diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 05e24ad9d7..6a432c0cf8 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -1,6 +1,6 @@ name: Lint -on: [push] +on: [push, pull_request] jobs: flake8-py3: diff --git a/.gitignore b/.gitignore index 2c3face445..f915537ab0 100644 --- a/.gitignore +++ b/.gitignore @@ -103,7 +103,7 @@ venv.bak/ # mypy .mypy_cache/ examples/scd_lvsegs.npz -.temp/ +temp/ .idea/ *~ diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 77d37a5c6b..e6bb748b47 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -1,20 +1,34 @@ stages: - - build + - build + - coverage -.base_template : &BASE - script: - - nvidia-smi - - export CUDA_DEVICE_ORDER=PCI_BUS_ID - - export CUDA_VISIBLE_DEVICES=0,1 - - python -m pip install --upgrade pip - - pip uninstall -y torch torchvision - - pip install -r requirements.txt - # - pip list - - ./runtests.sh --net - - echo "Done with runtests.sh" +full integration: + stage: build + script: + - nvidia-smi + - export CUDA_DEVICE_ORDER=PCI_BUS_ID + - export CUDA_VISIBLE_DEVICES=0,1 + - python -m pip install --upgrade pip + - pip uninstall -y torch torchvision + - pip install -q -r requirements.txt + - ./runtests.sh --net + - echo "Done with runtests.sh --net" + tags: + - test -build-ci-test: - stage: build - tags: - - test - <<: *BASE +coverage test: + stage: coverage + only: + - master + - ci-stages + script: + - nvidia-smi + - export CUDA_DEVICE_ORDER=PCI_BUS_ID + - export CUDA_VISIBLE_DEVICES=0,1 + - python -m pip install --upgrade pip + - pip uninstall -y torch torchvision + - pip install -q -r requirements.txt + - pip list + - ./runtests.sh --coverage + tags: + - test diff --git a/.readthedocs.yml b/.readthedocs.yml index 0fa357b9ca..9a68f0626e 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -7,7 +7,7 @@ version: 2 # Build documentation in the docs/ directory with Sphinx sphinx: - configuration: docs/conf.py + configuration: docs/source/conf.py # Build documentation with MkDocs #mkdocs: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 043d6c20dd..fa7527db87 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -57,6 +57,15 @@ License information: all source code files should start with this paragraph: ``` +### Building the documentation +To build documentation via Sphinx in`docs/` folder: +```bash +cd docs/ +make html +``` +The above commands build html documentation. Type `make help` for all supported formats, +type `make clean` to remove the current build files. + ## Unit testing MONAI tests are located under `tests/`. diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000..a3e6fbe3c1 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,35 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:19.10-py3 + +FROM ${PYTORCH_IMAGE} as base +RUN apt-get update + +WORKDIR /opt/monai +COPY . . + +ENV PYTHONPATH=$PYTHONPATH:/opt/monai +ENV PATH=/opt/tools:$PATH + +RUN python -m pip install -U pip +# remove preintalls +RUN python -m pip uninstall -y torch torchvision +# install dependencies +RUN python -m pip install -r requirements.txt + + +# NGC Client +WORKDIR /opt/tools +RUN wget -q https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && \ + unzip ngccli_cat_linux.zip && chmod u+x ngc && \ + rm -rf ngccli_cat_linux.zip ngc.md5 +WORKDIR /opt/monai diff --git a/README.md b/README.md index ac077c5ebd..413321bb0d 100644 --- a/README.md +++ b/README.md @@ -1,243 +1,51 @@ # Project MONAI -**M**edical **O**pen **N**etwork for **AI** - _Toolkit for Healthcare Imaging_ +**M**edical **O**pen **N**etwork for **AI** -_Contact: _ +[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0) [![pipeline status](https://gitlab.com/project-monai/MONAI/badges/master/pipeline.svg)](https://github.com/Project-MONAI/MONAI/commits/master) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/?badge=latest) [![coverage report](https://gitlab.com/project-monai/MONAI/badges/master/coverage.svg)](https://gitlab.com/project-monai/MONAI/pipelines/) -This document identifies key concepts of project MONAI at a high level, the goal is to facilitate further technical discussions of requirements,roadmap, feasibility and trade-offs. -## Vision - * Develop a community of academic, industrial and clinical researchers collaborating and working on a common foundation of standardized tools. - * Create a state-of-the-art, end-to-end training toolkit for healthcare imaging. - * Provide academic and industrial researchers with the optimized and standardized way to create and evaluate models +MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/master/LICENSE) platform for deep learning in healthcare imaging. Its ambitions are: +- developing a community of academic, industrial and clinical researchers collaborating on a common foundation; +- creating state-of-the-art, end-to-end training workflows for healthcare imaging; +- providing researchers with the optimized and standardized way to create and evaluate deep learning models. -## Targeted users - * Primarily focused on the healthcare researchers who develop DL models for medical imaging -## Goals - * Deliver domain-specific workflow capabilities - * Address the end-end “Pain points” when creating medical imaging deep learning workflows. - * Provide a robust foundation with a performance optimized system software stack that allows researchers to focus on the research and not worry about software development principles. +## Features +> _The codebase is currently under active development._ -## Guiding principles -### Modularity - * Pythonic -- object oriented components - * Compositional -- can combine components to create workflows - * Extensible -- easy to create new components and extend existing components - * Easy to debug -- loosely coupled, easy to follow code (e.g. in eager or graph mode) - * Flexible -- interfaces for easy integration of external modules -### User friendly - * Portable -- use components/workflows via Python “import” - * Run well-known baseline workflows in a few commands - * Access to the well-known public datasets in a few lines of code -### Standardisation - * Unified/consistent component APIs with documentation specifications - * Unified/consistent data and model formats, compatible with other existing standards -### High quality - * Consistent coding style - extensive documentation - tutorials - contributors’ guidelines - * Reproducibility -- e.g. system-specific deterministic training -### Future proof - * Task scalability -- both in datasets and computational resources - * Support for advanced data structures -- e.g. graphs/structured text documents -### Leverage existing high-quality software packages whenever possible - * E.g. low-level medical image format reader, image preprocessing with external packages - * Rigorous risk analysis of choice of foundational software dependencies -### Compatible with external software - * E.g. data visualisation, experiments tracking, management, orchestration +- flexible pre-processing for multi-dimensional medical imaging data; +- compositional & portable APIs for ease of integration in existing workflows; +- domain-specific implementations for networks, losses, evaluation metrics and more; +- customizable design for varying user expertise; +- multi-GPU data parallelism support. -## Key capabilities +## Installation +Clone and build this repository from source: + ```bash + git clone https://github.com/Project-MONAI/MONAI.git + pip install -e MONAI/ + ``` - - - - - - - - - - - - - - - - - - - - - - - - - - -
-Basic features - Example - Notes -
Ready-to-use workflows - Volumetric image segmentation - “Bring your own dataset” -
Baseline/reference network architectures - Provide an option to use “U-Net” - -
Intuitive command-line interfaces - - -
Multi-gpu training - Configure the workflow to run data parallel training - -
+Alternatively, pre-built Docker image is available via [DockerHub](https://hub.docker.com/r/projectmonai/monai): + ```bash + # with docker v19.03+ + docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest + ``` +## Getting Started +Tutorials & examples are located at [monai/examples](https://github.com/Project-MONAI/MONAI/tree/master/examples). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Customisable Python interfaces - Example - Notes -
Training/validation strategies - Schedule a strategy of alternating between generator and discriminator model training - -
Network architectures - Define new networks w/ the recent “Squeeze-and-Excitation” blocks - “Bring your own model” -
Data preprocessors - Define a new reader to read training data from a database system - -
Adaptive training schedule - Stop training when the loss becomes “NaN” - “Callbacks” -
Configuration-driven workflow assembly - Making workflow instances from configuration file - Convenient for managing hyperparameters -
+Technical documentation is available via [Read the Docs](https://monai.readthedocs.io/en/latest/). +## Contributing +For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/master/CONTRIBUTING.md). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Model sharing & transfer learning - Example - Notes -
Sharing model parameters, hyperparameter configurations - Standardisation of model archiving format - -
Model optimisation for deployment - - -
Fine-tuning from pre-trained models - Model compression, TensorRT - -
Model interpretability - Visualising feature maps of a trained model - -
Experiment tracking & management - - https://polyaxon.com/ -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Advanced features - Example - Notes -
Compatibility with external toolkits - XNAT as data source, ITK as preprocessor - -
Advanced learning strategies - Semi-supervised, active learning - -
High performance preprocessors - Smart caching, multi-process - -
Multi-node distributed training - - -
- +## Links +- Website: _(coming soon)_ +- API documentation: https://monai.readthedocs.io/en/latest/ +- Code: https://github.com/Project-MONAI/MONAI +- Project tracker: https://github.com/Project-MONAI/MONAI/projects +- Issue tracker: https://github.com/Project-MONAI/MONAI/issues +- Wiki: https://github.com/Project-MONAI/MONAI/wiki +- Test status: https://gitlab.com/project-monai/MONAI/pipelines diff --git a/docs/Makefile b/docs/Makefile index e3e3658fe5..bea205e654 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -6,7 +6,7 @@ SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = source -BUILDDIR = ../docs +BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @@ -17,12 +17,8 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - sphinx-apidoc -f -o "$(SOURCEDIR)"/apidocs ../monai - rm -rf ../docs/* @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - mv ../docs/html/* ../docs/ - rm -rf ../docs/html ../docs/doctrees clean: - rm -rf ../docs/* + rm -rf build/ rm -rf source/apidocs diff --git a/docs/images/end_to_end_process.png b/docs/images/end_to_end_process.png new file mode 100644 index 0000000000..e837f64a93 Binary files /dev/null and b/docs/images/end_to_end_process.png differ diff --git a/docs/images/sliding_window.png b/docs/images/sliding_window.png new file mode 100644 index 0000000000..3cd3aeea10 Binary files /dev/null and b/docs/images/sliding_window.png differ diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 9abd415f49..0000000000 --- a/docs/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. MONAI documentation master file, created by - sphinx-quickstart on Wed Feb 5 09:40:29 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to MONAI's documentation! -================================= - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - MONAI API reference - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/requirements.txt b/docs/requirements.txt index e3a16afce8..65d0917d7c 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,8 +1,11 @@ --f https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl +-f https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl torch>=1.4.0 pytorch-ignite==0.3.0 numpy nibabel +parameterized +scipy +scikit-image tensorboard commonmark==0.9.1 recommonmark==0.6.0 diff --git a/docs/conf.py b/docs/source/conf.py similarity index 73% rename from docs/conf.py rename to docs/source/conf.py index 5cebe24689..338e9661b4 100644 --- a/docs/conf.py +++ b/docs/source/conf.py @@ -14,6 +14,7 @@ import sys import subprocess sys.path.insert(0, os.path.abspath('..')) +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))) print(sys.path) @@ -21,16 +22,30 @@ # -- Project information ----------------------------------------------------- project = 'MONAI' -copyright = '2020, MONAI Consortium' -author = 'MONAI Consortium' +copyright = '2020, MONAI Contributors' +author = 'MONAI Contributors' # The full version, including alpha/beta/rc tags -release = 'v0.1' -version = 'v0.1' +release = 'public alpha' +version = 'public alpha' + + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [os.path.join('transforms', 'compose.py'), + os.path.join('transforms', 'adaptors.py'), + os.path.join('transforms', 'composables.py'), + os.path.join('transforms', 'transforms.py'), + os.path.join('networks', 'blocks'), + os.path.join('networks', 'layers'), + os.path.join('networks', 'nets'), + 'metrics', 'engine', 'data', 'handlers', 'losses', 'visualize', 'utils', 'tests'] + def generate_apidocs(*args): """Generate API docs automatically by trawling the available modules""" - module_path = os.path.abspath('..') + module_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'monai')) output_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'apidocs')) apidoc_command_path = 'sphinx-apidoc' if hasattr(sys, 'real_prefix'): # called from a virtualenv @@ -39,9 +54,10 @@ def generate_apidocs(*args): print('output_path {}'.format(output_path)) print('module_path {}'.format(module_path)) subprocess.check_call( - [apidoc_command_path, '-f'] + + [apidoc_command_path, '-f', '-e'] + ['-o', output_path] + - [module_path]) + [module_path] + + [os.path.join(module_path, p) for p in exclude_patterns]) def setup(app): @@ -77,11 +93,6 @@ def setup(app): # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for @@ -96,11 +107,18 @@ def setup(app): 'sticky_navigation': True, # Set to False to disable the sticky nav while scrolling. # 'logo_only': True, # if we have a html_logo below, this shows /only/ the logo with no title text } +html_context = { + 'display_github': True, + 'github_user': 'Project-MONAI', + 'github_repo': 'MONAI', + 'github_version': 'master', + 'conf_py_path': '/docs/', +} html_scaled_image_link = False -html_show_sourcelink = True -html_favicon = 'favicon.ico' +html_show_sourcelink = True +# html_favicon = 'favicon.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +# html_static_path = ['_static'] diff --git a/docs/source/data.rst b/docs/source/data.rst new file mode 100644 index 0000000000..8cd41dc58b --- /dev/null +++ b/docs/source/data.rst @@ -0,0 +1,55 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _data: + +Data +==== + +Generic Interfaces +------------------ +.. automodule:: monai.data.dataset +.. currentmodule:: monai.data.dataset + +`Dataset` +~~~~~~~~~ +.. autoclass:: Dataset + :members: + :special-members: __getitem__ + + +Patch-based dataset +------------------- + +`GridPatchDataset` +~~~~~~~~~~~~~~~~~~ +.. automodule:: monai.data.grid_dataset +.. currentmodule:: monai.data.grid_dataset +.. autoclass:: GridPatchDataset + :members: + + +Nifti format handling +--------------------- + +Reading +~~~~~~~ +.. automodule:: monai.data.nifti_reader + :members: + +Writing +~~~~~~~ +.. automodule:: monai.data.nifti_writer + :members: + + +Synthetic +--------- +.. automodule:: monai.data.synthetic + :members: + + +Utilities +--------- +.. automodule:: monai.data.utils + :members: + diff --git a/docs/source/engines.rst b/docs/source/engines.rst new file mode 100644 index 0000000000..e5cbc64b02 --- /dev/null +++ b/docs/source/engines.rst @@ -0,0 +1,12 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _engines: + +Engines +======= + +Multi-GPU data parallel +----------------------- + +.. automodule:: monai.engine.multi_gpu_supervised_trainer + :members: diff --git a/docs/source/handlers.rst b/docs/source/handlers.rst new file mode 100644 index 0000000000..9089701470 --- /dev/null +++ b/docs/source/handlers.rst @@ -0,0 +1,41 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _handlers: + +Event handlers +============== + +Checkpoint loader +----------------- +.. automodule:: monai.handlers.checkpoint_loader + :members: + +CSV saver +--------- +.. automodule:: monai.handlers.classification_saver + :members: + +Mean Dice metrics handler +------------------------- +.. automodule:: monai.handlers.mean_dice + :members: + +Metric logger +--------------- +.. automodule:: monai.handlers.metric_logger + :members: + +Segmentation saver +------------------ +.. automodule:: monai.handlers.segmentation_saver + :members: + +Training stats handler +---------------------- +.. automodule:: monai.handlers.stats_handler + :members: + +Tensorboard handler +-------------------- +.. automodule:: monai.handlers.tensorboard_handlers + :members: diff --git a/docs/source/highlights.md b/docs/source/highlights.md new file mode 100644 index 0000000000..f7895bf65d --- /dev/null +++ b/docs/source/highlights.md @@ -0,0 +1,97 @@ +# Modules for public alpha + +MONAI aims at supporting deep learning in medical image analysis at multiple granularities. +This figure shows modules currently available in the codebase. +![image](../images/end_to_end_process.png) +The rest of this page provides more details for each module. + +* [Image transformations](#image-transformations) +* [Loss functions](#losses) +* [Network architectures](#network-architectures) +* [Evaluation](#evaluation) +* [Visualization](#visualization) +* [Result writing](#result-writing) + +## Image transformations +Medical image data pre-processing is challenging. Data are often in specialized formats with rich meta-information, and the data volumes are often high-dimensional and requiring carefully designed manipulation procedures. As an important part of MONAI, powerful and flexible image transformations are provided to enable user-friendly, reproducible, optimized medical data pre-processing pipeline. + +### 1. Transforms support both Dictionary and Array format data +1. The widely used computer vision packages (such as ``torchvision``) focus on spatially 2D array image processing. MONAI provides more domain-specific transformations for both spatially 2D and 3D and retains the flexible transformation "compose" feature. +2. As medical image preprocessing often requires additional fine-grained system parameters, MONAI provides transforms for input data encapsulated in a python dictionary. Users are able to specify the keys corresponding to the expected data fields and system parameters to compose complex transformations. + +### 2. Medical specific transforms +MONAI aims at providing a rich set of popular medical image specific transformations. These currently include, for example: + + +- `LoadNifti`: Load Nifti format file from provided path +- `Spacing`: Resample input image into the specified `pixdim` +- `Orientation`: Change image's orientation into the specified `axcodes` +- `GaussianNoise`: Pertubate image intensities by adding statistical noises +- `IntensityNormalizer`: Intensity Normalization based on mean and standard deviation +- `Affine`: Transform image based on the affine parameters +- `Rand2DElastic`: Random elastic deformation and affine in 2D +- `Rand3DElastic`: Random elastic deformation and affine in 3D + +### 3. Fused spatial transforms and GPU acceleration +As medical image volumes are usually large (in multi-dimensional arrays), pre-processing performance obviously affects the overall pipeline speed. MONAI provides affine transforms to execute fused spatial operations, supports GPU acceleration via native PyTorch to achieve high performance. +Example code: +```py +# create an Affine transform +affine = Affine( + rotate_params=np.pi/4, + scale_params=(1.2, 1.2), + translate_params=(200, 40), + padding_mode='zeros', + device=torch.device('cuda:0') +) +# convert the image using interpolation mode +new_img = affine(image, spatial_size=(300, 400), mode='bilinear') +``` + +### 4. Randomly crop out batch images based on positive/negative ratio +Medical image data volume may be too large to fit into GPU memory. A widely-used approach is to randomly draw small size data samples during training. MONAI currrently provides uniform random sampling strategy as well as class-balanced fixed ratio sampling which may help stabilize the patch-based training process. + +### 5. Deterministic training for reproducibility +Deterministic training support is necessary and important in DL research area, especially when sharing reproducible work with others. Users can easily set random seed to all the transforms in MONAI locally and will not affect other non-deterministic modules in the user's program. +Example code: +```py +# define a transform chain for pre-processing +train_transforms = monai.transforms.compose.Compose([ + LoadNiftid(keys=['image', 'label']), + ... ... +]) +# set determinism for reproducibility +train_transforms.set_random_state(seed=0) +np.random.seed(0) +torch.manual_seed(0) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +``` + +## Losses +There are domain-specific loss functions in the medical research area which are different from the generic computer vision ones. As an important module of MONAI, these loss functions are implemented in PyTorch, such as Dice loss and generalized Dice loss. + +## Network architectures +Some deep neural network architectures have shown to be particularly effective for medical imaging analysis tasks. MONAI implements reference networks with the aims of both flexibility and code readability. + +## Evaluation +To run model inferences and evaluate the model quality, MONAI provides reference implementations for the relevant widely-used approaches. Currently, several popular evaluation metrics and inference patterns are included: + +### 1. Sliding window inference +When executing inference on large medical images, the sliding window is a popular method to achieve high performance with flexible memory requirements. +1. Select continuous windows on the original image. +2. Execute a batch of windows on the model per time, and complete all windows. +3. Connect all the model outputs to construct one segmentation corresponding to the original image. +4. Save segmentation result to file or compute metrics. +![image](../images/sliding_window.png) + +### 2. Metrics for medical tasks +There are many useful metrics to measure medical specific tasks, MONAI already implemented Mean Dice and AUC, will integrate more soon. + +## Visualization +Besides common curves of statistics on TensorBoard, in order to provide straight-forward checking of 3D image and the corresponding label and segmentation output, MONAI can visualize 3D data as GIF animation on TensorBoard which can help users quickly check model output. + +## Result writing +For the segmentation task, MONAI supports to save model output as NIFTI format image and add affine information from the corresponding input image. + +For the classification task, MONAI supports to save classification result as a CSV file. diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000000..26ea8545b1 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,80 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. MONAI documentation master file, created by + sphinx-quickstart on Wed Feb 5 09:40:29 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Project MONAI +============= + + +*Medical Open Network for AI* + +MONAI is a `PyTorch `_-based, `open-source `_ platform +for deep learning in healthcare imaging. Its ambitions are: + +- developing a community of academic, industrial and clinical researchers collaborating on a common foundation; +- creating state-of-the-art, end-to-end training workflows for healthcare imaging; +- providing researchers with the optimized and standardized way to create and evaluate deep learning models. + +Features +-------- +*The codebase is currently under active development* + +- flexible pre-processing for multi-dimensional medical imaging data; +- compositional & portable APIs for ease of integration in existing workflows; +- domain-specific implementations for networks, losses, evaluation metrics and more; +- customizable design for varying user expertise; +- multi-GPU data parallelism support. + + +Getting started +--------------- + +Tutorials & examples are located at `monai/examples `_. + +Technical documentation is available via `Read the Docs `_. + + +Technical highlights +-------------------- +- `public alpha `_ + +.. toctree:: + :maxdepth: 1 + :caption: APIs + + transforms + losses + networks + metrics + data + engines + handlers + visualize + utils + + +Contributing +------------ +For guidance on making a contribution to MONAI, see the `contributing guidelines +`_. + +Links +----- +- Website: _(coming soon)_ +- API documentation: https://monai.readthedocs.io/en/latest/ +- Code: https://github.com/Project-MONAI/MONAI +- Project tracker: https://github.com/Project-MONAI/MONAI/projects +- Issue tracker: https://github.com/Project-MONAI/MONAI/issues +- Wiki: https://github.com/Project-MONAI/MONAI/wiki +- Test status: https://gitlab.com/project-monai/MONAI/pipelines + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` + diff --git a/docs/source/losses.rst b/docs/source/losses.rst new file mode 100644 index 0000000000..50e4563ca1 --- /dev/null +++ b/docs/source/losses.rst @@ -0,0 +1,23 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _losses: + +Loss functions +============== + +Segmentation Losses +------------------- + +.. automodule:: monai.losses.dice +.. currentmodule:: monai.losses.dice + + +`DiceLoss` +~~~~~~~~~~~ +.. autoclass:: DiceLoss + :members: + +`GeneralizedDiceLoss` +~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: GeneralizedDiceLoss + :members: diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst new file mode 100644 index 0000000000..9f2de95f7d --- /dev/null +++ b/docs/source/metrics.rst @@ -0,0 +1,17 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _metrics: + +Metrics +======== + +Segmentation metrics +-------------------- + +.. automodule:: monai.metrics.compute_meandice +.. currentmodule:: monai.metrics.compute_meandice + + +`compute_meandice` +~~~~~~~~~~~~~~~~~~~ +.. automethod:: monai.metrics.compute_meandice.compute_meandice diff --git a/docs/source/networks.rst b/docs/source/networks.rst new file mode 100644 index 0000000000..5d456c1528 --- /dev/null +++ b/docs/source/networks.rst @@ -0,0 +1,94 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _networkss: + +Network architectures +===================== + + +Blocks +------ +.. automodule:: monai.networks.blocks.convolutions +.. currentmodule:: monai.networks.blocks.convolutions + + +`Convolution` +~~~~~~~~~~~~~ +.. autoclass:: Convolution + :members: + +`ResidualUnit` +~~~~~~~~~~~~~~ +.. autoclass:: ResidualUnit + :members: + + +Layers +------ + +`get_conv_type` +~~~~~~~~~~~~~~~ +.. automethod:: monai.networks.layers.factories.get_conv_type + +`get_dropout_type` +~~~~~~~~~~~~~~~~~~ +.. automethod:: monai.networks.layers.factories.get_dropout_type + +`get_normalize_type` +~~~~~~~~~~~~~~~~~~~~ +.. automethod:: monai.networks.layers.factories.get_normalize_type + +`get_maxpooling_type` +~~~~~~~~~~~~~~~~~~~~~ +.. automethod:: monai.networks.layers.factories.get_maxpooling_type + +`get_avgpooling_type` +~~~~~~~~~~~~~~~~~~~~~ +.. automethod:: monai.networks.layers.factories.get_avgpooling_type + + +.. automodule:: monai.networks.layers.simplelayers +.. currentmodule:: monai.networks.layers.simplelayers + +`SkipConnection` +~~~~~~~~~~~~~~~~ +.. autoclass:: SkipConnection + :members: + +`Flatten` +~~~~~~~~~~ +.. autoclass:: Flatten + :members: + +`GaussianFilter` +~~~~~~~~~~~~~~~~ +.. autoclass:: GaussianFilter + :members: + :special-members: __call__ + + +Nets +---- + +.. automodule:: monai.networks.nets +.. currentmodule:: monai.networks.nets + + +`Densenet3D` +~~~~~~~~~~~~ +.. automodule:: monai.networks.nets.densenet3d + :members: +.. automethod:: monai.networks.nets.densenet3d.densenet121 +.. automethod:: monai.networks.nets.densenet3d.densenet169 +.. automethod:: monai.networks.nets.densenet3d.densenet201 +.. automethod:: monai.networks.nets.densenet3d.densenet264 + +`Highresnet` +~~~~~~~~~~~~ +.. automodule:: monai.networks.nets.highresnet + :members: + +`Unet` +~~~~~~ +.. automodule:: monai.networks.nets.unet + :members: diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst new file mode 100644 index 0000000000..de5bb45975 --- /dev/null +++ b/docs/source/transforms.rst @@ -0,0 +1,390 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _transform_api: + +Transforms +========== + + +Generic Interfaces +------------------ + +.. automodule:: monai.transforms.compose +.. currentmodule:: monai.transforms.compose + + +`Transform` +~~~~~~~~~~~ +.. autoclass:: Transform + :members: + :special-members: __call__ + + +`MapTransform` +~~~~~~~~~~~~~~ +.. autoclass:: MapTransform + :members: + :special-members: __call__ + + +`Randomizable` +~~~~~~~~~~~~~~ +.. autoclass:: Randomizable + :members: + +`Compose` +~~~~~~~~~ +.. autoclass:: Compose + :members: + :special-members: __call__ + + +Vanilla Transforms +------------------ + +.. automodule:: monai.transforms.transforms +.. currentmodule:: monai.transforms.transforms + +`Spacing` +~~~~~~~~~ +.. autoclass:: Spacing + :members: + :special-members: __call__ + +`Orientation` +~~~~~~~~~~~~~ +.. autoclass:: Orientation + :members: + :special-members: __call__ + +`LoadNifti` +~~~~~~~~~~~ +.. autoclass:: LoadNifti + :members: + :special-members: __call__ + +`AsChannelFirst` +~~~~~~~~~~~~~~~~ +.. autoclass:: AsChannelFirst + :members: + :special-members: __call__ + +`AddChannel` +~~~~~~~~~~~~ +.. autoclass:: AddChannel + :members: + :special-members: __call__ + +`Transpose` +~~~~~~~~~~~ +.. autoclass:: Transpose + :members: + :special-members: __call__ + +`Rescale` +~~~~~~~~~ +.. autoclass:: Rescale + :members: + :special-members: __call__ + +`GaussianNoise` +~~~~~~~~~~~~~~~ +.. autoclass:: GaussianNoise + :members: + :special-members: __call__ + +`Flip` +~~~~~~ +.. autoclass:: Flip + :members: + :special-members: __call__ + +`Resize` +~~~~~~~~ +.. autoclass:: Resize + :members: + :special-members: __call__ + +`Rotate` +~~~~~~~~ +.. autoclass:: Rotate + :members: + :special-members: __call__ + +`Zoom` +~~~~~~ +.. autoclass:: Zoom + :members: + :special-members: __call__ + +`ToTensor` +~~~~~~~~~~ +.. autoclass:: ToTensor + :members: + :special-members: __call__ + +`RandUniformPatch` +~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: RandUniformPatch + :members: + :special-members: __call__ + +`NormalizeIntensity` +~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: NormalizeIntensity + :members: + :special-members: __call__ + +`ScaleIntensityRange` +~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: ScaleIntensityRange + :members: + :special-members: __call__ + +`PadImageEnd` +~~~~~~~~~~~~~~~~ +.. autoclass:: PadImageEnd + :members: + :special-members: __call__ + +`Rotate90` +~~~~~~~~~~ +.. autoclass:: Rotate90 + :members: + :special-members: __call__ + +`RandRotate90` +~~~~~~~~~~~~~~ +.. autoclass:: RandRotate90 + :members: + :special-members: __call__ + +`SpatialCrop` +~~~~~~~~~~~~~ +.. autoclass:: SpatialCrop + :members: + :special-members: __call__ + +`RandRotate` +~~~~~~~~~~~~ +.. autoclass:: RandRotate + :members: + :special-members: __call__ + +`RandFlip` +~~~~~~~~~~ +.. autoclass:: RandFlip + :members: + :special-members: __call__ + +`RandZoom` +~~~~~~~~~~ +.. autoclass:: RandZoom + :members: + :special-members: __call__ + +`Affine` +~~~~~~~~ +.. autoclass:: Affine + :members: + :special-members: __call__ + +`Resample` +~~~~~~~~~~~ +.. autoclass:: Resample + :members: + :special-members: __call__ + +`RandAffine` +~~~~~~~~~~~~ +.. autoclass:: RandAffine + :members: + :special-members: __call__ + +`RandDeformGrid` +~~~~~~~~~~~~~~~~ +.. autoclass:: RandDeformGrid + :members: + :special-members: __call__ + +`RandAffineGrid` +~~~~~~~~~~~~~~~~ +.. autoclass:: RandAffineGrid + :members: + :special-members: __call__ + +`Rand2DElastic` +~~~~~~~~~~~~~~~ +.. autoclass:: Rand2DElastic + :members: + :special-members: __call__ + +`Rand3DElastic` +~~~~~~~~~~~~~~~ +.. autoclass:: Rand3DElastic + :members: + :special-members: __call__ + + +Dictionary-based Composables +---------------------------- + +.. automodule:: monai.transforms.composables +.. currentmodule:: monai.transforms.composables + +`Spacingd` +~~~~~~~~~~ +.. autoclass:: Spacingd + :members: + :special-members: __call__ + +`Orientationd` +~~~~~~~~~~~~~~ +.. autoclass:: Orientationd + :members: + :special-members: __call__ + +`LoadNiftid` +~~~~~~~~~~~~ +.. autoclass:: LoadNiftid + :members: + :special-members: __call__ + +`AsChannelFirstd` +~~~~~~~~~~~~~~~~~ +.. autoclass:: AsChannelFirstd + :members: + :special-members: __call__ + +`AddChanneld` +~~~~~~~~~~~~~ +.. autoclass:: AddChanneld + :members: + :special-members: __call__ + +`Rotate90d` +~~~~~~~~~~~ +.. autoclass:: Rotate90d + :members: + :special-members: __call__ + +`Rescaled` +~~~~~~~~~~ +.. autoclass:: Rescaled + :members: + :special-members: __call__ + +`Resized` +~~~~~~~~~ +.. autoclass:: Resized + :members: + :special-members: __call__ + +`RandUniformPatchd` +~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: RandUniformPatchd + :members: + :special-members: __call__ + +`NormalizeIntensityd` +~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: NormalizeIntensityd + :members: + :special-members: __call__ + +`ScaleIntensityRanged` +~~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: ScaleIntensityRanged + :members: + :special-members: __call__ + +`RandRotate90d` +~~~~~~~~~~~~~~~ +.. autoclass:: RandRotate90d + :members: + :special-members: __call__ + +`RandCropByPosNegLabeld` +~~~~~~~~~~~~~~~~~~~~~~~~ +.. autoclass:: RandCropByPosNegLabeld + :members: + :special-members: __call__ + +`RandAffined` +~~~~~~~~~~~~~ +.. autoclass:: RandAffined + :members: + :special-members: __call__ + +`Rand2DElasticd` +~~~~~~~~~~~~~~~~ +.. autoclass:: Rand2DElasticd + :members: + :special-members: __call__ + +`Rand3DElasticd` +~~~~~~~~~~~~~~~~ +.. autoclass:: Rand3DElasticd + :members: + :special-members: __call__ + +`Flipd` +~~~~~~~ +.. autoclass:: Flipd + :members: + :special-members: __call__ + +`RandFlipd` +~~~~~~~~~~~ +.. autoclass:: RandFlipd + :members: + :special-members: __call__ + +`Rotated` +~~~~~~~~~ +.. autoclass:: Rotated + :members: + :special-members: __call__ + +`RandRotated` +~~~~~~~~~~~~~ +.. autoclass:: RandRotated + :members: + :special-members: __call__ + +`Zoomd` +~~~~~~~ +.. autoclass:: Zoomd + :members: + :special-members: __call__ + +`RandZoomd` +~~~~~~~~~~~ +.. autoclass:: RandZoomd + :members: + :special-members: __call__ + +`DeleteKeysd` +~~~~~~~~~~~~~ +.. autoclass:: DeleteKeysd + :members: + :special-members: __call__ + +Transform Adaptors +------------------ + +.. automodule:: monai.transforms.adaptors +.. currentmodule:: monai.transforms.adaptors + +`adaptor` +~~~~~~~~~ +.. automethod:: monai.transforms.adaptors.adaptor + +`apply_alias` +~~~~~~~~~~~~~ +.. automethod:: monai.transforms.adaptors.apply_alias + + +`to_kwargs` +~~~~~~~~~~~ +.. automethod:: monai.transforms.adaptors.to_kwargs diff --git a/docs/source/utils.rst b/docs/source/utils.rst new file mode 100644 index 0000000000..8ffbf1d2f5 --- /dev/null +++ b/docs/source/utils.rst @@ -0,0 +1,22 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _utils: + +Utils +===== + +Sliding window inference +------------------------ + +.. automodule:: monai.utils.sliding_window_inference + :members: + +Module utils +------------ +.. automodule:: monai.utils.module + :members: + +Aliases +------- +.. automodule:: monai.utils.aliases + :members: \ No newline at end of file diff --git a/docs/source/visualize.rst b/docs/source/visualize.rst new file mode 100644 index 0000000000..e6506f6849 --- /dev/null +++ b/docs/source/visualize.rst @@ -0,0 +1,12 @@ +:github_url: https://github.com/Project-MONAI/MONAI + +.. _visualize: + +Visualizations +============== + +Tensorboard visuals +------------------- + +.. automodule:: monai.visualize.img2tensorboard + :members: diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000000..f723201500 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,9 @@ +Most of the examples and tutorials require +[matplotlib](https://matplotlib.org/) and [Jupyter Notebook](https://jupyter.org/). + +These could be installed by: +```bash +python -m pip install -U pip +python -m pip install -U matplotlib +python -m pip install -U notebook +``` diff --git a/examples/classification_3d/densenet_evaluation_array.py b/examples/classification_3d/densenet_evaluation_array.py new file mode 100644 index 0000000000..ce89ebe309 --- /dev/null +++ b/examples/classification_3d/densenet_evaluation_array.py @@ -0,0 +1,95 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +import logging +import numpy as np +import torch +from ignite.engine import create_supervised_evaluator, _prepare_batch +from torch.utils.data import DataLoader + +import monai +import monai.transforms.compose as transforms +from monai.data.nifti_reader import NiftiDataset +from monai.transforms import (AddChannel, Rescale, Resize) +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.classification_saver import ClassificationSaver +from monai.handlers.checkpoint_loader import CheckpointLoader +from ignite.metrics import Accuracy + +monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/ +images = [ + "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz" +] +# 2 binary labels for gender classification: man and woman +labels = np.array([ + 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 +]) + +# Define transforms for image +val_transforms = transforms.Compose([ + Rescale(), + AddChannel(), + Resize((96, 96, 96)) +]) +# Define nifti dataset +val_ds = NiftiDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False) +# Create DenseNet121 +net = monai.networks.nets.densenet3d.densenet121( + in_channels=1, + out_channels=2, +) +device = torch.device("cuda:0") + +metric_name = 'Accuracy' +# add evaluation metric to the evaluator engine +val_metrics = {metric_name: Accuracy()} + + +def prepare_batch(batch, device=None, non_blocking=False): + return _prepare_batch((batch[0], batch[1]), device, non_blocking) + + +# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values +evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch) + +# Add stats event handler to print validation stats via evaluator +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None # no need to print loss value, so disable per iteration output +) +val_stats_handler.attach(evaluator) + +# for the arrary data format, assume the 3rd item of batch data is the meta_data +prediction_saver = ClassificationSaver(output_dir='tempdir', batch_transform=lambda batch: batch[2], + output_transform=lambda output: output[0].argmax(1)) +prediction_saver.attach(evaluator) + +# the model was trained by "densenet_training_array" exmple +CheckpointLoader(load_path='./runs/net_checkpoint_40.pth', load_dict={'net': net}).attach(evaluator) + +# create a validation data loader +val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available()) + +state = evaluator.run(val_loader) diff --git a/examples/classification_3d/densenet_evaluation_dict.py b/examples/classification_3d/densenet_evaluation_dict.py new file mode 100644 index 0000000000..7d4a62a4cb --- /dev/null +++ b/examples/classification_3d/densenet_evaluation_dict.py @@ -0,0 +1,96 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ignite.metrics import Accuracy +import sys +import logging +import numpy as np +import torch +from ignite.engine import create_supervised_evaluator, _prepare_batch +from torch.utils.data import DataLoader + +from monai.handlers.classification_saver import ClassificationSaver +from monai.handlers.checkpoint_loader import CheckpointLoader +from monai.handlers.stats_handler import StatsHandler +from monai.transforms.composables import LoadNiftid, AddChanneld, Rescaled, Resized +import monai.transforms.compose as transforms +import monai + +monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/ +images = [ + "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz" +] +# 2 binary labels for gender classification: man and woman +labels = np.array([ + 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 +]) +val_files = [{'img': img, 'label': label} for img, label in zip(images, labels)] + +# Define transforms for image +val_transforms = transforms.Compose([ + LoadNiftid(keys=['img']), + AddChanneld(keys=['img']), + Rescaled(keys=['img']), + Resized(keys=['img'], output_spatial_shape=(96, 96, 96)) +]) + +# Create DenseNet121 +net = monai.networks.nets.densenet3d.densenet121( + in_channels=1, + out_channels=2, +) +device = torch.device("cuda:0") + + +def prepare_batch(batch, device=None, non_blocking=False): + return _prepare_batch((batch['img'], batch['label']), device, non_blocking) + + +metric_name = 'Accuracy' +# add evaluation metric to the evaluator engine +val_metrics = {metric_name: Accuracy()} +# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values +evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch) + +# Add stats event handler to print validation stats via evaluator +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None # no need to print loss value, so disable per iteration output +) +val_stats_handler.attach(evaluator) + +# for the arrary data format, assume the 3rd item of batch data is the meta_data +prediction_saver = ClassificationSaver(output_dir='tempdir', name='evaluator', + batch_transform=lambda batch: {'filename_or_obj': batch['img.filename_or_obj']}, + output_transform=lambda output: output[0].argmax(1)) +prediction_saver.attach(evaluator) + +# the model was trained by "densenet_training_dict" exmple +CheckpointLoader(load_path='./runs/net_checkpoint_40.pth', load_dict={'net': net}).attach(evaluator) + +# create a validation data loader +val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) +val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available()) + +state = evaluator.run(val_loader) diff --git a/examples/densenet_classification_3d.py b/examples/classification_3d/densenet_training_array.py similarity index 61% rename from examples/densenet_classification_3d.py rename to examples/classification_3d/densenet_training_array.py index 07ac3ffe04..e8ef70c59e 100644 --- a/examples/densenet_classification_3d.py +++ b/examples/classification_3d/densenet_training_array.py @@ -17,20 +17,19 @@ from ignite.handlers import ModelCheckpoint, EarlyStopping from torch.utils.data import DataLoader -# assumes the framework is found here, change as necessary -sys.path.append("..") import monai import monai.transforms.compose as transforms - from monai.data.nifti_reader import NiftiDataset -from monai.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch) +from monai.transforms import (AddChannel, Rescale, Resize, RandRotate90) from monai.handlers.stats_handler import StatsHandler +from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler from ignite.metrics import Accuracy from monai.handlers.utils import stopping_fn_from_metric monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) -# FIXME: temp test dataset, Wenqi will replace later +# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/ images = [ "/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz", "/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz", @@ -53,37 +52,42 @@ "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz", "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz" ] +# 2 binary labels for gender classification: man and woman labels = np.array([ 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 ]) -# Define transforms for image and segmentation -imtrans = transforms.Compose([ +# Define transforms +train_transforms = transforms.Compose([ + Rescale(), + AddChannel(), + Resize((96, 96, 96)), + RandRotate90() +]) +val_transforms = transforms.Compose([ Rescale(), AddChannel(), - UniformRandomPatch((96, 96, 96)), - ToTensor() + Resize((96, 96, 96)) ]) -# Define nifti dataset, dataloader. -ds = NiftiDataset(image_files=images, labels=labels, transform=imtrans) -loader = DataLoader(ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) -im, label = monai.utils.misc.first(loader) +# Define nifti dataset, dataloader +check_ds = NiftiDataset(image_files=images, labels=labels, transform=train_transforms) +check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) +im, label = monai.utils.misc.first(check_loader) print(type(im), im.shape, label) -lr = 1e-5 - -# Create DenseNet121, CrossEntropyLoss and Adam optimizer. +# Create DenseNet121, CrossEntropyLoss and Adam optimizer net = monai.networks.nets.densenet3d.densenet121( in_channels=1, out_channels=2, ) - loss = torch.nn.CrossEntropyLoss() +lr = 1e-5 opt = torch.optim.Adam(net.parameters(), lr) - -# Create trainer device = torch.device("cuda:0") + +# ignite trainer expects batch=(img, label) and returns output=loss at every iteration, +# user can add output_transform to return other values, like: y_pred, y, etc. trainer = create_supervised_trainer(net, opt, loss, device, False) # adding checkpoint handler to save models (network params and optimizer stats) during training @@ -91,46 +95,59 @@ trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={'net': net, 'opt': opt}) -train_stats_handler = StatsHandler() + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't set metrics for trainer here, so just print loss, user can also customize print functions +# and can use output_transform to convert engine.state.output if it's not loss value +train_stats_handler = StatsHandler(name='trainer') train_stats_handler.attach(trainer) -@trainer.on(Events.EPOCH_COMPLETED) -def log_training_loss(engine): - engine.logger.info("Epoch[%s] Loss: %s", engine.state.epoch, engine.state.output) +# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler +train_tensorboard_stats_handler = TensorBoardStatsHandler() +train_tensorboard_stats_handler.attach(trainer) # Set parameters for validation validation_every_n_epochs = 1 -metric_name = 'Accuracy' +metric_name = 'Accuracy' # add evaluation metric to the evaluator engine val_metrics = {metric_name: Accuracy()} +# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values evaluator = create_supervised_evaluator(net, val_metrics, device, True) # Add stats event handler to print validation stats via evaluator -logging.basicConfig(stream=sys.stdout, level=logging.INFO) -val_stats_handler = StatsHandler() +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None, # no need to print loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) -# Add early stopping handler to evaluator. +# add handler to record metrics to TensorBoard at every epoch +val_tensorboard_stats_handler = TensorBoardStatsHandler( + output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_tensorboard_stats_handler.attach(evaluator) + +# Add early stopping handler to evaluator early_stopper = EarlyStopping(patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) # create a validation data loader -val_ds = NiftiDataset(image_files=images[-5:], labels=labels[-5:], transform=imtrans) -val_loader = DataLoader(ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) +val_ds = NiftiDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms) +val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) @trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) def run_validation(engine): evaluator.run(val_loader) -# create a training data loader -logging.basicConfig(stream=sys.stdout, level=logging.INFO) -train_ds = NiftiDataset(image_files=images[:15], labels=labels[:15], transform=imtrans) -train_loader = DataLoader(train_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) +# create a training data loader +train_ds = NiftiDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms) +train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available()) train_epochs = 30 state = trainer.run(train_loader, train_epochs) diff --git a/examples/classification_3d/densenet_training_dict.py b/examples/classification_3d/densenet_training_dict.py new file mode 100644 index 0000000000..a009c9e3df --- /dev/null +++ b/examples/classification_3d/densenet_training_dict.py @@ -0,0 +1,162 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +import logging +import numpy as np +import torch +from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator, _prepare_batch +from ignite.handlers import ModelCheckpoint, EarlyStopping +from torch.utils.data import DataLoader + +import monai +import monai.transforms.compose as transforms +from monai.transforms.composables import \ + LoadNiftid, AddChanneld, Rescaled, Resized, RandRotate90d +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler +from ignite.metrics import Accuracy +from monai.handlers.utils import stopping_fn_from_metric + +monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# IXI dataset as a demo, dowloadable from https://brain-development.org/ixi-dataset/ +images = [ + "/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz", + "/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz" +] +# 2 binary labels for gender classification: man and woman +labels = np.array([ + 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 +]) +train_files = [{'img': img, 'label': label} for img, label in zip(images[:10], labels[:10])] +val_files = [{'img': img, 'label': label} for img, label in zip(images[-10:], labels[-10:])] + +# Define transforms for image +train_transforms = transforms.Compose([ + LoadNiftid(keys=['img']), + AddChanneld(keys=['img']), + Rescaled(keys=['img']), + Resized(keys=['img'], output_spatial_shape=(96, 96, 96)), + RandRotate90d(keys=['img'], prob=0.8, spatial_axes=[0, 2]) +]) +val_transforms = transforms.Compose([ + LoadNiftid(keys=['img']), + AddChanneld(keys=['img']), + Rescaled(keys=['img']), + Resized(keys=['img'], output_spatial_shape=(96, 96, 96)) +]) + +# Define dataset, dataloader +check_ds = monai.data.Dataset(data=train_files, transform=train_transforms) +check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available()) +check_data = monai.utils.misc.first(check_loader) +print(check_data['img'].shape, check_data['label']) + +# Create DenseNet121, CrossEntropyLoss and Adam optimizer +net = monai.networks.nets.densenet3d.densenet121( + in_channels=1, + out_channels=2, +) +loss = torch.nn.CrossEntropyLoss() +lr = 1e-5 +opt = torch.optim.Adam(net.parameters(), lr) +device = torch.device("cuda:0") + + +# ignite trainer expects batch=(img, label) and returns output=loss at every iteration, +# user can add output_transform to return other values, like: y_pred, y, etc. +def prepare_batch(batch, device=None, non_blocking=False): + return _prepare_batch((batch['img'], batch['label']), device, non_blocking) + + +trainer = create_supervised_trainer(net, opt, loss, device, False, prepare_batch=prepare_batch) + +# adding checkpoint handler to save models (network params and optimizer stats) during training +checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False) +trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, + handler=checkpoint_handler, + to_save={'net': net, 'opt': opt}) + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't set metrics for trainer here, so just print loss, user can also customize print functions +# and can use output_transform to convert engine.state.output if it's not loss value +train_stats_handler = StatsHandler(name='trainer') +train_stats_handler.attach(trainer) + +# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler +train_tensorboard_stats_handler = TensorBoardStatsHandler() +train_tensorboard_stats_handler.attach(trainer) + +# Set parameters for validation +validation_every_n_epochs = 1 + +metric_name = 'Accuracy' +# add evaluation metric to the evaluator engine +val_metrics = {metric_name: Accuracy()} +# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values +evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch) + +# Add stats event handler to print validation stats via evaluator +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None, # no need to print loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_stats_handler.attach(evaluator) + +# add handler to record metrics to TensorBoard at every epoch +val_tensorboard_stats_handler = TensorBoardStatsHandler( + output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_tensorboard_stats_handler.attach(evaluator) + +# Add early stopping handler to evaluator +early_stopper = EarlyStopping(patience=4, + score_function=stopping_fn_from_metric(metric_name), + trainer=trainer) +evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) + +# create a validation data loader +val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) +val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available()) + + +@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) +def run_validation(engine): + evaluator.run(val_loader) + + +# create a training data loader +train_ds = monai.data.Dataset(data=train_files, transform=train_transforms) +train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available()) + +train_epochs = 30 +state = trainer.run(train_loader, train_epochs) diff --git a/examples/integrate_to_spleen_program.ipynb b/examples/integrate_to_spleen_program.ipynb new file mode 100644 index 0000000000..887e48d7f9 --- /dev/null +++ b/examples/integrate_to_spleen_program.ipynb @@ -0,0 +1,520 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spleen 3D segmentation with MONAI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial shows how to integrate MONAI into an existing PyTorch medical DL program. \n", + "And easily use below features:\n", + "1. Transforms for dictionary format data.\n", + "2. Load Nifti image with metadata.\n", + "3. Add channel dim to the data if no channel dimension.\n", + "4. Scale medical image intensity with expected range.\n", + "5. Crop out a batch of balanced images based on positive / negative label ratio.\n", + "6. 3D UNet model, Dice loss function, Mean Dice metric for 3D segmentation task.\n", + "7. Sliding window inference method.\n", + "8. Determinism training for reproducibility.\n", + "\n", + "The training Spleen dataset is from http://medicaldecathlon.com/\n", + "![spleen](http://medicaldecathlon.com/img/spleen0.png)\n", + "Target: Spleen \n", + "Modality: CT \n", + "Size: 61 3D volumes (41 Training + 20 Testing) \n", + "Source: Memorial Sloan Kettering Cancer Center \n", + "Challenge: Large ranging foreground size" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 0.0.1\n", + "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]\n", + "Numpy version: 1.17.4\n", + "Pytorch version: 1.4.0a0+a5b4d78\n", + "Ignite version: 0.3.0\n" + ] + } + ], + "source": [ + "# Copyright 2020 MONAI Consortium\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "\n", + "import os\n", + "import sys\n", + "import glob\n", + "import numpy as np\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "import monai\n", + "import monai.transforms.compose as transforms\n", + "from monai.transforms.composables import \\\n", + " LoadNiftid, AddChanneld, ScaleIntensityRanged, RandCropByPosNegLabeld, RandAffined\n", + "from monai.data.utils import list_data_collate\n", + "from monai.utils.sliding_window_inference import sliding_window_inference\n", + "from monai.metrics.compute_meandice import compute_meandice\n", + "\n", + "monai.config.print_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set MSD Spleen dataset path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_root = '/workspace/data/medical/spleen'\n", + "train_images = glob.glob(os.path.join(data_root, 'imagesTr', '*.nii'))\n", + "train_labels = glob.glob(os.path.join(data_root, 'labelsTr', '*.nii'))\n", + "data_dicts = [{'image': image_name, 'label': label_name}\n", + " for image_name, label_name in zip(train_images, train_labels)]\n", + "train_files, val_files = data_dicts[:-9], data_dicts[-9:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup transforms for training and validation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_transforms = transforms.Compose([\n", + " LoadNiftid(keys=['image', 'label']),\n", + " AddChanneld(keys=['image', 'label']),\n", + " ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n", + " # randomly crop out patch samples from big image based on pos / neg ratio\n", + " # the image centers of negative samples must be in valid image area\n", + " RandCropByPosNegLabeld(keys=['image', 'label'], label_key='label', size=(96, 96, 96), pos=1,\n", + " neg=1, num_samples=4, image_key='image', image_threshold=0),\n", + " # user can also add other random transforms\n", + " # RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0, spatial_size=(96, 96, 96),\n", + " # rotate_range=(0, 0, np.pi/15), scale_range=(0.1, 0.1, 0.1))\n", + "])\n", + "val_transforms = transforms.Compose([\n", + " LoadNiftid(keys=['image', 'label']),\n", + " AddChanneld(keys=['image', 'label']),\n", + " ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set determinism training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_transforms.set_random_state(seed=0)\n", + "torch.manual_seed(0)\n", + "torch.backends.cudnn.deterministic = True\n", + "torch.backends.cudnn.benchmark = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check transforms in DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: torch.Size([371, 371, 222]) label shape: torch.Size([371, 371, 222])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "check_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n", + "check_loader = DataLoader(check_ds, batch_size=1)\n", + "check_data = monai.utils.misc.first(check_loader)\n", + "image, label = (check_data['image'][0][0], check_data['label'][0][0])\n", + "print('image shape:', image.shape, 'label shape:', label.shape)\n", + "# plot the slice [:, :, 100]\n", + "plt.figure('check', (12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[:, :, 100])\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[:, :, 100])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Dataset and DataLoader for training and validation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# create a training data loader\n", + "train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)\n", + "# use batch_size=2 to load images and use RandCropByPosNegLabeld\n", + "# to generate 2 x 4 images for network training\n", + "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, collate_fn=list_data_collate)\n", + "# create a validation data loader\n", + "val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n", + "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Model, Loss, Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n", + "device = torch.device(\"cuda:0\")\n", + "model = monai.networks.nets.UNet(dimensions=3, in_channels=1, out_channels=2, channels=(16, 32, 64, 128, 256),\n", + " strides=(2, 2, 2, 2), num_res_units=2, instance_norm=False).to(device)\n", + "loss_function = monai.losses.DiceLoss(do_softmax=True)\n", + "optimizer = torch.optim.Adam(model.parameters(), 1e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Execute a typical PyTorch training process" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "val_interval = 2\n", + "best_metric = -1\n", + "best_metric_epoch = -1\n", + "epoch_loss_values = list()\n", + "metric_values = list()\n", + "for epoch in range(600):\n", + " print('-' * 10)\n", + " print('Epoch {}/{}'.format(epoch + 1, 600))\n", + " model.train()\n", + " epoch_loss = 0\n", + " step = 0\n", + " for batch_data in train_loader:\n", + " step += 1\n", + " inputs, labels = (batch_data['image'].to(device), batch_data['label'].to(device))\n", + " optimizer.zero_grad()\n", + " outputs = model(inputs)\n", + " loss = loss_function(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " epoch_loss += loss.item()\n", + " print(\"%d/%d,train_loss:%0.4f\" % (step, len(train_ds) // train_loader.batch_size, loss.item()))\n", + " epoch_loss /= step\n", + " epoch_loss_values.append(epoch_loss)\n", + " print(\"epoch %d average loss:%0.4f\" % (epoch + 1, epoch_loss))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " model.eval()\n", + " with torch.no_grad():\n", + " metric_sum = 0.\n", + " metric_count = 0\n", + " for val_data in val_loader:\n", + " roi_size = (160, 160, 160)\n", + " sw_batch_size = 4\n", + " val_outputs = sliding_window_inference(val_data['image'], roi_size, sw_batch_size, model, device)\n", + " val_labels = val_data['label'].to(device)\n", + " value = compute_meandice(y_pred=val_outputs, y=val_labels, include_background=False,\n", + " to_onehot_y=True, mutually_exclusive=True)\n", + " for batch in value:\n", + " metric_count += 1\n", + " metric_sum += batch.item()\n", + " metric = metric_sum / metric_count\n", + " metric_values.append(metric)\n", + " if metric > best_metric:\n", + " best_metric = metric\n", + " best_metric_epoch = epoch + 1\n", + " torch.save(model.state_dict(), 'best_metric_model.pth')\n", + " print('saved new best metric model')\n", + " print(\"current epoch %d current mean dice: %0.4f best mean dice: %0.4f at epoch %d\"\n", + " % (epoch + 1, metric, best_metric, best_metric_epoch))\n", + "print('train completed, best_metric: %0.4f at epoch: %d' % (best_metric, best_metric_epoch))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the loss and metric" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bxSsHxrClbwzHLOxA/1gaL/eNIucICCGwYfcwjlnUiXntSbx6+RwAwIVHLwAR4TWHzUH/WBq/+OMuvHZVD+666kx8890nYs/QpL46IISsDvaTJ7bjlf5xNMUtjKayGEtlsXdoEs/vGsLlNz+OP24fwK+f2wMAGA28Lzb3jmLjnuICO4ynt0uRnHUEnt4RfrVCCIErbnkS7/zeY/j1n3YDAAbGpct+x7odOHxeK05bMQcjqSzOOGwu7t2wD0Pj0U/IohCr6N5qhI5bsJPMMAzDMCXZOzSJi775IN50/ELc+N6T8x4/WOe1d2RSC+21rwzg+V1DOHZRB4iKK93t7iS7nYPjGEll8a5Tl+KslT3obkngv594Zcrj2do3itcdMW/K25fD8GQGv352D7529wsYGM/gk+etxDPbB3HWyrmhz/+Rzftx3a+kON437EUG9g1P4tkdUkx+/uIjceV/rsWaF/pw5WNrcclxC/FS7wjOcZ/TpccvxLpt/bjgmAUAgNccNhcAMJ7O4W0nL8FJy7px4tIu3L52Ox7f0g8AGJrI4Oo7n9Gi+eyVPfj9xl70jqTw9u89quMY312zGbsG5PuhKW6HPmchRMm/rckfXujFMQs7sGHPMNbvGsaZh/fkrXP/pj6sc8f2FTcrnbAtPPbyAWzuHcXX33YcLjtxMTbtG8HK+W1IZx10tsQjjyEKDeIku3GLOsocMQzDMEyteMjNgv7mT3uwfzTle+zOtTvwDmPC3lRKiSmRvbirGQ++2IdL//1hPPBi8fzpd+/fjP96TArhfUNyTIfObQEgrxiXOwwzh/zktv6qlETb3DuCM772Bx0R6R9N4103PYb3//AJLU6DvO8HT+DlPul0DxpO6CsHxvHDh7fi+CWdeN0R89DTlsC/3LsJOwcmcPODW5DJCZyzSorL951+KO69+nVY0dMKAFg6pwWLu5oxtzWBC46WwpmI8K33nKRPFoYmMkhnvSsJZ63scY87pgXyKYd248GX9uv3iFMghjNQpoPbN5LC2e7YJzJe3OLRl/fj/G/cj5HJDO56ehd62pI4ckE72pIxnLysC7uHJvHP92zC/PYk3nLSYjQnbJy4tAttyRjmtCbKGkMUGkMkWxy3YBiGYZioPLx5v75938ZeADIP/MCLffj2fS9hzMiJmiImKrsHpSP6puMX6mW9w6lCq2Myk8M//3YTdg/J7VTfg3ntSQCARQSUqXHNaMDdz+3FV361Ho+9fAAHRguP42D53M/+5HNU71i3Q0+wu31t4QmIbUl5YT+dc2BbhETMwo1rNmPX4AT+5uKjQET4wpuOwYKOJM4/aj4A4KyVc3HG4dIxti3Cyvltvn1+9S3H4hvvPAEJI5O8oKMJt37w1YhZhKGJjM9cPG2FjGxs2jsCAPj6247DFy89xiekC51nBJuX/GnnIJZf8xts2F04inHS0i7YFvn2/6/3vogt+8fwwIt92Ds0icPmteJnnzgDT/zt63HFmcsBAM/sGMTHzjmsoKtdSRpCJNsct2AYhmGYSOQcgUc278ebjlsIImD3kHR9b31kG6645UnsHPBHLcbS5eeTlZP8zlOW6GV9oykIIfDUKwN64pYieEwA6GyOIxmTQsiiwi5mkMHxNO7f1KsnDH7k7BUAgOd3D+MDtzyB/3hwS9nPJwrprINndgziQ2evwGPXno/3nb4MADCnNYH3nb4M//f8XqSy+SccRMCHzlquXeCOphgWdjZhNJXFos4m7fC+5aTFeOjz5+OWK1+Nf33nCfintx9fNOJw/lELcJ4rqP3HI3Q2xzE0kcHO/nEcs7ADf3/pMVjSJV37F1yR3N2awPFLOtHT5jm0haqE7Ar8/da8IK8a3PqovyKHbQTLj1vSiYRt+V6Tbjcucf+mPvSOTGJ+exLtTXG0JmNYPrdVr/cO4301nTSESI5znWSGYRiGicQTWw5g/2gaF7/qEPS0JbHHdX23uiW+Fnc1+9YfS5XvJO8ZmkR7MoZVC9qx5WuXoD0ZQ99ICt+9/2W8/XuP4h03Pear1KCcyG+950RcfOwhAID5rosMSCc5qki+fe0OfOjWtegdkY7xqcvn4E3HLcQLe4aRyQntlE4FxxH4zn0v4f/d9hRGJjPoNTLEuwcnIASwbE4LFnY24/B50tk9ZmEHVvS0Ip11kMr6r3gLISCEFK4dzVIgdjTHsaCjCQBw7OLO0HG8/ZQlWNLdMuXn0dkcx/Z+mft+28mL8eGzV6CjOYZkzNIT8ea0JkBEOHphh/f8hcD63UO47bFtvv0FT3KU8F23bcD3d0648di5rQks7mpGMm75nGSVSV/zQi/2Daf06wBAi+TmuI2ulspHK8JoiIl7ugQcxy0YhmEYBkIITGRyaEnkf83f9fQutCVjuPCYBfj+Q1uwxxV62/aP4TWHzcF/f+Q12DM0gZ8/tQv/9vsXMZYq30neNTiBRa7YtizCvI4kth0Yw11P7wIgXddP/fRpPLN9EI9ccz52uCLr9BVz8Ud3stY8QyQTCl/qDzI4noEjgD63bm5HUwzz2pM6QvLSvtIiWYlXK1BS44GX+vCNe18EAGzdP4ZN+0aw9fpLQERaKC7pls/7qEPaAchJdSqyIgIyRT0n2yJ0KZHcFEdHk7x97KIOTAcdzXE8t2vIHa8U20SEBR1NnpPsCtHzj5qPh16S8RzHEXjrjY8inXPwjlOW6v2pk5x01sHf//J5bO6T3f627B/DnqFJ5ByBnQMTWjyffGg3iMh1kh3sHpzAx297Ci/uG0VrwsYBNxNtnih1tsTxj299Fc5emT/Jb7poDCeZ4xYMwzAMo/nhw1txzBfv8VVLUDzwYh9ef/R8NMVtLOxswl43brFl/xgOm9cG2yIs6W7BKYd2A8CURPKeoQks7PJcwHltSdy/qQ9DExm85cRFAOSkwV2DExgcT2PnwDjiNmF+exJzWqUwMgWSihVEqd88mpIxiz7XSe5ojvsE9+6hSV2DuBCv/sff4803Ppy33CwxtskV28qxVkJRieQzV/bgns+eg3e/eqnMVAPIBcavroBbJN1dOd6YLh13zMLpEcmdzXE9SXDpHO/KwSLjb6Ymwl155nJcde7hevzK0d9mNBdRWfL1u4dwx7odviY0w5MZvPaf1+Dy7z8ORwCXn7YU17/tOADQTvK6Vwa0aL/QrdABAPM7vL8bICcoHmrELqabhhDJNnfcYxiGYRqcG+7dhOXX/CaSUPyNW9c2OKEKAEYms1qALuxsxp7BSfSPpTE4nsFhPZ4AaU3KPPB4kWYPJl/99QZ85EdrMZbKYtv+cV9sY7572bw9GcNrV/lLsT2+pR87ByawuKsZlkWY62Zg5wXiFoCs7VsKNWGvb1QKzY4mv0gGoKtJFGL/aBrP78qfdJbO5l+x3urWYN4xMA7bIhxiRASOPKQdRKQd6WAsVAlOyyJPJDfFcdwSGbM4ehpFsmLZHC+2oSINZIh2IsK5R8pssxDe3+Vl1y0GvBMF8/mp91jw/bOipxU9bfIx5STHDMfezFEvaG9CLWkIkew1E2GRzDAMwzQm375vM4DSQnFz7yhs5VwGNJ2KYTS7lQEO6WzCSCqrJ9IdNs8UyTKqMRrRSf7hw1vx+429uHHNZoymsni7MblqniuKXrW4U2dvFY+9vB87Byaw1BVrc10Hc74hkNT8tCi55BElkl3h1t4U87nSQLTIBZDvXKdDmq+ods07ByawqKtJl6U1URowuD8tkskTyZ3NcVzzxqPwm0+frV+TSqOOdVhPK9qbvL+HmjxI8E+yUzdzjtCv5RbjRKPPvWIxYrxXVi2QmezxQKZdTcZUt1NZR3fdO+XQbrzhmEPQ0STfe0Enudo0hkh2M8k5ziQzDMMwDU4xmfjb5/fgghse0E0YgqJSTRxLuiJ5YacUouryuOkqKpE8ns5CCIEvr16P591L4sW4+cEteNNxC3Hysm69TFUtOHx+qy53pli/exi7Byf0WNRlfr+TLH9HscKUUFO1faVIlvtW4mswYl3fYP3fsA6FW/ePI5tzsLl3VFeICGIXiFvoTLIhkjvcqh7HLgqftFcJ1LGUY61Y7orkoOeonXAhtAusnORlc1rQO5KC4wh9ggJ4k/SC1VHMNtmJmKxuMelmlb/7vpPRnLBxuFvObh47yQePOtvJcCaZYRiGaXCKualPbvW3+A020Jh0J5BpJ9mNBuxwqwqYLl9rQt4eS+Uwns7h1ke3Yc0LvSXHl3UE3nLSYt+yoYmMPl57UyzvsaGJDLpdcXzckk6845QluvQZ4GWSozjJKm6xfzSF1oSNmG1pwa0mExa78my6varagiJMJN/0wMtY+Xf/h/W7h3HWyrmh+7T0+P3LVTzBjDd0NE1/TQX1PlAVOBQresLzvraRCVd/CyWSD53bgqwjMDCe1q99S8LGB9y6xiqKoTDrNidjMpM8mZGva5P7/ls1vw3Ncbsqr0UxIolkIrqYiDYR0WYiuqbAOu8iog1EtJ6IfmIsv4KIXnJ/rqjUwE3itnKSWSQzDMMwjU0xnTg4kfbdDzqXqspCsyuAVUMGtdys5qAqY4ylslochsUNFKoca3PcxmtX+SsQXHHmcpy8rAvvfvWyPCe5dySFdNbRFR1aEjF8450nTD2TbDjJba7ImtOagG2RzkkXu/KcCilJ9oeN+zCRzoVmkgFZD/r7HzgVnzxvZejj6nUNnrQoQW5b/hJw040aharAoVhWIN5hGfEd9V54uVfGLVQkZN9wSk+aXPt3F+DoQ2Seemsg/22eiEkn2dGiPRmXeu5T563Cje87qaxW19NBSYlORDaAGwFcCGAngLVEtFoIscFYZxWAawGcJYQYIKL57vI5AL4E4FTIv8lT7rYDweMcDJ6TzHELhmEYprERRUIHQ4F4QNA80o6dK0aCV2JtQ5QkYhYStoUxQxwG6/x6+83pfbz15MV53dCWzmnBL646C4CXLwakk6hc5s4i4pB0prfgKholkgfHMlpo2xbhhCWdOPnQbvzhhd6iTrJZzWNH/zh2Dozjwz9ah4uOXYAjD/FPpGtviuET5x6OT7zu8KKCziqQqfaqW/gn7k03n7lgFZbPbdEtqxXq7xZ0xN1UK3KO0O8FdWKlhPVPn9yOTM4BkXSS1Qnalv2jvn0FneQDrkgm8qIYy+a2YNnc6cljl0MUH/s0AJuFEFsAgIhuB3AZgA3GOh8FcKMSv0IIdT3mIgC/E0L0u9v+DsDFAH5ameFLuJkIwzAMM1so7iT7RXJQlKkJUipuoRxCZTIFygKjJWljLJXVDnIqpEX19Xdv1F3svvbW4/Bet9NcIUwneUl3s640UUwkFxKZYahL/iOprG/imxLp37h3U56ja2JWY9h+YFw3U/n9xl4cFognvOGYQ3DVueHusYldsLqF/G1ZhMPntaKnLYEjA+7udNDRFMefn7E89LH1X7lIX6FXWEbcImhIKpF82+OvAJAnDkSEFvc9ZlbBAPyZZDlxL4fJTA5NMbvmznGQKHGLxQB2GPd3ustMjgBwBBE9QkSPE9HFZWwLIvoYEa0jonV9fX3RR+/CJeAYhmGY2UJRkTzuj1uks+FxC+UYqu9P5Q4Gm2e0JmIYS2f142FxC7PNc1dLaRc0GbO0uWV2jSsukqNlklPZnG+MKlZiErOouJNsTDTbNzKpnemcI5AJlCtrTkSb2lUoU+1Vt5Bl8tZ94cJpK/sWldZkzOf2AobIFyLvPRCMaLS7J0Ex20IiZmFHv78bX3DiXjonM8nq6sZMolIjigFYBeBcAJcD+D4RdUXdWAhxsxDiVCHEqfPmzSu9QfDgquMeT9xjGIZhGpyicYsJfyWBoKBJBSbuKcNQiUY74OS1uk6yilKkMvki2RS3XRHytESk3eTF3V4t5WIiWVHKCxud9D//5ni+SLYtKnrlWTnHMYvQO5zyxS/2j6bQmozp+EfY/sOwC0zcUyI5+LrPNMyJh8FcdvDv1mZMtmsNOUkJm7g3kcnlRXRmAlFE8i4AS437S9xlJjsBrBZCZIQQWwG8CCmao2x70MR03IIzyQzDMEz98JMntuPjt63DW7/7CN5102ORtilmpg5NBJ1k//di0ElW4iebC3eSm+Kyjq26xJ4yRHfOEdgzNIEFRi3bqL8RKaMAACAASURBVJPOlJBaElEkWzqU7F+ezTn4zO1PY+Me2fhjJCiSQ51kq6iTPO46yct7WtE3mvLViX56xyASMUuL48gi2cj0mpiZ5JmMjrs4+U5yU9zGY9eer9tFm3GasLbohSbu1atIXgtgFRGtIKIEgPcAWB1Y55eQLjKIqAcyfrEFwD0A3kBE3UTUDeAN7rKKEuMScAzDMEwd8rd3PYd71u/D09sH8eS2/kjbFIscBL8Hg/nRYHULJc7SauJeQCQnYxZSGUdP2DOd5Luf24PX/cv9PiEUJW4BAG1JuZ7Zla+YwC6USd52YBz/+8xufPK//wggv/FJFCf5+V1D+Ms7n8HAWBrb9o/pTPLyua04MJrCsJHz3jM0iYRtiOQQERhGobiFMDLJMxkzU53JOb4To6a4hYWdzXqiXZsx8bAl4S81CHgVLAB/CbiZKJJL/nWFEFki+hSkuLUB3CKEWE9E1wFYJ4RYDU8MbwCQA/A5IcQBACCir0IKbQC4Tk3iq+iT4BJwDMMwzCyh0DddWHmy4DJV3aI5kEkuNHEvEbMwmXFCS8Cp0m3meLpaEpGeg8qtmpnkYjVxC4lMJcJUA5E8JzlEeMlMsvc8PnDLk+gfSyOVdfDM9kH89UVHAACWz22BI/y1ktNZxxcXaI6Yo9Vxi8CfyHOSI+2mZpiZ8HTWwYKOJuwblvWPlbhVnfiajNenJeldMdjrduVL2MFmIg5S2dyMzCRHOgUSQtwN4O7Asi8atwWAv3R/gtveAuCWgxtmcbSTzHELhmEYpkHI5Bx8d83L+PBrV/guYZs6cWgig2TMQlPcRv9YOm8fheIWys2z8kSyX60lbAvDE16dZLO6hS4FZkx0C8ughhGMW7QlY6HtnBWFOu4p0axyw3lOcsh4gk6ymuy4bf8Y+kZTOpOsus9t3e+v8xu3CbalnORoz1eVUCs8cW9mq2Rd51kIZHICCzqaAMjui6oShupqOGm851SFiyXdzboLpN9JtpFzBEZTWd1IZCYx82T7FNCXAThuwTAMw9QRiSLC8OdP7cS//f5FfHfNZt9ysyPcCV+5F++++XEAwMB4iEgOxC0mAyXglMOZcYVNftxClugKq5OslilRCSByCa+2ZAytCVvHM0pN2itYHcIdjopIqGYWirBL+DGLfBP9lV7ePTiBdNbB8KTch+o+t6UvKJK9uEXUiIBuxlFIJM9wK9kOTNwz4xYK5SSbJ03qJMKcoJm0/ZlkABieyMxIJ3nmjWgKxAJnwgzDMAxTDxTK4e4ZmsCeIXl5Omj/KJ2lvvOe3TEIwJtwZpIftwhM3HNVQKZAdYuEmxnVcQtjf6lsznfcz7x+VehzCeOohe04emEHkjEbTXGr5IQ/JTKvvuMZfOjWtXp5UHRGqm5hh1e3GHAbsewfkScbqrTZ1v1jOh4C+EVy1Il7Zp1hE10neWZrZD2+nDtxT2XKTXpckWzWmVbvm562pBbBwUwyIKuyRHXlq0ltm2JXCCJC3Cb9T84wDMMw9UBncwz7R1O+ZRPpHM64/j59P9iBTX3T7Rmc9C03xYki6CRPZHKIWaQvkdslqlsokawm9ilhDBhOcjqH01fMwdUXHlH4iQa46tyVuglHZ3O8aB4Z8DruPbL5gG95UOyqbHJrwsZYOqczyyZmdYvJkOYo+0dTaEnYmO+6pemcg/kdSYyls3CEdP+b3P2GVW8Iw5v45l+eK3ByMtMw4xbprIOEnT/eOW4e3Zykp5rXzG1LojluYzLj5GWSAddJnoFxi4YQyYA8s8uETFpgGIZhmJlKmIMadH+Dk8OUG7ljQE4oU1EFM/ZQaF8TmZzP/fQ67olQNzPpTqwKayaSNtzlYPOJcjiksxkLO5uKrlPIaQ3GL0Yns4jbhPamOMbSOS1mTcxM8s6B8bzH+0ZSaEnEkIzZmNuawIGxNNqSMTTFbYync24JOPl8ozcTkb/zO+4J9/GZLZKViFfvg7C/97K5LfjOe0/CWYf36GUqA9/TmkBLIobRVNZ3IqYEczrnIFmP1S3qhbhdvO4hwzAMw8w0wrK4wQhBKiB01VfdDrfqwiK3jNpEJj9uoS537xmaQCrjltoyhKMSLOmck5dHBvLjFmYJOFOAB9sYl8NN7z/ZVzs3jEIiMiiSRyazaG+K60v7oXEL8qpbBLvBAUDfaAqtSXfC2ZyWPJEct6nsTLJtxC0mM7IrYEdTXGeqw177mYQ6mVLOe6G/96XHL/LdVyJ5TlsCzQk77+9sCuOo0ZVq0lAiOaxdJsMwDMPMVMyqFYqsE8wR+++rjnvKSVYTpoo5ySq+8daTFvsmSJkl4MImEaoSXVokh0zcA6DbTE+FhZ3NJdcptPegMzuayqItGdNirFSd5LCKIPtHU3pMS7ub8eyOQbQmY7q0Wdy2dH42ejMRb+LeF375PH721E787upzfG2pZzIqu65EbyJm4eG/OS+07KBJ3N2wNRFDc9z2taQG/BNXeeLeNJKwieMWDMMwTF0RVvorKPwmswHxq51kvws6EZZJzjo6b6zWMYWd53CGu5nJmI10ztEOsm/inrHfYuXbKkGhEmnm+UQqm8PIpBTJKg4QFoeI2aSvPIdN4Bscz+hSdmryXnPc1q5x3C25J/cfTSSTUR1CTbS87tcb9FWDGV/dwlJOsnzB47aFJd0tOGxeW9HtbvrzU/CZ16/Cku5mNCfsvJiGOYlvJjYTaRiRHLMtrm7BMAzD1BWmRFOVmrKBcqZB8at03a7BCfe+WyvYrTJhOnKpnIMX9o7o+5PZQCbZUAFhk8eU86f2bU7cM6MXxUrZVQKrwO7NaMrwRBajqQzammJ63GHCy3SSC8U0VWm6pa5IHk1ltcAzO+61xMubuOc4AgvdeMwrB8Z1vnzG10kOxC2i/r1X9LTi6guPABGhJZHvJCfZSa4OcZu4LTXDMAxTV5iZWnUr6G6mAk6yiluoahZKVE+kc0jGLJ+AyWQd/HH7gL4/kc75hKMpjMN0mhI1qrSaI7xKGGbE8WDiFlEoJCLN12poIoPRVBbtyZh2KAt23MspkSyfQ3egnbbqHLjU7QjYP5bWr1vCttDVEnerXESTUWZbbXXVe+/wpK52MeOrW5DfSZ7KRM3FXc04JDBB03SSOZM8jXAmmWEYhqk7DD2ccwSEEHnuZl4mOVAnOWc4ya2BjHM652DjnmEAshX0ZCbnax1tRiwKTdwD/J3sUlkHMdtC2hDv0x23KIR5kjE8mcHoZBaHz4tBuNXxwkq02Rbp10yJ5e6WhK6TDABzWl2RPEe6vgPjaRw6VwrmeIxw+WnLcPqKuSUnHCp0MxFH+GpOq0z0DE9b6PGp6M9UJmr+/aXH5L23E0ZjkZlY3aJhnOREjOMWDMMwTH0hAq1CMjmh3dETlnSipy2RV8vX0QLPFcmO5yw3x22f2E1nHT3ZLpWT1S1Mx86sGhEqku18kazLwWWrGLcomEnOd5LbkjE9nnAn2dKvmfrdmecky/uLuprRlozh8xcf6WWSbQvtTXGcsLSr7PE7wt/4bM+QjMzUSyY5ZUzcK5emuJ03UZUzyVUiblt5OS6GYRiGqSVrXujFKwfGCj4uBLBqfhuueeNRAOTlfxUBuOq8lVjU1Zwnkj0n2S/0xlM5tCZtnW0GpJBVQjKddTCeye9spgRQmBBVIiboJAMzJG5hOMmjk1k5ca/Ji1uExSFsy5u4p37PMdx1QDrLgNQWz3/lIrz1pCW62cVUTghssxlHTminerebK5/pmWQiApE5ca8y4zVfywXt+a2ua00DiWTiuAXDMAwzo7j6zmfwn49sK/i4EDILHNOl2DwnOWYRmmJ2XtxCkQ46yZkcmhMxxAwBk8k5MP2j4Yls3gQplYcNd5KlMDTbPauMtOkkT3fcIqgh1YQ3f3UL6Zp3NMV1lrpQJjnnqNdO/u7KE8n59avV6zaVqIHZ1jmTc7C0W8Y4drutx2uUVikLi8ibuHcQzWNMFnQ04fglnbj2jUfhtBVzKrLPStJQmWTzTJdhGIZhak0q4+RNvDMRELDIaxOdzTna3bQtQjKe/92mzNO8uEUqi9aEjeEJo7pF1tFCEJC53eBlbcsCkAt3M8MyyUocpyrUTCQKQf2eyQkkYuRzkgfHZb7XrJNcqLqFN3FP/u4qMHHPRO1zKgLRbOucyTlY3N2GZ3cOYY/rJM/0jnuAPJmaKLO6RSmaEzZWf+rsiuxrOqiDc5doxLkEHMMwDDPDyOQcpLOFo4AqUqvc36xjOsmyHu9kxtHOqdxGuPsOxC3SObQkApnknOOrACFEvruqnOSwMmvJAhP3gGAmeXpFXlBEqu/7YCYZUCLZQtymUPEes8mXSbYIOHtlDy46doFeR8UhTA7GSbbJEMlZB62JGOa0JrBHOcl1IJItCxV3kmc6DeQkEzJFPogYhmEYppqoShXFooAybkG6M1km52iX07bIFck5X1UAdSsdqG4xns6iJRHzZZIzOQfBw+c7yW7cIqKTHCaSp9tJDo4sGzhBAAyR3BTDO09disPnhze6sI2Je1lHIGZZOO+o+TjvqPlYfs1vAOQ7ywCMiXvlC1o9cc8B0jmBeMxCT1sCL/eN+R6fyci4hddMZDbQMM+SnWSGYRimGvzmT3vQN5IquZ4StsW7wQoQDCfZzCTbhKaYJUVyznSDC1e3yHOSs46vTBoQ4iSriXuhHff8dZKB2mSSgyIy40ZIHF/cQork9mQMRx7SjstPWxa6r5jl77gXlsXuag6JW8QPJm4hf+fcuEXCthC3PbFeqFnKTMIm0n/72eIkN8yzTHCdZIZhGGaaGZnM4JM/+SM+cMuTJddVwraYgaMn7qlMslHdwrYIzQnlJHv7cIQUd8pE9YvkmE+wprNOXnOSpkB1CyVAiznJppOdNkrK6fWmu7pFQK3ouIXId5KDtaKDmB33MjnH57wrwkTgwU3cU+2/pUiO2+Q7bj04yWZ1i+ku+TdTaJhnyU4ywzAMM90oSba9SFk3hXI7i8YtIMVHvFB1CzeT7C9xKnzfd6oJiYxbhJSACzjJTQEBaBWpbmG2EW51xXUqKzPSVY1bBESkF7fwlqlJZaXq7Uon2XPh7YgCX5WAm1Im2VLNROCKZMvn3Ie99jMN26p8dYuZTuNkkmPEdZIZhmGYaUVJmWDnsDDUd1K6SNxCCFndQjvJOeGrbtEUszCZzWnBLbdBnkhOZR04AmhJ+uMWpuhW5NdJdp9bWJ1ko6NcW1MMY+kcUlknT/hPewm4wP1gt0HAy0qXEpymkywzyd76v/zkWQWjNKr28lQEonppZdxCIG5bASe57F1WHbO+NGeS64yYxXELhmGYUhDRxUS0iYg2E9E1IY8vI6I1RPQ0Ef2JiC6pxThnKsGIQzFUZrjYVU5HwJdJ3rBnCL2uSItZFpJxG0IAE+mcb5uMYQplHYFx9/GWeMBJdkvKmZfHC1W3CNM9piBU3dJ2D07kCf9qNxNRz98JiYFEEck6k5yTE/cUJy7twoXHLAjdzmsmUv5zVa+x2bHOrru4hTdGdpLrjI7mOMZSWYxMZtDelD8rlWEYZrZDRDaAGwFcCGAngLVEtFoIscFY7QsA7hRCfI+IjgFwN4DlVR/sDEVPmosgkjOq012JuAWItIj9m58/px9T1S0AYMSYOCeMuIVtERwh0DsiS4nNbUvmicRUJofmhI30hNwmKJKLVrcwlPOS7hYIAXz9/17AUYe0F1xvOsgXyf5Ji4A3oTAsY2xiW4ScUSc5atTBbEtdLmr8yu2WmWQr7/GZjPn+mO6ToplCw5wKvOawOXAE8PiW/loPhWEYZqZyGoDNQogtQog0gNsBXBZYRwDocG93AthdxfHNeKI4yArtJBcpTyqEW90iRKjJTHJ+CTYzbtEUs5DNOdg1IJtSLOluzttXKuugxYhYJMupbmF052triuHf33sSAGBLnz+TXe2Oe9oJFuU7yf7qFo6vQ2ExDmrinuV3kuO230muh0yyOcR4PZTjqAAN8yyPPkR+pm/vH6/xSBiGYWYsiwHsMO7vdJeZfBnA+4loJ6SL/BdhOyKijxHROiJa19fXNx1jnZGUoZF1JKDUpHKzuoWJ7balBgJOshG3aIrbcASwy+3ctri7We9LuX3prONzjwvGLUo4yQnb0u5nqspxi+DQ1GsqQjLJpZ1kf53kqAK1s1lepW5rKv8ivDrGpHaS/ZnkOjCStdC3KPyEqhFpGJGsznaLTZBgGIZhSnI5gFuFEEsAXALgNiLK+64QQtwshDhVCHHqvHnzqj7IWiFEGU6yk9++OX9/MpMcJjJjNulJdh/98Tq93BFCu9RNcRtZx8HOgQkkYhZ6WpNafCmBm845vsl6wYl7SqCFXfKP2ZZ2EOM26XVVlQNF7eIW+cuiOMnKgc4FJu4V45RDu/FfHz4dJy3tijpsjTpEymjrXG9OshrjdF81mEk0zDPVHwYskhmGYQqxC8BS4/4Sd5nJhwHcCQBCiMcANAHoqcro6oBynORIdZLhVrcIuXxtW1RQwKmcczIuXdFdAxNY3NUMyyItZlSsIh2IWzTF/cfy4hbhY1QVLhIxTzAHRXK1m4noEnBTiFuo6hbCrTRhR4wOEBHOXtUTWgWkFGr8umNdjHwxj3rIJKsxxutA0FeKhhHJMfesLJ3LlV6ZYRhmdrIWwCoiWkFECQDvAbA6sM52AK8HACI6GlIkz548RQmCNYeLkYlS3cJx6ySHOcmWhROWdqE70CLZF7eI2cg5AjsHpUiW27kiOeY5yS0JLyKQN3GvSJ1kwKtkILO4/glo6li1ils4Tn7cIoqTDEgXOeeENxOpNN7EPS+TbArj+hDJ8jc7yQEilAy6koj6iOgZ9+cjxmM5Y3nww7iiJGwLqQw7yQzDMGEIIbIAPgXgHgAbIatYrCei64joze5qfwXgo0T0LICfArhSlJMxaHDKEclqcljROskQIFDBTPKCjib84qqz8sag4hbNCTeTPDCOJd3N7nb+er6ZPCe5wMS9AkJNdbJbNqclz0lW3e2mu25uUMdmcl5cQuE1YSk+FtU8JOuIsjLJB4POJGfCM8n1YM7aVTohmkmUTJ9HLBkEAHcIIT4VsosJIcSJBz/U0iRiXCuZYRimGEKIuyEn5JnLvmjc3gDgrOB2jKSc0wXPSS5W3QIAFa5uAQBdzQEnGV7cQkUn9o+mcUhnEwBPxJhOcsy2ELdlVYdkLDxuUUgsfuXNx8Ii4H2nH6onxyvXti0Zw9BEZtrr5uZ13HP8bamJvL9NeU6yqIro0ycXWTOT7L1m9ZBJVidRpU5CGokoUzR1ySAAICJVMigokmtOMmZxJplhGIaZNspyklXHvZxs4xyWZXU1cqgTq4RTR1Aku1lawGtwAXgxCp1Jdh/L5ARskvdjTv44lPgp5CRfceZyfTs4ca/drfQw3ZGF4N513EJ4HeDSZVS3AKrrJBPJSY+pAk7yVHLO1UaL5FnkJEc5HYhSMggA3u52Z/oZEZkTQ5rcMkGPE9FbDmawpUiwSGYYhmGmkbIm7hmtpAu6yUKVgCvsJAdFnCO8GsxNRoxCCe1gJhmQJbuSMSuvsgXguZxRcrHeBDS3w19i6g02yqFQxz118disrlGqPFnQSa6WM2oRaSc5bpOOfQB14iS7L9NsaUkNVG7i3q8ALBdCHA/gdwB+ZDx2qBDiVADvBfBNIjo8uHGl6m0mYlbRUjsMwzAMczCUN3FPGLfDv5tUdYuw5gyFhZMwmokYItkVxcFMMiBrICdjFppCYhFe3KLIk3FRWlXlrZXonu64RaHqFp6TrKIApcWmer5Zx6makwzIv4F2kmN1mEmm6K9xoxDlXV2yZJAQ4oAQIuXe/QGAU4zHdrm/twC4H8BJwQNUqt5mMmazSGYYhmGmjbLqJBsiudBVTqeAk2xbVPASvBBAWjcTMZt9+C+Hm06ybRESMcvnPCtKVbcwUWNS4lRFOqY9blGguoWarJfQJwilx1GL6haAfA7+THKdVbfgOsmhlCwZREQLjbtvhpw1DSLqJqKke7sHcjLItGWZeeIewzAMM52U8xXjj1sUcJKFqm6RL5ILIQBfMxFFftzCe0zGLey88m/msaLFLeRvdQKgYg7xaZ+4578fFMnB514M7STnBLK5KjrJFvkyyWaHw7oQyapO8izKJJecuCeEyBKRKhlkA7hFlQwCsE4IsRrAp93yQVkA/QCudDc/GsB/EJEDKci/HlIVo2IkbQvpLNdJZhiGYaaHcuIWpntcyMARcOskB+IWxcSe4xhxC8NJDgrFvLhF3ArtjFequoWJFXSS3eNXu+Oeins4QkgnvoznoE5Icu7EvWpNRLOJjDrJ/kxyPSQYZmPcIlID8gglg64FcG3Ido8COO4gxxiZZNzCaCpbekWGYRiGmQJTqZMMFI5bqN0FJ5sFxV5rwsZYWgosAYRWt1AiWWWSg3GLpXNakAwRs6WqW5ioNZSDe+Qh7Th8Xmv121JnveoWNlFZQt+sbpFzonfcO1iKVbeop4l7syluEUkk1wsJm6tbMAzDMNNHOXWSs7nS1S2kk1y4soWivSnuiWThxQ3MahWJWCCTHPeL5G+9O7xlQTlxCzVW9XT+7PhFuOrclSW3O1jy4haOV93C8onk0gJOOaLSSXaq1mbZtkgbeYmYv05yPZWAm01xi4Y6HeAScAzDMMx0UonqFnuHJnH93RtlS2UhQi+1B8XeJ8/zCkPJOslyf8mQTHKwTrJaFrOtUBdQHb+c6haqHXS1tF3wNcrmHIxMZpDK5mBZ3utVbnWLXBUzyRaRfk+YTnI9uMiAN05uJlKnJLkEHMMwDDONTLVOsvnd9Nk7nsbjW/px0asOkdUtQrYNir0/P2M5jljQjnff/HggbhEtk1zMJS6nuoVaV0VJqjXhLOi0ZnIOjvvyvQBkFEUJ/HKrW1Qzk2xGauI2GQ5+VQ5/0BA7yfVNT1sS+4YnMZ7mXDLDMAxz8Kx+dje+c99L+r7pJOdKKOagk7xveBIX3PAA1u8eBiAjggLhnfjCxJ5az4xbFK9uYcYtCo9zKtUtnGqL5MD9yYx30mFZ5DnJEQScmjDnZZKr5SR7t00nuR4qWwCAemlnk5PcUM/03CPnI5V18MSW/loPhWEYhmkAPv3Tp/GNe1/U9806yYXKuimyAZG8ae8INveOYmRSGjlNcRuikJMcIva0QHXjFrZFPldPTZ6zbTVxz4hbFHOSy8kkQ2WSqx238B/owFhK37Yt0gKu2PNUKHHqKCe5SqLPHFvcqJNcNyJZ10muj/FWgoYSyUvnNAMA+sfSNR4JwzAM04iY5nFJkWzELdJZR4tjhW2RFMmhmeQwJ1n+lnWSBeI2+QRWXE3cC4tbFHFL7TLiFuTuMlf1TLL/QPtHvO/58qtbeE5y1j3ZqAZkvM7qxxzPTMeLWzSUdCxKQz1T9YHADUUYhmGY6cCJUNZNEYxbjKYy/n0JAVdq5m0bPgFNxS0E0jkHccvyuXr5E/f8dZILUV7cwpv0FnWbShA8zP7RlPFYeYJTOcc6k1zF6hZAfgvtOjGSZ2Wd5IYSyerSEle4YBiGYSqJiln4neTimWSzBFw6J/KcZCHkfqNUtwC8uIWcuOcgHrN8IjVRJJNczEn2Ju4VfToAPDmvTPJaieS+UTNuUV4UwHSSq5lJ9kSyv551vTjJs7FOckM9U+Ukp7jrHsMwDFNB1EQxp5xMcsB1Hs4TycIXt3j2i2/AV958LIBwt86buCd03MLM0+qJe+7vRKCZSCHUQ8WEtLeuVxkCCM9TTwdBMW6ecMi4hV94FkO9ttmc41a3qF4zESD/ZKZeMslcJ7nOUW88dpIZhmGYSqKqJpkiuVS0L+NrJuJgNCiSAVndwpWanS1xdLXEARTIJKvthDx2zLJgasLgZfyoE/fKayYif6uJe9USeEVL2BkT98qpk6z+PlWLW1DQSa4vkTwb6yQ31DON2wQiFskMwzBMZRl3u92ZcYtS3zXK7QWkIBuZDMkkBybuBcu4mVjaSZZRj0TM8jvJrnP8qkWdOG3FHBw2r1U/VtRJVnneckSycpKrpCKKDc02SsBFqm7h/k1U7epqNhMBvE6Iahx1krZgJ7neISIkbG4owjAMw1QWTyRHj1tkHAfNcW+uTGgmGeEiuVh1C0fIqgyyIYX3uLqaumxuC+78+BnalS60P/2Yu+OZHLcoKpLJex3KaSaSylTXSVavbyLwN66bTDJXt6h/Etx1j2EYhjH41bO7ceb1fyjZ/KMYKm5Rbp3k1qRsbJvOORgJq24hvLgFYEQmirh1auJezLJ8GdygeDFd1aIl4MpxktXYq9xMxDzOyvlt/jGRFwGINnFPrjvpzl+qdjMR5STrk5M6iVuoYXKd5DomGbO5BBzDMAyj2d4/jt1DkyVFbTEmlJNs7CKdlUJxaCKD/31mV942WcdBc0I6yZmsyM8khzjJCbvwBDRdJ9mNW8Rt8gnboNgzoxhFm4mUUd2iVm2pzeMcscAvkm2LvMjIDHaSvdJ8tu9+vUR81VUUdpLrmGTM0m98hmEYhlGup5iCkayEzFiRuMWnf/o0PnP7M9jRP+7bNpMTSLid1dK5XIEScPC1pY5FyiQLOEL4GlKEjt02BXTh56h2EdYeO0hw4l61TFDzMEcsaPc9ZhHp1yuK4FVO6GTGdZKrVt3CH7fwMsn14cyq/yOuk1zHJGMWO8kMwzCMRslap4RK7h9L4+o7ntHRCsCrNexVt/DWVxP3nnplAAAwHJiYJ3PDFuI2IZMT+SXgoOIWHipuUarjniOEr4lGGL64RYTqFpE67rn7cWrYcW9xV7PvMdvyOg9GEZzKYZ9wRXL1qlvI3zpuUcZkw5mAeu9zneQ6JhGzkMpwnWSGYRhGosRxKSP56e0DuOvpXdi0d0Qva3In3qm4RVgmeTQlxe/geEAkOwIxW04oT2fDOu4VnrgXWidZd9yTPxaVmJBnPFap6haAPG61cGEOlgAAIABJREFUS8CZVTRsi/CVNx+Lnrakvq+d5Ah52bh2kmsTtwjWSa4TjWzELepkwBWgIUUyO8kMwzCMQunaUk6ymthnusWekxxSAi7n+ETzwHjat790VraOTsQsjKezWpR543JLwBnLVAOQMFHrddyTcQtCcSc5FlEkl1PdQo6D9GtaNZEcOP4VZy7H+UfN0/e9THKEZiK2f+JetSaiKRc+GfdnkuuluoX6/+E6yXVMMmZxnWSGYRhGo4RsqUyyEsCm8M2PW5hOssDuoUl9f2DML5KVkxy3LfS7j5kunHSShS8LXNRJJmM7oao6FHeI1TalmnEAXhygFOauqqXvzPGrm2a5PJUAiJRJtgKZ5CqJPnUyot5T9dZxT01a5eoWdQyXgGMYhmFMRN6NcJQANt1iJSDD6iSnsw5e2udFMwaCcYucg5gtneQDrkie397kG1l+M5Fijqg3cU9tV8r9VcIsipMc1dEkn2CtfnWLYL1eyygBF+U5qO2qXydZ/g5eLagbkcxxi/pHZb8YhmEYBjDFb3GVrB436ymrCWphIjnrOOgdTun7wbiFrG4hneShCSmge9oSxvFcR9hXJ7lYdQvvtoDwVXUoRJRL+sqFjSp41VrV1HbmsdRziRvVIXQZuwiDsl2H3XOSqyv0k0GRXCdKjOMWDUAyZrNIZhiGYTRK15aauJfTpeK8NdUEtbA6yZmc0OK3qyUeMnFPNvxI2BbG3Ml9XS2eSJYT8ER4x70Qt05XlhACjvCLw0LErNLikcp0ksupJFEpwiIeZtxCRQDCXrcw4pblZZKrLJKDTnK9VbeYTU5yrNYDqDQybsHVLRiGYRiJ+nIv5SR7E/yMbV1RPBaSSc7mHAxPZmBbhCXdzXlOcjbnZpJjlm4kMqfVdJKFrG5hbKM77oVWt/DG6bjiupTAi9Jko1yxpusqR1q7MlghEY+YIZLV41EFr22RnkhZ9Y57bjMR5chWK7JysOQcdpLrngRP3GMYhmEMBKJN3POqW4i8ZV4JOG/9TM7B4HgGnc1xzGlNhk/cswgJm3Qzku48Jzm8BFzROslGE5JSmWQ9OayC1S2oBk5yWCY5YXvOeizCyYBJzCb9N62W6FNdCoNxi7qrbjGLnOSGE8ncTIRhGIYx0XGLqJnkkLhFWCZZxS26muPobonnTdzLOQK2WwJO8fqj5+P0FXP0eAREGZlkTxSqJiSlM8mlG1Z4bnPRXWnUrqqaSTZuB+MWOUfo5xDVSY7bVtVLwGVzfpHsVbeoyuEPGvXWrxdRXwkaTiQnuC01wzAMY6BLwJVYTzcdMYSwmrinzBczipF1HAxNZNDRHEd3SyIvbiFFsifmABm3+Ks3HKn3JYR/4pZqNV3M1RXuT6lmIoDpJBdeR2nEqM5w7TPJ/riF40RzzE1iFlW9ukXWze7kTdyrs7hFvYy3EjScSE7GbKTYSWYYhpm1jKWyfqGrneTi2+nssvEVopzksChGJicwOJ5BV0scXS1xjExmkTW+f3JCOsmmSE7ErEBTECCY7v3s61fhklctzBuf1iZuJtkq0ZYaiJY3Lles1cRJJso7ropbyNd5Ck6yW90iqrA+WDKuk6yuLMTs+hLJ6r1fL+OtBA0nklUmudRlNYZhGKbx2D+awrFfugffvf9lvaz8jnv5meQwlzmTc3TcQk3GUkJIbWtb8MUtkjHL1xQEEHli8y9evwonLO3KG58SJwICjiPFYqnJdlFyr1Em94WNo9piSR0tWCdZvs7qOUSTNTGbdIQmETVncpB4TrLbca/MqiK1xuuyWNtxVJNI7wwiupiINhHRZiK6JuTxK4moj4iecX8+Yjx2BRG95P5cUcnBh6EuY3AumWEYZvaxb1h2wPvVs7v1MqdA3GL7gXFdws1cz1/dIugke49lcwKD42l0Nsd1VQrzuyfnCF0CTpGM2XriW1hb6mL4Ou6hchP3yhW9aq1qG4rBcaq4hRBCC86oTnLMIky4TnIyViWRHMgkK3FcL8asuqpSLed9JlCyBBwR2QBuBHAhgJ0A1hLRaiHEhsCqdwghPhXYdg6ALwE4FfJ/+il324GKjD4ELZKzjj5bYxiGYWYHYUJPub+O45fJ5/zLGiyf24L7P3ee73GfkxwQzuoxVW50eDKLzpaEdoszAZFsEfnqyiZilq+UmxS70Z4b6Y570BP3SmFFiVtMseNetZ1keTxhTNzz4hbluuHBCEw1UO8NHbewVMfA+hCdHLcI5zQAm4UQW4QQaQC3A7gs4v4vAvA7IUS/K4x/B+DiqQ01GglDJDMMwzCzC7MChKJYyGLbgXF9OxcQwoCXT3YCTnLSttDvlnzrao5r0RUUyTGb8uIWZmxCit2oMQf1fKQDHUWsRCmNZulMcqRheHWSq6yV1PHUeBM6bhHteZrEAicu1cArAefGLezyxlxrHD1xr8YDqSJR3hmLAeww7u90lwV5OxH9iYh+RkRLy9mWiD5GROuIaF1fX1/EoYej/mlSLJIZhmFmHeoL3BeZ0Hni4tsqxzmddbBzQIpnPXEv4EYn4xb6Rl2R3GKI5Kw/kyyd5AKZZMerUhEJ47k5QkRqZ1xOW+rodZLd9audSQ4c16tuUX4JOLM2ctUyycGJe3VWAk79T9WLqK8ElXpn/ArAciHE8ZBu8Y/K2VgIcbMQ4lQhxKnz5s07qIEk4+wkMwzDzFa83K7hJJc5ce/Lq9fj7H9ag4GxtJdFDkQxEraF/SMpAPBlkjOOv7qFbCZi6W3IaCUt3P1G7bimHWchZMc99/61bzwKP//EmaHbRHKSVdyi7BJwkVavGMHjmnGLcp3kYASmGqirDPVaAq7exlsJorwzdgFYatxf4i7TCCEOCCFS7t0fADgl6raVJmHLyxjsJDMMw8w+tOD0Ocl5i0JR6w27LaQPjKWMx/xxi0TM0nWR25viWggrISSE0E0ulAgLThBTbamjYj41M8v88dcdjlMO7Q7dxoogbMrt/ObtqxaZZO9vnDCc5PIn7lU/k5w1rkIARqfDOhGd/375Sfj4OYfhmIUdtR5K1YjyzlgLYBURrSCiBID3AFhtrkBEZkHHNwPY6N6+B8AbiKibiLoBvMFdNm1wJplhGGb2ouRGzuca50/ICysTGnSaJ9J+V9hcJxGzdAmxZMzKi1soMR2zvLiF+n7STrKrdqNqJJ23doRuS12KKA7rVMVatZ1krwSc/K3jFmJqJeAU1YpbZFxdoo5nWRSpIcxMYemcFlx7ydFc3cJECJElok9BilsbwC1CiPVEdB2AdUKI1QA+TURvBpAF0A/gSnfbfiL6KqTQBoDrhBD90/A8NF4JuNx0HoZhGIaZgSjhGDb5ztTAOSdEJAeWqbbF/n34J18B0hmMB8qPqpq4tuXFK9T3k9JxwnWSo07c01Ux3G2jaJUozUSozPiEGn+tM8nxkGYiUfVuPBCBqQaek+y9d2yL6qYE3GykpEgGACHE3QDuDiz7onH7WgDXFtj2FgC3HMQYy0KdqU9ya2qGYZhZhwiZpOeFGryF2RCRnMtzkj2RHKyT7K9YYXuZZNW+2v0KssiIW7jiSJdyc8cbuQScF0mGE7G+ss6RFmtLXWbcQo2/6pnkQHQkbrSlLttJVhUyqhS1ALwTJ9O5Vm3ImZlJw3XcWzqnBQCwuXe0xiNhGIZhqo2WwyGZZKeUkxxYpJpNACFxi0Cd3WAmWQkiOXHPn6H1KnCIsqpb6CYk8NpSl0KJxmJCLFZmKTKvBFyVnWT1W0/cy++4V05baqC6Ill1Y1SZZEBmo+slkzwbaTiRvKizCYd0NOGpV6atXwnDMAwzQ/Gc5PzqFqZwDnOSg3EL00kWxsQ9Iuh4BSBjFLGASNZOspFJVuLI7wiXUd1CbzeFTHKRdc84bC4+f/GROG5xZ6RxeBPoIq1eMYId98LjFhEn7tn+fVQTcwKnGcdhZh6R4hb1BBFh6Zxm9I2kSq/MMAzDNBQixDUWARcYKOQk+5eNprJ56wvXwU0Y4kpO3JP301l/XeVYSHULMzddVltq4zlGjWl4cYvCKzfFbVx17sqIo/AGUv1Msj86Eq9AdYtqOskK8ypEzJ28x8xMGs5JBuSbP+twJplhGGa2EWwfDXgRjKGJDHYNTgBA6HdEMJM85opk2yIjkywnzAVLiBWKW5hOshJkpiYSwQVF8HXqQ7SYRrnisZxxVN9Jlr/tYCbZcJKjVl6IByIw1eCzF6wC4L8C0Jyw0ZywC23C1JiGc5IBeRllIlNO9UmGYRimERDwYhEKJZhv+N2LGBxP496rXxepusWYG7eI2+TLNRNRXgmxYFtqpcHNEnCqIoavdbYoo7qFzjLD10ykGKr1cSXLdlk1c5LV70DcYgqZZPX3S8SqJ1A/e8ER+OwFR/iW3fT+U9DTlqzaGJjyaEiRHLfZSWYYhpmNeB/9Rgk41SRkIoPhCekOqxbBvm0Di5STHLctvb7jSCfZdIeJSGeU1Xq6BByRjll4cQtvrAJlVLdQVTGE/IlSyCFKJrlc1Dhql0mW9706yeVX6Khl3MLkVRFz4ExtaNC4BemC7gzDMMzsIcxJVpnknCP046El4IJOsiGSHWHGLUi7mEr46kxywEm2izUTgSt2yy0BB6Ed7VIocVzJMmPBesXVIjhxT50A+J3kaLJG//2qGLdg6o+GfHfEbQsZdpIZhmFmHd7EvfzqFjlHaPGcC/mOCHbhG9UimXxtqS0i7WKqCEWhTLIUyX5BrXCEiBybAPxVMYQQ5dVJrqCg1Znkiu1xasdXr+f7Tl+GE5d24ZPnHY5Tl4e35w4Sq0EJOKb+aNC4BYVeSmMYhmEaGy2SDVdYucc5IbQQjtJMxHSSzYl7REDcCjrJgUyyuy/bqG6hnWRLW8Ky417ZcQs1cS9CCbgyayBHGketnGTLf/yYbeGlf3wjYhaBiPC5i46KvC/192ORzBSjIUVyzLaQzbGTzDAMM9tQ4tSUu8o0Np3kaJlkOXEvYVtwhFef2O8kB0WyX4TbIRP3lLQsuwRcXn3l0tt4TnLEg0Sg5nWSjScTn2JcQjvJHLdgitCQ7464TciEuAQMwzBMYxPWcU/nlB2hRbRyhk2hF6xuoeIWyo1V4jQ4cQ8wMslZx7d/00lOhmSS8wZRhPwsc7RMskWV7Y5X+0zywe/Lq27RkDKIqRAN+e6IWewkMwzDBCGii4loExFtJqJrCqzzLiLaQETriegn1R7jwWJOsPOWyd85IbQQ1k6vIfSCzUTG017cQm/v1uQN5oyJSE4azwVEMpF2KxPB6hbu8cptJqKzzJGcZKuiUQvAE9zVdpLV4SohzuMzpLoFM7Np0LgF6UteDMMwDEBENoAbAVwIYCeAtUS0WgixwVhnFYBrAZwlhBggovm1Ge3UKT5xz7utjBRTcAW9ldGUqpPsimTHqyqhqigkjTq7cdvCrsEJvLhvxBPJtlceTsctdAk4kTeGYvgn7kWrr9ySsNEcr2wt4FrXSa7EcdlJZqLQkO+OuG3ps3mGYRgGAHAagM1CiC1CiDSA2wFcFljnowBuFEIMAIAQorfKYzxo1MQ8X9zCcJeDcQuzYliwuoWauJfQ9XiF25Y6XGTFbcL/PrMbb/i3B31OcjywrhJ5YZGPYpAvbiEixQ6uPGs5bv3QadEOEBHP0a3obktS0biFmrjHmWSmCA357ojbFDpzmWEYZhazGMAO4/5Od5nJEQCOIKJHiOhxIrq40M6I6GNEtI6I1vX19U3DcKdGeCZZks053sS9kLhFsLrFRMZ1kmOeqHUcKdYSgYl7gF8wm5nkntYkPvraFTj/KGnMqyOqC57laD4iKZClo116/Z62JE5eFq0sWlS0k1vXTnL+349hgjRm3MKS5XqEEBWdrMAwDNPgxACsAnAugCUAHiSi44QQg8EVhRA3A7gZAE499dQZ40o4Rqk2vcxwl3U5uJCoQyFvRUUrVDtoy2hLnYybTnK4SLYswt+96Rj9mJ6AJ8pzkgEpqNXzqHbcQVFJR3dqx62ASOYScEwEGvLdoS5tcS6ZYRhGswvAUuP+EneZyU4Aq4UQGSHEVgAvQormukF96odmkoXIc5LNcmLB6hYKJX4dRyDnTpgLKyHmE8lGneQgSuN5cYvooo+Iyuq4Ny3ULJPsThisgHKJcwk4JgIN+e5QH15Z7rrHMAyjWAtgFRGtIKIEgPcAWB1Y55eQLjKIqAcyfrGlmoM8WELrJBsNRLwW1WrinrdesC21IqHiFkadZK+ZiDlxz5wEWEwkkzuuyE9LYxF0zeZaaWRv4l51j1vJ6hY8cY+JQkO+O9RlFHaSGYZhJEKILIBPAbgHwEYAdwoh1hPRdUT0Zne1ewAcIKINANYA+JwQ4kBtRjxF3I99EfLx7zj5TrIpYoMl4BSmkxysk1wybhEi6IIl4MoRfQTS1S2qLVLNMZi/q4WaZFmZiXtcAo4pTUNmktWbnitcMAzDeAgh7gZwd2DZF43bAsBfuj91SZg7qytaGCI4LOpQUiQL+WNmkgvGLYo4yVOtbiFXhhu3EFUXqYpge+iqHbeCmeRgxRGGCaMh3x3qDHFz72iNR8IwDMNUE4F8oauSd8qBFULoK42mhi0Uf1CCSjUToQJOshMiwkPjFoF1ypF8FgEQ0jCvlZNcSbFaDpVsYsJtqZkoNOS7Q53hv+fmx9E3kqrxaBiGYZhqESZ0g8JZCC+T7CsBF2HinnA77nl1dr1M8qRbMg7wXOtYESd5atUtyK3XXOaG04BVZQVR2Y577CQzpWnMd4fxOXfRNx+s3TgYhmGYqmI2BLnghgfwq2d35wlnR4jw6hYl4hZmnWTdRc9wkiczXsQvF7J/ha5uodtSl1PdwjsRqLWTXPVMsnu4sJx3uXCdZCYKDfnu2Dc8qW/3j6VrOBKGYRimmpg6d3PvKP7ip09j79Ckbx1HhMchSopkHbcgxK18kZXKGk6yU9hJ9krA+e9HwSIKrfFcTdRTqlUmuRLHbU3KKwBtyfjB74xpWBpSJHOzPYZhmNlJWCZ5/2gqb51sLl9oFqoamrC9eITjVpUIKyGWMpzkbBEhqxxYR8ctyqluYWwXeavKQjXLJKufgz/uMQs78J9XvhpnHD63AiNjGpWGFMkfO+ewWg+BYRiGqQFhQjfrhGWS8/PAwbbUipiOW0ihbNH/Z+/M45wq7/3/+SaZnWHYBmQVUBAREBBx39Gi3mpbu2jtYuuv6q3a9treFrXtbfV6azftZl1ua/XaVmvVtlRRW63iLouCCqjsAoKswzJ7kuf3R85z8pyTc5KTTCbJJJ/368VrkpOzPAkzJ5/zPZ/n8xV7Mp+Zk9xhVJJ1Y5JIOFXQhexKcg5iV9JbOQpBsSrJIpI3YS4iOG3SUM+JlYRoylIk11WHMXvcIPv5gc5oEUdDCCGkUHjJXPeEPNOTbPpbla9ITka2peQkG5VkM5vfa/+aZDOR3NpS+9lCCkdxKskhKZ4Pm1QmZSmS3Vxx39JiD4EQQkgB8BKQqSIZiMZ0xz3vdAuze161nZOcbAc9rH8tqiMhHDy4Pu04vKq9elE8lwi4UOl4kgstWEMixWvFTSqSQCJZROaKyDsiskZE5qVZ7wIRUSIyy3o+VkTaRWSZ9e+OfA08GxZv2F2MwxJCCCkwQYqs/ukWyXXqqsx200a6hVVJHta/Fm/fMBfTRg3wPIb2PHtP3HO2pc7Wk6wn/BU93aIInmRWkkkhydhxT0TCAG4DcCaAzQAWi8h8pdRK13qNAL4K4FXXLtYqpabnabzBMU52tcbJjhBCSPniZ5lwrBNPVo3N9c0qdENNBPs6ElY9bbeIKy2SLbuBS7E11kSw37L3ZfINixgRcNnYLURyylfOJ/q4hT58KI+eZEKCEKSSPBvAGqXUOqVUF4AHAJzvsd6NAH4IoMPjtYJjznBmDiIhhFQGQdy6ZiXZ1NSm3aKuOrWSHFfJnGQvHvvKSZgxJlFZ7rLKvV6VZCCxD9tukYXwCxniunh2i+J13KNIJoUkiHocCWCT8XyztcxGRGYCGK2Uesxj+3Ei8rqILBSRk7wOICKXicgSEVmyY8eOoGMPDCvJhBBSGQS1W+iOe2ZBxbRb1BsiudpIt4gr5dtpbszgesw5fBgAoNvD82ySsE3kEuUmvp0BC4V+S8XouEeNTApJj3/FRSQE4BYAX/d4eSuAMUqpGQCuAfBHEenvXkkpdZdSapZSalZzc3NPh5RCbRUryYQQUgkESX6Iq2T6hKk348aT+uqkG9H0JCuVvoKqX0rnSQasSrJybhOERMe94laSdeW7GB33GNlGCkkQ9bgFwGjj+ShrmaYRwBQAz4rIBgDHApgvIrOUUp1KqV0AoJRaCmAtgIn5GHgmzPOkmWNJCCGkfAlSY1VQGT3J9Q67hdlMRKUVp/o1XUn2FXViNgXJ0m5hp1sE3iyvFLPjHu0WpJAEEcmLAUwQkXEiUg3gQgDz9YtKqb1KqSFKqbFKqbEAXgFwnlJqiYg0WxP/ICLjAUwAsC7v7yIDXmHuhBBCyo9AE/eMSrLDk2w88Uy3sNtS++9bC8juWCIFw89vHJJcc5LFaGddaZ5kpluQwpIx3UIpFRWRqwA8CSAM4G6l1AoRuQHAEqXU/DSbnwzgBhHpBhAHcIVSquB5bA3VGd8mIYSQMiBwBJylNM3qsbmtFsbm40QEXHpxaFaS01kDxPAWZ6P7nHaLLDbMI/qwhT6+MCeZFJhA6lEptQDAAtey7/qse6rx+GEAD/dgfDmjz3VVYUFX1KNPKSGEkLIjW0+yubY5Ic4UgFVGBJyycpL9CCqSQ5Kbt9ic8FesWWy2J5kd90iZU/Yz2qrDIXRGY8UeBiGEkAIQqJIcT3qS4z6eZDPfuCpipltkqiQnfnbF4p4tqTUiRkpFljnJRa8k05NMKoTyF8mREDpZSSaEkIogSCXZ9CTHja8HM93CFLjOttQqbQVVi+toTKW3W4jRcS/jiN3bZT/hL58k21IXw5NMkUwKR9mK5OvPPRyTDmrEzDED7VB3Qggh5U3QZiKxWOqaZhycKca02NUV6HQVXAnsSUZOzUSkJNIt9MS9wh434Uku7DFJZVO2InnmmIF44msno6m+Cp3dFMmEEFIJBEm3MDvumZXnmI/dQovdmMqck2ymW4TTdNsIhcSoCAcn0akv+bgYJNtSF9qTTLsFKSxlK5JtFLClpR3LN7WgvSuG/3t5g+OWGiGEkPIhWLoFkh33jPVNgW2EW9jCLJFu4d9xz1w3UUn2X08AxHKYf5fYLgefRh7Rle9Cd9wb0q8aQ/pVF/agpKIp+2y0l9ftAgDc+tS7GNZYiz8t2YTxQ/rhxAlDijwyQggh+SZIDUT5VZId6RaplWSlkLGZSNgUyRmi4uK2bSIbu4UYdoviepILnW7xrbmT7CYthBSCsq8kD+lXAwA47KBGvLBmJ4DEZD5CCCHlhwrgSlZIto02RbWvSDYqyUHbUnfH4ginaWRlit1c21IXy3igbRaFPn5tVRiNtVUFPiqpZMpeLf7m87MAADXhELa0tAMAc5MJIaRMMUWvn/iMK2UIYu9mIuakO20r0B33guUkqwwRcE4PdFDMnORC2x00xUq3IKTQlL1IHta/FjWuGDjmJhNCSJliCM8qHxUZjwPRuO64l1zumLhn6D8tBuNBOu5Zh4zGMzcT0R7o7NItim+3sD3J1MikzCl7kQzAQySzkkwIIeWIKXojPnYHs5JsTtbzayZipltkzEnWleRohpzkXNtSI1gWdG+SbCZClUzKm7KfuAcANVVhR/WYlWRCCClPnAkV3iLO0UzE0JtmY5GwCB758vFYtXVfspKsYHmS/Y9v5yTH46iJhH3XC0lu6RYhEbsKXryJe7otdVEOT0jBqAiRXFsVcmQlMzeZEELKE1P0VvlksJmVZN+21CKYOWYgZo4ZiN2tXYnXdQRcwHSL+mp/kSw5p1skHSXFEqn6sPQkk3KnQuwWYdotCCGkAjCNCBGfkq/ZTAR+nmRz4p71MFhOcuJnwm7hv2JPUir0OItWSQ7Rk0wqgwoRySHaLQghpAIw7Rb+lWSkVJKV1U1P45i4p9tSK4VYPL0X19GWOq0tAzlFwIUcE/eCb5dP9HhZSSblTsWI5PZuQyTTbkEIIWWJCjBxT1kT8IBkIdndhMSMb3PmJGeKgEv87I7FEUnXllrEGGt2dotk19gipVvo41IjkzKnQkRyGPvao/Zz2i0IIaQ8MX3FfnYLhdRKsjsxwivdIm513MvUSQ9I5CSns2WY7aWzbSaStFsE3y6fMCeZVAqVIZKrQtjX0W0/p92CEELKE6cn2S8nOWmt0EXZmKuUbArAZLpFIic5bQSckZOcqZKcy8S9hN0i8bhYEWx6vPQkk3KnMkRyJIS97aZIZiWZEELKEUcl2Tcn2RDF+ofbbmF8O6ZM3AtcSU5nSjYqyf5reW1mv8die5KFfgtS5lSISA47RTI9yYQQUpaYYtdv4p7pSdY/3S2iTSFsNxOxKtBB2lID/nYPvV48B7sFjIl7RYuAYyWZVAgVIZL71UYcJ07aLQghpDxRATzJ2lsMJO0Z6ewWImLlE+sIuMyVZPdjN4Jk85KsPMlITtwrlt1CH5Ud90i5UxEiec7hQx3PabcghJDyxCyI+HXc095i/TixnVMku7cNiyTbUqc5vrmZTyHbWs+oJGdhWwhJCeQk25VkimRS3lSESD7x0GbHc4pkQggpT7LtuKe1cUol2SWSQ6HEhLl4PPU1E3HYLdI3E7GPmVW6heTchCRf6LdPjUzKnYoQydWREGaPHYSQADPGDKDdghBCyhRl5FtU+eYkOyf4KaOyrHHr4JDoCnSmCDjjcQYxHc+hImzaNIpVyU02EynK4QkpGJFiD6BQ3PvF2YjG47ji90s5cY8QQsqUuMNu4V9Jjhsrmh7lSEgQjacK4bA1YS6WoZmIadNIP3HP6LjnvzuP7SSnfOV8oqvl9CSTcqciKskAUFf7sDwRAAAgAElEQVQdRmNtFWoiYdotCCGkXDEqxCHxrnYq5UyzMNMutMh1V2kTdotEukWQttRe+3Cul1tbauS6XR4J2SK5OMcnpFBUjEjW1ERCtFsQQkiZYlaSQyKeQjXusleYucnax+y2SoRDyeg1vwmBiWMmH2eKgFO2JTk7u4W5j2KQtFtQJZPypkJFMivJhBBSjpie5BED6nxEciJGTb8UV8kOfFoAu+f8hUUQtczAQXOS03qSkVtbakc0XfDN8kqyLXWRBkBIgQgkkkVkroi8IyJrRGRemvUuEBElIrOMZdda270jIh/Kx6B7Qk0kjC172jF/+fvFHgohhJA8o1SigvuzT03HN+ceBi9bsrZXmJXelEqyS7mKCLqimfOJgzYTyTWlwjx02o5+vYiufLOSTMqdjCJZRMIAbgNwNoDJAC4Skcke6zUC+CqAV41lkwFcCOAIAHMB/NraX9GoqQohGlf4yv2vY09rVzGHQgghJM/EVUK8fWTGSNRWhT2FXMyyW+iqcdzwJOtEDPd24RAQsyrJ6ewW5maZ1ks2E8nCbmGsWiyJSm1MKoUgleTZANYopdYppboAPADgfI/1bgTwQwAdxrLzATyglOpUSq0HsMbaX9GoiSTfcneMtgtCCCknFJSz2uolkuM6ySLxfWBGwiXtFqnpFt1xHdnmf3xzu/Te5dxSKkz/crHSJdhMhFQKQUTySACbjOebrWU2IjITwGil1GPZbltoaiLJQna3OxiTEEJInyaRPpF87qXjojGnIDYn8iXtFs5tQiFBd1R7koPZLdJWkpFbBJzzAiCLDfMIc5JJpdDjiXsiEgJwC4Cv92Afl4nIEhFZsmPHjp4OKS1mJbmLE/gIIaSsUEplFKrJSrIWyUhJrkiJgJNEfrLXa871ko8zVZI12dktSqiSTJVMypwgInkLgNHG81HWMk0jgCkAnhWRDQCOBTDfmryXaVsAgFLqLqXULKXUrObmZvfLeaWmKvmWGQVHCCHlRVxljkmLuqPcVKpwTrFbhMS26KXThqZwTdeZzxxkdnaLJMXSqHZb6uIcnpCCEUQkLwYwQUTGiUg1EhPx5usXlVJ7lVJDlFJjlVJjAbwC4Dyl1BJrvQtFpEZExgGYAGBR3t9FFph2C3beI4SQ8kIpVwybh5LTE/Aiht1Cpdgt3JXkpE0jXQU1eCU5+Ti7jnvmdsX1W7DjHil3MopkpVQUwFUAngSwCsCDSqkVInKDiJyXYdsVAB4EsBLAEwCuVEoVtXxr2i3Ov+1FPPvO9iKOhhBCSD6JK+Wq0qYKuW7tSbaSLBSSmcURnW7hUUlO5iTnw5OcD7tF4M3ySjInmSKZlDeRICsppRYAWOBa9l2fdU91Pb8JwE05ji/vmHYLALjkd4ux5qazEXEnxxNCCOmThDJYHuxMZCvdIq5UyjL3diERdMUye5IDp1sYXzm52y2Km5NMjUzKnYpThqbdQvPLf60pwkgIIYT0lFg8KXCBhODNlADR7co7jiuFqOU31oWUlHQLEXud9J7k5OPAE/f8d+ex/1KqJBfn+IQUigoUyalvee2OA0UYCSGEFJ6edFAtRa595A1cff9r9nO3J9nLyuCOgINKWjB0IcXTbhHIkxxw4p5Brs1EilVJDtGTTCqEQHaLcsKrktyvpuI+BkJIBWJ0UD0Tidz6xSIyXym10rVeSgfVUmVLSzsOdETt53GlnJYEj1KQTrfQ/uO4SjaX0oWUlIl7IbEr0PnwJOdcSTYfF0uj0pNMKoTKqyRXpb7lBopkQkhl0JMOqkVnT2sXbvnHO057RTw56Q5ITMLLFMOmbRNh3XEPCl0ukeyephI20y3SaMOg6RaZGp4E2a54EXBWJbk4hyekYFSeSPawW/z2hfV4Zd2uIoyGEEIKSk86qBad51bvwC/+tQYbdrXay2JKIWakeaoUT3K6iXvJSrIWwElPsn9Ocnrxm30lOZuKrHPdYtktrJ8VpyBIpVFxv+JedgsAeHDxJs/lhBBSKWTTQbWQnVI1WtzGjUqyUgrKrCQrV5awh450NxOJx5Vht7A8ya4NRcT2Lafz4gZNt8hV3pZCJTnZlpq1ZFLeVKBI9n7LOw50FngkhBBScHrSQdVBITulamyRnNTEjpbSiefKkUGcrpKsPckAknaLKm23cFWSJZmTnG5CXnC7RW4pFZneWyHgxD1SKVSeSDY8yWMH19uPdx7oKsZwCCGkkPSkg2rRiSvl+AlYEXBpKsleQrXb5Un++p+XY/mmFgBAjU/HPUe6RdoIuGDpFg5PchZ15Vy9zPlE6EkmFULliWTDbvHMN061H+9r7y7CaAghpHD0pINqKaALxnHltFto+8XSjbvxwf5OV5U2TSXZUruL1u/GH159DwBQU6Uj4JzbhAxPcroKqimgzUp1uvWym7hX/EqyPirtFqTcqbhYB9NuYZ5susyZH4QQUqb0pINqsdHi1tDIiYl71oILbn8ZADBqYJ39umczEXdOsoGdbuGuJAsCTdwLOiHP2ZbadzWP7YqPfl9sJkLKnQqsJHu/5W6KZEIIKWn0BD13BFw8DsfkvczpFonzfcRLJFd5NxMJidiV7HTi0BTQXvu392d8FWWXbmHuo0iVZHH+JKRcqTiRHHGHX1p0dlMkE0JIKZOcuOecqBeLK8fdQEc118uTHE9TSfbxJJv7SVshDihiHZVk37W89m+OI4sN80jIFslUyaS8qTiR7Ed7d4zVZEIIKWFitic5uSxu2S06jEKHo+Oeh46LxZyeZHPdw4f3x6FD+6G5scbxWjiD8E7uI4eJeznaLbKZ8JdPxLZbUCST8qZiRfIRI/qnLLt/0Xto64p6rE0IIaTYaEuFclSSEznHnd0xe1kmX7Ad5eaanVcVDmHqqCY8dc0p6OfqxBoOBavgOkRymol7zipsbiq5aDnJRT4+IYWi4ibuAcCr152RcgIEgO/+bQVWbNmHH358WhFGRQghJB2eOcnx1EpyJiGpm4lUuURstY8dD8jsc/Y6XtA85WwKso5jF81uoXOSi3N8QgpFRYrkYf1rfV9buXVfAUdCCCEkKF4RcNqT3BENXkmO+XiSq3wmdrvXTe9JDjhxz4yp813LY/8++ygkugBPTzIpdyrWbqG5+5JZ+PCRI+znu9h5jxBCShK7mUg81W7RYdgtMglJPf/ELWLTidpwFhPmtKBOP3Ev/Rh9twtY0e5NtBeanmRS7lRkJdnk9EnDUFsVxt+Xvw8A2NnKznuEEFKKeNktYh52C2e6hf9+vDzJfjjSLTKo5JAAMaQX3bm2pc61Ap1P7Ai4Ih2fkEJR8ZVkwOlD64rG0WnctiOEEFIaeLWlTnTcg7OSnKHaqpuJuDviVaezW2TR6c5Of0grko3HObalLlolmekWpEKgSEZq9eBTd75SpJEQQgjxI+6RkxyzI+BMkRzMk+yu9Lon8pk4m3+kH6d+Pb0nOfk4O61ZChP3nD8JKVcokpEqkpdtainSSAghhPihbRZKOZclJu4Fz0n28ySntVtkUUkOBai05ppx7Oi4V/R0C6pkUt5QJAOojvhXGgghhJQGMY+21DozuaMruN3Cz5Ps15E1sa54PvZCH9Nt53CsYxwqq2Yijgp0cUTqoIZqx09CypWKn7gHeFcPuqJx1FWHizAaQgghXnh5krXgbTUaQWVqS61zklM8yelEbRYT7fQh0+Ukm/XurNItHNsF3iyvHD68P16+9nQMb6orzgAIKRCsJMNHJLNFNSGElBRxr2Yi1uM230py6n6SHfeC2y2C5iQDSWGeruKcqye5FCbuAaBAJhUBRTK8T4zdFMmEEFJS6NOycjUTAYDWzmQlOePEvZjfxL10nuTk46B2i3Tr5ZpuEfHKtCOE9Ar8a4P3jOauKEUyIYSUEkm7hbHMeuKoJBvbeInkqF/HvbQe4uA2B9tuEbTjXhYF4dqq5Nc2I9gI6V0oksFKMiGE9AW8PMlaMJuVZEcCRDpPcjZ2C4eoDZaTnLaS7PM4EzWR5FwZamRCeheKZPhP3COEEFI6eE7cUx6V5AwtpHURJJuOe450i4wRcJnXE6ffIjA1rCQTUjACiWQRmSsi74jIGhGZ5/H6FSLypogsE5EXRGSytXysiLRby5eJyB35fgP5wLzFNn30AABwtDglhBBSfPQNPnfHPcCdbgHjcTbNRPKTkxzO0pOcjditiZgiOfBmhJAcyCiSRSQM4DYAZwOYDOAiLYIN/qiUmqqUmg7gRwBuMV5bq5Sabv27Il8DzyfmFf1Xz5gAAPjwr17AX1/fUqwhEUIIcWGnWxg1DM90C5jWiNT9RH3bUufHQxzEbuHYX/rdOaitMu0WVMmE9CZBKsmzAaxRSq1TSnUBeADA+eYKSql9xtMGAH22E0e1cZW+4M2tRRwJIYQQk3Q5yW1dZrpFchsvy4NfBFy65AizyJwx3SKUeT1nBFxulWRCSO8S5K9tJIBNxvPN1jIHInKliKxFopL8FeOlcSLyuogsFJGTejTaAmDebmMzEUIIKR20/1hrZDMKrq0zeMc9XX12i+K0dotscpID2S1yqySbE/cIIb1L3i5JlVK3KaUOAfAtAN+2Fm8FMEYpNQPANQD+KCL93duKyGUiskREluzYsSNfQ8oJs5JcV8WTESGElApaE3u1p/bvuOe/v5QIuDR2i3CGyYAmWeckZzNxj5VkQgpGkL+2LQBGG89HWcv8eADARwBAKdWplNplPV4KYC2Aie4NlFJ3KaVmKaVmNTc3Bx17r2BO4mMlmRBCSodY3Gm3MPOS/SrJumLrJUTdE/eqg07cy6CS9app0y1M33QWtWQz3YIQ0rsE+WtbDGCCiIwTkWoAFwKYb64gIhOMp+cCWG0tb7Ym/kFExgOYAGBdPgbeW9SwkkwIISVJzCWOTW+yXyXZTprwEKzuiXvpPMnZ2C2CpFs4PMlZ6N5afi8RUjAimVZQSkVF5CoATwIIA7hbKbVCRG4AsEQpNR/AVSIyB0A3gD0APm9tfjKAG0SkG0AcwBVKqd298UbywYimWocn7dfPrkV9dRhXnT4hzVaEEEIKgbI9yf5NRdxoMRoKScpKKZ7ktHaL1H36ERJBSNJPyMsxJpl2C0IKSEaRDABKqQUAFriWfdd4/FWf7R4G8HBPBlgo3vjeWagKhdDS3uVY/pN/vEuRTAghJYC2W9zz4gZ8/+8rsey7Z3qu54xr868kuyu96ewW5rpB7BbpqtJ+YwwCJ+4RUjgCieRKoH9tFQCgtYtX6YQQUoroQvC6na0A/DujeqVbeFkf3HaLfKZbZNDIPWhLze8oQgoF/9pcNNZGHJP3smX9zlZcft8SdHTHMq9MCCEkMHGXXaLTRyQ7PMkhvSx1vZR0i8Ad99KPMxTKXEl2RMDl2JaaENK78K/NRU0kjNU3nYNxQxoyrrv6g/244e8rHVmd3/7rm3hyxQdYvKFkrdeEENInMT3IgL9INjWnu5JsCmN3uoW7smzijIALUEnO2JXPHG9wlVxLuwUhBYMi2Yd03jTNJb9bjLtfXI/393bYy7p1u9NM99oIIYRkRcw1Oc/vjp14eZK1SHZUmZOPjxzVhCkjmnyPna3dIpLhOySbNtcmrCQTUjjoSfYhyInIPdMaALpjicpGTywbhBBCUglqt3B6kvVPQyxb2tosZvztqhPTHtvUvJnTLTILaYcnOYuviyAFHEJIfuBfmw/m5Igzfvos9rZ3+1YtzDuAevZ1pioCIYSQ7HDbLdzn5HS+Y0+7RRbFjJBPBdpv3UxfAWZlOhu7Bb9bCCkc/GvzwWxPvXZHK875+fOY9J0nHJUMfRsvGjcrydpuwUoyIYTkk1iGSrJutGHe3dPnaa+Ui3Qd8dxkE9kWEsnKcpdNJZkQUjgokn1wZ1FuaWkHAOxs7UxZV1ssACBqPXZXPAghhPQM92m101VJ1ncATS2dbCaS+KkLGEEsESZhY7tMiCBjBJxDdAceBSGkkFAk+1Dr40nesqfdfqzPcWZWp64quysehBBCekYsQ7qFriSbRQq7gqwryobtIpsKbrq8Za91MzcTST7OppkIIaRwUCT74NfVSFeUTbbv78DYeY/h6VUf2FVlVpIJISS/BLVbmKvZFWDrp64ki2QnksPGdkHWzaSlHR38qJEJKUkokn3wS6fYZsS9aZa91wIAuPvF9YjGdCU5ddtHXtuMZZta8jdIQgipIFSGiXvabuH0JCd+hl2V4LBIVhVcPV8uiI85SFvq5sYaY32qZEJKEUbA+VDt0/rTrFzo85peJpC0dotrHlwOANhw87n5HCohhFQEmewWNfbEveQyt03C6UkOfuzkBMDM6ybaUqdfcfSg+uAHJ4QUBVaSffBrT/rjJ9/BBbe/BCAZ22OLZAGicdotCCGkN4i77tB1Rp2V5Fp74p7pSdY/nbaLUEiym7jnSslIR311GA3V6TvjjaFIJqTkYSXZh3SB7Us37sG7H+zH+5Y/ucvwVujbf5y4Rwgh+SU1J9m7kuw5cc/VcS8k2aQTp3qb03H9uYfbcaB+DG6ozuLohJBiwEqyD352C81Ztz5nWys6rRP1y2t32Sft7lgcn/nNq3h13S4AFM2EENJTUifu+VWSk8vcFeSwI90i+5zkIHaLUQPrMW5IQ9p16EMmpPRhJdkHP7uFF7qSbDYV+cafl2NPWzfW72zFi/NOd8TEEUIIyR53JbnTr5IcT7Vb6LnYZt5xdhFw+ifFLSGVAkWyD6ZIvviYMfjDq+/5rtsVTW1XvaetG0Ayb9mvpTUhhJBguG/IBfIkuyrIyYl7WXqSs7BbBKVfTQQHOqNZb/f3q07EB/tSk5YIIfmFItmHSQc1AgB+/PFp+MSs0ZgzeRj+/fdLUzxwANJWiXVup3sWNiGEkOzIVEn2ykkOuSbchR0iOfixQ6Hgdoug/POak7Fq676st5s6qglT0ZS/gRBCPKEn2YfTJg3Fo1efiI8fNSrx/LChGNFU57nuM+/ssB+Pb3b60PRJ26+S3NLWhV8+vdpxe5AQQkgqbk9yh7uSXJWak5waAReyn2czdS+bdIugDG+qw+mThuVtf4SQ/MJKchqmjHReqQepBvevrXI81ydtv22vfeRNPP7WNrR2xfC54w7GiAHeQpwQQioddzEhxZMcSa0kTx3ZhGPGDcLgfonmHbrHRygESBZlIrMCTQipDFhJzoIgvuLGWud1x1tb9uGJt7b6brtpTxsA4I6Fa3Her17s+SAJIaRMSfUku+0WqZ7kqaOa8KfLj7P9yv1qEoWMbCPgtDbO0EiPEFJG8M89C9q6Movk/nXOSvLe9m5c8fvXfCvJe9u77cc7D3T2bICEEFLGuDvupbalTq0ka7TIHdSQOEeHc524x0oyIRUDRXIWtAeoJPev9XawmCdz0y/X0tbttXrFsWFnKzbuai32MAghJYzK0Jbay5Os0eJ2YH11yrIgaE9ymCKZkIqBnuQ80+jyJGvMk3lnNG5P6NvfkX38Tzly6k+eBQBsuPnc4g6EEFKyZGom4tVxT6O17UCr011rVzS7nGSrkkyNTEjlwEpyD3FXjoNUkt2TTQghhGQmsyfZ324B6EpyopCxvyM7kdwb6RaEkNKGIrmHHDK0n+O5XyX57W3JLMzueOLEzi58hBASnMzpFqkT9zQ633iAZbdo64plFQHnjpIjhJQ/FMlZMMSKEDLRTUc0/Wq8K8lPrdxuP47GEifw7fvZMYkQQoKSMnEvJSc5UUn20MjGxD3Tkxz82DrVQlhJJqRioEjOgse/ehK+cMJY+/knjhqFf5s2wrFOg49Ifm93m/04alWSP9jnTLMoVoXiX29/gO/89a2iHLscuPaRN3DKj58p9jAIKXsyddxLX0l22i3MZUEI90LHPUJIaRNIJIvIXBF5R0TWiMg8j9evEJE3RWSZiLwgIpON1661tntHRD6Uz8EXmubGGkwZkWww8j8fm5oiihtqwp7btnfH7JOrriTva3cmWxRr1vQX71mC+17ZGHj9zmgMc25ZiBfX7OzFUfUd7l+0CRt3tWVekZAiE+Bcfo2IrBSRN0TkaRE5uBjj9CPucqj5VZI9J+4hIXCb6pKV5Fw8ybRbEFI5ZBTJIhIGcBuAswFMBnCRKYIt/qiUmqqUmg7gRwBusbadDOBCAEcAmAvg19b++iyRcPIEWRUO2ZFDmvpq/7enBbWuJG9uaXe83ldC6jfuasOa7Qfwvfkrij2UQGze04ZVW/dlXpGQMibgufx1ALOUUtMAPITE+bxkcItftxa2K8ke0z1EBCKCJiPLPhvrRDLdgiKZkEohiCybDWCNUmqdUqoLwAMAzjdXUEqZCqQBgD51nQ/gAaVUp1JqPYA11v76LFVh50emw+s1dVX+qXrar9wdU3j9vT0pFoeO7nhKpFEpor+oClVR+cHjq/D7LCrdbk784TM4++fP53FEpLdZtH43/vr6lmIPo9wIci5/Rimlb4u8AmBUgceYFrcn2Y0+P3vlJIskKsnVkdSvvYuPGZPx2CGh3YKQSiNITvJIAJuM55sBHONeSUSuBHANgGoApxvbvuLadqTHtpcBuAwAxozJfLIqJhHXGbLGdcL1s1skXkt83Nf/5U289l6L5zrXPvwmbvnUdCzduAeX/G4RnvvP0+xcz95GKRWoSqKzSv3Wfe29PTjQEcXJE5vzMq47F64DAHzm2JK680t6kU/e+TIA4CMzUk4XJHcCncsNLgXweK+OKAuUUp4T8kz0+dmz4x7E85y1+qazA1nd2EyEkMojbzf4lVK3KaUOAfAtAN/Octu7lFKzlFKzmpvzI6x6C3clWXvgNPXVmSvJfgIZAJ5bvQMA8Mt/rcb+jihee29PrkPNGndQvx/6VmbY57fnY79+CZ+7e1FOY/CqABWKNzfvxe7WrqIdn5BSQUQ+A2AWgB+nWecyEVkiIkt27NjR62MKcnoK2yLZOwLOS95WhUO2lSId2g7HnGRCKocgInkLgNHG81HWMj8eAPCRHLcteUxPMpBoHjKkX7LS6+VJrrbUpF88nIk+AWvBGvFTor1ANKBI1rc8e6Oi0hUrXnb0h3/1Ai686+WiHZ+QXibQ+VhE5gC4HsB5SqlO9+uaQhc3glzEh9NUkuuqw6izzs8XHzMGl508Pqvjh9lxj5CKI4gCWwxggoiME5FqJCbizTdXEJEJxtNzAay2Hs8HcKGI1IjIOAATAORWYiwRIlY5oc6qIEfCISy6bo79el1VqkjuZ3Xh87Ni3POFo+3H2/d3Yvu+DnRbYrHKp8KxeU8bHnlts2PZOT9/Hj9YsCroW0khcCVZ+dst3GH/2VKsBivaC/7uBwdy3kcxq+CEBCDIuXwGgDuREMjbPfZRNLyqw260kPX6W7zk+LG45wuJKTE3fXQqrjvn8KyOz2YihFQeGUWyUioK4CoATwJYBeBBpdQKEblBRM6zVrtKRFaIyDIkfMmft7ZdAeBBACsBPAHgSqVU6c9MS0NrZxQAMHVUMgrOvFXndduu0RbJ3pXk4w8ZgoMH19vPv/R/S+yYuE//5lX8v3sXY1+HMy7uU3e+gmseXI7uWBzxuEIsrrBy6z7c+dw6dEXjaGnL3jYQuJJsref1XbGz1bfwFAi3SO6p6A5Ka2fi17InVaKgn18+WbV1H5Zu3N1r+1dK4ckV2wJfQJHSJeC5/McA+gH4sxXpOd9ndwUnG5Hste7gfjWYPnpAzscPsS01IRVHkIl7UEotALDAtey7xuOvptn2JgA35TrAUuOIkf0BAN/80GGBt7FFso9fuSos6DbE4fLNe3GkIcKfWrUdn797Ef7y5RPsZVus+LiO7hg+/MsXsOtAUhRP/HZirs36H5yTVVyRlxD64RNv4/nVO/Do1SfZy/RYvSoqe9u6U5Zlg9tu4c5BDUJLW5fdejYoBzoSFz9edwKC0h2Lp3jWexud2rHh5nN7Zf9PvLUN//6H1/CtuZPw76ce0ivHIIUjwLl8TspGJUIgu4X42y16Cu0WhFQefSSZt3QY3lSHDTefi1ljBwXeprEmkcvpd5tORNAVc3WSclVUX/eZ7NfeHcOGXW3Yb1W4Td7fm13ba53fvHTjbqzf2QoAuP3ZtXhrizNjWAtZr4pKdyy/dov2ruxE8j9WbMP0G/6JReuzq67u70yI+3Q515nweu9Pr/oAf1/+fs77LDbb9yfuDLzvyvQuFKxgE02QX4WQ+FeSe4o+fdNuQUjlQJFcAHQlOR3drgrq29v2B9q3uy2ryTvbEuJ2+76OFLuGF1qQXHD7yzjtJ886Xpv38Bu48dGV1li13SL1yyJqpPjnYpVIEcnd2YnkV9YlxPEbm/0TRLzQlWR3Wkk2RD0mHV567xJcff/rOe+z0nH/XZDKJcj5RKxvtN6YHiAiCAntFoRUEhTJecKdl2yiJ+6lm9iVqxjoSCMi329JVJJn/8/TOPOWhfbyVVv34Zm3t6eMJ13V7oHFm/DbF9Y7xupVUTGrqZmSKpZvakmxZ7gr6Onenxf6+yvbL8kDViVeV5IfeW0z/rT4vaz20dMqOkmlmGknpLQIUh3uzUqy3j8LyYRUDhTJeeLV687Aq9ed4fna8KZaAEB9mgi43EWy/3ZmZfODfYnb5ks37sHZP38eX7hnMf68xJmOEfTWth6r1yRFcx/pqtxKKZx/24u4+Lev4A+vJrvpdUZjUErhF0+vxsZdrWjvSv+5/OvtD3Ds/zydIqYVgr2X5ZtaMHbeY1iyMZFHrT3J1zy4HN96+M1A+9Ck+z/827ItGDvvMext75lnOx3xuMp7woayk0yABW9uLXh7b7+0k/0d3bjtmTUFm9hJik+mbntA8u/3G2cFnzOSDaGQsJJMSAVBkZwnBtRXY1j/Ws/Xrj59Ar459zBcePRoz9cBYGij97Ym1zy4DEff9BS27k36Q9PZEbzSFrTXGADe3+v0mQZNZ0jaLTyOaQjFdC22tZh+a8s+XP+XZHvu9q44tu3rwC3/fDSyGucAACAASURBVBdfvGdxxol7//3YKmzb14HNexKddNN9fXkJyMfe3AoAtm+4tiqMtq5Uf3cQ0n1+d1hdAzfvacN3/vpWip0lH5x560Jc+cfX8r5fzZf/8FrB23v7XXjc8PeV+PGT7+Bfb5dUShnpRYJc/4VDgg03n4svnjiuV8YQFopkQioJiuRe4iPTR9iPa6vC+PKph6b1u/7p8mMz7vOR17Zgx/5OPGRUgNPZETbsasXRNz3lWGaKWHczkKwryV4T94x9vLllr+8+/ARle3fMHseBzmjKxD2lFO57eYNdkdWJIe4IN68vVK/3p9+BtnlUR0JYt6M1Zb0guAWdWeWMWV7tSCiE+17Z6LhYyRdrd7RiwZvb0l6cZEux67R+dyN25bEzYlc0jtcL2NmS5Ib77zdIc6Z8Ew6J3XmPEFL+8M+9F9hw87n42YUz8MiXj8dNH51iL083K3rUwHrf19yY3xXpRPKyTS3YsT+ZW6yUcohYEWd19axbnwvUjCSdSDZF+KX3Lsm4Dzft3TFbsMbiqR7llVv34Tt/W4Fv/Hk5gKSHuM0tpj327SXM9RI9HhEJNMnRC/d7OmBUpNNlS+eK+X9nCohiNWTpDU71qbh3GRc1PeX7f1+Bj/76JWzohQsXkj/cItmvOVNvwol7hFQWhb8UryBmjhmImWMG2s+rMpQgmhtrHKLWD3NSSkcaQeSuFI+7doEjaUNEUmLi7nxuXcbja4HiabfIsiGJm/auqC38lVIpwlN3PNTeWN2gRVsk0uVCr91xAEeMaPJ8rcWaQBiSZFU6W674/VKcMWkYvnfeEQCAfYb/WHvHg/gqg2JOajOFcW/EppWaLMinSH7Nilc84BGjSEoH959O4m+/Z82LsoWeZEIqC1aSC0gknHpyveMzR9mPF3zlJDx69YkZ92NWENNVkr38yvs7kkJg0+42PLx0c8o6mdCeZK/KeNSV8LDrQCeO+8HTeGCRMynCLwmivStmv6eYh0jWz7dbExHrrEpyq1VJ1iPy0qLn/uKFlGWdHp/RASszOdvvwk2723HPSxuM/SQ/ay3i3Z9PTzCtKOb/dT47/2nBnakpzdodBxwTMHsbbSmp8vibyhZ998Pr75OUDu4LTNNucf+Xjg107uwpYaZbEFJRsJJcQLy+hOdOOch+3NxYg+bGmoz76TSE4x0L1/quly75AkjEumXLC6t3YktLYpKcV7qFmZMMJCwcu1q7cN1f3sSFs8f4rqe5Y+E6fNKa4BiPqxQxrUWyrqI2aJHsqgIGTbdwp03E4ipj973vzV+BAfVV+NqciWn3/cRb2+zHujqdLgGjw7KaNNVVBRq7KYzN95/PSrLb7uLH+b96EQc6o/j07DGBuzwqpbBuZyv2tXdjhnHHxY1XJ0M9rny8Vf15+fxKkhLBHetmdjA9Ztwgz/NRvgmFpCDHIYSUBqwkF5BInmZ8tLQmhV26SWbZNuJws3xTakOOz/z2Vfz+Fasq7CFQ3KJWT7CKK+DnT622l/tVVLft68Avnl5tb+MWlW6/bb31Rblf+4it768fPfEObnpspecxTPZ1OMV1XCm7e6GfSL7npQ342VOr8eKanb77XbfjAH5mvF8t6tNVeT/zm1dx5Pf/kbJ85fv78I8V21KW79yfnLxmVq29jvG/z63DjBtS963pjMbwg8dXJT9HPW5bjKZXo/r42YjWK//4Gs746UJ89NcvpV1vT1vqJD39eeZ6QfDoG+/bGd3dljpm45LSxh3311BjWscKMwamWxBSWVAkF5Cg7UxHDqhLO3PbSzR4kW0jDjfn3/Zi2tdjcYWlG/fgf59bh988vw6xuEK7ZSt4ad7pOKh/LUYPqrPXv/Wpd+3HQQRJXCn7Vrj+7NwiXCeGaBuJGO7Z/31+fco+9Tg1bh9qNKbsqmym2+8X/+ZV39f8/K3p3rfOanYnepzzi+dx2X1LU9Z/coVZqTYqyR4XIDctWIU9bd147t0dnsd+/M1tuHPhOvzwibc9x+uVMnH+r17AGT991rHM7w6BFwvedAr/tTsOYOeBVI/pbo8kCz0er5zkTNnJqz/Yj6v++Dqu+0siB7s7mlifIrm00XYLfRp1z68oBJeeOA7nHTki84qEkLKAIrkIHHWw/61lAHj2P0/Fa9850/f1f6z8AAAynqz3dwSbiHTKxOZA67mJKYXL/m8JblqwCv/92CqcectCfOdvKwAkBObwAbXYdcBb0AfxzUbjyr6trr8Y3UJG+7PdwtKP/35sFRZY2chA4kKi0bggiauk3aInmsnv7aXzJOvv+XU7D3i+rt/j06s+wP+7dzHeb0nmXDsryf4D/9zdizyX64uyTbud2dm6ktzmccG1fPNerHXdyeiJ1eOMny7EyT96JkXkursyAslK8v7OKN7elmxw8vzqHRh/3QK8k6at+1Zrsqq+2NSfF7v7lS5tXVF87YFlAICIZb0pRrrFl04ej5NzPF8SQvoeFMkFZuF/nor/++Js3POFo3Hzx6Z6rlMVDgWatX/EiP45jeGCmaPsx9eePQlfmzMhp/3E4sqRV7vOiNCKhEKoCodSotk0ptjVMW5uuqJx/PdjiUg6XSlye2S1KNNNR4IUlH76j3fwqTtfhlIK7V0x9Dc8wNF40m4RpLWtV97x8k0tvtt6CdhYXGFvezdGDUxU3TfuavPcdtu+hLi79N4leGrVdodVxEzk0J+JUgrrdngLbhOlFO57JTHpbuG7O3DJ75JCWv8/tQdssNLTSYNtXTHb/uDeZ2c0ZleVtXi/4vdLMfdnz9vPF63fDSDR4dAPLY4H1lcDSN6dYFvx0uXJFdvwtnXhU2VdMTcUISeZEFJZUCQXmIMHN6ChJoJTDxvqmMiWC6dNGprTdqce1ozBDQmBMLC+OmVSVFDSVQ0jYUmbPGBWVIN82YVF0NEdw6bdSQEZiyv7FmxHdxxd0TjaAsR4bdjVhlfX70Y0rtDeHXNMlIvHlW1TiXpUFt2V7Dm3LExZ5/zbXvRtD+0lxL7/9xUOL7K7GUh/67by1pZ2LN24x3O9Vg9P8p+XbMbpP12IV9bt8hwLkHi/l/xuMRYaNoxn39mBPa1d6I7F7epqOn+72aHQy+oRhNUfJCu/+nfjrMnDEs+t9/P5uxdh5o3/BJB87/pj1mMYPSiRN74yTftsHfc3oD7x/67/T7vLKF+63GisSf6N6kpyv2qKZEJI70KR3AfwqyoPrK/Gt+ZOynp/9dVhOzqtviac80SUhT7+ViCRCe0lvrV4NCuqDT6VZBORRNXwJqPZSUd3zL41/9DSzTjjlmdx78vOGLKx8x7z3WdXNI4Ol0iOxpUxWS2x7O1t+3DbM2sApDYt0RcKpj8S8LdVmMv1Z/HgkkTKiLZTPP/uToyd9xg27GzF5j1t9kXEztYuXHB7cpKb6Tnf75FusXxzYuKlKUDd7/+xN7d6/j/OuPGf+NoDy+zKvWlnmffwGw57h47jA3KvJJ9563Mp+9B+c92t8JV1ux1jN7EnDlrbrnh/HzbtbsN9L2/A2HmPOd6jFsmb97Rj7LzH7P9TepJLF3M+h774ZiWZENLb8CxTwrx87elYv6MVxx86xFPsNdVVeVZrBzdUp23bW1cdRlNdFTbvaUdDTaRX8mEjYfFM8+iKxVETCTsqqvUBKkKxuMKz7zjFXHt3zJGd6vbTZqIzGkd7V8yuKOrjaGGohfzcnz0PAPjSSeMdVVOTC48ejSUb9+B1qzGFn7/1l/9KJl7E4gqRsNjCWQvRR15PWAVeXb8L33r4TXt9d6XTjPjr8MhJ1sLCT7dO/Pbj3i9YPPbmVnzY8r2bFwcPLN7kiA80J9vlI35OV/BrqxK/P17Vd/dhbKFrvbBjfydO+tEz9uuPvLYZhw7th/te3mh/Vs+vdv4+0ZNcupi/3/r3uhhtqQkhlQUrySXM8KY6HH/oEADA3CMOSnm9OhJCjVVl/rdpwzFyQMLT+vv/d0za/dZXR+zqqSB46kY2REKC6kjqfrft7cA1f1rmmFQY5MvOy+M77+E30JZjdzwgUY102y3e3rYfz69ORLt1dMcdX85dsbhDLA41Mq07o3EcZWT9+rWGftuYUBazq+qJn+7JcW7/tbvSaY7NrPTG4nHsbu2yuze6hatSCne/kJr84YU+Rjq7hdnG27xD4Gc5yURqJdm5Hy8bjK4k+1kmqsMhfPX+13HHwrV41fItu4X2Vx9Yht88n7njJCk8HYa1SF981wW4A0UIIT2BIrmPcMdnj8KnjxmTkkShLQ0N1RFMHNYPQFJc+FFfHcaMMQOsxxFEekEki3hXkm9+/G088voW22IAJCwfmfCqJj61artddc2FA53diCukbd6hPbAA8Nvn12PLnkS1elj/God46+yO+7aJ9uODvc64M7emdMeu7XNlGHdG4/b/naOSHFOYeeM/8bjVzMR9gdEdU7jh0cwZ0kDS67uvPTVhwh5Xu3cjk3HXLsCX/5AaXZcJ/V70xVM07hT1XhVf7cn2s0xUR5KTSN1Z0CY3P/6272ukeJh3TfTds964uCeEEBPer+pD/M9Hp6IrGnfcJj9iRBMA4Ohxg3Dm4cPw7LvbMW5IQ9r91FWF8R9zJuKYcYMxe9wgbN7jnabQU7w8yXanNENMFctbqL2p/dOIZLNyfOtT79qT6AY11GCVMTnMLdyC3Lo/+cfP4K7PHuX7ujvnendrd8rrNZEQol0x/MYQke72ve5KbDbe28XrExMF93jEsGmcleRksgaQyEKOxuKIxlXGizfN+y2JFI/G2sT/Sywed4h6nWtsotM9/DzRBzqj2NWauCjxS1wBvLtIkuJj/i3opBs29SCE9DasJPcx3JP4po5qwuLr5+CCmSPRVF+F86ePzLiPuuowIuGQnfeZrhPglacd0oOxpn6J6VSCbkPMFGuWum5JHbQNNJDs0OeeqNcZjTmEcdB2zs+mmfzorhzvbnVWntu7Y56TOt2i2C2a02U1uwki9k3rjD62Kaovv28pJn3nicDHvOh/XwEA9LM+Y3dVvjOWKnJ1Jdmvgv+3Ze/jA2uCYbqqODVyaWJWknUMYG/cASOEEBOK5DKgubEmq45T7lzidBP3/vNDzvSM6aMH+K7796tOdO7Xa+JeVLcTTn7pnTc9MTmsOscoulzRleRcJgC5m5d0dscxdWST/TyI3QIAlmzY7fuau2Ogu/NcZzTuKZLd1VS3jcOdQ5wuqi8IpujUAvwDK9MZAJ5+e7s13uz847pq726K4/XZtnalt1uYpEvgCLM6WVRef28Pnl71Qcpys5Lcvy7xe0G7BSGkt6FI7qOcNGFIztvWRpwiuS7gbXAA+NTRo30j23QagcZdwQRMkZx8bcKwfthw87k4YmT65ignHDo48Dj96G9UgHVTiaA2ABN3ykVnNI5Ljh+LQ4f2w8Rh/QKL5Hc/yNzsQ+MWyV3RRFKIG/fktdSJb87nhzT3CzwGL8yK94ZdrRg77zG7q6FZcT/s28Gryea27hbfXlV6LaR72swkFBJs2t2GQ69bgLe27O3Rvkj2fPTXL+HSe5c4cr+BxP95VVjw4OXH4YxJifxsWmMIIb0NRXIfZM1NZ+PeL8zOahuzWuj+cslGJE8Z0YQVN8z1fM0tNt1RXkBS4JiZt3q7TB7DbGwRfgwxEil0Y44xVgOKbLjlk9Mdz7uicYRCgsnD+6M7pnolTmyvh03Aq5J82X3OyXLu6qr7ea7NZLzG9dLaRDLIr59dCwDoX5v6f7Z8Uwu2tGSO69Oe5J89tdqx3C2gAOCu59bhG39e7vCJ50I4JFhkNZq5feHaHu2L5M5jb2x1PO/ojqG2KozZ4wbZ5wlW/QkhvQ1Fch8kEg5lXUW55ZPT8fTXT8Ftn56Z8pp7X5877mDPffz5iuMwdVST52sXHzPGti2Mb05MHNSJFEP6JYWpV4VVV7YzfenlQyQPb6q1Hz/+1jaEQ2KPNyjVkRCOHD0Av/38LHuZ7vRWFQ6hKxoPXEnOBq8JZzUB2pe7t3PbHjLZLdLd1m5urHHsXwvumCur2eT8217ECTf/K/2gker71rjtF9WREPa2d+OhpZvt+L5cCYtgkNWNck0WVX6SH/Sk48esOxETrl+Anz31LjqjMftiWl/TpZlKQQgheYGnmQph4rBGHNLcD+dOG55xXT9RNN4nNePK0w7BTR+dioEN1bj94pn4y5dPAJCsWOoW2ID3rXIt0g6yBOy504Zj3tmpnQSb6qpTlgGJbnxBi0rNhmAHgFED61BbFc7KvqKro2ZO640fOQJAYrJid8wpkvNV8MpVJP/WSr6YPW4QAOCOhc4sYK9q9IShSQtGOpE8dnC9w5/trkq7N732kTcyjlfT6FGFBlIj3I4dn70NZ9TAOs/loZDYv6OtPo1jSO+h70rsbu1CR3cM3TGFnz21Gh3dcfuOFCvJhJBCQZFcpjx6dXIS3fLvnoXDDmoMvK1A8KOPT8OCr5zkWO5XyZ0wNLnvs6cOt9fT3tcB9Ulx67YhXHHKIfakw5s+OgU3f2wqfnXRDEzyGK95fHOS3xEj+uP6cw4HAN/4O72/xtoqzB47yF7eYCVr3HfpMRhhVJnToSuculPg8KZa+3FVOITt+zvxlDH5KMiXeZCZ+l7d/vxalnuh/dgPLd3sWO4WtvPOnoQFXz3JzuQ+7TBnNrdJQ03E0WjkruecAtxtobl/0SYExa+SbLYmB4ATDsleJG/e4233CIvYv6P3fOHorPdLciceV2ix5gm0dkYdXveO7ph9x0mfL8IhwWUnj8ex4wel7owQQvIARXKZMsVIWmiqz86mEBLgk7NGY/II50S6iCGmdOOSR68+Eedb6RRudPc1U9y6bQjXnDnRftxYW4ULZ4+BiGDW2NQvvoHW+/i3acOx7L/OxOHDE+MLi6DZ8hofPrzRUyhrK0j/ugh+cdGM5Hs1/gKCxrbpmfY6JcScLKYTPczOeqbn20/4BcmK9mqoUu0xcc8Pv8qsO1WkvjqMqnAI935xNl6adzquPO1Qz+1+87lZqKsKpyR9mPRkclWDTzSgu/34p44e7VvtzsZvDwDb9nXgvV2tAIDqMDu6FZL9nVG7C+L+zqgjNaW9O2bMXUgsC4UE151zOB647LhCD5UQUiEEEskiMldE3hGRNSIyz+P1a0RkpYi8ISJPi8jBxmsxEVlm/Zufz8GT3iGIsPnrlSdg6bfnYMrIJt/4uS67kuwtkn94wVTfSmi/mggevNz55TeooRr/+I+T8dNPHon66ghGDqi1xztjdKIl9CkTmz2zbidYov7gwQ2OYwqSKwcVyVqQa5Fspkd4RZANtOwm910629MTbu4rCDd9dIr9OIjdQuOOutMpJVXhEB77yom2ncb8/xwxoM5TaP7ukqMxZ/KwhEhO07LanayRiW+fe7j9OGjEV2NtFW791PTMKwbkJ/94FwBQU8UaQiHRVeTBDdU40BHFXqOT48ZdbfbvOu0WhJBCkfFbQETCAG4DcDaAyQAuEpHJrtVeBzBLKTUNwEMAfmS81q6Umm79Oy9P4ya9iPu754xJQ3HoUGdMWH11BINd/l43Ot3CrCR3GJPG6jI0EZk9bhAe/vfjcMHMUfZ+Jg5rtGPPtEc5LIIxg+ux6oa5+OQs76riNWcehl9eNAOfOGqUQ1iaq7o73HkxckAdvndewn+sLRZRQxhrH+t15yQ91VokV4dDvkkS2VQ8jzM8uNnYLdxVbN2soyoSwhEjmnCsti24ovvcx/jK6YfitElDE+OuDqf93LKdwDh2cANuv3gmLjl+bOBtwiHxbQKikF6k/8eciSkXY5pC53ZXOjq3fNTAOrR3x7DHiDxcv7M1WUkOsS01IaQwBPkWmA1gjVJqnVKqC8ADAM43V1BKPaOU0r2NXwEwKr/DJIXE7SP97SVH46lrTsl6P6MHJhIfhvVPen1N/VUfQBgedfAgfO+8ybj5Y1PtiWcabb/QX5p11WGIiGeU3JB+1fjwkSMgImioieCKU6xOgsa6d372KDsOzu/798V5p2PmmETVWgtbbfsAkk1GRg5IxsoNssa5t73bt3FLXRaV5Hrj4qImCyHntlvoyrIWg36Sw53FbIrmuqpw2jbP2UbhhcOCs6cOty9EgjLn8GG2hzobTpvUjNnjBmGKR0Z3NhcgpOe0tGuRnPjb2brXaatJTtxLPKdIJoT0NkG+BUYCMGfbbLaW+XEpgMeN57UiskREXhGRj3htICKXWess2bHDv00vKQz5+u753nlH4H8/N8u3S19Qi4HpVTbRNg73LX33l+fHZoxM2VZPRjOXnnH4MDz3zdPw9NdPwX/MmQg37kpsXXUYD15+HO76XDIKTgtG871de87hmDYqUan1+2yDfhaRkDjEm1+d9IbzU0Wme/xaNKeMyfVZuS0dZjW8rjq93aIjjYD2oirHXK/aqjDuviT7iXb6AsArd5wiuTC0dkbx2nt7bLuFTh7Z0tLhWK/GladOkUwI6W3y+i0gIp8BMAvAj43FByulZgH4NICficgh7u2UUncppWYppWY1N2dfDSLeLPvumVj67TmB1n306hNx2cnjAQAnHJp7Nz+Tuuowzpw8LKUTn/l6T9CpGe5IMPeX500fnZqyrV7H63vWqwPdFacc4nlbfva4QQ47iU6gMEXvxGGNmH/ViehfW+XbMCWoIKsKhxy5xn4d5o72mPjotnRo0ZyyC5fdwu3NdVSSq8Mpba9N9luNP2aM8W9nbpKuRXomchFN+ndzcL8ax/sKSbDEEdJz7l/0Hj5xx8vYuCtxMzIpkhOVZD0HQKdb2BP36EkmhPQymafUA1sAjDaej7KWORCROQCuB3CKUqpTL1dKbbF+rhORZwHMAMBWVgXAjF7LxJSRTZgysgnfOOuwvFfQxg3ph0/NGo0/LXHGf9Vn8CRnor8lTls7ndVK95enV7MMXVn2E+rultpeuc1e6C/0QQ3VqK9OtSL4fbEH7XpXFRbHurG4t53BPUkPSG2+MG5IA55fvROb97SlrGvi9ua67RZBGJLBv67xa2wye9wgnDV5GA50RlM68PUE00pi+qerIyHfCakkv2xpaUcsrrB8UwuAxGRRAHi/pR11VWGMHliHHfs77QsaYSWZEFIggnwzLwYwQUTGiUg1gAsBOFIqRGQGgDsBnKeU2m4sHygiNdbjIQBOALAyX4Mn+ac3bjGHQ4IffnxayvJsEh280J5mdwc5XQEcaX3ZRjwE6PTRA3D5yePxk08c6bnveJapDJoffGwafnnRDEwY1ogXvnU6Fv7nqY7X3SL5j186Bv/8j5OzmiRmVji9YuEAH5HsOvZUKyZQx9X5aUL35+ewW+QgktN5hyM+dosHLz8O/++k8RkvJhZdd0bguyeAfzoIJ+0Vjp0HEjaL5Ztb0FgTsW1U77e0o39dxJ7TkOy4x3QLQkhhyPhNoJSKArgKwJMAVgF4UCm1QkRuEBGdVvFjAP0A/NkV9XY4gCUishzAMwBuVkpRJFcoXz/T6fPVVddc0SK7s9tZTdUT+X708Wl443tneW4bDgmuPedwDG/y7rzmriQHpamuCh8+MpEbPaihGgcPdmY2m9/r1eEQjj9kCCYMa0RVwIuTfR1RhENiXwB4RaydPLHZM3f59ElDcfExY/D8N0/Drz49AxfMHIVjxg3CLZ/0vlDwwxSWftnLbgY1JNYbUF+Fe7+Y6v/VZKoOZmqhPbR/LQb3q8EDlx0LIMU5koJfzNu+DnbbKxQ79yduPO480IWRA+vQrybxu7J1bwea6qpskZyMgEtsx7bUhJDeJtD9bqXUAgALXMu+azz2LN0opV4CkGoIJRXJ1WdMwPb9nbjvlY0AkpWhXKm3hGCHq5JsVpj6BxRxbs6afBBueyb/rqDJw/tj0kGNeHvbfofVY9QAp1gf39yA93a1eXqORQTf+bfJuOL3S1Nef/6bp6G5sSbljsBPP3EkGmurbH/2aCvF408ePutMlwdmlXWkT3tnNwdZFyNfOmm8Y/nJE5vx3LvJybpmAwkvgtpSjhwVzANt2i3GDq7Hhl3prSck/+w8YLvzcNTBA9FQk/w/aaqrwpB+CduYvnDlxD1CSKHgtTgpKDd+ZErmlQKiK8luy4H+8sy2kYXJkaMHYMPN5+LDR47Af33YHQueO6GQ4L+tz8C0m3xtzkRHRffpa06x38ddnz0KXzxhnGM/ugIadUWsjR5U73nxEcTaIr4hcE5MoTrKRyRfcvxYDG5IeuJnjx2Ef/7Hyfjyqc55uz83moBMGdkf03ySUDTa+pFpUl1tVQinTGzGnZ89Ku16ptB66N+PT7suyS/tXTHc8+J6bNuXTLGYPW4QGmuSF7b9a6vQZM2t0BdQwmYihJACQZFM+ix+fthpoxJe28H9gk9c9OOXF83AF1wCtadoO4gpXOuqw/jYzGS8uIjYQvDYQwbjeqMTHZDMR/ZLt3ATpAI7rH/C/pJpwqdZpTaFsMk35x6GRdcnbzD1q41gwrBGW+Bou4hpC3n06pM8vdSOY1t2i0ydBkUE935xNk49bGja9UyCTi4k+eF3L63H9/6+EvsNa8ussYNSKsk6PWavJZLNttSEENKb9CxegJAcuP3imRjSQz8yAE/fLQBcc+ZEnDl5GI4Y0dTjY/QG+qvda/z3fnE2Dlii4ZITxuK2Z9aiJhJKubWsPcxBJxgGiVa7/JRDMHJgHT48bXja9RxtvY1q3meOHYPfv/IegESF1hyzW/z+6fJjsXjD7qwnimqxX1sVRmuWGcwAcMy4QfjhBdNw6k+eTbue2fqb9A7mRNJzpw3HoPpq++JJM25Ig71skHVBdtKEZnz+uIMxKIv0HkIIyQWKZFJwzp6aXoQFxc9CEAmHMMPqileK6OYbXpVwM/nhG2cdhmvOPMzTe6mXdQcUyUHSGqrCIXx0hnezzIeuOA6f/e0itHfHUqrStVUhdHTHcc6U4bZI1k1BJgzth9XbD6DB9X81amC93VktYgCJJgAADK1JREFUHXd85iiHwNZ2i0yVZD/mTjkIY4c0+L5+4dGj8egbW3HxMQfntH8SHNMOdd6RI/ChIw5KWeesIw7CYQc14vaLZ+IUqwHQoUP74fvn8yKGENL70G5B+iy5CqVi02blOvtVwjUi4js5SYtQv5xkAHjr+x/ChKGJxihB0zP8mDV2EA4ZmhCXbsGtxf5gw66gb4X/8UvH4t4vzvaM4QvC3CkH4cQJyeY2tt0ii0mfnz02KXi1heXpr5+C+790bMq6N18wDW99/0M5jZVkh+6wB/hbXSYOS/z+nj11eI9z1QkhJFt41iF9lr7a7GF8c0JsfvjI7Crq8686wc4R1uI56pOTDCQsDlqI56M7mZ4j6BbuR48dhH+s/MC+HW7S3FiDUxrz10WzKodK8o0fmYJoPI77F22yxfshzf08OyuSwrGnLZlk0uwSyTd+ZAoGN1T32b9xQkh5QJFM+jxTRvYv9hCyYnxzP7x949ysI/CmGbFm2mOcaeJeUs/mnvSh0Ukabn/zrZ+ajuWbWnLOvb7qtENtC0ombLtFlp+dvphgIkLp0NLWjYnD+uGnn5iOMYOd1huz+k8IIcWCIpn0aZZ+e06fvA3b04zog5oSDRY+c8wYfO/vif48OgXARFeQe5CGZ6M9pO5KckNNBMcfOsRrk0B840OHBV5X2yVqIiF8bMZITBkZbHKmzthltm7p0NLWhcENNZg6qjQn2BJCSN9TF4QYDK7Q2K7+tVXYcPO5AGCL5Oe+eVrKerpwmmubbRMtNDNlFPcm3VY1u7YqjFuMjOVMjLYmCerubaT4tLR3255jQggpRSiSCenjNFSHUVcd8awkzzl8GBZv2IMRA4J1xkvHMeMGYeOutsCtqHuDzmhCJGc7afPq0w/F9NEDcPLE/PmjSc9oaetCUx1j3AghpQtFMiF9nOX/dZbva5edPB4fP2pUXiruN35kCi4/5RDPCXqFQtewh2TZKCYSDuG0ScEbi5DeRSmFlrZuDKwv3gUXIYRkgiKZkD5Oung1EcmbJaUmEi56IsQZhw/DN+cehs8dN7ao4yA940BnFNG4wgCKZEJICUORTAjpM4RDgi+femixh0F6SIsV/5apBTohhBSTvtmNgRBCSJ9Fi+SBFMmEkBKGlWRCSN64+5JZeL+lo9jDICVOVURw0oQhGDGAaSOEkNKFIpkQkjdOnzSs2EMgfYBJB/XHfZceU+xhEEJIWmi3IIQQQgghxAVFMiGEEEIIIS4okgkhpIIQkbki8o6IrBGReR6v14jIn6zXXxWRsYUfJSGEFB+KZEIIqRBEJAzgNgBnA5gM4CIRmexa7VIAe5RShwK4FcAPCztKQggpDSiSCSGkcpgNYI1Sap1SqgvAAwDOd61zPoB7rccPAThDRASEEFJhUCQTQkjlMBLAJuP5ZmuZ5zpKqSiAvQAGu3ckIpeJyBIRWbJjx45eGi4hhBQPimRCCCFZo5S6Syk1Syk1q7m5udjDIYSQvEORTAghlcMWAKON56OsZZ7riEgEQBOAXQUZHSGElBAUyYQQUjksBjBBRMaJSDWACwHMd60zH8DnrccfB/AvpZQq4BgJIaQkYMc9QgipEJRSURG5CsCTAMIA7lZKrRCRGwAsUUrNB/BbAPeJyBoAu5EQ0oQQUnFQJBNCSAWhlFoAYIFr2XeNxx0APlHocRFCSKlBuwUhhBBCCCEuKJIJIYQQQghxIaU2H0NEdgDYmMOmQwDszPNwCkFfHTfQd8fOcReWShv3wUqpispE43m7z8BxF56+OvZKGrfvObvkRHKuiMgSpdSsYo8jW/rquIG+O3aOu7Bw3MSPvvoZc9yFpa+OG+i7Y+e4E9BuQQghhBBCiAuKZEIIIYQQQlyUk0i+q9gDyJG+Om6g746d4y4sHDfxo69+xhx3Yemr4wb67tg5bpSRJ5kQQgghhJB8UU6VZEIIIYQQQvJCWYhkEZkrIu+IyBoRmVfs8ZiIyN0isl1E3jKWDRKRf4rIauvnQGu5iMgvrPfxhojMLOK4R4vIMyKyUkRWiMhX+8LYRaRWRBaJyHJr3N+3lo8TkVet8f1JRKqt5TXW8zXW62OLMW5j/GEReV1EHu1j494gIm+KyDIRWWItK+nfFWssA0TkIRF5W0RWichxfWHcfR2es3tl3H3ynG2Nheftwo+Z5+wA9HmRLCJhALcBOBvAZAAXicjk4o7KwT0A5rqWzQPwtFJqAoCnredA4j1MsP5dBuD2Ao3RiyiAryulJgM4FsCV1uda6mPvBHC6UupIANMBzBWRYwH8EMCtSqlDAewBcKm1/qUA9ljLb7XWKyZfBbDKeN5Xxg0ApymlphvxO6X+uwIAPwfwhFJqEoAjkfjs+8K4+yw8Z/caffWcDfC8XSx4zs6EUqpP/wNwHIAnjefXAri22ONyjXEsgLeM5+8AGG49Hg7gHevxnQAu8lqv2P8A/A3AmX1p7ADqAbwG4BgkwsUj7t8ZAE8COM56HLHWkyKNd5T1B346gEcBSF8YtzWGDQCGuJaV9O8KgCYA692fW6mPu6//4zm7YO+hz52zrXHwvF2YcfOcHeBfn68kAxgJYJPxfLO1rJQZppTaaj3eBmCY9bgk34t1S2gGgFfRB8Zu3fpaBmA7gH8CWAugRSkV9RibPW7r9b0ABhd2xDY/A/BNAHHr+WD0jXEDgALwDxFZKiKXWctK/XdlHIAdAH5n3Sr9jYg0oPTH3dfpi59jn/qd6GvnbIDn7SLAc3YAykEk92lU4vKmZCNGRKQfgIcBfE0ptc98rVTHrpSKKaWmI3GFPxvApCIPKSMi8m8AtiullhZ7LDlyolJqJhK3t64UkZPNF0v0dyUCYCaA25VSMwC0InmbDkDJjpsUkVL/neiL52yA5+0iwHN2AMpBJG8BMNp4PspaVsp8ICLDAcD6ud1aXlLvRUSqkDjZ/kEp9Yi1uE+MHQCUUi0AnkHidtcAEYlYL5ljs8dtvd4EYFeBhwoAJwA4T0Q2AHgAiVt3P0fpjxsAoJTaYv3cDuAvSHzJlfrvymYAm5VSr1rPH0LiBFzq4+7r9MXPsU/8TvT1czbA83ah4Dk7GOUgkhcDmGDNJq0GcCGA+UUeUybmA/i89fjzSHjH9PLPWTMyjwWw17iFUFBERAD8FsAqpdQtxkslPXYRaRaRAdbjOiQ8eauQOOl+3FrNPW79fj4O4F/WlWhBUUpdq5QapZQai8Tv8L+UUhejxMcNACLSICKN+jGAswC8hRL/XVFKbQOwSUQOsxadAWAlSnzcZQDP2b1AXz1nAzxvF3DIAHjOzvagff4fgHMAvIuEh+n6Yo/HNbb7AWwF0I3EVdClSHiQngawGsBTAAZZ6woSs77XAngTwKwijvtEJG5ZvAFgmfXvnFIfO4BpAF63xv0WgO9ay8cDWARgDYA/A6ixltdaz9dYr48vgd+ZUwE82lfGbY1xufVvhf4bLPXfFWss0wEssX5f/gpgYF8Yd1//x3N2r4y7T56zrbHwvF3YsfKcHfAfO+4RQgghhBDiohzsFoQQQgghhOQVimRCCCGEEEJcUCQTQgghhBDigiKZEEIIIYQQFxTJhBBCCCGEuKBIJsSFiJwqIo8WexyEEEIyw3M26S0okgkhhBBCCHFBkUz6LCLyGRFZJCLLROROEQmLyAERuVVEVojI0yLSbK07XUReEZE3ROQvIjLQWn6oiDwlIstF5DUROcTafT8ReUhE3haRP1jdrAghhOQIz9mkr0GRTPokInI4gE8BOEEpNR1ADMDFABoALFFKHQFgIYD/sjb5PwDfUkpNQ6Lzjl7+BwC3KaWOBHA8Ep22AGAGgK8BmIxEd6ITev1NEUJImcJzNumLRIo9AEJy5AwARwFYbBUM6gBsBxAH8Cdrnd8DeEREmgAMUEottJbfC+DPVu/6kUqpvwCAUqoDAKz9LVJKbbaeLwMwFsD/b++OUYOIgjAA/7+NIFYWtt7CzjtYxEYIwdoTCNp4Ci29hmAh5AyWqaxsgqiFiIxFttDXCBFj1nxftcw+Hvtgd5h9O7DHf39ZAP8lOZvdUSSzV03yamae/BJsny3jzvvf9a8/HX+PZwXgT8jZ7I52C/bqTZKDtreTpO2ttndydk8fbGMeJjmemY9JTtve2+KHSd7OzKck79ve3+a43vbGha4C4GqQs9kdb1rs0sy8a/s0yeu215J8S/I4yZckd7dzH3LWA5ckR0lebAn1JMmjLX6Y5GXb59scDy5wGQBXgpzNHnXmvF824PJp+3lmbv7r6wDg9+RsLjPtFgAAsLCTDAAACzvJAACwUCQDAMBCkQwAAAtFMgAALBTJAACwUCQDAMDiByvXIpnzQQmKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure('train', (12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"Epoch Average Loss\")\n", + "x = [i for i in range(len(epoch_loss_values))]\n", + "y = epoch_loss_values\n", + "plt.xlabel('epoch')\n", + "plt.plot(x, y)\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"Val Mean Dice\")\n", + "x = [val_interval * i for i in range(len(metric_values))]\n", + "y = metric_values\n", + "plt.xlabel('epoch')\n", + "plt.plot(x, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check best model output with the input image and label" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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SBJfXEnz6ZWoARBh8/3YCGUPyiRfn4FNXSimllFJKqfmjAYUb6PQf3Qu8xJKvzu+Cg46fChIYEhKdEPn+ftwFTEw4FbutmYEd9YgHtbl23M6zWMEAVlMDI2sqyTRbAISHhXy1RTEp+I4Q6xFieYOxJypyyg/smmpKkQB2weCFBQoF2LkfC4g2NhBuX0qxCvygQUIejAaxioLlQvwMuBHBzkO+DgJpCGR8Kk5beCFwoxAcNWRyNpkWyNdY2MUW4kejcDgFxgdjAB8TDgLlIJIpFKb3YRhDwxePQkMt535rBxVnXMJP6owFpZRSSiml1M1JAwo30NH3fow3HXyM4DO756X+rj/aQa7ZpfKQRdN3+vCOnVzQWQnXYsVinHvvPWSaDXZBKFb65OpaaHo+iWdbFGIO5x7wwfEI9jskOiDTLLhRg5fwKSUsMk0Bon0GO+9dSJ5YcrF+tJdIMIgVj11YlmDZuL19NHwmR+ShNViuIdUXJjLkk2oDqwSFSsGNGey8UKj2AYtiwqLqSBYv4hDqGccEHZIdUcbbg6QWG/o22Qysr8PJ1hEeNgSyBqtkcPI+gcYkgZEc3sGj0/5cvKFhGBqmfmK2RvHhTYS70/gHjsztD0AppZRSSimlZkkDCnMo/dRS4o+cmvQ1//6NbPy/d1D/d/OT2T/z+FbyTR7x0w5NnzuONzCL3SNEJr5pnx/pd29jdLmFVYRIv5Bp83GyQikBHW+NUXXYYLngpAQA34HhDT7hXpvQqGDGbJqfy1KoDTK61KHrzVGid28j9aYM8WiB9yzr5J5IJ18e2cRzPSsZP1jD8idGEN/HdJ0j9qXycpOJFAlUrlmBFEpgDF51nFxzjOFVDpn1eUaLYXwnQikqRONVRHtyOM/spn5JO6F7G8k02OTrIF9nSK8t4vQHCQ9ZFCotQqMBfCdK8u5tVH77EDTU4h2f/PfjaoJPv4xv2bBlHbmmCJGv6YwFpZRSSiml1M1BAwpz6GrBBICOt4VZ+qH5CSaM/dI2RlYLsdNCaMTMLpgAWNEofiYzR727lPvgfYyssMjXl4MIdl4I91sUan38gCFxyiI07mHnPMQP4gcNlUeEYsIuJ0tMQyBlCBztxo0vxnnjEA+1HOcvm165oq03NL0MTS/DffDu1z/IruOLaf5WFU7OEBwr4ew9gTgOxrZhbAgJBDC7OwnvhkRsG4XqEHYBBu8R7IJQqnDI1cSp3gVuxxmSQLi9hkxTkHSrRV6CWEUoxQ1+yCCeUKj3Gfct7AfXUIoJyaoYUvIxew6W23ansfzF92DnfqKb7qb793bQ/Be63aRSSimllFJq4WlAYY6M/eI2kv969UR6i785f3kM+u73iJ0O0PzDFPLKYWY7t2C+ggn2mhUcf3uA0AiEByyKVT7iQewsNO4sEeoaxQQDWOksfjyKXQgRSAnjSw2xs1BxxiOQ8uh8m83HP/wNNoQu7JTx96Mt/PX+BykOhSHiYTk+97Sf5cvLvwvA55c+Q3pxntdVvYfAk5W4UYuBR+9GPLDzgh+qQnzBKiwjOAbRQZ/mH7u4YYvWp0bKjUzM3DD3rMHfdxj3dCf26U4qgArKu1Bk1rcwtiRAvk5I3V1AxgNklpbwwgHciGFkdRzxhPh926k+nMPpH4fRFP74+JS5FszLB2h+uTzbpfuBCG3/XQMLSimllFJKqYWjAYU50PfbO2j422vf3Fk/2DMvbad+dhvVu4VEdwn7SCfedL7xXiCl+jgm4BMetAiOGbJ5CycH9c/1lZdYBANYI+OYWITRdZXgg+VCaERAwHeEsaUBfv0N378kmPDVTJyPHnsDpf4I4QEbu+hgLMg0XyjzV8NL2ZtqpVB0iGUMdtEn0udguZBtMrjRiWUWvpBtMogR0i0BQsMGPxTAyuQxZ7rx83mscHjS9+d29xDq7qHhvrWMrkkQHA0R6/UZWudgFUDCYGzwIj7jywRjR4j2h6jY52PbFiabw0tnptx+0npuD4uHVjL2M1uJf2H+dgtRSimllFJKqWsRM49r5aerQqrNVnlwobtxXaYzbb3v3++g4W/m/ttk6+7VdDxeTcsP8tjPXjnl/2aSeXwrpahFulWwi+UkiE7GEBr3yTSVlzPk6n2SxwQvXN62sfKEByIExz1KMYtMo834Mp/40jH2bfnMFW38+fAyBksJzuUr2JA4y3srD/JSvoJvjG6gv5DgQF8T+VwQMxAidsaiWFkOJPhhgwn5BPscvLDBi/kERmxK1R6BERtjgbENkT6rvBvFqwXCL5/Az+QwpeKU7z397m0YCwoVQmopiAvGArsg+E45KaX40Pb0GGb3QbBs8L3yNpRT1D/869vJNgit/0NnK8yHl8wzjJthWeh+3Ci38lislLp93UljsY7DSqmb0bXGYZ2hMEtTBROcpkaSp+Z+1oD3pns58ViQqv1mVsGE6dy0ToddUUFu+0oyjQEAIsMe4Sd34rS2kN7YQqbBJtHjMtBoERy1sAuQaQYxFqWGIsGeAH4Aqo/m8R0LO+/hjGQp1cQQ3xDZdYb4oiYaXhJKySiblnyA9CJo2dHN/1z+WdYEo/x+9UkA/mRwFR7CH557E/92egWFTBB7KIC4QmLNCJmuMIUqg1mexR8MI8kiMhgCA17URzwhNCoEUg6lpMGtcrHSNuKDeND9QBDzE3cRGhHCQ4bIkEf8+0fwxscn/Wziny8vhUkADVVV5LYso/++INlWF/EF1xUw0PnWJMk12wgPecT2lnetcM/1XvNzr/7nF6gGzv7hDtyYYfEfvTDrn6VSSimllFJKTYcGFGYh944tRL567az7x39nCUv+YO5v8vo2hxHXJ5Sa5QwTM7uNJa1EgtzrV1NK2JSiQrFCKMWhFHOILV/C4PZGiknBtzmfr0AM5OvL7dp5QTI2VkmwSobBu8t7L4RGfSJxh1LCJlNvw8bVZBsNwfFyXXYB7Bz0fa+Vn/ne75Ho9Mk0W3gh8MIGVqeJhEoUcwEo2ATSFqFhSFtV1L9iKEUFryuKk4PBe0MERy3cmCE0aGMErALYBvINBilZ2EXBzkNw3ODkIV8jeCEYXQljvs2iseXTCux4IyM4eY9ACuysRSBtUaz0Eb88a2FsmUW+2mJk5VIiQz6VR2oxLx+Yst6274zRv6UC2bwOs2v/rH6mSimllFJKKTUdGlC4TgNfX0Xd26fewm/ZZ0aZ3S37lczrNgCQOGVRufMss5n/cPEMC3vtKvB9vMPHgfJyDqumGnL5Sb99F8eh83fWUViVw8+DFC0qjtjU7y7iByx6Hm1ifLVHaMAmkIJ0k40X93GTBmxD+GwAJwulap98s09lY4rRigRSsAiOWIjv4IWExk/vx4rHMNVJZHgMky/gjYxc0Z+K1x5YNnZFHG9snHpjsMJh/PzVk2JWfvrCY6e1hYEHFyG+wQ9AvNPCDUNwHDItBjcq1O4v4dsBvEg5IIIFXQ+GMI9sx0kJlgeVxz0i/UUCBzrAcS7ZecP6wR4afgAtixeRX1bH6LIgpXg5QFFKGMZX+jhpCz9kMbApTmL7DsIjPsknXsSuqcYbGr7y57j7IHW7wW5t4dwHtlP3MZ2poJRSSimllJpfGlC4TvZXq6dVzt93eM7b7nooSnAMYn0eJhKa+oRpcJa0k16aJFNvUxsNYnWcA88D30Bgkl8Ty8ZubcZywXctnGGHSL9QdaxI5KXjmEVNpFuqiJy1CWSgUA1eUJBkEUaD2OM2hVqPYklwRhz8kGHsTBK7KARHLJp/nCfYl4aSi5dK4adSMMX0//N8D2907MLTawQTLuee7abqk93YNdVIRYJiazV+0CJfEyBfY2FsyNY7pJb6BEctShUGP2AwFkhNgVLWwY65dK+yCHdGSCxbTXTAJfTNK7fydDvO4HScoWFvNdTX0PVTdXgRg4l6OL02GMBAttlQTFokt6xD+kaxYdKgwmv9Dw+3ceKvtrH8d6++64hSSimllFJKzZYGFK5D6AeNVD8w9TfAqZ/dRuJzc3tT1/s7O/AdQ+KMT+KpA3hzsMWjhEKk1zYwutwhV28YX1GBH0hQeVgIj/rEzmSxWhuQXBHv2EmsRILu31xHKQ5OFkJdQeKdUPf5/fipFP7mddhDKaA8jV88sIogBmJ7IgTHDNFBj8RLZ3B7+8qdmCQ56LX3Ophf3tAwDA1jne7EAuJAYvM6pOgysLkSPwC5pUWsoIf0hpD6ApblE6nNsr6hh9FihNIim82PdXJfrIMDf9LKvrEW9p5uw+4LsfjJPIH9p/BGx8631Xz4OE5LM6X2OobXBCjFBSctRHvLORb6tyTAJGj+asc1+5743IskPgf9v7WD0Kh/ze1MlVJKKaWUUup6aUBhhjbsgb0fqAem/rZ8bKlFYo7bz7YYwv1CaNTFFAqzqstZvAivLkmuIYIXEqyLqvMqPMR3KFRYlFbFcQqGikOjIIJVXUmxsvytvDMkVJwxhIZLmFXtyP7jpBZH8VbFyLRA9JwhMuhTu6+AVfQQw/k1/ldbqvFaokh75TKkUMRPxrDGs7gdZ2b1fmfL7NqPAerdVXihGkbvERgv51+ouCtFphCkKppjuBBlNB9hbXUvTcExbPFpDw2yquEc65I9vDS0mGMNDdT+211UHs8iLx44v1Wk292DdPdQm1vL6F0JCtUW+RpAIJAqB2Vya5uJTJS9lsZP7Sf7wJp5/1yUUkoppZRSdybdNnIGZONazJ6D0yqbfedWol95aU7bH/qN7RSTQu3+IoHvvDzr+gY+sJ3IoE/v64TgsIVdhEVPDmEci8ziBJG+PKnFUXI1Fk7OEEz5iIHh1TaVJ3yskqHi345BXTVjG+rI1lu4EQgPGYJpg5PzCT85dZ4Je80KTv9sHeFBGNucx+RtAiMObsQQGrJwcuVkiNVH8gTOjcPIOP7oGGJbWJVJ3P7B8zfkN4oViyFN9eQXV1OsdBheZeOuS9NWO8ob6k5wMluLLYZNFR30lyo4na3BFkPGDdIWGWFr4iS70ktZHB7k7w49gHsswYq/PY2pqsA/3oEpFbESCSiVKG2/i3RLkEyzRazbx8kb8tUWVUfyWM/tmbKvmce3EuvKwk5N1jhTd9JWZXDrjMVqfj3ds/eKYw83b1iAnihVdieNxToOq9dcPBbrGKwWmm4bOQeqflzNoS8naZr6/g0A8ec+UDN0r0fLM0J496lZLQewa6rJ7FhOoVKwihbxTsENQ+3+Et7BowBE9oHT0kwyW4G/vhIjwuB6i0RHeZlD8sAofjzIsf+yCtNQwC+4NH/HIdNoUfflQxAK4fX1T9q+09TI+LZ2RlbZZBa7xBvS5EcL5NshXpEnFwhS8gRn3EY8cKMQ6QdnJAeODck4VqmIVCYxw6M3PJgA4GcycOI0gROnCQCJe9ZQfDHK8KpWPrGxgXBlnrcv38+ZQg0n07UELY+QU6Q5MkZjaIx/6rqfhxsOcTTbyFuWHKFl9Qjf2nw3nT1xwsc30fr9LFZHP253D/b3XyEJJLesY2hdnHyNBcDwXWEauxZNOXMj9qWXsGuqyb/5PgLf2z3/H45S6pYyWQDhamX0olYppebP1cZjDS6om5kGFKYp7wZo+svnp10+8rWpv5mfiYEPbCc0ICROjFw1Id+0NdQC5a0XxUC018cLCpHO1PkdKey7VpJdlER8g5M32AUfy3UQY0ie9uh4vJrNjxzg6fYf8o7jD7PvZBtuSKjdn78kIeLl7OVL6H2wkWyjkG90STSmKBQC4AmSt4nUl8ikwhAoB2SMDbEeQ7TfxRrLgOthKhPQVI/pHcQUi7P7LOaInO7Gji0hkA3S8pRFrjbO58c3gWUIhF1et+QU7ZEhQlaJ+6PH2BNexJgX4Wy2kqpgjhErxtua9vM9ZzWDtTHOBGup2d9G5U4bt7Or3MjO/TQMtNP91hZyDQY7L5x7tIWmJz3cs93X7J83NEzge8OM/eI2zamglDpvOsGE1+hFrFJKzY+ZjMVK3Ww0oDAN7zw0wFfumsEJW9bN6fTyU3+6nfDKMRr/MTY3u0YYQ+RshvjLQ5jqJJRcTCSI9A1dKFNyCT61C3EcAhNbS9Y6W3n0j5/lP9cevaS6qlCWxMEglZ++esCl8OhmercFsNaOk+/zsSqL2GJoSqQ4NtSIZHas74gAACAASURBVBycnDDQVYWVtRDALgjFSp+WHxZxIzZebQXWWPb8LIqbiTc+jjy/j6rnwWlvw7mrkUDGIZjySbWGeOHYOl4QQ6G5xMcyD/Ebb/o+CTvPumQPAfHoylcxUEzweOMr9JWSdNZV03V/FR0rFtP8wxqCJ3txz/Xinu6k+fM5Sqtb6N0aYWSDS6GqnabnG7B/sGfS5JYXS/7ri2TetZXYF+d2OY5S6tYy04tXDSYopdTc0iCCul1oQGEa/mrfm1nCvmmX73h7nMVzOEHBWZom3RenIXO1NIYzU2iuwCp4WPkC/okO7JYmpG8Yd+DC1ob+6fI0euNeaLP/53JXBBMAnt2/mqV7J08Qaa1fTXZRBWNLy79qrmuBD6a/nMzwpKnDDnt4ruAZGztjYZUEL2SwihAesBheE6JQKdQeiBHePb0cFgupsKSOUtwikDEgkDxVIjJk4+R9RtJBMPAPL99PvCqLMcLPLNtDhZOnM1vN1/o3MJSL0pYYJeoUya3KkzoTJhJvIzixbabX14/V109teBO5eodsm0vf5jDVic3TylkR++JLjP3SNpJP6EwFpe4013MBq8EEpZSaOxpIULcbDShMYdXLAdg0/WACwNLPjpxfOjBb6Xdvw9llUVEC54V9zDYzgxWLMXh3CD8AodWriQ54ZOttIoM+sY7a80knLw4k5B7bwg8/9nHgygFwb6HA4i8anGcuXZtvV1Ux+vAqSlHB2JBpNXhRHzMWAsdgHIPvCJE9EaJ9htGVkDgDoTFDcKxEtj7A8DqDeOCFy/k/Aqm5CajMN/vZV4gDztLFpNbV03O/U96lYbz852aVIHIqiO8FKdT4vFC5hFP9NbTVjjKciZLNB/F8i5Jn0do4Qtf9tUjJorplO9WHs8jz5d/H4NMvszS9gdEVEUZXGnpeZxNau4OWP3tpyrwSySdepO+3d9Dwt9NfxqOUurXp8gallFpYswkm6LisblYaULiGyA8aOLqpb8bn+QeOzEn7Pb+/g/SKEg0/hJpnOnAn2ybSsmeWlNCyqD5SxA9YDK53yLQ61Oz3SLXZRLutK/vwoR3s/98/ev75VzNx3hFLn3/+X974LoKdF3accNrbyg9E8AJCaMwnV2NRs8+QabJJdsDYEhsM1L1axC542JkSibNBxDXndy1wHtiI+CHsEjg5j8G7HQLDWYoP3ndJ8MIKhzGue0kA5GbhnuogcqqDJV8D//Ub6Lk/SrHS4NaVCPQF8CKG0KBF97faieVgMBinFAW3zmfkeAzjQKYIVtxgbEi9JUO2KU7bRTEA+fFeqn4Mwce3MrLKJlfvM/IrW6g+kMLsPnjNJRBN//AKQ7+2napPvHADPg2l1M1KL1KVUmr+TSeYoOOxuhVpQOEacg/MPJjQ86EdNP/Z3Hzr64UgcSRAxeks7sR09yvMIJjgtLZAwEE8A8HyN+XhQUPF/kEqXjV4x09dUr748KZLggl3Pf9LHNrxxPnnS772PlZ2XjrFPrumkeBwHns4jVMwOFkfJ2qR6MxTikWwXEO0z2AXDaG+DBiDnBvCGR8Hzzs/A8MZyuEtC+M7UHFonIp4JamVSVJtNi29q87nUfDz+Wm//4Vk/Wgv7a9W0PW+u0knLDDgx138sSBu1GDGy7MwxEBw2CKQAWOVd9SIdYP4kOuNY5VAHOeKAEryxx3AYroXGYbusfCCCRo6a/EuWsZyOT+fp/ZrR3B33HN+1oNS6valF6pKKbVwHm7eoNvyqtuSBhQmceyfN/G29fs4uqk043NzG7Jz0ofRX96OGzU07iwhP56btVajO9qwSoZctUW+Tqg56BLtHMc7dvKKslYsxvf/5R8vOfZaMOG/D67mufVhVnJpMEFCIbyQ4HQP4fUPkhQBEQKpBE6qQOPTQ5ixcSITu0Bca1mIf+AINaG1FGojDGytIt7r4oYtwkOGvtdXU/ypHYgLTX9160zZ98bHaf6Lcn9l41o63lmBXYDA+jFy2RDBQxFyS4pEKvPIcxUUag2I4KQFHAikIVdv6HvfFhI9LtFv78NMzFpxe/uIf22IxuAmBjcKqcUgb19O/RcKeOPjV+/TyAiBzghjj28l9iVN1KiUUkopNV80eKBuR1fOcVecfuQfryuYcOKJjbR9KjDr9guPbmZ8qRA9J4RPDU19wjQ4rS34NgTHXbCg8rhH9Luv4r86+fKMc79+z6THN3zkgzy3PnzJMSsWw2ltwRQKxI+N4Hb3YEpFpORiznRj/WgvxrLwevquuaXk5eTYGYoVNqW3jdL1kE1qUXmphO8I2WYfywW7pnr6H8JNxOw5SOv3C9S+6pI7WQG9IQJZkKBPIR8gX2so1rtk1+fI10G63TB2l4sfhHwtpFodWL/y0jpdl+qXeomdFYwFlgtEwpN34CJudw/J5ztxmhrn6d0qpZRSSimlbkc6Q+Ey7zw0cN3Rw/+2+et86pfaZt2Hsw/aBNLQ/MRhvJGRWdcHMLa1lepnOyAYQLw6kKsvF7DWr2bvH370iuNv/M3fpPF7u69MDOl5mEIRKxbDO3z8/GG348z5x68le5wuCYXwUyniX3iJ+BfKx879xx0MPZInsi9C1QEhebqANzQ8o3pvJvb3X8EGln2j/Dzzrq1kWoIYC7yIAU/wUwFKFX55+cOYjZMXvLvSjI6HyLTEWZ5ZiYyMYzJZvPFx3FMdNH96jNzW5fRuDVB613Lq/65/yr6453ov5L9QSimllFJKqWnQgMJlvnJX3XWf+6lVs78hO/PhHUT6wckwZ8EEp72NwfU24eFmnB8dIFAZx993eNKyVixGz5su/db/ro9+kPavDxN6ddeku0z4+Tzk81jhqb8Nn4rct5ZCXZTws/uvaKvpLy9d3lB8ZPOs25suu6oKs6jxqp/bJWUb6hl6eBnRfpfgU7um3Ubsiy+x7Ivlx7JxLbmWGOPtDplWg5vw8aqLSNDDLToQ8PFCho6frkW8WsJDhoZn+/GOncQbGSH41C6ii7aTqxeKD28i+PTL124ccDu7KDy6mdjRAdxTHdPut1JKKaWUUurOpEseLrJhz0L3AEorsyQ6fcLDc7XxJJhYhMgAFCoD2C2NSNHFrkxOXvaupWQWldtO+3nW/PiXWfKPJ6+6NOJi15sg8eKp9tbZAcT1MdfYneA1M7lZny0/m8VNhKZV1uvrp+brhwgN5sq7cFwHs+cg4W/sJN7jER4UQoM29rkQfk8EGQqCJ2BBbnGRbJtLtlFIra295LNs/G4PTS8UGF0enHa74e/tY2i7Ln1QSimllFJKTW3KgIKI/LOI9IvIgYuOVYvId0Xk+MT/VRPHRUT+RkROiMirInLvfHZ+Lv3HEwfZu/H6zx//9rJZ92Hw/duJvBKl8t9OUvGZF2dd32u8Q8dInixhrPIyBO/w8UlzGTiLF9H1UAIv5rP6R7/Mlr//XRb9eTnh33xyz/Vir11VzoeQyxPuSeFuXzuvbc6UKRSwfjT95Jje6Bjm5QOX7MJh11TPeBZH9Csv0fSXz7Pow8+z4s+PsuJfRqk4bhE7EcQqCs5wACtvgUD/fRa9b1ty/lz3dCfOM7tp+uwR7LtWTiu4YUpFkv/6It6bbpk/3TvGnTIWK6XUzUrHYaWUutJ0Zih8AnjksmN/ADxjjFkBPDPxHOBRYMXEv/cBH5ubbs6/v/iVX5jV+UsqZreWv/jIZsaXQuVJ75pb/V2vUtzCcsu7BkxGHAcTcChWGHB85ECCtu+lYef+Oe/LZMzJTqipQmJRSjUxxtunNxvgVmE31OOuaiP34Dpk49rrylfgDQ3jHzhC4yf30fJsGnzwHYPlCm7M4IUNxUq5YvaJNzQMQ6Ngpj/rxQvZ2A31M+6jmlef4A4Yi5VS6ib2CXQcVkqpS0wZUDDG/BC4/G75MeCTE48/CbzjouOfMmUvApUi0jRXnZ0vG/aAPL/vus8/9s+bGH375Dfq09XxmBDtFRLPHptVPZezK5PYDfWML7GJPf0qTLaUwLIZ/blNnHtLI1WHIX4kyJIvDcGLr85pX67Fqq0hv7iKwupmjC2ML4P0u7chG2+umQozYTfUYy9fQuktmxh+aCnO4TOEvrkLs+cgXl0SuW8tsnndjOv1Mxl48VVW/n03yeOCsQx+cx4v6eFGoOs315J+9zbsiorz53h9/ZP/7K8i+NQuSqtbZtw3NX/uhLFYKaVuZjoOK6XUla43KWODMebcxONeoGHicQvQdVG5sxPHznEZEXkf5YgtYaLX2Y3ZO/bRLbBx5+wq8ZnVbgNn/s8dVB6EyhOlOUvEaK9ZwfjaaioOjZBdkqT1qeFJcxzYFRWYxc0EUz7RfpdiwqbqaA7v4NE56cd0SCgEAYfw2XHSKyoBqDgFg+sFN1SBd+924j0usf09mFwOb2gYp70Nr7YCs/vau0c4rS24Z7tvxNu4hL1mBSZg44cDBL67m6QxeBe9bl4+AFvWcfbNCQLbdlDR5RLtzmF2TX9GiNtxhrqPnaFuyzrOPpjAD4FVhPTyEpl2i+i5ZQRP91/3+7d+sIf+D+6g/qPPT11YLZTbZixWSqlblI7DSqk72qx3eTDGGBGZ/lefF877OPBxgAqpnvH5c8FesZSVH5xlMAFY/bcZrjeFYubxreTbioQHg0RODF5y0zkb+bYk6WabxDEL8bhqUsXMG1ZTqLQoRQXxbBJnSzd0ZgKAXV2FiYQwnd3IsnJAIZA1iGeRagcnB2fW2wS3L6JQ79HwnIXvCMWk0DK0CDM6hjc6hhWLYQoF8g9tpJSwwRgG11skTy6i6hMv3Lg3JAKDowy8fTmxPo/w1WYG7NzP4r42uh9rI9XqMLg2QWLNdkJjHomdZ3DP9U6rOevgKQJb7yFTZTAJg+RtnJwwuD5CZbSJ4CwCKg07x/F33DOrGTzqxriVx2KllLod6DislLoTXW9AoU9Emowx5yamb7220X03cPHi8NaJYzelk7/awOI/OjXreqazleDVdD9kiJ4M0vztnjnbqi/z+FaMBclTLqWaKMHRwqTl/Ac2UkhaBFM+qUUOhWqfmn+aenvBueae60UGBrEXtTKw3sEEyse9sMHY4GwaxQzFKOJg5SwGfyqPV7Cxgz7HWlsID7SSWeyRPGhTtzdDscIm02zR+pmTxL9QTiiZ+rltYCDxublLdnk1YttQKFDzTy+Qe8cWrEQCP5WatGxxUS0tX+7EPdtN7rEtdD0MEncJbF5C83Ot09ru0c9kaPjb58m9Ywvdb7AIjloUFhUYrbAZX+6w6uQSvBOnAc7nRfD6+q9V5Xnm5QOUHtnM9PeJUDfYbTEWK6XULUzHYaXUHe16t438OvCrE49/FfjaRcd/ZSKz7TZg7KJpYDeV3t/ZweI/mv231tl3br3uc+2aasLnHJKn/TkLJgAEx1yydTahkSKhUwPYx89OWq53cwTfEcIDBUpxQyC9wLuI5vKERiGQAicL8S4h3gWFQgAsg9OQxQQM3liQNYvPUV2Zxq0ukV7msuzzRRp2jiPP7yPxuRdp/XLXJbtTFJLC+JJ5en+XJbq0khXQ1oRsuptizKJ034pJT7NXLiPYPXJ+SULkaztZ8UQBpydEqdJn8J5Lb+PtmuprdiP67X0EUhaFphJ2yMPOWgTHLHLLas730evrB9ed0dsLPrULZ0n7jM5RN8wtPxYrpdQtTsdhpdQdbTrbRn4GeAFYJSJnReS9wEeAh0TkOPDmiecA3wJOASeAfwA+OC+9nqVPnPkRkaHrXaRwqWzd9d+kdnxgNdFzhuSh0TnpC4BdVwdA3b4szsHTuJ1dF/I7WDZ2VRV2ZRLzug2ExgzBtE96UYS2Z4os+vDCrZU3rovJ5Yj3eNh5cDJQfbiAFxSKQ2EoWdARIzhsYWcsTj+7mJEDtUjGoWaXTWAgU85LMMHvu3SnjLrdKSpPeHN6Y2wlEtgrlpJ5fMulCRCHhvEOHuXUuxJkGy2MLZTesgmnqfF8GWdJO6WGCkwme0md8vw+lvzBC1TvtcjV++WZFZSTa06Vp8MUCiz72ElipwIEjkUJpCx8x3DmEYfB923DXrH0fP+uttvH1RTaa2ZUXs2923EsVkqpW4mOw0opdaUplzwYY37+Ki89OElZA/zWbDs1335t0etJMjdT38eXQe11nGd23IMbNdTuTV81v8H18Nsb8AMW9o8O4hVL54/bDfX4ixrgTB/9b18GPiRPFQkM5zn7cJLEZ3fPWR/statwKyPIj/fO6DxvdIzEs8cIr21nZHWErgeD+EGfph9aRHuLnHy34KRsln8mhZsI4cZsYocHMJEQqVVVVGTaMNEw3uHjVySgHFsRJ19jEVlUjXW689KGLRvZuBprLAuhIPT0T5kc01nSTrGlCjceoOLQCMV7l2M/+8olZQJjghuG8fYggawh0F4PE3kRTCREoHfsqksPGr57FkwrAxuh8rvlmQn28iWQylxzuYLb20f7Ew6pTS0MbHDwA2DnhGyD0P3WRhr/urzEZ/SXt1HzfO/5pRBTsZ99BWfxItyOM9Mqr+be7TgWK6XUrUTHYaWUutICz3G/8R54NTen9S37wuRr46dy7vUx4l3Azuln9Z8OK1fCjVlYtTXgX0jxaBqqMbZAIoYXFDItQqHSIb00Tu3+0jVqnDkZTSEvXN/78kZGCJ4dxiqBHzSIL2QaLJxUgcC4TXBUsLsHsZ99hfB3yokCi3Uxol95CRMMTN6fUAg3IgTHDbnaK7MB2PEYw3dX0PcTjRTrYngrWnEay0ma7aoqJBDECocvOaewuIbg2WGCw3kkV8B+9pUrylgeOHnINggYsIczSChUzqlw7BRSuvrSA7ezi5p/eoH63QZqqvCGhjHn+qeV+8A92038hQ7EBT9kcGMGP2hILfWw16zAisXAwLm3NCH3TX9bztHNutuVUkoppZRS6oI7KqCQfmopP1gfmbP6ht67/ZJp9tM+7ze3U4pB5fHinPUFwH/9BgqNcUZW2OV18pYNlG+oJV9CXJ+xjfWkFxkKNT75KotiwiL0zV1z2g+vf/CSYMZMuR1nqPr0Tlp+4OOkBasEA/dV4KSFig4fd3H5Zt+UirinOs7PDPCOn8I7fPyK+kyhgBcCNwLR3nKCyteWhgB44+NUffIFaj/+AvmaAN0/kWDs9YuxYjG8VW2wYRWy5EJeJblvLeIbTDCAdfAU7sSMBz+fvzSo4EOhypBtcynFBL8igikU8FMpjOtO69v++OdfpP/+eoqPbMbPZM4ffy254tV4ff20f32Y5NGJ3Alhg3EMp36+luxPrKXyaJp0u6Hzrckp+3C+L1/cifvgfdMur5RSSimllLq93VEBhfgjs9/R4WKBd00vU/7lxpdCIA2Rw3ObmyfdFqZY4VC73y0nJJy4qbeWL8ZPRinUhMnW2eWby4hHIGMoxWe2ln46TGkOAiW+R+zoEHX7XHKNBjcm5Js83Ijgh2wkMP19B5zFiwhkwI0K4vlYsRgSmzywFEx5BMcMbkjIvHkt+bpygKBUHz9fxsqVyDSG8JNRrPorF7zYDfX4928kMmiwXKHiqEMxKRRqry+YFR3yCA1eOrNGrKn/dP1Xj9Dw/Ch2QbDzQmDUJpASctU2frD8e5BfVMRpbZleR4wh1Ju+nreglFJKKaWUug3dMQGFsW8tn9P6Mo9v5YV7vjTj87w33kvlEWj6cQa3u2fuOiRCoVIwNoS/sfPC8W3rybckYO9RUq0BEBAfpGQR78rT/GTX3PVhluyaauyqqvPPvWMnSew9hxszpJaVt4WM9bkEu0amH7TYtp7h7c3ka4RA2jC0NoZVX8vo5iZOf2Q7I7+2/ZLifkBInPXI1VqMrHQIP7kTs2s/dubCshDv0DECWR8vZOOdvTQo5OfzeH39uDGH4XWGfHOJ1FKf2lcLBL9zaY6FaX0mDfXEj42W8ztcxJ3IxTAVf99hVvzpUYoNLn7AkF5VZOgeQykRINFhYY84ZNc2TTnj4TXewaP492+c8ftQSimllFJK3X7umIBC8idPzGl9sQ9286V0xdQFL9N/bxgEjDW3MwOsdauIDBkqDl1IJui0l6fpOxkXu6YKLyR4IbALQrTLxhnN43ZOHlCwEok57d90eEPDVyRDdDu7iPZYBIds/ACUohZ+PHyVGq5k5UqUYkIxCcN3G4wDQzuayNVaRM4JmSah77d34N+/EaelmXBPFi8oiA/WRMxCQiHcxIUZEU57G+PtDj1viGKtXDLpjgmluIWTEUK9DjiGobtCmG13l+vbvG76n0lfP+N3VTG+vm7qwlerY2iY+uccwoMW9qhDIC0MrQvgZA0IjC8OULyrddr1uVH7uvuilFJKKaWUun3c9gGF/t/aQcf/tX3qgjP09Jpv8PGVS2d0Tucf7yBXbwimfKwfzWwHhGsxO+4htSJJ4lQaevrOH3c7u0i3RSglAgy+ZSmWZ3Aj/4u9O4+y47oPO/+9t6pevf293jc0esFKgNgIgCBA0ZREmZQtWdZiybJlKR7JnjhWrGMnURw7juPxTOTJxEk83jcda5JYokxJ9LFkS9RikiZFghsIYie2RqP3vfvttd07fzwQC9FAN8AmTdD38w96uVX3vnqvcU796nd/PwhaA6QP+uVrbwFRxZsrNvl6aDriI33IjETIkGV3xbC7OjnxmRTFXvCaIhITkjApKHdJtBR0/OHz9Dw0RmY4QluCcGQU/eJRUl99higO1XbNwOf2MvvRO3Bmq8hUCnn7RoY/2E3zS1Uy5xXl/hxofeW8q7qY3WARxcFvUGir/ntZrRdh1M/VC1Zaa/uWtd0g9ZVnyB6fA2nddKCn+TsDtD7v4ZQkUQxKG3waj9VoPgAL6zXDb48vu0BjYrjI6L/dd1PrMAzDMAzDMAzjrWPJtpG3utY/eOp1Oe+vTmy94WMsDzKDkP2Hs9x8ycKrzW1M4hYUHD5F5F0oOrhpPUFTitIqC+lbtD21wNC7c0QJTfalGG3PlVem1sEbQDmSWAEmdktaX9BY69cQnTxzxRiZSl1RtBBg6KO94IX4bSH2rI1dheKaiMaDkqZDJXTgE50eIPmq1olWWytBut5u0fLrBQ0L67PkJ9KESQc/C/Pr4vgZwcTbFNn+fXR9c+LimsLhEXq+nuHMr8ZwBPjlGF4TYF8Zv1MNabQl8Dd3EHvk+eteg+jYyfoXwc115AjHxnHGxolt2UetNUIWbObWO8QXFCKSxBZg8D05Vi+je2h09GVWl7q5do8KwzAMwzAMwzD+KXhLZyic+r09r9u5H/7rt93QeH33duwq5AY8oqmpFVuHcGLEiorEuIe+EEwACFpSVFtjKAf8LGAJUqP1IoGpcYXfsPyihv/YSl317Q5OUVDLC1Qqjrx9I+UP7cHu6wG4KpgAIAPAqWcHiNUVChtCRCgIMoIg7y46l7BtwrWdIDTxGRARNLzsITR4G7uQtQC/QSED0BaIREitRaNyySvPMzZDMBcn8GwIBCKA8qok5Q/tofbeO/Hesxuv0aW8KkGx26HygeV9VtVl7/HNaDrskTltIwNBuVtQbq+34pQBBDmFcBe/Lq92ra0yhmEYhmEYhmH80/GWzVD4nXNP8Yu9r8+5T/3eHtb9wo1lPgy8L0HDcY07OLuiT3Zr929DS0GUtC9Gh6y2Via2JfAaIXtGkx2oop8/grXuLlLDYHsKq6ZWcBUrSFpXt5zUEKSh92uziJqHKFc59Qt9BC0huf1XP7GXmQzF+zcRpMBKBTgvJwnWh2Armg5auAsR7miJV66ATKXQfgDb1hPkXMbvdFExjZbgZzXza12SUxGxlwYQqSQdT+SY2iHqrSPLNkFOIedKV2SdRFNTtDyzlqm7NfacTZDRjO+ViAiUU69doGL1f0WgmXWg09pD6ivPYHe0M/LhfoSC1FhE6qvPXHYtrtxecaOc775A19MpTv3mFmo9PpEbo31/xPheC6coWfjgDnJfe/GK4NS1+O/eTexbK9ty1DAMwzAMwzCMW8dbNkPhp37rX79u5/7ae3/3ho+JOjxkqAnPreyTXT9jEStESP9SgCBc2wmACECGGmv/kYu/K3cJRAixqauf6L8pvDqYANg1iM9ApTeLqHqEY+MIBfacTTgyelVdgWDXOrycRMXAPZIkPaQRI3GEZyHDeqAA61IhReHYWM2NyPkyxe4YUUJjVQVeI4RpTZgQ1PIWaIXOpsg9M4w7J4jNCxIjNtIXRK/aNgHQ8IWn2fQfzgMQW6gXetQWOAWJ9AUiEohAEJ+SNBySoOsFG6NVLcQWNMmJiOzh6ZW9vtSzOVLDEnsiBgK8nAUalKWZ2SpQOzcu6zxO4dbYMmMYhmEYhmEYxuvjLZmh0PZ0FvY+/bqc21q/hu3ujRdUTB2Kk5jyF71hvhl63zZqrS6xhYhYIcA+OnDxCfnM5gSZoYggJSh3SvKb1qIPncCpKlLDguRQETEyuSLruB67p5twaPQ1v+bIBS3BnfUIelpwoojeX3sa4cQQWzcizo8ht91GmHHxG2LM99u485pVj1UZeJ9L5jz0/9v9WPkc0cYe2H+Iy/MzovkFmF8AoCnpomUD03sC0ILkeZuGk369K4cTg+FxyGbo+K9PIW/fyMv/PIeOKcTuLReLLV4uHJ8gtrCG2EI9KNL+yDDajaHjDpwZumqrxiv5Bw0XHvyvZK2Ny7U9W2HwhxKgYXYTJMcElS5N42EorU6QWUYCjnjqpcUzSgzDMAzDMAzD+CfhLRdQmPz0Ptj7+hRiBPjcI1/k9+bW3/BxYRLiTx5npTYazG5OUmkVtL0Q4JwZI5xfwGppYebda/FzgvlNAukLQDO1u4Gm+BacYkS2FKFeOo7d3vaa5pepFDMf3kpqPGR+rUOtCRpOKKKYwK4pSp0WTUc94o5NdOYc9upVaDd2VTHF5Wh9bILpu9vqN7BASD1YoSamCHMJwj3rOP9DNq3PQOP+MZxiE16Dw9jeBCoWMbtJsvDr+xARxArQ7m1Gv3h00bnUgjFAJQAAIABJREFUoRO0VPuptLUDECtC8tAwqimPcGOQSRGePVcfe+QE2rkTd8LGb3BxrrH+nq9OMPATbTQe12+a2gPi+wfJ9+9lbhPIEGQEdlmwsE4jlCTf0rKsWh+lH9tN+q/2vwErNgzDMAzDMAzjzeYtF1B4vbo6vGK76/LxP3gPnSx/Hru/t75nfpHCgTdL+vWuEVFcoOYXEK5L1NdOtVUQpEE3+ljnXKyqIL4QUViTIj4XIZTG7upEFW6yLaQQiDs2MfC+HHpjiXklsJ0quWSNsc4GRCDRMYU9L2k4KfFXNRCbnEHlUoiJ2ZuaMjp1luaYQ3DPDuQTL174oUJ2tlPojVNpE9gdJeZuSxGkO9EXdjNoCdrSOCWB9AFRr8VQ6k+TlpvRLywSVBACnXSRQT0IpCVEnU3IQpVwVRO15jjxVwIK9+wgPmqTGtXE5jwur24gnNjFLhrRyTPY1TbS5ys39fpfL/lTZaJ4itmtilpgoSWESU1sXlC+q4/415cOKGRPzBPt23Yx2GMYhmEYhmEYxj8db6mAgrW2b9G97Cut87dvLGgx8c6OeseBFZSaCImVJDLQqFoNu6Odlz+Swqrpeg2AcRct650O/LTErmrm1jkIDbGpBiylUMXlBRWsfI5ofgFrXT/8aY312Ze5Q/p8vGE/x/x2niquZUNynI0bRznrtwLwbLGfl7Z2Mldzsf9uM9UWQd//uvmASnT0ZaxdtxNdCCqEwyPYfT1M79DIELr/zGHiToFdVSyskXitEdkTFtlTNiKE9GhEtbleMmRuvUW1KUv7SBsIAW4Mv6cJ+fiL2D3dzNyeI8hq0oP1mhPjd+eIz2RoODxP/BuHALDb26gmLDr2+yhHML0tTdtYF+HwCAAi5lzRltOd09hjc2+uVov7D9EotzG7NU6Y0iTGBVZNEqY0I/dKOuw9JB9+5rqnUIdOEDywi1unZ4hhGIZhGIZhGCvlLRVQeCOCCQNB6YaPUQ4Xn5qvCCGwahFhQpIYrncrKO1aTdQUEAGx4Vi9mGAuIlZwKHcKMuc1Czt80NDx0BTR7Pyyp9ORwmpoYOAn2/m1zi+zxpnkobnd5KUiI6tsSI4zGWRRWpC3KuxLDBFpydliEx9f/Qz/rfouGnNlZs510WRZF7cM3Cj9/BEkUHvvnYQJQaHXwvI1XY+FON99gXzDHuyqJj0ksDwLEUHLoSqRa+FOlEFksTxNFLNxCxrdlMdvTeGemiB2vt59Q83MIVQnyTFoOF0jVnaptEjyL5dQh05cXItqa8TyIsKEjfQUc5s1rU+nYfjCW+Rc+afVcLL2ptnucDl7voo7laLWGaKnLqxZQ5RSFLttktc/HKhvCXlTBUoMwzAMwzAMw3hDvCUCCjOf2kvT51+fIoyXs/I5Pr3lh4HCso8Z/M29SE/QsX/pNnzLpjWFHpdqmyD58HGC+3dx/r2a+KBLrT3Eb47InLYprlEoq57Gbv/UJF9a/xB/NnkvYyTrT8+vUVDv8nR9AL2xl/PvyrDt/hPMhmnWOJPcmz3BROTQbS9wfzLgqH+GvyvdznSYYSZK88xCH7/R+zeMRzm+e/cf8JXCVsb+dY7v/vlewlQnbc/VsB47cFMvP/6NZwFIv+rn6YfqT9Pjr/q5BBSQeqneUjLdmCdsy3Pmo43IQNCa7iIxXkU03c7kzgxBStD2bAX55EEyQObOLejnj1x5zkKF4pYcudMV2H+Ibmc3YqYepBE7N0PFv1joEUA+/uJNvdbXW3TsJO3P5hh9m025N0JLjV2wSAzbqGsVhXiVcGwcGY+jarXXd7GGYRiGYRiGYbypvCUCCm9EMAHgjw/9LT+7+m3LHu8/sAt3ThDGwf7eCyuyBuG6qJ0biZUUTgXsVV2M3xZDBBG1Hg9RcHCKgigGVkVieeDn4MmtXwMsku3f5d/lPgYTk9eszn95MAHg5Z9zuX/LiySsgDE/x9/4O+iNT/Oy18FnG+tFFjfHEmy+8PVYWCIuAna7gs8XshxEkbMqvKvxGOpTgn2Z0/y3+97F/L59JCY1TX/+xrx/AKpYrG/1GByi99l6kEit7SbIuYzf6RKf1SSmNPLJyzp5PLtI94aBQXIDgxe/d7/5HMO/tI8wsYbWAwGVliz+fS10fn3odc9MELaNDi/kCNxE14XYt56jzb2T8bsscqeg0iaotSrCqqD443eR+fLSRRf1lnWwSJcLwzAMwzAMwzDeum75gMLp37mLtb/4xlSZX22/+pn49Q3f55A7qUmP6KUH34AoYWPXFPFvv4S/ZxPVNo07aVFr1+h4BEWbKA6yq4qaThE2XEpIz4iw3rLweoQAfWnNP3HHs2xPDRIXAUWV4FClm9XODL5tLXp4h52m25nBEpIPpk8RaM2UM8t3S5sItMWm2Di3N47x6JYM1ek4uft2rljA5UZF8wvw/AI20P2ki/ZuIpPkwk38qgfPghtj+p4utITOh88RjowufsyrrvGNzHM5q6EB4cYIxyeQqRTFd99O6qvXr3uwmOT5MnpfFhGBO69xSpLIhelt0PB4G+H4xPWXVqpRe9dOnO/+47yPhmEYhmEYhmG88W7pgMLAb+1l7S++MU+35z+xFzi45LhXnPr9PcTH6zeN+X8YWLE95hOf2onQmrYn5lCBz/z6BKq3ShhIxHwMmfPQMzZWDfzZOPe8/xCfX/3kxeMXlFOvBXC9G9rLfr7wsbsY844CPayJT/Kp3Dgfy8zwmdHddMdn+RvGeF/q6u4F9yfrVSibrVR93Z7HD6WPMJrIcFssyZ+sepqP+ElK7S6nW5pZHd1BbGSe6NTZFbpSN+6mgglQv8m/ayvq8BlUuUz+3HmAa77nwr25wIWMOejIuiKDJJqbu/i1DkOyh6cJL3RdsNb2UdnQjPu3zy15bv3SCeI/uIeFHy4RjiVRboQIBc3PSxbu7iX11esHFPTAEJU7tpO74VdlGIZhGIZhGMatSv5jL+C16PuVNy5VvutnTvOC5y85Tuy6vf5vKLA8aDpaXvLp7o0Ik1DpADlfRDgxgpRASoWwNFZVEFVskmOCaleIM3v12/tI6fb6k+5lPB2XWzcyuU+zK3uOnF2lxS7wYLEBgM+1P4GnHKbCLANBiQVVve65ZlQST1uc8ds45NeItOJdTce5t/kUXU0LnP0kTLyj7eYuypvB/kPLbgt6s4EL2dyE7Ou+7nmjk2ewDl8Iyjg2pc5lxgxVRGpEoXW9gKiIBNrRFHsF09uW/m9C1Wrkzry52mIahmEYhmEYhvH6umUDCsWP3vWGzve1td/h0//hM0uO088fwe7pJj4hSUxq2H9oxdYgdt2Oci50jUi41O7fRmFjBGdTxI8miBIaLI1ywMr5WJ64IjsB4JebTmGv7lpyLqupkZEfbOS+XUd4Ym4dC2GCQNt8KD3NZ8d3kJZx3pY6yT3JMwQIcjJx3fOtcxY44bdzT+I0/3N2LzOqSrs9z6fyB9nVdJ5Pbn+K2R0Rs5/ci927+sq1NDRgr1p6zTfLamhY0fPJ+KvLQl5JODfeZNFqa8XvbYGp2SXHXmwHOj5N65ePLnuOxq8fQw+m0I4iOWxhz1vY1QsBhuWsef8hZCaz7PkMwzAMwzAMw7i13ZIBhdlP7iXz4BtTN+Fyuf91/TnFzs0AvPwLXcRnNbkz139qf6OKfSkyQ4rsGaj1NTK33sEuSLQEoSE1IkmcieE1aqKiQ7UnWPQ8Kp284qZXplJXjYlmZiltrbEQxPnVrr+jI7ZAUcVxhMV/aX+RQ36NXW6F0TDDU9W+RdtpltSVVf9b7AIPLuzmYw37OexnyVsVDnh5mp0Su5ID/OzdjzO9N+TcT6y6cr3FIuHwyM1csmW5fNvASliq28HlWxbsrs7rjrWyWeY/sRfv9m7kkwdvaK1q7SqiwvI7kkSFAo1H6sU8/bzGrgpECDIUzH1057LOUbx/07LnMwzDMAzDMAzj1nbrBRTu3ELjkatvXl9vXy1llxyjX6g/DdYWKFtgeTdWbf96ZDzO7GYLGUJyKiJyJYlJRZTQqJjGqta3Q2gLgpxCli2swuJFE5FX3vReK1U/la2xNTvC/mo/02GavHUppX00zJGWcR4t3UZSevQ5VxesTMtLQYupKMZet8rWxBDbXZf7EhHdVolWq0TOqvBMeQ1nKi1gK2T0qhtt8do+pmLH5td0/OtFZjKopiU+V+0tWJ4mfnb6xid48fgNH9L8D8NkzkmcBYGyNepCYoKXF8s6Pj6zeBDLMAzDMAzDMIy3nlsvoPDs4UXb+L3e/vzD71n22OSopOFlD/38kRWbX3a0oSyNnxYoWzDfb1Npk6hkhFCQPxPScEIhA3DmJSquyK1fPD1enBu9KjVdplII163XVwCs9WvY1zWAKwM2u8N8IHuAY9X6toPjfoV3Jz2+XXHYmRxgvTPJ96rXCF5c0GMHHPIt3pua4ahf5QXP55HyBtY7gi3xIT7V8Cy/1fUIn7hjP13vHmTqXT0XMydkPofV0nJT181av2bFPuWXZ3Lc7DYJde8OrLV9AJz7pS2M3dtYP19TY71Q5hUTWvidORqeGSW8rEXlRULU1/Hq415ZY3fXDW8VCQeH6Pj7afy8xqoJgpQmimsWtvnL2vZgPXbghuYzDMMwDMMwDOPWdesFFP4R6H3bUC8t82mvtAgTEB8t3nhbwGudMh6n1t9MmNZYPoRxgVDgNWhiEzbaBi8vCZIC5YDfFYCtaUwuvuUi2thzaZ/9BapcRqZTF9sSimKZoXIDx0qd3OXCdtelzVkA4LZYkumozHZ3vn4+BE2ywkHP45B/KfPhuH8po6HZSnFX3OL/nt7GUJgnIwN6Y9MoFH+zcAer7DQ1rSmFLpUgxsK6y9Y7NUU0NXVzF29m7mLmyGt1eSbHzW6TqDbHqK5pgju3YPmQPxsgbJtoZhZhvSoooyKsxw4QnjtfDx60tWJtWo/YvYXg/l2IXbdTfPt6qj+6G7FjM/L2jfXAxOXzbWyvB4pugB4aQ8XqgQRtg1UV4EuiPcvcznDX1huazzAMwzAMwzCMW9Mt3TbyjfLPv/Awf7q+f1ljJ39+D4kpDRM3kaJ+DbKpkdmNLvFJCFKaaoskOa4JMxoRgnY0hV6L9LBGBoCGVHOFhL14+nmUclgsnyCamcVubyMcnyDoa+NfrnqI/aW1fKeaYFtsht7YFN+quLw76TEVCW6LJVnjzOAIxXiU4litiy+P7OL84Q5+7v7v8KOZQwwEJVbZCR4uNxJom0/kn+VU0ICD5nBtFb62mA8SHPQ8Dnr9/HD+JfZkzvDLgx9BdLYhh0ZRtRpi5+abCgxEM0sXMXwjWLetI2xIMrvRwq5aNB0T5E9FWDWFDusNJl/59yp3bqHWlqCWt3CqisJqCxFBudtGJTTxMRu9vd6wseFEHi2g2iJx5zSxkiIW3djWG1Us0nBEUG0VWD5YHoRpi+mtSVqfXPr46a0pmt/4EieGYRiGYRiGYbzBTIbCMrw3NbPssQu31bcgrOSNbHl7F9VWsGsQpAReW0TkCqyqwClK3AmLxFS9uwOANWdjScXJicW3CfhZ++LWhqvY9d8p1+LhmZ14ymY8zPFErYv7EhGddj1LwdMW01GZzbEEX5rfzTOVNTxT6GdtdpqO72s+/9UHOOG38Hi1n6c9i/cmp9gdP3+x1kKfk+azjWe4NzHDb7R/hxmV5OnCGtqtErNhmrVrxgnac4S7NgIgTw2t2PX8xxAdP4XYf4T8aUWYgPk1DjLSyCXqbNhdnZS7k8hA41QVlqeJXAjSgACrKNE2hHGNFlDslggF8RlFYjakuMq6dqDiOjLDAU4ZggyUVmlQEKYhvG/p4owNJz3UPTtueE7DMAzDMAzDMG4tJqCwDK5wljVu/uN7EYHAnV+ZrQ5wocp/v4NQYJc1pU0+dmONIA35lyE1qql1B8xui/CzAssDbUNhNoW3EOf7NXXVOe2qwu5ou/j95R0fwuERUBGlrhjlMEZrrEijVaIYJdhfi3i8vIEXPB9X1G+ES6rGA5nD3JE4h0RzeKaDwscLqA0l/nz0B3ggeRYLRUUHrHfqNQja7UvbLXIyQYed5r5ExH/vfJz91T42umPc33aMmdvj9c4G77gDVbm0feKWpSLyh2dRMai2adKnFpBPHlx0qLW2D333dmobO6jlJcUum8S4R3GVhZ+rb0dInZekhwXKqhfmDLKKzHlF9nSR+HxEtdEmOako/viNt1h1//4QLQcqoEG5GgGUeyJmNi+9fcJ67ABzG67fOtMwDMMwDMMwjFuf2fKwDB8deCewdMZBoV8QnxAkpr2Vm9yyKKyPEIEge1aABgaT1FoVXjPEpyTumI30BVpAZkSh3lbEP5ElaAv45Bc/zc53nOCLfY9ePGWlxSZ+2VPrxdoc5k5X6EwsMOln+Fhukml7jn9x4id5bOuD/PrknexNn6bfmWNB+Xxu6P2MlzJMn24C4F/c9x2mgwwWisEwwVDQRE0XuC8REWnF1tjiN5tJGWN7/DyOUOxODPAXjQJrXT88eqCeOfEWML+lXuMgfR6ioy9f9Xu7o52wu4WZDWkq7YIgUy+O2Pash3YkmeGISoeFUxL12hkZsKsCvzMAT1JtskmNOCQHFoAcyhEUO22Sb78D99w0eD7h2PiS69SBj3zyIKkNe4lPS0qrNe7k9QtvXq7tm4PceF6EYRiGYRiGYRi3EpOhsAwvPrphWeP8BkV8RiPUymUoRHNzSF+gUhFBGuJZj8jVqHxAbFYSpDRBWiOiC+npnZJqOQYCnCmH2LzgheHui+f73PSG+paMiclrTyoE1kyJhSBBoC2O+Q0UtMvGhkkWlM99maNIFIOhz3hk0eSW2dc+QGpIkhqSfH9mLUnpc6LYhiMi4jLglNcOgHVZC0hPX13jYYOjmFdxIgQI0Il6ZwHhujfdWeHNxPYUIoIotnhnhtqmLmptCbQFCAgy9a0M8+tiLPS5zG6wSQ2DO6vx8pogo7Gr4Ew6YGvmbw+ptrmU1udJnpol8TcvIEONn7epbGxj6v4+7N7Vy15v44kqfhacoiByNdWW5X22w5HRZc9hGIZhGIZhGMatyQQUlqH3155e9lhtQWxkYcXmnv/4XpyiJH/IIUgLgsEU7rRElG2CjEYG9WCD16xoORiRG4iwhuKgIXsG2p+r0vD1JAD/x9Qm/vLB+6g1ScSu26/zIjS1ngZ+quUp3pk7Tk07nPLa+Ynm/fzcwPv58vQe3pmYJS/hv4//IBPVDH/3yG5iRU3D6ZBjY208eHInobb4T0PvYTLMstEdI9BX1guQSA56HhXlX/zZYKhZZ5eItMTyQSVjCNdFlcvX7Kzw6naG4k2azSCcGIXVNrVen47Hri7aaTU0YNUigpRkYQ1U2hXKVQRZzfzGeu2E5IRG2YJYUZN+payEqndisOdsiCtqjZLCapvo5Jn6NoszPplDk8SHi9iepritfflr/v5BWg/4eI2KKKkRSjD/ib3LOlbda+ooGIZhGIZhGMZbmQkoLGHhp5a//9yqCoKUqN/IrRA/IwjSCqE09oWuhVFc03iw/tZZPghfEiUV1UZJrUGiJTS/pMkO+Fj7j5E/XuTnR+7ioS+9nTChSU4q5ML1axLMbXSZidLkZYXxMM9TC2vZEy9wX/MJVidmScs445GFLRTjxQzunKDhZQ+rptGDKfyRFOOlDBOVDH9xbi+ddhFHWLzgXQoezKka212XpLwUENgcq9dUaLLK1Jo1suQjs9nrrlUH/pXf30QRwjeCDgOqLRp70oGxq9tglt+2rt5qVIM7X89gyJy2ic0LdDyi2Atzt0GsqKk2SZyKRvoQJiG2AJlzYE85LKyB9qcv1amIn50mPHsOdeQE+W8dR0uBtX7NstedODCI0CBCQGiqzWJZWQ6F1aaOgmEYhmEYhmG8lZmAwhJ+9t//9bLGVX/0TlLD9SfHK8W6bR3JqYjEhEQGUOrRyFAQJesdHbqeCCmvDiEX4DTU8JoEiZmI9X8ySu4bh3G++wLa87Am5vn249vpeqxE/lS9E8D8ztZF55SpeuHEagt8b34TB6q93JM4zedXP8m/GXkXn84P8R9bjvGnC51ULrSVmBvOIUNAQPLkFG3PKtY+WEE93MzUXAYvsPn14R9hMiqz040xHdUjI61W6pqv/YTfgd1XYvwHGqGl4YZugBe9lm+C7RLCdtAOqJi+mG0hbBth21htraROzzF7W5LxezS1Jl3f5pIBGUD6lEN8pv7eT+6LKK3WlDskyTGBcqH1QJX0WIRyNGu+XEA/d/jivOHA4MWvo/kFkg8/Q2FLM3ZfD4jFt15cLpqaIn1OYlcEUQzKqxSVjW1LHpce9ZccYxiGYRiGYRjGrevNmRv+JvKJ7Ah/xdIp4uN7LdxpyA1c3VXhZo3d14KfgSgO2UENWqABpygJk1DqsEmdF5TXaIQbUmvW1CYtEpfdQEK9c0PPN9uxh2domi4ys6+d+TWSjBD1J+KXUeUyMh6n/69mmHpHmv+t+UkeLuygkjnMp1v/HnD5zzPr+Ej2RSwBH2t+ms++59v82Is/Q2U0h/XoOUZ/oxE5lWTD7w6Tmuiglk9y8sPwv4cf5F91P8J42ElcBNybqLfjzMnExfm/UGilrFyarBLBYIr+vx0iHLy5lpFWWys0N1DtzmKXAqCHIOdgVyL8rE36qQGiqaszBV4v1Qe2Y1UEq7/lYeVzRPMLF7MpoolJZEsjs9sVqUGLMAF63iY+A1ZVkx4NKXXaxKcFbkFQbhMsbIxIjFokxzRz6+OkR0O6vxOhXzy65FpSX32G+Q/vIV+tEXW3Is9PXLeuRmY4IopbxL161kyhR9C8xBz2917AWr9mRTN2DMMwDMMwDMN48zAZCtdhNTTgiGVWtlegHAgTSz/xXa4gDX5OY9Wg0iyxqwJta7wGhQih2ipIDylQEAYWkasJUovPb3/vBXShiAgjnLKqt6Hs6V50rKrVUAmHsXKWjPTJ2RVO+B2cDOpZDQ12mYwUlJXkcK2bc0GeH+o5TpASWE2NiOkYytX4vc0kvnmApm+cIHiyiUNHemmSVSaCPCnp8aXCWp6pZS/WVjgZlMnIGo6IOO210fsNn2j8OsUjlyBSSfyWFHPrHcqr4oQpm4Veh0pbjPm1NrQ2Yrcv/aR9Jdgd7ZTbLOwKiFBBc+NVY8prsmRPWaRGFU4Z4tOCMA5Cw0K/AxKUDYkpH8vTxMct0iMap6xBgIoJnMryt3vUGiWTP9xPmHag+foZHKlzJbxGTbW1HoCqtC/vc65SZtuDYRiGYRiGYbxVmQyF6xj//1r5zzPrlhyn7tmBiKDj6Rp2OWBFNj0IgQih4TiUVkG1vd7NoeklgV3TTO/QuLMC5QhWf10QpFzyf38GWhqIrnHKyj0biM35KEeQmNYs7OogW65e9ZReODH0C0eZOH0nn3F+nA92vsig10ybs8Ahf4LVzgxfKmxiS3yIde44D83cScr2UA6IbIZ1f1kEIai1JJBhSDQ3R+f/8xTCdfnsrzzA6V/dTOq2Of5oy1+yyq7yl8V+Ii3Z7I6wJz7KcJjgEw//S/oee/rStZQWqGu9ssWpTILpLXG0BWP3RRBJnDlNqUcgPc3s9gZk2EDmyxM3/PYsl9XQQDQ3R9TZhIqB16Tr9QiCq2/8lSMIUqAtSXFDgCxZZM5JZnYqnLn69ob4rMLLO8TnFU1HqlTb6wU443Ma6SnCuEVskXUspvlPnkbevpHZHQ3EUzbx6yQ26IPH4CN3gYZYARa2BNj9vYRnz11/kiOnlrkawzAMwzAMwzBuNSZD4Tr+/cZv8uDZnUuOm1sfJ0rUW/MJf2UKAsp0migOtqexgvq2B+1oqq2CMC5QtsaqXsqKyB2fJ5qaQg+OXPOcXtai3BUnPhuSGo8odVioVa3ITOaKcTrwQdefgE+XUhwtdzFQbeaRmc2cDZrpsecY9hs4UO1lJkrTl5imHLosrNPM72pHnhlGP3+E1Ikrswu05xEVCvR/uUDwdCOPFLcwGCZJSo/N7ghHvS7+qrCVP5x4B+v+ZPjStchksNb03PA1jNIuqQmFnwVCAaLeXlN6EDQqKu0S21NYLS03fO5l0/UtMGHWpbQa7JKg1hxDpxJXDVV2PdCDAlmycIoSu6KJTVvEFupFL52qwq4qLE9jFWokRqvEpzzcGQ8rUCjnxv6k1ZETaAlBeonjtMYpCISuf+bQ4PVcnWVx1WGBqaNgGIZhGIZhGG9VJkPhOj6ULvD5fxdd84n/K6ptgsSkwKpFyOkFVqSKQhDgzkFywmfsAQGehVWWVNsUlU7QMc3CNh9Rs+h+RCMXyijqNRCuJfeVA1jNjUw90Ef+ZIXSvZIpP0vLscVv+nofmuBsrI3O/hcZqDTzQNNRni6t5WnWcn/2MC9VexjwWnhf9iD9jfCBHY3MrE8yu3kzvV+ZJTxyAruvB5VKIItlwsEh1Nu2c/pjMbr6xvniiV18zd1GOu7x8dXPMBum+eZ/fDuZo9PofAwulIJQxSIU61kPr675sBiZySBiDmM7kiSmFLXOAHfCrt84d3vosg0CElOaxHgNXseuENF8vYVoYbVLbEMB8VSOzOHJq57s272rURZkhkLm+x2EAulDuUvQ+YRHpc2h3BnDywmaD9dw5j3mtzaSPVVEBIriugyWp0gfn1ny8/pqQVpQaxZklhgXK0KhReN1BqAFM5vitD56g5MZhmEYhmEYhvGWYQIKS4iOL52y7TUqrKogPlwkHBldkXlVrUasoLHKAVQT4CpkV42g4AIQH3VQlkQ7MLVVsNDXTeeXvesW1tOBTzg2Tsu3NLopT/p8ChloZDJJ5HlXHzAzR/v+Rv7Hhjvpbp6nMdbBJxu/z+9M3kdZufTEpvmfY3v5UPYAaZnkO7d9nc+M7ubrE1le/tk8OnYnCBCRoLUvZGJ8F7mDMWSmSqQFfsXBrzjIFxr4ytEHqDU6ZI9MoYdGUbXaFUuT3qDVAAAgAElEQVQRtr3sdpCqWETv20ZmKGTo/RFEArWuQjDnYjkKFQncaQvLU8xtSBHu2Iifq9cE6Ph+FfnEi8t8l5ZvdqtGn8+w9refYrFXEZ47T+19q5jaDdqJcFsr+J0WzX8bp9bkYNc0Ybx+U+/nbMK2GJan0Zak3Jskc7aEqIXg3PifdOsfPMXcT+/Fum3ddT/vnV8fgvd2U1hnox1N5C7v/MH9u3C+/fwNr8swDMMwDMMwjDc3s+VhBQglsDyBrNSWHnwDojjMb0jX56hY2EfSxEfqrRqlV583itef2Bc2RFR29SwrfT/qamZhUx67qtEWiPg17gyDEBlpwokkAyc6aI0V+JXBD5CwAh6e2cn+0hqa3DIvB5daUP5u53PE8zV0PEIkQ7r7pmjuneUX13yPe247iX7nHBu6Jvhg90FExUaUbPKnfOxSQPbJgXrtiMTV2wGWG0x4hTM4RaXZIv+ci5P2EVJDXJFKeuhURK0tZPJOmL5DU+qGaqvCy2uKPe5rbzEprSuKPYodm7HLAqt6/UKG2oLsKYk7ZVGbTrCmbZpij8TPCMKEIDEdkjtdJYxLZKiRQf29d+dDSr0pVNKBRWozLEf+ZAVtXb8AaTg4dKH4o8IqynoxyGVQ1soVKjUMwzAMwzAM483DBBSuo+9bP7OscbE5QWwBtL3MjhDLIG/fSP60R2GNQAQSuyxJTmj8nAJHUe2KCNZUkZ7AqUDuuMX0FofKrl6gXljxmoQAIUiNRYQJgW7MLTomKhRwvv08G/94hs5H4VipkzXpKZqdEh9oeoGNiTH+uPtxysplLCwBEOiIn964n7dvO0EuX8GxIjY3jfP7A+/AEYrPbHiU3Y2DDFRbkHmf3HGL2CPP4ze4TL2n3mJQVSqLvgZ99/blXbt4nIW7uolcgdcAnEsR1GzctEdhPIOT9OnqnyZKKeJTEvrLqFyItjXz6yTzD2xY1jzXpCLC8Qmshgbs3tWI0+fp+bsy6/7fs9decypF17emqDWBXYHccZvzj6+msirEaxAUeiWVVqdeA2MuxPI1Vi1CuTZOwSc2H4IUiOK1t7xcj3jqJea25S9+b7e31T8nr9L+xBzYGhXTVNrEsgJY7rdMdoJhGIZhGIZhvBWZgMJ1ZI4ur15+rUURpIG5worNrR0L6UWIEOKTkvwJqLQKdExDIJG1+s2eDKBwW4CfgdqWKud/WDL+S/uwmupP2a21fRQ/etcVLSLlyfPEChFRXKBsKK3NYa1fc/H3dkc70r2UtaDOnid7bJbHX15Ha6zIoUIX42GeB1KnGQhr/GDyPElZD6Z4OuCXm07xf3Z+k45sgSCyGC3nWKjGWQjipKTHjuQgCenT0ligvFrjP7CLcz9i8WO/9F3kttsQsfp114GPlb8U7Ki2usj40m0IRSaDjDRuQdGx34OeCloJAt8GDVJqJufTuI1VVEwTlGPYMw66zcPr9qm0rsyfRTQ3R3juPKpYxBoYJxy/djcJVa2hHYuGk4rIAT8DfqNC1iReo0Y54OUFdk2hLIFVjYgSFiomQSmEBlkN0JnUTa83SIv650AIvI1d6H3brl7nS8eJjzjYFYGKaao7e5c8r1gi88EwDMMwDMMwjFuTCShcQ+nDe+j4r09df5C0sNavIXtGkh7WV7VffC20axEmbRpPRLQeDJChprahVg8kCOq1CSZcor4asmIRJjWRZyEafAobA05/uh/vh3Yz+JEOprYLzn+kG6upXpVfNOZJnF+g1GGhJcyvsZnZ01p/ot7Xw/j7+q6oYaA9j+j4KVq+6/KB7EF+pv0fWIgS/OHMPtY7KVqtFDmZYDgskZb1G/5VdpovrH2IfLzK6vQc968+wU+0Pcs7k8O8XOsAIOfWCFoDhu9zQMCfHXob47+pGf4f3ch4HLu/l2hjD9baPgCSDz9zVW2FxURTUyT++lnsqqbW5ND2V3EIJEJqpCdxYyGxWIT1YoYgrbHiET/4jhcRlkYu2Pg/UCB4105kPI6wV6bMyPVqWwCgItShE8QWImKF+vYHEYHKRBe/tjxNkJQot14ANIoJgoyF35QgyFhEaZfo5JmbXmN8TlPY2kztPbsZepfLqU84i46TASgb7IrAy1nXz4bhxrerGIZhGIZhGIZxazABhWv47Of+cskxwrGp9jdS6dAXi/qtFC0FzndfIHtomrG7bCZ3gy7b9eKPIw4qpolPSaKig4ggSmiyL8VgygUt8DsDhh6wkD4IBbGCprazH4DitnZmdjWRPR8SZMBr1CysFcz8yEbCgUGa/+TpRdfU8KXn+PXhH2FPvEBSXuoMMRnV0+xX2WkWVPXi9odIa3609SAfb/k+u9MDOCJkJhL0uZNsSo7y4Y4XeNfm45z6qT/iv9z/JX5/zxd5cfeDHN7zRU79p+2c+tkOTv28zelPtjP3z/Yu+9pdntVQ6JEM/0gEArLpKtrRFOaS2I/maDoWoi1gzOWRJ7ejI0Gqb4HqTAK7GqH8ALZvvNG3bnlrvMZWgeTpGRpOBWQGNXZJ4o7ZRAmFn1fYNYhcgTsXEMUtnLJivt+m0OMQJCRB2sbafPPbNdz5kNzTQ8S/8SzJUYHbUMN/9+6rxjUej7B8QMPcRonV3bn0621rXXKMYRiGYRiGYRi3FhNQuIb3p0pLjtGeR5iQJMfFsiveL5dy62niamCIMK2xywJZlQSNCjRoSxMmIDFUT+OPGkO8RpCBAEsjahba0cio/iS50A+F1U69dsKZAvG5CKumSA9r/C6fKK7xs9cOirzSZWGomMfTipcr7WxPDVJRPmVVL853MiiTkwnOhkkAOuw0cRmQEj4fzcxxb2KGTltwutbOC6VeAm2Rd+r1Ej6ULrAtNgPAg8UG1u4YIuz0eN/mQ8Q2FpjdqpGpejq/3d973Wv3SqvG9GCZ5ISm3k8TGpJVRNYnedIlNaHwshaxeYlVFaSGJSqUFMczJIYc7GODCMdGuTZ2X89Nv4/XXOM1slmiU2dJHh7BqShSo5ogoxGhQMXrzUhlqNFSECUsorjAqoGXExd/rhKLZxUsh/OdFy5+7RYUjhMt2qYzPusTxetZFMrReD2NS5476mu/6XUZhmEYhmEYhvHmZAIKr9H8GgvL07Q/fXPF8ACQV+8x9/L1G0Md+GhbEyU1sQUJoSBKaOyChdcZUF3j0/SSoPUxh+S4JkooZNFC5HxEzqfapklMasJ8yPxGzei/2YuYmCH94gjFVQ6Ffkgfd0lumGd+W3DNJ9yvpK3PPdnO+w7/M96ZO87xahcv+jZ9Tr0TRY9dT32/O37pY2WhKOsYkVbkZILZKOLe9HHenT/ERJDjmalebvvTnwfqAYivlrKsi01waqSVu9YOsC9zCtcJSIxLVLl+jcOz55Z1WaO4TeOBOdb9RUjynMPASDOWXa83MP4+j8m7I/y+Go27JqntLpE57JI7apM/pajctRa2rgetmd/dgbWuf1lzroRwZJT0Q8/Q8oUDOAWJDASxGYsoBlFMUFzl4uUkIoJYUZMeUdg1jVMJkfOv4XOoNaUdqwDwcpLEX+cotztXdKwAsL5/mMYjUOuI6jU4upauNeLnllePxDAMwzAMwzCMW4cJKLxGkQtagqy+hn3iKrriW6utleKqS0GGhsOS2LxEORp3ThKmFNrWYGniGY8gDXZV4ZTAnbKwOiskDyVQvkXL9glK3SB8SXJUIkMY+8g6dDZF2/dGSI4KUGBJhTNjg3dpK8NigQ53DqZmM+xyx2lzFiirS6kZrnCItLp0bbSirFzarTKWqH/U+pw03y5uodeZJWl5rMlNk5i49BT8Q+kCO90Y1lCc97ccoKJc4k5IYurKJ+VWPndFocnF2PM11JETiEiRHtE4Qy5RJEkPK/RcjERzBWlpHKloypWptmuEAqFharvD+fszeE0u2RPziKp36cSLdD94PWjPIz2kCRsDoqSmsAYK/fXPXKyoEEpjBZpYSVFttNBCwPTsa5rT8hXCieE1QLlTkB30wH1Vt40wJDUeoKVGBuBnlr4eTjF4TesyDMMwDMMwDOPNZ2Uqzr3FnPu/9gIHlzVWO/WnxirpsFK3mdHEJJ3fy6OcGFZbC4X+eivBsLeGfS6OM39h60MkqE0lCPug1CNpfV4Rn7ZQ8YDi7TXwLGafbke7GnIB2rYIkhBkFE3H0liPnqTlYBNnfsyFoTzJ9QUGfrKDricasB49cFWgA6DzG0PY1VU8vGMz571Gnin0s6Xzm3TY9SyFVwIHAE/UbD6VGweu7DywPj7OKb8VpSVrklMcK8MDnfWWkHrfNuyFGs6PCv5o8O24Vsjo2WY2PTrM5SGbaH7hirkWo9Ix7PY2aimHYrcgaIiwh+N4eUGsrYg+kiU1ByO7QUhN7rYZSl4TMhDEZzSl1VDqtIhPOFS3dOIOj9RPvMg2gOupvfdOCqttWg+UYP+hGzq29fEJong7pW6N9AVBTlFeJam22HQ8XcNPW5RbLVr3zxLm4oiGPFzY8nEzCt0OsQ/eQZDVZM+A/eQhwkWKKiaOjpLc2YsModypWap5pDM4BR3thGPjN702wzAMwzAMwzDeXEyGwiJuu+fs8gcrCFMgIrX02BsQHTuJDnyIItJDAr9RIcZdxPoSYVqTPmshSxaJUZvI1QQNIQt9FqXVmnI5jrQVwo2QAQRNIToSlPpDap0B0hc4M/XaBbGhGXRM4U5YlMdTeK0R82uuXRAiHBwiN+DxvemNtMYKtLpFHq92MxyWLhZjhHqhxjvdGgPBlT8bCEq8MzHIGmeK05VWJv0MlnepPoJ89ihidgGvSdGSKJGN1cicsdGxq2sDRHNz17+I+w+hWhuoNjsIBTqmsIv1sE94PkUY11Q6NapmoabipGIBQgkqHYJqa/3mvdgLE3dlGLnXRu/bdnGdy2V3dZL4VyPc8YlDTOxO39CxUK+pkJhWyADsKsRmJUHmQpvIQKHlhVoGCaeeJbMC2RPKhvSgwPKu3aEhHBmtZ+b4EFtYxpxKUdl2/YwSwzAMwzAMwzBuLSagsIi/XvfIxc4F12Kv6gIgyCrypyJEcPXT/JUQdrdQ6tF03TaBOyNxnsugEori+oDsGUlyop6mj6MpbfII8xG5bJnYsSTal1T6fdACdyiGiASyYpEaFqhDJ7BaWghbc0hP4q2tYRcsiARB6uobRKul5WILRevRAwQfs3not+/ntsQonx9+Gz95/ONMRA5fKLTy+YV2vlzYRFLGWFCXAgGtVoo+J02HncYRip9ueYImp0yl9UJ9BGmhw5BwbBwZCKaqac4XGqg166uyAtTbtl/RzeFa1KETNDw+QGwBWr5vkz+tWFinkF1VZAhRUiHKNkjN8HgDXmeA118jSOt67Yq4xmsEpySYuiNF6f7bAZCZzJJzy3icubet5r7WEywEcSpdN5bZ8IrMg/vp+oeA/KmIxJQmSkf4ec3Y3UmcqiIzElJZlSRojN/U+S8Xn1ckZiKsWn0rxfVoi/+fvfuOkuu6Dzz/vfeFepWrOgd0RCYyI0CKyrIoaWTLWnktzxlLtiXLtjx7PGOfWXtm58wcz86xZ8Zrz+p4JFmW7bHpXduylbVUoiiJFHMCCAIEiNjd6FQdK4eX7v5RSA2gE9iUSOl+zuEBuvrVe69eNR77/uoXKA+EVPpWD6b5U9OoH06liKZpmqZpmqZpPyS65GEZh77wO2zhyWW/Hy7mAYjOSBppRXpynjV1UZDGDUsJrnVpqsL0oSSJEZgKuwg2BaiYjzVlY24rkT+gyD5rQXsDyhaZYyaFrSH+d9sIs1cWr5GWGmo2QeplA2VAxycfB6By1xB+TCJ8QCisksAYLuMuJin8i4O0fuc8/nQOuH4qgT8+QfavJ/j9g+/j7QeOM1FN86XirfTai9zmjHDWa+dIo0GL4dFQEonEEgYN5RERFl8v72ZrJIclAroeL6BgyXWJzAnqvsnsfJLUhAC5NPZljy+gQoXZ24OKRgjOnF/2WvrTOTo+1XwdRmcH2YcFQW8bHHseYdv4t27BHplj4e5e/KggnoP5WwTMmcRyivw2hVMQdH9rmjARBdNE9HVj1lrwz48ue9ywXif5uSe5v++dZE/7DH3lCYytwwSn15EBc5H17WexpEG6rwezuolap6Cyu46bimBWBXYe0qMKty+LXGPTyhuJf+EpZn/jEKkRn8BZOd4ofaC9gX0uirFlaMX3AECENxdQ0TRN0zRN0zTttUlnKCyj/xsrhwdkNgNAaDSb0vl9q1WRX7SGYILR2oLR1trMIIiDm4YgERLpqGLYIV6XR+NCAnPeotYpmqUQnsCPgVkT1FsUZlUgrBBzwaJRiiADgRcHN9VcVAN4cUmtVRCkAkLXwM2GNAoObiaknpU0dvRiDvbfMBNARJplEds/W2GsnGWuGqfDKvJcaYAj9X5G3TaOuz0ECg43msEEaDZu/H5NkjZqnGt0cL7WhizVr9t/o03xhs5zZDIVRKAQ/tLrFoxPQtQhmJlb10I1XMgTdmSBZtPDsFJFPnwYf/QC2WdnyL5cI7AlkbzCLimUhEheEJsOcTdlqPfEyH/wdrz2OOHM3JqOuenTLxD9ytPN876JYMKVkw8Ik3FiMz6hCfFkHbclwEsqkFDPGFS6Xvn80khe4cxUSZxduReDXQSRi2DWWNPoSDd1fZNPTdM0TdM0TdNev3SGwjLsbz274vdVOgHj4KUUiQkwyg02qughmF8AaTD9W3cR2tAYqkPJolG1MHIRRGeDMBJilQziFxSlIUHQ4lPpFUQWJIGjcFMK1TAwBstYR5MkLiiq3YLuxxrQksZ0IiCazR57vy3xf2WenMiQfcYicJq56TO3OjTe3otZ2URoQ3xc0fnQBGE6jpKSxb0prJoi86sV2qKSr/3pXnakc2y2Z3h45g3c0XGeUT/F8UYvRxsBuyLjHKkP0G4WAfiH0duI2y72qbPXXQPhQcJo0Jcq8OLdcTr+amrp9fd9gtwMsPYxktAcwxkmInhJi/Cf3UnieA7//Chy7w78pIOXtPDiAiUgnguYPmjAYBn3VJx6q4NVVrR/f4IwFVvzMS+Nu9wI6uQZ4vlOlNlLaTENB3y8LhcxZqOkQPqvvJdHvUWSHZlq/hyuoOfLo7z8r/uot4d4SWPVm0n6yOyG/RvRNE3TNE3TNO1HTwcUbpIym8kdZllg1gLwN3apZKQS+A7U+10iUY9gOkJ0Uw2OOlRMm+ispLazTj5mY5Uk5lyzV0FjuE7qOQc/Bv6mgEY5gpEJWUwK4uPgTJUJTpwGIDbUjlAK4YWMlmJIO8BoWAQ2NFrAjyr8Dg/PDLFGI8y/wSU214V0FZFvPEP2MBjZLP7F5ojzf3eIr9zdzUPZbQy1LHDW7aCuLEIleabUz972C4RK0mGUyMgqiy+20cgJuri+bCCIKhqhSbtTJn4kChuULm90dqCKDcr9UTLfPIFqbwVATMxgJeKovlYilsB3DJJPjTG3exheTICA+FQAAvyRMYzODsJlGha+mpTv449PkAgC6tkhREOiTEV+V0j6hIGXkKyvbeQNjmEA0kAmk4Sl0rLb+eMTROYHQEFgXynTWU44Mv4Kz0zTNE3TNE3TtNeSVQMKQog+4H6gE1DAnyulPiGEaAE+BwwCI8D/qpRaFEII4BPAu4Eq8EtKqedfndP/0REXPwkWISRGq7DG9Pe18m9pjuQTFYMGEZyqIAwllR0e9rRJ4IDyJM5wicpsDFkzCOMBuAblgZDYhMS0fYyoou4LzEWT2GxIcOIMAEYmjXj0KME9e1jcEcMwSvS0FHDL3cRmA5QBviPJHTQRASTHYL5LMv3BBm1fjuL93F2kvn7s8qQFEYnQ+hdP0PoXzfNvAP/5z34azJC37zlBIzT4t2ffz3Bynv/7W++i78GA4W88sfSaXlyQmr09KAnfmdzO7ESGW740ie+5G3Jdg9wM5GZIvWwTBMHlEYvB/AJqRz/WfIXAMWj9i6cJ9+2k7w+ewuxoQ9XrKM8n2LcFgLCnHRbyS/a92gJ8I/lT02TunyY1eoDCoEO9VVDuUwSJkNZ7DyB/cPim9jv7G4eI5UJoy+B1JTFLLsZsAX/0wvUbSwM3pTBcyG+RxO/ajXhs+XGraoPew59E+j6saZr2o6fvxZqmaddbSw8FH/gdpdQtwEHgN4UQtwC/BzyklNoKPHTxa4B3AVsv/vcx4NMbftavsk/m1zDezvUA8NKK/I4EwY6BDT0HL2VTb1eQ8qAhcbMhlVwchMIbaCA9wJXUazYiGqAshXFxSkOQ9al1KbxFB/dCHHyBnRekj+Uv93AIKzXCO3dBCNVOgTua4MLRbnxHUGsxSDx8msSFOh1PgeytsvjGOrHzFl7RpvILBRppSe2NtxC85Vag2Y/gWtt+/Wm2ffRZxu6qkDtUxH7HKOMHy2z57SeJfOOZ67a/9On23FsHCBMBs+MZMNW6ShrWSnnudf0sxGNHEMUKzgtjAIQvnIAwwJ/OEeQLhJUK4vEXMFIp1OHjiIujLC+NkvxhBROuJh8+TPblCuV9dQwXEucMzr0/wtRv330TOzMwaxCfqMNEDuN7z2MslC8HEy5N+bgsDDCrAumKZmlO1t6AV6Qt4yfuPqxpmvYapO/FmqZp11g1oKCUmroUTVVKlYATQC/wM8DfXNzsb4D3Xfz7zwD3q6YngYwQonvDz/xV9NWpfatuIzz/4p8gPUUY2biGc+bQAEY9wCwLECAC0ZzE4DQXwKYV0GgLcaZNgpKF8iX4AtkQYCisnIVsAKbCrAhiF0yiM4rw2MnLx1Cei/BDah3NRWDY5qIkeHGwKyHB4iKF4SiFzRI1EkdIqHUHGGUD0wjw4oJ6i0G5x768oN4IjffcgZsSyJiP8CTmrLX6k9YhvPcA5sAKAaMwRF0bGJBL39ug2OwBITvamk+pVNY0wvLVYo7kUJ7EbQkxLva3DG9ibV/76duwSyH2yOzl13h1MEcOD2C0L20+ahfAaIBZ4XLvDW3j/STehzVN015r9L1Y0zTteuvqoSCEGAQOAE8BnUqpS53ypmmmf0Hzxnp1fvT4xceWdNUTQnyMZrQWh7U3uHu1GVuHqf/XFixWrvdW8SgA0hdYtQBzscYrb4fXFM4twEArZhX86QjO1gL+4QzSs/ETIX7DIDlYoJiK0/qERX67pGdPjqnDXVhzJvELgrbjNazZCsFLpy7v91JKvjnQh4o55A4kMBogA3BOO8SmFEFUkDxVIASyJ8vUW5J4SQiKFtFpg8idC+TPtmB0KuRb84TfbaHxS/vo/t7ckmPdrMgDz9CVTBKbuYXJ+wJEURK+6QDG48dfccq8OdDHqfc6hN1tWJEMg38E6pkXm9dm9w6EUvjHX0Y6DsI0kbFYc2G9zGSOq0dGBvmVJyK8mvzcDLGzm/FjilonGA2BF1cYt2xb83siHYdqu0HnQ1P44xM33Gb+rg4Mt53k564aISqhsilslucExivu36CtbiPvwxf395q8F2uapr2W/ST8TqxpmrYWax4bKYRIAF8A/pVSqnj195RSimYt2Zoppf5cKXW7Uup2i1c+6m6jFPa3E31x9eZxSjQ/jTVr0EgZKHvj+lsK26LaZeNmFCKARt0itJqXNz5mgC8IQonh+DSyAmeu+TYK1WwSGSmGyIcPE55aOqIw2DOMuH03CIGfjZE545IabWBWIYgo3IzAaChEcPGtDEOcRYXf6iETHpEChEpAW4NIXpCfTuKmodyvqA6lL4+jfKXCUonkmTLmvIWfDJnbHX3lwYSuTsJ0HD/jY0V8BtsWGHtnkvo/uxOg2ZyyXG0ev15vll903XgU6HWp/z9K0sDYsQWrAspq/gwIrxnoqg6kkbt3rLoLY+dWSu/Zh/QgmLhuvXlZtev6DARnvvkzapYlxipvkdHZsaHZLD+JNvo+fPF5r8l7saZp2mvVT8rvxJqmaWuxpoCCEMKieeP8f5VSX7z4cO5S2tbFP2cuPj4BXJ1TvuniY68L3i8t4E9Nr7rdpfIBPwpuSlAe2LiFUjC/gLPgI11B4CgCX+LHFV6nh5dU2AVBdSKBCgRmGdx9FeZ+0I3X4VHvCmh5bh640pNARCKc/y+HuPCOOOWBOIXbuyn3R4lMFnFTJrFcgJcNKW3zKG6G0x9uwRweJL8jSbVTIComhhVQ7VIUZxIY4w4A9qxJoyMgNikYfb+ifGgQcduuV/TaRaT5P1J1+DjDv/c0Pd+H/B6f+nvvXMdOBMbWZvDkUimCP50jPHoSZ9zCsgLOzbSSODRL7k4Ddfc+wlPnr2s8GNxgnKWRSa84yUA6ztrP8xUQkQhIg/DevYy/ux1nPsTOC0JDIQOBdKHcYyLClfNmZDJJvS+Nm5BIX92wFwaA0drCpj97keTnnlzyePpsDbMiUFI1Jz1Yy9daBLkZSvftXv+L1YCfrPuwpmnaa5W+F2uapi21akDhYofavwROKKX+5KpvfRX48MW/fxj4ylWPf0g0HQQKV6WBveZ9bvf/XNf2mVMhfhT8Da4fLwxaRBZBNgRy0iE6WCLxkk2jzyXcWsUqSMKKRWFXgBN1aWyvsfOPi2z77cOXx0IaW4ZovOsOCu8/QOo0SA9m90uSJwtIXyEWi4gArGqI8Jv9F979jmcwh8pQrVHuk1T6g2YK/YJD4CiSL1tEc4JqV0hogki7VHuafRumDxrN6Rdy7f0krv60X5jm0gVtGBD/wlPc8vtjJF5cPchzKRiBUlAso549dl0pQv9/ehzzOxnUaJxCOYoSYL40suYMiNVKG8J6fU37eSWMbZthz1bmf/lORt/p4MWh1C9JnQ9xezyED8mxkI5/Or5iyUPtZ+6k+uaduEmD0AIll/8ZDuYXbth0Ujx2hLajIcoAL7H6JAexUXVBP2F+0u7DmqZpr0X6Xqxpmna9tWQo3AP8IvBWIcSRi/+9G/gvwDuEEKeBt1/8GuDrwDngDPBZ4OMbf9qvniErsa7t/aggsKGRXnP1yAxD90IAACAASURBVJq0HqtSb1f4WZ8gFlKdSuAngHpzsW6VBVbewChL6icymKMOwUunLi/ojJ1bKdzaydh7JPntEhlAaIK7yQUJzpzHzLuHsSo+btIgMVggmqlzIt+FEAp/OkdsWqESPsKHyFzzuOW9dcyqAgHKUoSugVkRIMHYXGb8p7Jw5y7cd96O2duz6uu8+tN+o7cbmUxet40/NY0/Mrb6vq4KRgS5mct/v5SlIB0HYdl0P5jDaIA/EcNtC6gd3EbjXXesuWTDyGZ/ZE0YRSRCeWcr83uTmPVmucElviOInbFJXFBkXi5fbqx4w/1YNuUeoxkIiEm8pKDaJQjvPbDuc4pNuxgNQSTPqiUNzpweHXmTfqLuw5qmaa9R+l6saZp2jVWLwZVSjwLLfXT5thtsr4DffIXn9bphuAq7KIgubOxHr0HUJLAh84IFEoIIlHZ4REcs3KrEzSj8aLNEr+/BkNhIEbFliOK+DuotkvmDHi1PC8y2Kq4XAwStxwOC0xaTb2nBTUP/t8vM745jVRTywSwRAe6ZKNZOi/BNB2h5oUh8OsbUIYHbEqBMhQDqLYIwEiASPtGYi523MXKwmIhBFM5+IEbmhKAwPEDmbA9W0YUnj676mq8tOdiwa5kvYNyyDWUZVIZTJB86wcB/OIvcu4OFfVkCJ8CLSmJtWbgqELHs/hYXb/i4dJxmhoI0ljRyNLJZRDy2bLPDtap84C4aKYlZUxgNhRcXpM6DEopqtyAx4RJaNtm/eWLV4k3/nt3YJUVgS8yGIjGhyDwzRW1zG/LOPfD0i+s6Ny8V4qahp78HLmbI3IibtvjhFIX8eNH3YU3TtB89fS/WNE273muou9zrVxABN76xJQ9W0SVImkQKgkqPpNYZIqoGQVShDFABqEhI9IJJ4qVpVMRi+h1d1FsFtU0+smBSaxf4c1HiE5LsyTJGqQFzi1gHB1ncbuIlLEILau2SWrtCeoJqh4l0odQXITrbzEqIzoKXFqh4gKqYKAn2gkFYMui/I8d4LIWbFCTOG806+kBQ7RYYLszvtElMGmRmBpeMIPxhC8+OohoNEsEORCwK5QpyvkhyPIaZbxAkbILjL2NsHSY4faWZpbDsJWn813695BiXyh2umQpRftNWQlOQftTHn86t/+SlgdnfizPvIT2TRsqgkRWIoDke0i4qkqMKq+xhl9c2ZtMsu4jAxosKQhuk25xa4RRL0JrlxnMtbsyaK2OWo9hF8DoSyBMrHLe2nj1rmqZpmqZpmvZapgMKr1ClS+LHIJJf2/ZGJr2mEYPqmRdp23WIRrbZYE+Egui0wN9fpr+lwPSjvaAMYjnFuV/swY8pwogijATIqkSEIAKwFySRRQVPHye4uNCNfmWG6MVz6T6X5eyHe2i5fYbeRIHnTg3S/aBJaICXMDDrIbGZkLYXGky+IUrggJ9UmGWB11/n1IVO7GjzWOXBgNQpAz8GyoRGTJE+A5Uug/zP99DxfBv2t569qetsDvTdXAbDxWyBS+UQ4bGTiJ1bYbAT/8mjGBOTKK7U/gSnzyHjccJKpfk+XB1MMM0bBhOMbHbZrAUAPyJJnq/cXDABMLYM4rUnWdwWwU03G3EaDUVgC9IjfjPAJARu2iY2vbaSApmv4Cw4LG63cZNguNCaTBLML8D8wvpOcGYes9aOdMEouitmR9TaLa4vatE0TdM0TdM07fVIBxReociiwmgIpMeShehy1hJMuKTtyy9d3t5ob2fq57ZSnXeYlgo3GxKmfNycjXVgEeEbNMYToCA2KQki4KUUfiIkepjrPjUH8G8ZxEvZWHvzWDLkyIVNbLk/4MJbbTb/+Qjzb+knNuVS7o6RPO8z8IUcKEVw+hynP3kXO/qmuX/LP9FmXKmb/8tCFx9JL22gOOWXufuB38Z5oMylwhAjlSLc2occmWouYm9AJpOXGwFeG0wwtgwx8sFuGjtqZB92aP3sE0u/39lB2N+Jeub61P3gxGnMoQHCfTsJ4jbi8Reaj7/lVmptFl5MEtrQSAv6/+kCqlgmKBSXne4QLC6i7t53eT/Xynz/3JKeDpcIy0ZsH0ZM5KC7fUkDRRGJYGQz+NM5Fu5ox00JAlvgR8GsNAM4hqsodxukRzzm9tjEJwUt3zvP8jMorjrnM+exz46Q5TZmD9i4aYVMJW/YeHHVfc0vIALw41DviRE5vPy2dkFnKGiapmmapmnaj4uN7ST4Eygx7TcXUm0C0du1ofteEnzIJKm3g91aJx2vETohwgiRPriuSa0Uwc5LnJxJJK+obfLxYwqjJomPlZfs18hmkbt3EERNYscmKS/Emscr2jQyFm5LwOIb+mlkJMqS2GWFaHigFG5vhlN/cTvnfvYzfH3715cEE4DrggkA3WYCp61GZTiFsXUYgLBapTyYYOrndyw/anJ4E+Zg/5XpDReJSIT6YAuNlpD2lhLzh7zrr11uBo68vOy19c+PIsamsUZnLz+W3xwhNAWpsQZtzxdJjocgBHS1QRg0mzF2dmAOD163P+P55Y91o2ACgGzJNPtj5PMw28xwuNQYUtg2YamM0dmBDJplDV4C7CK4KZqlKm0CL9kstTEaEMkHhJXqsudxHaVwHj2BCEFJ8Htb1/7ca/jxZtNPVmklEp1Yf8BC0zRN0zRN07TXJp2h8ApFHngGte9ukhdC1MTqow1vhozHUdOzWKUu5KMJFjvjyIRCFiMUdviY5xMYfTWCHWXUWBxlQMthg8VditRZUM8dB8C4ZRvnP9DGgXee4J7sY/zJt96D+Eg759/8FwA8shV+NfshVD7Cwi4Tf3ONzvfP4NdiFI2Ax/Z+caXTXNGJe/4W7oHhL/wa2/4qiqzUaaQlhYN1yv0pzPfejR9VDP9uM9NA7t5BrSdBOJhk4rea0yJ2fGISvyONevpFItMVup5Ik+tMYjg+Rns7wezskmNeV67g+4jbd6OePdb8vu8TTExidncRLubpfOA8KEWYLxDW62SKwwRXvafB4mIz82F2HgCzqxN/Ooc52I8/NnGlKSNLsyuWE+RmLjeBvHTul4IPl55r2DaNtMCLC2p9PmrSRJkKOQexGUUjI/CjkuhsSLXTJNbVDuvIMggrFdLnAha3Gsiqt1o8YFlWEUrbPfJFi85vLL+d2x7XNx1N0zRN0zRN+zGhf7ffANKDcrck0966asnDzbi0z0aLwu3wkTEfISASdTFfSGM0oGY7yKLEqkDXNy8w+d4+eh4JST87cTkF/kNfepAPJq/U+v/mz//ZkuPcFfHwJ2NIAX5Msa0nx+/0f4u3RTcuTf3c//IZDpz8OMmJOEqAaQXsvec0z5/r5w3bzjC7byfhi6cI0g65OyzcTIhqcTm45TzPfmwHHc+HxJ9u9kIwttxJULTYtm0S0gm4JqBwtcvlCkdOXn7scjnF1DRy7w78oyeXPCc40wwwQLM8wejtujy+UkQiqGqt+fyLj4X1AKOzA1Uo3lTpwLWEZRMO91DcDGYZhN9sxGhVBCJQSA8i+WYvBaFAegrhXp+tsSoFzvxqcyFWFp1TlPaEiFUiEpGp0roaPmqapmmapmma9tqlAwobwCo3F3fjP7uJ3m9GCFYYmwdr+/T6akZrC6U3bsWPKYQTIKYjKBMqtkWyBJW+EKMm6HuwitEI8Ecv0PlnU4holNDzELft4sw/T/LB5JFljzEXVPiFUx+k+1FFYajZWPHr27++5nNcj8P/x6doKI8Dn/otfNfguTMD0DB4/LFb6BkMSebaOPe2GO/46WeYrKVpjVR4S/okHe8sYd0X8FD7IboeWSD65afZ8U0HsXmA4Eyz5GC1a6uCAGHZyKE+RKmCP9XMQAiPnlySYQBgbB5ExSKER08inAj+yBjm0AAqGmn2O4hEMLZthulZiDrM/9RmSgOC2LQiOh8S+9JTlzMjboaMOpQ3xWl/XrG4Q6CiAbXNAa1PWDiLIY2URAnIbzFInwtJjjXW3bjS2DJEYcggtCCMrW1CxI1EZ32c0QhqlSKqek+S6ELnTTeo1DRN0zRN0zTttUP3UNgAXrxZyx5aNHsNrEBY9ro/vVbdHYSWIDkiST7nkDwvaT0iSJ4xMVxIjElajitqnRGE62Nk0sihfmR7K6O/cyunfiXB3Xe/tOIx2ow44wsZnAWP1FiIWV9x81csIiwQYI84ROIuzqSJ8AXJI1OoIKTe5fOBlmeImS5bYjPcP3mI3sgi9yZfZnFfgKg0swPCep3g+MuX+xqsem2VQtgWWCbKX/pZ+dXBBOk4ICWi5mJ2dSITF3tFeD7VoTRGKkVQrhCcOktQKkEqwcxbPGp9HoEjiOSbPwc3G0wwhwehq51Kl4EfEYSmwk64WDEXLyYodxtIXxGbDbFLELnJZod+Z5pYTtF6wscoN25qH3BlHGSld+VMBxkogsU1jkTRNE3TNE3TNO01TWcobIB6u8KsCOJTiuK+TmLnRjZ0/+P3tRCdU6RGfYx6iF1w4cmjVzYQ4nJqvhzoY/GdO5nbLwgiCtVax4z43D/wyIrHONJoYFs+CzsSdH9rimJ/z5rOrRDWSMvo5a+PunX22s6anvvSxz/F0AO/SvSFJKmRkMzJEm5fK27W5kP3PMY3C3u5K32e54sDfGzTI/zR2Z+iNboVrJCT/6qHrse6SfzTUwCEs/O4993RHEupVl7UhpUKHH8Zs6tz+W3qdYzZBUQmhbu1h7m9UVpf6kG5IfnNFlZxM9axEcLhHupdMeY/WmEwMUuxHqGSa8V8orHi+MRVKUVtuIVyH7g9HlbUg7Nx/E0NykMhylIkzhqkRgPMqqLSZZIYD7GHB/HX+PNX+5k7qbUatD86g6jUbjr4AWA+epTYtjuo9IoVtzMqHuzeAhf7emiapmmapmma9vqlAwrX+HZ1/WnfsUlBrUvhxQSWs8KCSogljQJvxMikCYplZDxGWCphtLdT3ubhJS1C0wBl4O2wMfcdojQAw/9UZPItzT4KzkKIMsBNCowtZbzZKOff8Vdreg3brOZ5Lx7wKW7uInrjwQTXuTqYAKw5mHDJv33DA3z5vx0kTEQRQcDs3Wlqbyjz1ZE9/Mdb/j9cZVDwHOqhxX/a+hX+Ye4gtcEc7U6Z0R1ZzGcH8M+PIhNxoqN5glWCCdBs+BgeO0nQ14FRrxMUy5g9XbhDHdQ6bBAQ//xTKN/n1G92s+22MbbbdSSK3mieR3PDnB9oZ+A/Jml1puk2Xe6OFOi2Cxwtb+LwWCtyfHZJrwCjvR1VrSKzGfzxiaXnc02ZhtHZQZiKgVIoCfaEhdsLYlMDczJCaCoIBeWhALMmic2GKCko9dtEz6392ispyJ6sQqGEv8wkijXvy/eJFBT11lUCCqU6yjJeWbBF0zRN0zRN07TXBF3ycI2XG2v7ZP5qMoDQUvhxqHatcEnXsNhFSFDh5QVmMDuLUTJobHLZ89FjzN/t0fbecTI/N8HtbzrJqY8kMN88T36vx+IOycItgmp3c6rC+ff9+ZpfQ0zaNFwTa8HErEhCA/662LHm59+ss/UOSrvbUYePEx49SWiDCgXb22aY9xN0GCU2J+aIyQb//tT7mK0nkChm6wkMoRj7QC9IA+UHlLdlEQeaIyjNgb5ljynGp5p/nhwhyBcwUgmwTAqbHQpDBtX25nsoYlGim4sUGg6n5tvZnsjRZpX5Hzv+nradc+zJTvLx7u8RNTwGnTkey29mKDpHfofC23rl58jYthmyKWRn+5Jggjk8iDk0QLhraMnoTFUo4mWjlPotAkehDDAWTWzHw0sFWGWJVZIQCqpdgkZKYpUC0ucbqNLam4I6cy7W+dyyYy3XS/oKuUqSg5+N4bXGNuR4mqZpmqZpmqb9aOkMhWs8kR8GFlfd7mpGXSFCQXJs5Rb3xpYhgrMjKwYWgsXrj33mF65MYzja9eDSLICh763rXG/k3+X28vAf3o3aK7AqgiCqCC3BV2f28UupB1/x/m9kyi/zuxPv5olHdhG+UbHlS83Hy/0h3dkS72s7zL9/9n3s779Ah1Pmk2NvJR2pc1/7MY5W+giU4PhCN33vHmHMuIv+z5wgMu/S6Ihi01yUG50dBDOzV8pBLmYCBPkCAGGlCtD8Ol8ge36Uts4OgoFOzO4uzv/KMIe6j3JrapRea5F/mLmT2VqCrFnhzo5RtkZz/P6599IeLfPl8n5Sdp2/OXEXdDco/bsyuZE7sfIGA9+qIx8+DIC6Zz/FQYf5fQI/GRCdMOl5tI4BGLu2Exx/mbBeJzSbEx2kKwhthWwIGgtRjJrEjyqUoTBrze95cUG9xSA0TZKyH/Oh5addXBK+YT/WS6MoaSBME2GazeyJqZsffZr85jFKH9u34jbWuWmq+/owbvoomqZpmqZpmqa9VuiAwjWeOLaFbTyzrueYdUX6lGBxu8AuCdLLbBecOb/u8zGy2SVff6d8C2H8BPsjkXXv60Y+eP6tPPeD7bQZCmdeUB4IMeoCFVEceWGY8wNlhqzEhhzravlQMlHJEEYU2WMCc1Mvfk8LQ7smeX/PYaQIGeic51/3fptKGOHXDv8Sn37r/ZxudPG29Ev8fe5OOmIlsnaNc7eWmP7gDuLTzZKPaDbbDMxc1VvCHOxH2RaUSrj33UE9axDYgtCG1s8+0dxmUy/B7BzMzOLfsZtav8em6CIZo0qXUSBUglanwoSbZbya4bGpIRZyKcacFowxBxFCbM8igRJUXYt9u0aZrcU5G++gZdshQhPyBxuYkSp+2QYlUBLMUgMjl28em2bjTmUI3LQgjIRE5iVeQtHet8j8QgI57uBnAwJAOAEVGSE90gxmhdbako6s6QJIAxFzcPftw843qLY62LlZCK8Ua6xnSoVqNPBWST4IutuInl/UoyM1TdM0TdM07ceADihcxRzow55Z/yUp9UlqPQG934Vay8ZVkZibesnd1w9cyUL47ZZzwI2DCV+uJPjG4h7Oldr4zf7vMe8n+Ej6xp84//LYvRyd7WZxJItq9/BiFkEEkmclhquotQuMusH/mHsjf9z9/Ia9pkv+dPatJK0G77v3aZ5++A4wJLXuKG32NF+cPMBvDXyHW1su8EBhP7Nukg/d9Th/cPbdtEXL9McX+Y2e7/H1/D4GnXn277rAF//yHcwesGlkFaGxneTnnrxYPtJcugbjkxhdneQ/dIham6B+Z5lIxGeoZYFj995K/JhD39dmUeMNxG27mN+dQEQbTNYzeKHBGdnJv+x5iL+dvYfbYuc5UeqieLyVnX81Q3FPG8lvHiWsVDA6O5h+/2byhxqco4UdbTOE2wT/9t3foNfM83cLB/nexFaKgUHLgw7tD08QXJggjEQQtt28OLdsodZuIj2IzEnqXQHKDpnNpRE1AwSISEAyXaM0miY5AuNvk8QvSDqfrq3p+oejE5f7eVhj4yiluHh0jG2bCU6dBdY3pUL5PuYqh5elKtXtbUQu7l/TNE3TNE3TtNev10VAQVj2qs0MN4Lb30bm1PqflxwLCaKSWhu0PV98xQ3njEwatambcHQCL75yk7urvS9e5n3xJ656pLzstk9+fQ+GC5mCotIrqXUInIXmmXsJgQjBLoIpVy7juBkP1Qw+0vYIn8q9lYl6BqPePIZRDzFlyH1dx/nK/K18vPO7/PHkO/n17u/xP2fuZWt6lh2JKV4q95CRtcvntuDHGf1pA7Og8DM+Zl0g9+0kfOFEc7/t7SAF4WIeZ7GX2TsEe7tzLNRjtEYqdLQVme2xUUYzEV89dxyn/y5QUPFt3tNyjkFzngcrt9Bul0jJOj3RAkeHywSnzhI/dRaSSQCC3Aztn56h/dPg3ncHJ/Zksd84xx+eeReWDLlwrp34qMm2z5wgWFzk0nL90sJd3bOfaleERkZS2aSw8wLhCcyKSbCpzm3bznP8ge1EpxxKt4Fsa5DfEcHurlC2YjjzDtkfLL3eRmsLIhqlvr0L86Hnmse5+t/TNSU4wel1dHa8Rmp0ldyDUoXYy+gMBU3TNE3TNE37MfC6CCio4Iez/Fjc5pAaXX/gQgYgwovp9Ruw/g7yBQxADW+i1nllsfcnC8O8WO7l6Gw3lVoEpQSbO+aImS67UlP8fvvaR/H5CUXgQyMLylRYZUG9VRCfVAQ2pEYCpu8W/OeO52CDK97bjQpJ4fPifDdZp4abMoglYjgTZQ5mzrPJnueC2cKfz76JuOny+YU7eOTJXfz2Tz0AQEekRKfhsjM6yf/10jt41+BLyLogPinwFy0WdoIIkzgvNI8XzF7pKRAdr0CYJFdNMH2+lU378iyWYhBeadZotLcT+9JTxLfcTXJnnXONDuqhRY+VpxpEeLyylc3OLKa5/fJ+r57ScIn9zWfo+SaUxg6S3yJBwNDTLtaDTyw7jaLaFcFNSnwHUCACmiUoRrOC48RsJ422EM8TqLLZLHswFWEgUWZII339exX2d6NeOAnbu5Z+Qza3FbfdQmhJrOkCYW62OVbzJiVGKysH1FIJhHfz4yk1TdM0TdM0TXvteF0EFKRtEdZf/aCCmxFYf7X+9P5Kp8SsQmw2QI7PLPvp63rq0YXjcOaDGaQHt3zy48RmFIUt4Ld6ICHbVuKNvWf5yuH9yLLJyeltPPPVXdR7kvzsJ77D/5YdXXH/h+49zg8O78DKNpBnY3hxRXRWYNabowrndxuEdoAlrixQT7hVdtrr79AfqBBDXCkFcUTA840eaq7FcGaeqV5J7lCWlqOCvz17Jx/d+hi1wGZLbIaCHyVmuBy47QwP5PbwsU2P8PdjdzBVT/GBtmc51DvC/vgYX+zcT8GxUdGA9kcsGinJjQZYqsPH2VHZjPB8svnTFOIxhjoDjInz+BebNV4KQPR95hjf3r6L1K11vja1h5/vfZaz1TYSpssTk4NUyxHM4UH8cyMY2Syqr5Pw6Mnrjpn83JMk13it7FIAAmrtzdKGIAJBTKFMRfR4FDfqYG4v49YtcCWyaCI9QdCIYlcFXgIWP3yIluMlZKEKpoE6MwaWeTk7AZoTJgoHOjGrIcoUJJ4fJ5iZQzgR5N4dFG7JkBypIssu4Uunl/RVWIl6buWglqg1UMXrgy+apmmapmmapr3+vC7GRsrO9h/KcRoZtbbRjjcgAkgcn13yafjVjFRqXfXofn8Hmx5yiY9DbcCjvEngzF0sfwgE+dEMj0xshlCgLIUSECQiOBNF/v7/fNeq+79/4BGEJwkCiV0QyEBg1hSRRR/pN8sdkn3Fpa9BrHxtpvzrSyzKYZ1PLG4B4B/LzXaV26w4W+0ZTBny9JGtlLZ7DHzdp/WFIpWazZFSP+dKrXxnZgcj1VYenx/m7a0nGJlv4f6pQ+zM5rgrfZ4QyVB0jl5zkbBmkugt4iQblDcJCpsl5tDADc8zOHUW//woweIiwXQO9dxx/Onc9dsViwx+XvCLLU/w5o7TnK13MBSbZ2/8AplYs1nA4p1dBG+5lSCfp9qfaqYRrEAs00zTaG/HHOyn1mpS7DcILQhsRW3QhdYGIu1S6wwJogqlBEJefC8kiBBS5ySxaUHmTEhiykMWayAEIl8iLJVQjcaS4zX6W0h99xSRbzyD87Wn8ScmUZ5LWCohpuaRvoJQoaIWRnvriq9pidX+/SiFcl/98iVN0zRN0zRN0159r4sMhaAlBSt/4L4h/KH6up9jDvRhVRTSX2WKQ9SBYnH5718lePOtnPtZm+Q5SSSvMEoGjW6PwS2TvHymh8i0idsakr+QwW6rsb1rhuPpbqbrSZJjMZL/8CTvHPkQ594f5/S/+PSyx3nLXcc4XWjnwmYLM29QbxUQWiAhNOBP93xuyfbbrPiK591tXpkGUQhrpGWU2cDnFmcCgIPOBL889i5+reP7DJg+W1tnuWB75E61Y337SRQQef5uoltctqRmuS05yqA1SymMMusn2d89gRsa7E+O8bcjd7EtO8NEJcOzdj/pFy0KB2JEkw38hCJ+QYDrrXqtbxjkuWo6hPPYSX7vrp/mxB/08wu3Po1E8dmz95DPxzFtn4X3e8ydTbB1pB9nprbqgvrahf3lx6tVCm8eJjHeIDmmKPc5RHOCRqtNrTMktBVGXRAkQuTZOGGbj3Aldl5iVqDziQLGXBEMA//cyIo9CkQkQuTlSfwbjCiFZoZG6geC2oEBigMmnVUXcjMrvq61UvEo4cT6/51pmqZpmqZpmvba87rIUDAWfzgp0vHE+hc60/dtwi4rYrMrZx8E61iQzdzqYNQFxX0uLR8eIzInSR23mCymcCYtQhsiHVXs9irBeIxjLw7Q3lKitL9Oqf9iicKTR+l6YuWGDn/Z/yi/NvgIGIqO55tZDuUBCCxBaMGbo+tvCPHYxQaL36l28vGJgwxZCe6LNfg30wc40uggbdU4Uu/nL/O3sz81DkBkXiJu24XcvYOe//Y43/38HRhCcTB6jhGvmZ2yx7nAVDVFa6TCzydP0hKtsiOeozNa4sxCG/GpAMvxCUNBZE5Q61R4gx1AcwFtbBlaeqIrZRIoRXjvAcQde/D3b2HqA1uInbH57tQ2xutZ8qdbUEUbPxdDCPB7G0y+qxeefnHd1wuAg3tp3LOTaofB3L4oE2+MoQRIHyLzCqskkS0ufirAKEuSo9D5sMHA1wJaXwywiwpjch5/9AL+uZFVDycHNq26TZCbwf7mM3T83THc9pUDSesRpqIbti9N0zRN0zRN0360XhcBBb8r80M5Tjyy/lTs0iD4jsAqb1yjuWpviJ8OMByfPZlJ6h0h9TZF/WQGLxGCgnS8hueaGFVB29ACrm8g5+xmM7+LzOrqde8fTMyCL/AdgQzAzguUCY22tQcTCmGNP1rYDMA9TvNH6m5nko+0PcKT9YC/Lnaw2ZnhcHWQmHQZa7SyNzrGcGSG9/cdQXpg5PKEx5r9B3p+UOVbj+5nxGshVIJPnH8bv3vqA0QMnz5nkf8w/TZMGTLvxcnVkgRPZZE+7O8bZ7h9HgTEJ6DU37wYqtEAKRG3775y0tdkqzt2JwAAIABJREFUEgizmawj7tiDsWUIe3wB0fAoDDtkX24QnVMsFGM8NdWPMytJ9hZRVkgjF0NIRWxu/cEXGY9jDg1QHGoGEOxSs6lnrTfAWQxw8gF+TGBWIfN9B3vOIHFB4McE1S6JmzJIjFWxiwqVWWuXBvDbEvhTNx4neq2wVCIyMrfu17acWtf6e3BomqZpmqZpmvba9LooeWhkI9y48nxjbc3Mcn0l/cqi0wIRKKxjoxsyCi//i4cwahDEwLICvva1Q4R9Lqpu4bV5yJJJiGLupTYwwE8q5kZaUE6AMKHa71P5wF3EP/8Ubnr1t/eZhiLeWaHalYYQsqd9hK848nufXfM5O8Lk37ScZdwv879feC9/N/Q9JgObFxub+Mep2+mOFokYPl5o8Ox0H/9y2/f57yPv4L9v+UcCJHYJ/J4WTKUIZubgsSNsre/itzK/wL7hcXoSBfKNKDHTZZO9wFcv7GF2JsVLdjct33aI2opKl+TkXAcCqPQHNCqSjmevBA0q21qxSx5WKkVwVelJeO8BRBAy/uY4dgGkp3DycRZ2StytNYwLkByH9r87SqVnH97uEl5rSHU+DgKUHRI9HmV2v2LqTXehnIDMYZuuv3nxhpMfLjF2bae4I4MMFHY5pNpp0UiLZi+O8wZzuw1iM6pZ8jKrkJ4iMR4iQoXRCDHLLurZYygg9cz6xjBaU3mWC38Z2zajojZ+ykGZAvvYBfyRsXXsfWV2afUyFE3TNE3TNE3TXh9eFwEF6W3ALMZXifQgnvMI5heW3yaZXHFxeTV1scmeVTCoWxGiHhgLJkoCvkTZCpFxUTMRIvOSRkuIUZGIljpByUQkPOpphzgQnV054+K+k+9BKUG9ZmPdUaIxHcNZMJDrXPNFhAXAPxb30uGU+GS+jy6zQJ81T8quk7TqLLgxfqXjUQqeg6cMBpMLPFcfwJEepaGQzidCaju7ETu6seeqqKOnGfj8PkaHNlN7Y4lUvM5MkOCL6lYWCnGsaZvIgiA256GkoDBkUj2bJkgEdG2Zo/BYJ1fPL6y1GTTSkvTDF4MJ0sB7+wHK3RblPoGXUrgZQIE3I3HTISpv47X6LOyI0JkfbD5WtUhMSRp9Ll7dRIUCN6MI4iEy2+yPEETsVd/vMGoR2IJyu4FZbU7W8JJg1MGoARK8OGTOehi1ACtXRJQqqHgU4QevaJGvqjWMrcMEp881GzeaFspzEaaJ35ZASUEQkZi1gHCNfT/WKjTl6yMtStM0TdM0TdO0Vb0uAgqBY2D9EI4TNdb/6akywTm8cnbCWoMJIhJh7s5mgMAsC5i1EAHNRnx1SaKzTKXkYJgBqiYIbUVsSlIe8olaAeacpEEEefFkjO89zzt79mMO9tPzuXke//I+2l70qbUaBBFY3BMiW1yyDzmkRl2UETD5BsnJjy7fyPFGnqwHHG/0kjaqvDU1zf3Td/NSrovdXVPckRnhxVIvZS/CH468mwuLGX5/09e4xZng0fJ2vj65C2NTlfM/myI2JUBBMmkQfcEl8sAzdADml3oo3bGJmX9e49hiD85ph/YjPvGX56gPZGlkTRoZCOIhVsrFMX3mEorEWBXZ2YEQzU/+W56+aqRnGDC/y6Y0GNL+HLgJQWFX0Cw5aJd0Pg2z+yWtxwS5t7rU3iRhHOScjXtXCS8XQyV8ktkqYbpOdTLBUOc8Z0Y62fT/nFl+dOiBXfiZCNUOGzclcJPNiRqFzSAD8JKK2KRA+gqjAc5ECbFQaE6huMkJJNcKcjOYUuL91O14cYMgIjDrIaEhqHQZzRKPc/P450bYmCNe4abM18dNR9M0TdM0TdO0Vb0ufrc36htRTLC6rkiRkXWELry334YXB9IJWGZcJIDR2bGmpozl9+5H1gSyAV46RAmIFwS1QBC/IHG2epTdGL40iFSaIwKdeRdUBNkTUHcUYcqn7anCkgWtPzLGxH1ZenZVabTa+DGodgl6ts7iBgYKh4WdEVBw8qOfWvPrH/PL9JsJPjPzZrbGZhiIzPHN/F6G4vOcNtvpcoqcqXawOTZHzk1Rs+vErQanvTYOOrNUYyO8YdvL/PKXfx0xXKNsR5EuKNMksXMrLBQIcjP4E5PEelpJPZDAbChmDyhiF0qIchXrO+eIxOPwkU6KNYd4xGViPo2yFMXhOMlnmte97UGWjIYUpknXE2VKW6KUN0miOUWko0pXpkjFtSnPtNN+JCRzdIGZQ1msbIAnQPgCbyKOSvlIK6SUS4CpMCuSxb/fxNCIe+P3WhqE9+zFqHnkt0RopAXROUViXFHYAsoA0QCvM6AWGMSmBMlxF2UIqFTXHExY689a6a5+Sr0mfhT8GEhP4iwoOj75OMCyJRGrEZHIspMsoJmBo2mapmmapmnaj4fXxa/3/v/P3p1H23XdBZ7/7jPdc+fhDffNg/Q0W7Zky7Ys2zgJITgDFYpAEgKLVV1Jp2gKmq6kizTdDSyqqotqii5WNVUsiqrQBQQChAyEhBCTwY4d2bIly7KtWe/pzfN9dx7OtHf/cZ9sy5rekx3FgvNZy2tZ9+5zzr7Du2vt3/nt3y+h35LrRMTmllH1XguzBnJm/rrjNtzhQbUXXNIEJUB1urhpUELR6JVU6jbCkqhCBGlBvU/D/MYxvCRU5pOosTpIYLlw5RyKRYyqQ/LZaZysILAVPzbwAmm7hdKgslWy4yNnNjRNTwU83tQ43Bxkwa9haT4Xm51caOUZr3QyYq/yka1HMUWALhQvVfr4qY7DNAOTpUaSbr2KBhSCBLvNOjIRoGkSqyjwMhInC3OPduFt78fo70OzbZr5dneAVlZgNAX10STeaL79tu0coeFY1BYT1B2LwNNR3Q6R8qthldcGE6DdLtJNWxhlDf+uGs28oFWNMHW2h2rDJrqqcFKChbd3gi8QQiFtiZ8IkGkfY9Ukk66D2V7oW2VB9+EC0ZNzV3/TZIA1u0azN4aTFegurB6QVEcFmfMQnxXoDsSmDaxKO0PBTeqIpTXUUB9aPI73rgPo27de97Pxx/qu+7wwDIK3301xzCBakESKCiEhsKHzvzx93WM34nrBhFAoFAqFQqFQKPT3y22RoaB5b3bi9dUV/RhsIsm7lWt3RLjeIkrPZgmKxRueq/n++yhv0ZEZFwkgwZ6waXVLzKyDp0WwXkzS99AiCyKN7Ja4Z9vt/Lb+9znm39OPH08Qa8HCh3bS+6cnCUrly64hT5xGAn1P9lIZtfncnftZnM0R6xJ0nIDZXRvrpmEKnZNOPx9KnuH3i/dwfGWAg/lJPp59lv2xSY43Rni6MMonhh7j3089ymotzhdiB0gaDvd0zrAcJKlIGx3J/1e+k1xvmVI5jkorIss6rT4fa3+VCzsTdBwdxi4OojuK0k7wOnyGviwo3GGQNgXpfDezj6T51M4/568772KynKMvVWF8qZPqgEHHaz+Lri6ClRXqP34/9R6d7merxGctSl0Rgg6JMCQiHRCMJ8icq7PwYIL6oESlPGqzKVLjOm4SWkMuQsLaYpq+wQKLp7vpe7JJcPr8NTMJxIE7qHdFaWV0IkVFeStYBR3htb/fTk5gNMFLKSJFQfacgzVTbAej1gNS5mNHb1h8UX9xHJFJX/HZX7L08ftw0zDwG4dfeSw/MoQ73HHFWGNk6E0tyAgQW9h8a9ZQKBQKhUKhUCj01nRbBBT8qHZLujxIJdhMQEEZYFWuP34jwQQAaQikAbgakVwTpxBdv4bCq1kIT+BmJPNLGVTDoGt4jeUuG+7bC3MFsmcdijsiNLvBXoWgfHkxPWN4EFWuQFcHeAG6q1g50U1iTZA5H5A8ucr4WJ7lPXW69fgN5/sjidN06gk6zSofGHqBtN7g94v3caw0RMxwUUrw35ceotyyKa0kWMvH0VBM1juIZx3OOz385tEfJhL1aBVtRkaXWYklaDYsTCOgWolilAzsosSqtOsdSFNiz5r40QAlQCiY+OdbefiHT3ChlSdnNXi+PMjI0Bpd2Sq+jL4yX33XNpYPdZKZGKTWq9PMK6bfncTpai/RjcE6bstEn7HpeTZAe+Ec9o79iIfLVBaSCF8gfGiNOuBrZE9DcZdBYSFPZhb0lo+WyVzz89YXi0S9gOpQpt0mc71eppBgNiXS1Mi87CMnBUoD6/g4QamM0duz4RaP0K7XoXfkrrn1wKoqIq/7zvqT02ivCxyISITGjm6sTQYUbrTlIRQKhUKhUCgUCv39cVtsebhVnlsd3tR4q6RwsgItfuMF+I2UR3V0B4QncNaiYEqcjgCjroGmEIHAqGuYtg8KlldSKDvgwk/Gmf7JEVbvjJA745A5L6kPKrh/L3o2C0DwtruZ/tAgSx/cTdCRwEtZ2Gs+W75UJ7aoWNulUzjYjV0QHPzyJzY038PNQQCGzAJTrQ7ON/N89uw92LpHzmowVcgSN1x6ElXu2jbD+3In+IX8N7F1j38/9Sh/u3IH2weW+IHhC+gJj5nlHN7JFL2dZUa71tAXI2z51NOkvnmO+QctRn90nCAdgAZOWkN3oPLTFQYfnCVlNGlICycwODA0zemVPKOpNXxboNk2APL8RbqOlpg/ZNPzdIXIWnuLQe6ERt9gAXcphrbc7hyxcFBn7YP7qfcJmmcyiJiPTHtUt0isOQs97tHsEmTOQnyuXf8AbhA8MnREvUX300WsqsQqCZy8j4yAk9KJz7UX+UqDxFQDujraXRc2EUy4JCisXXVRbwwPUh0SlLYLnPfee9lzwrg8tqgNDxCdvnqWw/XcKJggI7dm+1IoFAqFQqFQKBT63rs9Agq3ZscDOzIbrHWwLlYI0DwQ/T1v6LoiEmm3LUyDikj0moZWNTBLGtJQaGWDyIqGm5F4joHW0tBWLbRIQGSwRv3OFs28QhqC9IUGmgvNvE3zvq3Ih/dTGYlgVhWpKQ9jconimMX8QyZONkKtXxCfV9jFgPSET+ZljbJs3nDOGb3BalDHFD574nPsiC3i+zqtwGSxmWS4o8h0LctMKcP4WgeHq2PssQzSZoulapIXp/pZqKS4UOlCBYKgYWDvLbG4lmL8+AB9T7YzB4Idg3gpSdM3MQoG8RlFbDWgdW+dh/ovMj7XxZboCqtOgojus9RMsqtriaMzg0gTZKudYq98H3niNJkLEmVoaH67bkBxt2JrepX82Cr2qobTqTBagvJYu5aFn5BopkTUDOhyCLY0MS5EsVcV0UJA+qJHatJHK9au+37JlUK7RWPQ/jInZwIiyzr2CuiOAgGxuSbJi3WMmRXUzDzKv3ZND6P/+rUSribobm9p0ZsCs+Kj796Olkyid+TQOy/f8iDjNjJhX/NcWjKJ0ZPf9Bw059YUWA2FQqFQKBQKhULfe7fFlodWTuON5wDc2PPL/XRybsPji2MGzR5JcG78DV1XCIGbk6BAT3hIV0PLuvgiglkRsLOG5yVAUyhfQ3Q5aHM2XtWkVbbQMi4igMmfgOjFBG63x8o+E6sE0YLErEtyL9XQi3WW3ruF8q4AK99g3kzQ84xP8ugsKhVHlKpEtvbw0P/7SV76X67f7eHRmAPEmfE6+Fh6gqdaNu/adobzlS50oTO5muPn73iCl+t9NAOTL57cR8WPMhwt8K3qdqiY7No6yfHZAYZ61pgrpKmsxen7mk76704TlMpM/usH8EdaxBIVXKljVQSFAz726Co5qfHMwjA/tPM0Umlsja1gax4Zo8FXp/awb2CO896OK+ad/PNnkI/sp7yn3SI086LJy1t7qVRjBEM+fVtWWTjfhYoGaJEAbTmCmI5i1wSuG8EqanScDGj80yJjXfO8/J/30vH8GsGFi9d9v7TuTvzzE8iH95P++mkaD+9g8DEHhMCPG2ReKsPsIug6QaNx3Tv9Rm8Pi+8dpvP3r18M9Io5nJ9GvWcPbkoy80M2eiuK5nWiuWCvKbJnugHQF9aQXgCnx68ZyxNC4G+02OhrmHNrN91BIhQKhUKhUCgUCr213BYBBaNxa1IU1hbTdG5ivFAQn3vjSR6y1UK4AhmTBGULs6bhBxEQ4HQH5KIOVZlAmYpIwsH3dfyERE+7GEaAPx3HaAhIuDidBugKabYL/Zl1QcfjM2CZLL2jl/IY9G1bodqK0PKhPGKwtmsEaYCX6MRoCpycZP9zH+b4vX92w7n/k9QyjzVivCvm8UuLQ/zKzr/hM4sH2ds3z8v1Pp6eH8H3dfaNzPCNl3bxwK5xUskm5bLFyeUe4lGHhWIK4+UE2/54Bn9qhgCofuggaqxBOt6ieibHZFcUM6XQUx4P5if48oW97Movcq7czYVKF6YWYOs+pxby/NY9f8mxxigvvq0PrhIXqfdESIxr1HY5lPZ52Mc6CPo9iEgWljLkx1ZZfbEbMezid7kY8xGSU4pgSRBYEPviERYPPsCRYicDn32W4CqZBPLh/WhPHn/l3+5QDr0zhfbkcQIg8tXnXnnOghsWW3wtf2GR7j8qtYt3bpC4dy+VoRi505L4bJPizhiapyjugtTFdjZGq9sm9sw4fmENZq9/vqByeY0OYRjo/b34UzPXnoNhgBCbmHUoFAqFQqFQKBR6K7sttjz40VuzCNHszd07lSakJt+EFG5NR0UUWlPDWtVBgjIlyg7I9FYoFhMoDXL9JdymiSxEUBFJULFwWyb0OjRHXcbyqyhLIQyJ0RDYBciMO6BrOEM5GnmB5kNntE6tauN2BrQ6Fc07mjQHfKSl8JISrdOhPJve8PS79Rovui2Kawl+9eSP8DM9h9GEouxFqS4kkVKw2kygx3yenRqmPJPG7GxSW0oQSA13Ocbgvzl82WJ06QHo7yxRnEvjd7mkXogQ9LeIxVucrvTwUzueI221cAOdbKTBYLzEYj1JJtnkG+U9PLc2jFO7einP5MU6ZlXR31vESjnoDhglA90OMBYiLE51IIYaBL6OcjXi04L4kkfmgkNquv0d2fZbFxj6r2fQRodeqVXxysdp24hAot2x89XXcyBKacebl2dzaSvHRs28M0l5i076hRXE4RPk/uBpOo4VSMyAkxb4tiA2WSEorF33PNfa5qB8H9W4/pyU77/pXSNCoVAoFAqFQqHQ989tkaHgpm5NQMG6EL3xoHXTv3YIzQUh34TsCRmgNdvV/WNLAjcFCB2v26PxUpZtD8ww/9IQ5Uoc5WsQCxA1A6FAWhranInsd5j85gipMmz/0AWOL22n97CD5kn8fIbFg+0WlEa+wamFPPqMjdUSOPkAVTERcZ/4eZv8e2ZYrCSpVUxedFvcaV17H/2nyz3YmsfJRj+e0snmauzrmucXn/wIP77vGH87tYt33n2Sb57ZwcJkDzIuufvOcV6c3IYnopi5FpVCnJ2fevmVu+36rm0U93Wg55tUWhGELzi4fYKT2R7kUgIZ17B1j08ffQihS373wT/h5498hM5slU+M/R1/MPsQq04CT+qMDK5cNl8tHofRQaqDMaQhmF/OYFg+rb1NEkeitJpROu5fxDZ85r8zQHwVYisBTkZhz5QJTp9HX59jcPo8wrTQkwmIReE1BRm1nm606VVUvYG+fSui3qS606PVZbDxMM2bZ/WfPfBKm8jXhr+CU+foOtVubSrSSZRptLuGPPvSNc/lLy5d87nm3cNYX1+55vOhUCgUCoVCoVDo75fbIkPBLtyiqoybuEwQVUgTEt85f9OX0ztyr/y/VdKQiQAn0670jwCtbGBVBCv1OH4cgqaOUTAxV0zMikB0OeAJjJoglnBodUn8OJSdKEpTNPIWbspkdV8Ce1WhNwXBfAz7uQSdLyrcrETpCqEEAz1FakOS2bUMjXqEVG+VDx/9GOe8+lXnfs6r86HkJKUgxqlKL08tbaHpWDx5cStW3OXIyghOy+SZz99F/5dMtv55hf5vwLFzIwQxBQkPMR6j+3ETWX/1GvWxLNVhDa8UYW05BSmPH+16nljEpWdojV/e87dMlnMM9hd4356XKAWx9nGORUZr8EBHu5ZB0mwxtfC6QoP1OlqtQbNTw02BZkjUVJzOv4lgNBVuR8DChS7W6jG8lKR0r8PaHh03JaiPZTG2jAAgqg20eJzSB+/G688hS+XLPktVKkMQgGGAUvhz8yRPmwjvpr8qb0hq8voXDopF/MlpgvMT1AdjGL2bLzKq57spbzExBvpvdpqhUCgUCoVCoVDoNnNbZCjcKrkzG9+VrrlglUG87u70Zrh3jqB/u51i3v9kk+W7o9QHJEHOg0CABqnda8zP5yDvY8Y9gqqBURcEW5sY52PIjoDmmIOYT6A3Ba0uybmJXsj5RErQyJs4OYEXV9gFQWwJzHpAo1sjOSFodguUDjMTXZjdTVrVCKKuU6kbRBZN3t38eZ545Hf4q9ouPp6exBTttn/bzXb6fkZvkI9WOLWQx6tbmDGPmO22F+VNk8ySIogIqlsSCAm7PnmeoHRlO0J9+1b8riTzD+ogFNFZg+awh4goSkGcrlgdW/f44vJ+hlJFNKE4W84z28jwi/u+xedm7+bp+jbmnTSFVpwd6SXOxK6Snq8UPY/NM//ufhLpOquFCE5Wx021t5kkz5qURRqt1yGXqlNdtJBGu1VlvN7A/eEDiOUmZ/+PfnqfgFanRWT/NrSnXnjlEkGpDKUyRm8P/vkJANIXA8SEatcQUDcXIFv5nx6g88Um4rsv3HjwOu+d9xBZczYcK2t2alh7BzA32a5y8ce20uwW+LNzmzouFAqFQqFQKBQK3b5uiwwFeYvCHumXChtux6cE+DFQ1eu3C7ye0tZX9/hb48ukL/p0nBDYFyNoFYNkrs78TPsuu93ZRM5H0VsCP6EIKhZBVIECrWiiZV3QQHa5RNItEqctmp0GzU6BVVZIC2KLisxnnsVNapT2SIJou7Ckn2x3mPBXbYwVE+EJhKehOyCWIjz8lU/wH7/0PnZ+62P8Xqmfj1x8O58u9/Dgiz/GRaeLbxy+C38phrlkEixEcY/kaE0ksactKlsE1QGNxOeOEP/8kcuCCZdqD+hjo6zd18XCoRi6I/BzPs6uJlu2LJFItNhqLZGxGlxY62S6kmW2mqEzUufO7Bx1L8IX5vcBMNvKogvFrswiZ8t5fnXvVy9/wzUdf2oGubBE1wt1VhbTZEeKSBOiK4r8kzpmXWEv6NhRl9L5HNlTYBcEqYstaLbwEjrn/mmC2IzBwjsCGt06y/fE2gUHX3etxp0DCNPC2DJCYY/O0j36TQcTAHqeKGwqmAAQWayztiex4fHaejKDnnl1c4YWj6Nv33rD46zKdYegd3VteB6hUCgUCoVCoVDore+2CCgEt6goY3DmAjKb2tBYqyLQHUC7+bk5mfaxWjxO6dAgmqcwWgrdpd3hwTHbA32BUiAjCqW3gxlGRUdaCjRQliKomvipAMMMEOvBjnqvhllT7cBAAJk/eRZkQL1PoCyJvaowawJ7UYeIRCV8lK5QlkLZAa3udsaGvWwQnxcIDT43fw9Hjuzg//rW+5mf6eCJlW2ILge9JdBbgvicRnxeofmCyBr0fddl8NOnr/5+r2d2BLkEuguBBV6yfU3DDIibLrl4gxPNYX44d5JsrIkfaCxf7MCTOkORNSSCpmdyd+cMBSfGmhtDR5KONHmm9ppFsKZjDPYhIhHU3m3oFQdhSNYW00gTylshtujhpgRCgu9rdD0PVk2SnA5wUyat+7YRWO3PTJogHI1IWSEjgHjdn5IMsL5+FOW5BOk49n0FsvestOs43AS9qwsZNV99YIPdEsT0PGt3KMSBOzY0vpUTuEkdIq8Gu2S9jrLM6xwFXlKgt24QLLlOK8xQKBQKhUKhUCh0+7kttjxo7q2qoaBY/IEc3S/feGirQxFdFldN398IY3SYSElh9OSpHhxmbbdGdElgVRS50z6VukE5ESE6abZT/wXoDQ2/w8NaMHE7AoQvMMoakTWBk1N4PR4R28NpmZgu1HZ4mCsGkZKg63iAPLQXvekRXVa4KQOlg9GA2j6X9AmLRq8iiCoSEzqNPoGQAqUp3JTEtwVB1WTeShOf02h1KrxOycR8J6pioTo8rJJFdFnR8ZUzdD2Rwp+aBRlcsyWilkwiq1VW7kmw/afPIitZlgspdAH3DMzwwkI/vZkKn5/dx+5suxhgyzXp3bpC2bP5u9Vd3JubwpEGM80saavFscUBXnDb+/iNfgm0gxZ6KkHl7j6E6sW3NVIXaqimQeqsgebA9r9cwOtJ07hLIqsmuW8kiC17VAZNnA4B6DT6JJoj6DwqqPdD6pzO/LtcopkW2uggwbnxV1+bbb/SiUGbXaa4MEKmp8qFX7mTsX/94mV1IzZCWCbi3PSrWxc2mOkQVCoIX9DsiXHt8pqv6vutdvFGtm9FFNZQ6y0x5ctnrnmM964DeDFIzF1/TiKXgcoN0hhCoVAoFAqFQqHQbeO2yFDwEreud32re2PjNO/V9PCbUduTx6opgoEuWhkdzWnfHS7tEESXWthFiZVyMBuArmjVLIKoRBgSZYCVa6FiAUZd4KYVkaIAV8NpmfiujtIBTRFbEOgt8OIac4/EOP9TSQJbIAA3KRCBwkq6GHVFEFHoDYGTbWcYiMEGqqd9V1laCq2h4bkGblphVgTWooFsGiBBj/kkZhXp8Ua7yN/FKZDXb6kpLBP14D7ctGCpkcQ2fEzL576RSdacGHf2zgPwUH6ClVaCrmiN3flFOqINzhe6uLiW41ytmyMrIxRacY4tDtCXqnBH7wLd6RrbEsuvXEsFkmaHRnXAQEjQ6g4dR3Wy5zwafQpVrSN8iTZro7U0YisSq+iQmnKR64EXvSXwkwFGSxFdUVS3SBAQBBped/Ky1/bato6yWMSeNyktpBAKWg/t2vT3xe/vQNwgS+BajIYgiAjkQ/s2dZx+jRaRl43ZvpXVOyycTomTvP7fqd/9/ehxEQqFQqFQKBQKhb5XbouAglW9RRkKwJ4fPLehcbEFQXrSv7mLaO3ChoU9giBq4ibbxRTd3U38uGL6hxPkO6IPAAAgAElEQVSUtguCmRiVXR561CfbUUNvaKSP2FglgRAKvWSQe3gRLytp7m+QOm2Q/rsY2VytvR0DqA1LKlsl9T5Bc7A93+qIRJoKJ6to5drXqQ8KlKXQfNCd9vYFMR5DNgwiBQ2rrKFMBQsRvC0trCrICFjLBj1PCXo/FyHzx08jDp/Y8Nuw/I93cOEjEbivzOrX+yl8rZ9oxGOqmuUneo/hBjortTgxzWWlGSeuuzx/cYiM1eC+3imysSbHzoxSdyze3n0O1zVImA4TxQ4e6h7nM99++JVrFd+/h+IdiuGfGGdtp4abT9L91+NIU9B1XCJ0DRk16DyhsJc17FWXVqdNeauFm1FIHbx0gFCC8haN/N9MsfVzTfSoT1e6Rnms3XJU3LsXdegu9NSrW2eU7zP8746x7Y8cRr9Ywyq7m/7KSEun9tDYZd0kNip/zGP+7TD1nijctxdhWjc8Jjg3vqECi5U7O6lu8xEBJBav//fQ6t54W9ZQKBQKhUKhUCj01ndbbHkwGrcuoHAoO8HXuX4dBX3bFlrdEFu9yXiMDAgiAgSYi2VSMyZBxECuRImuKNZ+oIVYtTAaAq3fxXd1hFAEtqLVJWj1eRiTCWRUslhIg4J4zAFiVEdA1m28XS6ioRMkAzAV1kiNTMRlcTWNNmsTWRPUxjzMpIuYjtHq9tEcDWfQRdQN9JpGdFkgAhN3dxNZsojN6LQ6JZEzUeKLAdkzPua3XkCL2ptO4QcoHPL4sbueZ6aZ5ehogoGxZTqjNc4s5/nD6Qf4xdFv0sqb/Prx9xK1PbbGVjh07wV+4/ij/It932Qstsxjxm5m1zK8UB7AXYjzwvQY++6/QMGLY9Tad8yNgX4a3RqJkSInzg7Rd2iRwj0W/lNjDH5xgeDCRQLTQvvOMskH7iR3pEwwt4C+dxtTPxIDAXVdI33KIL4YAAGFtw+RmmiiTUapPxWj92sXCAB9tcLSe/vpm0tflt6vHGdTwZbX0556AeOd96B6u6Gwtqljo4+fItd3J+Wt4GUimN7mAxrXUtitE5sGqwqRrz5zzXHCMLCq36e+maFQKBQKhUKhUOh74rbIUODW7Xjgkfi194pfUtvTid5i0xX7LxXkE6aFF9PwshK1uILmKZycwl5TIEDVDcyqRmAr5HwU5eisrSZRcZ9Wj4+VcZB9LWL9NTQ9QGRcKqtxguh6YcOiDZ5ApF3QFeaSSSbWZH6iE23WRpqKer9Er+h4dbMd2CjroIBAkB0uIq31Yo4KxKyNUdawC4qBbwd0nfBJnitjP3seZLD5YIIQGAP92EmHFTfBsaPbUJGAzmiNLrvGzu4lIobPV9bu4kIrz1h+lYTtIBH81fI+3r71PF9euAtPGkQNDykFfdEyvTuWCWKSqtuuFpCcbF/On52jNhpQq9rY2RZN16RSjdLol5Tuaaf1K88FpRCHT+BPTKIch1Y+CrrCqOhovsCsK6QhiC26GE2FjOjYqwKzoZCD3VQ/fJDmti76vjKLPzWzufdknZ5KXbXgonbnTiJHzqHOX3zlMWN0eEPnVLtGkUZ7i87cwze3beJagphCKNCb1/9bUFJhHN1Y9k8oFAqFQqFQKBS6PdwWGQraTe4suBn3RG6cDt7o0DHqoG+yWOSlhbfelyewQWsKgju3YjQCOl/UqYxoKAFoCn9bA1mIYHY30QFvMYZV0kAo/JSOWIxQ79C4d9sk5/90B36iXSgyUhTIiIbKuYjlCOQ8vHTA1EIHWBLQUXmH2MtRvLhCSBO/0wMJBAJ0xdpCmq4TAt2V9DxRQiiFjJqoo69Wq5QbfM16vptgeeXy4ItSVO4dYFd+gplalgfvP8VTz+7mZ/se5zMrD+ArnXfnT/JMaZSGtCi2oqQsh6PFYWZKGTLdDTrsOj+ZPsZfz92B7+pUfZu1apwt2xfJxyqcK3eTmmzfiRemhTIUA90l5lYyCKEQAvKHIf380lWLRq5+/AEQYK1A9rQivuhRHbAILEEzb+FFNfyoRWJOUrhDYFXjZL45jmo08G8iW+OS8qO7qQxreIl2m8+ePztD5R3biS67mAM9FO/pIP2ZdiaAf3HqxifUdIq7k1RHIEhI7AWdpV84RP53Dt/0HC8x+vvwsj5el2Loy5VrFt8EEPt3Io+dfMPXDIVCoVAoFAqFQm8dt0VAQd3CDIWNkBbojiI21+BmNmOoRIzAarcnbHVaJE4XKG3pptkt0V2B1tQJNMBURCIejUYENIXT7WNUdJKJJnXPRlZMXl7sJdFQlHdJVESSmjDwoxpOXCdIBZgLFn5KoukKVTUwawJ/1kbI9t3lICoxVkxkpF07AMBeVWTP1tEvLhIsLV//xdyA0DT03dsJTp697PFGl8ZkKUfTsWh6JmZF8PXyXqK6x/1dJzlaGSFrNeg0q6QjLWKGSysw0TSJKw0e7XiZI61BbMNnz9ACS80ke3oWcKVByY0xs5xj+/GLBLSzD4SrUW7aKCnwfQ25GkF3JSwXrjFxaPQIokvgR6GVM4iuBQSWoN6r03miibQ0vKSO5hkkz5YJVlYuP4em37Aw5etljswhZB9zjwa4o5Jm1y7QIH0+gp0wqA5qbKa0od7VQaQSoLcMEFq77ahBu33m9do4bmDu7mg3wtegJRDN67eEFE5wU38roVAoFAqFQqFQ6K3rtggoiLfYSqQ2BJGCoLQzQfro5o/3M1GkAXpT4CY0ars6SM14rO3TiU9oBJYAoRFYEExn0fbW0eoaSmvfZS6XYlhe+3hnOkHzYABCYa4ZmHWFO+JgLkTwUgFsqZO0PWqTaWQ8QFY0ci+3izGa5fY5la4Y/oqHmzGI/+URoJ16H7wJLf78hUVYWKT8UwfJHVkiuHCRC//hIHS16I+4dMXrnBvvRQw7lLwYZ0vdDNpFnMAgabQoenH6Y2U8pbE3Pc9jrZ1IJVjzEzSkxd7sPLtj8zxe3MHJ5R4O9k3xjef3sPXPfYLX1BoY+apPoztN5ZBENiy6j0P0S89e86668EFbLzUQWwkwawGlMYvUlE90RbCyP0p0RRJd8Ygt6XBh8rL37LVtIzf1fk3NEJ+aYftfgt6RI9jaz9w7kvgxQcM06Dvc3NT5VK2ONARGEzRPkD0boHuKiV+/G+GDl5UkL+h4MRj5UuHVwM8GAiFT74kSnRNI68bZEqJ1/YBDKBQKhUKhUCgUuv3cHgGF4K0VUfCTksxZDbu4ubvPlxilJtFVm2gB4oseRtVFeJLkRBovsd6S0gW7oFi5L8CcjKGtBxBalkAGGk6vT+yiiZsCvaXjDzhYF03W7gDla5hlQXzaoLTXxjMiJIYrNKZSRFfALgTojiI5A8lzJdyuOPrjz/Pa3fVyxzA899Jl89a7uhj/T70oKTgwNM1UNcu9XdMAPHZxJ7k/i5M8X0GeOH35CxaC9J88016837eXTzz6VR6OneeT4z/B+ZcHSE1q+FGT79qj/Ob+z3N3ZJmPrnyYiO7z47nn+MPlh8iYTZ5bG6blmpxd7WaulkYAph6QNRtUXJuf3fEk//HFt7PrU2faC3shXtlqYT52lDSQ/szGPiOrrjAnFa2cRnmLQfacIr4YoHQIIgKjoVAaTD9qkpwUiHTqsmyOmwkmvF5QWIPCGv3PrgcXCmsYA/1sdAdQ8Pa7WdkZodUh0B3oOukTf+IMaqiPXj9FrV8nWNbRPIXmiiuySG7Ej6t2tsMG/gyCCxdvPCgUCoVCoVAoFArdVm6LooxmbaM79m8Nva7hxQRG4+YCCqLpYNUkIoDIQhW97hIkLBKzAfF5RatD0eqEaCHAXjTw+x38eLtgo1EVWHMmRsEgPq9InwezJjDmIugOxOYF0YsWVhUCG6yCDoak2VyvDSEhOl8j8bkjpJ6eJDh5Fv3x56+Yo3pNMEHs30PpZx5g4b918KX7f49Ht53ik31f58ODx3ip2Me26BIf2vY8Kz/RZOlQBvnIfoTxmljVa+onOB02pgj447UH6I2VUXGf2KIkPqdw6hbzXpYBI4GvNE6v5TnV6ufJyS08kjrLWjNG0zF5dPg0vfEKfYkyU/MdnK91s1RrZyx4devVzIpNFs18LS8maHVoeMl2l5Fmh0FkzSOwNJy0oDoMy/eB8AVIELE3tyWi3tV12b8vZVtspJXjJa2ciR8VNPt9sud8on/7AkGlgnz5DPZXniX/F2fo++x5cqcdnCzo27ducpIKaSkS05s7LBQKhUKhUCgUCv39cFtkKDQ7DSLf70m8ht4SBFFw0wY3s4z0JyaJTc+ibd+CPDeB3t9L4QdyCAnJaR+jpSMCSSujY7RATUZwsxKzBl4S5FAL61QUJwuV7T5mUcePKYQv8BLgdEq8lCA1DtXtAVbMg3NxxI4a2qnEKxkEqnH99PnFf3GI7vfNkI0sgltiOLHGLivGjtgi5908e+0ZfmbnGf7N8iHW3DiPP/C7nD2Q4pf+1T+ja2YAf2ISYRgo/9V76lP/GP5o6iDdsSovz/eSOGuR+uxhxD17iBbi/GnffRyprHKgY5qY5rLgZehI1/kPE++kL1FGCMV4rZOGb3F6oo+fvPtZvj67k2IxwTd+JMl2jl5xzZuR+4Onab3vPry4xtoegZdUKM3GLgU4HRBbFPR8rojTHcd6/AT+G7zeZTSd5X80BmKMjv/29KYPF6aF3t9DcYeOm1HYSwbJo5P4r2kXqdk2QbEIgP74CoOPA9nshq8hH9qH3VOnWbGxqrfFz0goFAqFQqFQKBR6k90WGQqJeffGg24BfX3BZdba2xLeCOX7iJaL8n2C7gxGE0o7Fc0ug8AEoyEJbECBM+ChTNWu/L8gMC5EafUENLsUZlknuiTInhIEEfBjCmlJ0mdh7T6P1GkDzsdxOwPUuQRdx2uvzOF6NRL0bVvIvWeOT2/7LLbukTAdIuvtNt4TP81dkTlyeoO0FiVrNADoNRJsMSs0uwUy0W7dKCKXh4JSJ03m5nO8cGIL+pkEqcn1LA8JSofZC92cL3Xxs7mnKPoxyn6UxakO5heyPJCbYHd2iXszU5QdGxT8xbcOUVxLsP2jr3ageKPBhEviF8tkvzuDvSqQsQAvISiOGXgJiVVVSNskenLuTbveJVo8RmLeR0gwRoY2fbzyXPADmjtb+MmALX80jz83/+r5k0m8g7uvPLCve4MT1Fk8GMOZTSAaOrHlG7x+Td/E7EOhUCgUCoVCodDt4ra4tSiNt0abB1lrtwM0GqA0MGo3t+UBACFobOskWq1D3aG4K0FkVaO0TWE0NHxbUO8X2CtgT1tElxStHLhJyJyXlMd0vKQkuqjRc6RGYW8caUJ0RRBENco7YPgLgql/5KE1dbSmxugXK+jzhevuwdfu2sX4T2a4++GzPJScoyp1TCH5YP453hdbASyec/r5YKIMwHJQ56czx5hPRFkOWjzX6sO5u85kNMeQvRf5bHvrhIhEUK5LetIniFoICYOfPvNKKr86fpLocdj57RRqqI/3fOCXsPYX2dG5jIj65LvLfHN5J+dm8jy5sBd7WZD220EIf9Vm7UvD5KINam4E87dzWF+/iWqZr3OppkDni3nsNYu1PQo/Kek6opGcdTCWy+2ik28yWa1if+MEkSCg9OP3EjzYR+6x8Su7SFyDiEQoPDKIKMD2X3r+ssyES+c36t4VXRfEWnlj8zu0Fy+lkBFJ99M65mNHrjte3zW26foMoVAoFAqFQqFQ6K3vtggovFXofXn8qRnsNUmj+40ldwjLojpoEnvZRBoa0lT4ScichtIOhZsBswoIsIrgZNpBFd0DFKTGFSAIbIXSNfQWtLol1rhGpKDR7AtQQqDFPVRLR2igjp28YUG/yR/N8p4fepb+SJG03iSnBdybushOa4mY1s46uBRMAOjW45zz6uT1JjN+u07D3UMzPC8GaLwUI7He7UCL2pBJk/jOeXx7B6kL1cu6MFwSVCrwcoVRuR33iQTn9+ygfz6gOpinEofecYkXg+oweNubKAmZTJ245bI9tcx8M82xD+YYMu/D/sqzb+gzusQoO2RXG0gji+4IvDjo335+w8URr0ZEIjTfdRdWxUN74vgVz2uJOEGxSOa708y/fwQ6M+ieS1DawKI/CPBjgvisaGcrXIW+XLpi/v7SxgIW9X4bN61AQbRw43eh1Z/EPLmhU4dCoVAoFAqFQqHbyG0RUAiib42dGSrSXjAbLYUWgO7cfLFILWpT3gZWbQg3LogUBPaawmgpZASsooZVhtKdHtnjBkYLGnmBb0Jph6DjZUXiL54BQB8bRYzY5F4SZM41KG2P0uyH6feBpkCZku2frl9xR/q1xD17uPBJk3dte557ExepBja1wObTpQP8n51nAPuqxy34NWICGkrwteqdRNb3gng1i8IenejyTsThE+2F8HrXhex3Jm94Zz84dQ79FHR/R2f6V+7HaIC4t8zCrghUTEh5HBieRhOKI6e3kN86T9Zs8OTcVpIddUofg56v3MQHczWnLqB1ddL17Tp+bxZjahm2jKA0AYUSdGQ23cVAOQ7xJ86Arl+1dWXrni3orQD/qRfo/t35a7a3vJrShw9Q3KPIvnz150Ukgj81c+UTG2gVCbRbjlZBbxlEvnr4huPticKm5h8KhUKhUCgUCoVuD7dFQCG6+MZb8G3Un1Q7rvlccG4cgPhsg9JYkma3RfxmLyQ0rJJgZT+kJsBLKvw4+FENlCSyBuWDLWjpKE1QHVX4nS7GsonuCIxmgJ5KEVQqiFqDZreGNKFZjuBkBPFJHd2FyjZB53ENdWz9FvFrWile0nz/fez7leMMS4NHMy+y1SzwLy9+gF8a+lu+ULyHsmyS1qI0pIspdP63xXv5f3rbnSEOt/rYG1kAoBrYNAKLnNVAswNavQHGSpVAqfY++vUF6/WCCZpt03rbXqYf1VFZl9H+VfZHT9MKDB7tOsmql+Sz4/dQW0pwZrWbtw1cIJ5rUnFsDq9uQdMkSI1dnUuU+/vw5+YRhoG2ZRjRaKEq1evWjnh1Iq/Ot/xj+6kOa7S6JEFcgjWAGXMZy69y5sUdKEuC6CJ7QkdI6PwvGyukeL15mN84hjE6DOuvYTNSE01WDkTRr1F6RDnOK9+dzdKzWUq7JXpDw95YQkPYMjIUCoVCoVAoFPp76oa3/oUQthDiWSHECSHESSHEr68/PiqEOCKEuCCE+HMhhLX+eGT93xfWnx95o5MUzq27v3mu1XPDMfpyCWmCeAPdLFWziZdSKF0hgvXWj3WBVVZIExq9CqomIhLgx9ev5QnMmmgHG0YN5Pb1gn1CEF2WtHIKpbULRrY6FbElSXRep+OlGsbwIEZ/31VbKc69TUMTCl/qLPoZVoI4u1KLnHF6qfo2aa3dyyKmWZhC51PdTwLQkC57Iwu0lM7/Pv1+pBL82/yLTNZymBM2sRkd5PqbtMG735UfuYvFgyYP3n8KzVDMraXJWE12Jpc41ehDInBdAxH1aTQiaEIxlC2yLbPC1HIOU5eUZ9OcXs3jbG9/ltr2LUz8dDcX/8kwIh7b2Ae0Pl916C7WdgusQwW0viZ60mPX6Dxew6LTrkHGJdrVoH9kleK9HsVDDurBfRgjQ2h37tzYta7Bvzi16WACgDlfxCxrRCrXfs9vJpgAILf2o2yJ3+lhNG/cllPfROeI0PW9FX6LQ6FQ6B+y8Hc4FAqFrrSRvQQO8A6l1F3APuBRIcRB4P8GflspNQYUgY+uj/8oUFx//LfXx70xxq3b8vCFP3nkhmP8qRlQ4CZufl6y1ULbWkP4gmhBYjQhNSVxOgT2iobRFESWdSIxDxR46QCUwDxQpLotQPjQyrcX+iqXxslqjP1pET+qoQwY+jsXaUD/dxqo517Cn5q5YnEqTIulXzjEjn3TlL0oP5f/Fh9NzXLG6eVQ8gI/m5njV3u/xpfqCS56r3aH6NbjeCrgF2bfwWIQ5/1f+585/bXtHF4e5cfH38nFJ0awSoLM+YDGts4rXrvekUM+sh99bBTNttE7cgjTYvmfH6L6UxXYU8WVBlbEwynZJI0Wfz15B8+tDPHlmb3cPzRJX76EZfnsi0/zoz0vUPUiSCVouiZ2vs6uziWMmoc+NsrSwx1YpXaa/uuzI4Rx7SSd+gfuZ+Ffeuz6gQlc3yAec1ASpotZCAQvLfehWu0OBvPLGUYGV9BMyfgHbE5/spfp9+Yo/I8P3FSnhjfCn5ym70mHwHpz/270TJpWV5TYhIm1YNLz9dkbH2SZb+oc/oH7/v8Wh0Kh0D9s4e9wKBQKvc4NVxyq7dJq0lz/TwHvAP5y/fE/BH50/f/fv/5v1p//QSHEG2rToE0vvZHDN6XncAN99/YbjnPTimbXG+s+oc4lkJai2aFRH5A4aY3sOZ/cmYDYoqL7eR/zmSQo0JIeWlOjWo6iTEmjT+GkdLx3HQCg9+uLiMl54oserQ7F0r0RVu4B8d0XrnptYVpw5zbqA4qx5ArbYst8o7aHF1yfj6an2RtZ4NPlHv7r2iGSWpNRM8G0/2pQ4Yv1HD+X/xb/af4HGf2iZPCxCvOzOcbXOul9ykFvQWKiRvz49BXXDgpraE8cJ7hwEdlqIaJRFn7uAKU7fDLRFqbps9RI8s6Rszyw5wKPTe/EMny6onUqdZuIFnB/1ySmEfC744/wdHkrMcMFJVBAs2Lz/NwAWqmOqDdJj7ukpgKSs1fesb9Wy8fqhw5S69f5H7Y9w1IjQSziomkSM+Lz7pFTRBYMqnUbPeETjbgYZsDMco6gYTB21yzKkjSGfQr7A1Ye6b+Jb8cbE72wwf0Im2Fa1HsNNB/sFXH1Ogyvs+GMkNANvRV+i0OhUOgfsvB3OBQKha60oRoKQggdOAaMAf8ZGAdKSqlLq7FZ4NKqqR+YAVBK+UKIMtABrL7unB8HPg5gc4NFxzUWfd8L2lMvMPXLhxg4de6643SnXURRz2YJisWbulYQVcioRGk6ylI4WQADs67w4oLZvRpmBRAK2dIxXIHfMLBWdZQOlRGNxXcpEic7SE1LWtk8awddItOCwa+sIVwPaRhXXTQrz2X8Exb5jiU+knuGPqNJoGDUTFCTLXIafDS9COlFPl3u4XdaER6OnQNq/Nr8u3n85R1E5iy2/t4E5sJRxJ072f0rsygp8bbZdJxuIS5M41er134Pd22jsSXDar9B8EiZkWSNQGqM5VY5s5zn7lzAUjNJLOKSj1XZm57n3EoXM/UMc+VhaitxHt57licntjLYVWTf0Aw1L8K0r/Pu0VOc8QfxFxaJ6jrm7Fz7okJw7vcO0P2UQeaPr6x1IB/ax9zbYvh76kRsj8/P7uPtPec5U83jK51ZleZrk7sZfHCWpWoCQ5NETZ9UZ4kOu07Dtzh9sY+9O2c4Od1LOt2gVMlR/HcPMPK11lU7OnwvqHiU6oBG6ZcPMfAbNy6cuBFTH9uGm1UIX5GcvPF4LZmkemc30YkNDA5tyPf9tzgUCoX+gQt/h0OhUOhyG8qJVkoFSql9wABwH/DGNoe3z/n7SqkDSqkDJpHrjhWJmy59eFO+8rO/ecMxTt6nOixuOpgAEJ/VEFLg5ATxi+0iis28QurgJUFmPNzRFoEF9oxFagLsBQOlAxq08hICgZtW1Hs0smcdBv9KJz6n8DM2Xj51zTvwRk+ef3vvF/jAwAuUZIxOzcJej5knNJtOPY6jPE66Td6fGMcUPh859lHe/vn/ldlPjbH9Y0cZ/rXD+AuLaLaN/P/Zu/MoOa77sPffW3vv2/T07Csw2AGC4ApukihTq7U4tmVZdqJIcaQ8+73n7SQvJ3ZenJOT+L3EPu88PW9ybMeKZdFyJEuUKEo2LVJcBIIACRDEPsBgMGv3zPTeXV1d6/ujSYgQQWJIytHC+pyDcwZd99atRtXUQf3q/n73xFnc1SJeaY3ibVGsrIrUl33F7y7FYphTaco7VZoT8Os7/453D54ka5hsT5ToWiqXzSxVM0LH7k2bX+hk0VWXy+Us+weWuGvPOZbbaQzDIaI4HF8cYb0d447RS3x7bZKg1qsT4Iy9JO0iCJj5xJFrBhPkdIq5f2Rg7+hwz9QFPE/iAyPPoUsulqeS1kz29y9j2zIJ1UKVPfrjLWpmhI1WjPPlPJ4vEc+YnC/lEXJAEAi8EQsxblK8NfI/Jf2h/Y9uZfFdOZwkeAZc/q2DeG+5cVN95ZnpV9zmJALwe4GwSPn6BUSCbeNEvvS9Wboz1PP9vheHQqHQm114Hw6FQqGrvaYk6yAIasAjwO1AWgjx4gyHEeCFV8AsA6MAL2xPAeU3dJS284a6v1aTavy6bbQNGb3aezB+vRQzQHQF3WyAYgEvPKOJoFdYUbQUFLU3TV+vgmsIECA5An1DgA+S7hEoYCfAHNAwczKqGWDldYT/CkXzJJn2/jGGlSofTJzgRr2Cj8+gcvX31oVK09e44Bg8Wt1G9KEEWk1Cm78qsE7g9Q5cKApSIoF7a5Pld3m0dhde8buLaJRuUsZOBvTfWOJQY5o/OnEXbVfjdGOQXKbFnuQKA4kmuuqiSD4JxeJtI+exKr0lLBdbGfZkVpCl3via5hJRXZbNFC1Lv5K/r17e3PT/tZ/aiShYyIpH3THoNAweL28FIKlZmK7GsplitK9G3Y5c6Xfz0ALthkHS6OIjMFs6rtP71Wg0IqRTbeIxi/awjz2ag1v2bOp4Xg/n7QewshKxok9sMSBaDFBbULrJQNx8/XFfXMnkuynDQ7ixgEAN0Dckks9vXLPdVceSCv9T9A/l+3YvDoVCoRAQ3odDoVDoRddNeRBC5AEnCIKaECIC/Bi9ojKPAD8J3A/8E+DLL3R54IW/H3ph+zeD4BpLC7wGfrX2Rrr/g/A1aI8EvdkT7fbr2kdiyaU9pKJ0esGF1ggoHYFZ6L0NlmyBuBhl4IiHa0hs3CBwMh7GqoKvglaTcAoQXwCj6tNNCZyYwEYw+oXFV8xxX/2VW2nttThsbuHj6VNUPI8VXyAJi72awSm7w8/kAAcAACAASURBVC4twh/WhjnbGeQbD9zC5F8Wyc0eIgesfex29PoQqad6hR79m3cw/+NRPCNAG2nz0e1PccHs5+yDu68aV+g6QbcLQHfvGOU9gtz+NWxPxvYVkvEOHUel46g4rsyja1sZjVd5e/4sG06ceTNHpRslWeilL76lf5a/Xd1Osx5hdGieufUca7U4dd3AvJzEK50GuFKMUhkcuOaSlf49+1m816Db7zGQaVJczDK+tcKzxiiDkTp/efYmpvs3WK6naKwmGJ1ap2pGUCSfIBA8tzbE9rEihuxQMhNohstYtspqM0EQCLLRDpeKfYhcl4s/rYGkEbvnINmzLvFjy7gvpmN8D3TyKoWvzBEEAV5p7crnwcF9dHMG2i178GIq8iPPvqb9Ln5oAsUM0OqCocfNVww8vJRa7/KGfvFDV/lBuBeHQqHQm1l4Hw6FQqGX20wNhUHgz1/IGZOAzwdB8FUhxGngfiHEfwCOAX/yQvs/Af67EOICUAF+5o0e5CtN2/9+UhsCO+NDKgEveXB7Lcw+he6gg9uQ0eqCaEnQyQdoDYGdAi/mI1wZvWwjx1U8XUY4AsmGxKKPWZAIVBdfFbSGe58LD+wMVwUTpEQC/yW1DJrTLh/ee5QhtUo38HnOHuDtkQ3ikkHLt660W3XSLJlppj49d+VBXM7nsfoE9S0S8ctZWF5h/r1RjB01WotJIrrNscYoZ9YLjDy9yEvP3IvBBIBAABIU53PouQ6dtEo+1qbtaHQchbePnuPBuV3kIy0+e+kmstEOquQRV7t0Oho1O8opb5DxRBXLUdAll75km/V6HLOlY5QkkOSrlqt0i9cu7rl6e4RgWwthKTQ7BlLU5dDaJAPZBodWJpjMl6l3DXbkSzznKIzEa7S6Gvv7l9Fll4jisN6JseHF0GQPx5GJqV0ShoahuFwq5ejL9P795b7ebIpiMkVri0J09zh9zw8TP72G+wZrDchbp5Dt4JrfU3z7ObQXfnbeewvya9y3kwDJFgQClLMLbGYR0ODoydc4Sug6vu/34lAoFHqTC+/DoVAo9F2uG1AIguAEsP8an8/Ryx377s8t4Ke+J0f34j6/DwEFZWIMd/7lKxS8+JZbbYI1GOD0J5BevX7jK0rNWbSHIygdiK551KdkhA+uAQJIzCpINjgJFbOg4Cdc5LqMOeqCUPCMgBdrBXsaRNYDrIzA+K74hnhx6b5b9iACQA74xtJ29s4s4gUBH4i12PA8TK9Nvxxj1wtPnl/87D0MP9IkWH3+yr7O/N9jjH7Zpd0vX3lg3PI757jw69uQxiwU2WepmaZRTOAun8H68VuQuz76SgvRtfFm5wDoZlWcnEssZ7Kzv4gmuYzFqzz87C7klM2ldo4t+Q3WO3FuyK/w6OxWkskOk5ky24dK7E6u8FffOsjYrlVGk3WOl0dwfYl9w8tc/MwMA1+Zw/U95EI/5NJ4cR15dumqmhfOfTex+HYVqRvgz8eIb6vRLCZIDDTxAoEsAgRw/swIhakNFptpHFvhcjPDRLrCN09u5x/ffIgztQKrF/KoNYm+m0oEvmCllcIPBE1LZ7qwgY9gtZ7EbOkoqsfNU5c5tjSCf6PF6j6BU8+TOjVE/zMm6lL5SkBIzufx1q+fsqEMD7Fy3wBa4/ovPoyvPs3Svz5I30mX+MkS7qXLr77vqQmE1wtWpS/6eOXK9Y9noPCKAZzQ6/ODcC8OhUKhN7PwPhwKhUIvt6lVHt6MTv/rAjOfuMaSh5XeA+nApw4jfvFWGpMR0k+8vjHU05cxP7IFAhj69CmMm7exertB+mLvLbZRtunkVVbv6AUP9BWVQA6ILyh4Gmh1geUlaA8FKB1B7ttFygcHSH/m6oKDVx4An34eKZ8nkisQUV3eFysRleI80I5iBSn65Sb9kd7YH1+4kw//3N/z13fsR/7K7eSP1HCTBomTOpXt0H+0i1AUuGE7sz+VwIv54ApkyedfTH6L/6YeJPVEjpyY5d7sGf7j0XdhnM4y8p/mkPbtoD4lsWdmnqats9DIcEdhjh9LnaK5V8dyVU6uDjKY6RVVPFfrJ5nssKd/hVUzRcdRecYd4y23neRbc1soaS6d9Sj7d13iyMlpdj4wh7vWy/H3SmvIrktnZgvu5DYSf/UU8o6t+HGDSz8p2LZlgfV2DNeTsboqIuoykqqz2kgiST79iRYNPUZxPsfU1iLZdIuUbnFsdpybt1/isbUt2J5MeqzG9v1rlK0Yu0ZXSWsma50E51YHkUXAJ7Y+zunUEI8ubQHg8JkpCAQ375ij2E7SjlkMb2+w9s446+U+fLuAaClknxcUvhm77uyF5k0jdAoB/b/38mKT1yICCGSuG0yQd84w/4E+nGRA/pmAxGxzU2kMQS4NYUAhFAqFQqFQKBT6kRYGFF7Brbsvcq31G65M2/c9Ov0BcvcajTbJK1dQU10cSwFJAtGroO9EBUonoD2oYhYktAaoy2AOgJ31UEyFzqBH7LKMZIM77KIsqdijGeLL9iuOpwwU6Owe4Y7Rs5SsBI9ZCcpeHCfoTYC/6EVQxQp3GBJ9eouz7QJ3DV/kkfTNEARIXZdAhm46IHJyCT8SofRvHd43fPjKGE+tT/CRRJldU/+Dz9du5rb4Bd4XM/lPEgQHGiiDA7SH4ggfOq6KJnk0PJ2LrTy6tA3Xl5iIl3nu3Bh7ps5yrl5gKlFmtp7neGmYXMxElT3WWnE6rko02iVhdDE0h/laluRAkwu/OEXu1CSZp4u4c/N45QqRLz2NnE7hAa2ZDNWtCkamyb39Z/mDZ+9BkgMk2UMzHAzZoesodKoR1GGfTH8T09KwXIWOrbJjoMgZMUTZipHQunRdhUozxtOXx0nGO9SqMQr9ddpdjXjOxLQ0/nrlADmjTasRIfAEku4RT1g0bIN6x6Av3sb1Je4ZvMDnl25mdHyDuNblTHIQq2+I9GyB1DOr15w1A2AnJMa/2rrmtmvRqwHRJfO6wYHmtgxuJMBNu4CMtLC6qXSH9mQK49SmDycUCoVCoVAoFAr9EHpNqzy8WfzMpbdx/+Q3r9suuirwFfGGxvKXI8RP61Q+sBttrU1iHnwV1m8UWFmJQIZOwaebFtg5D7Uu48QDhCNobbfJn3AgALUN6rqJ8sjxVxzLG+1nY59O29XI6y3+pnIju7QVprQ11t0EE9o6p7ojLLgtXF/i9J/u4sRv3oAIQKyWqeyOYycCJr/S4cxvTjD7R9P8ux1f4WO5J/lY7kk8JH5+7DAPtKN8ubGf38gfpeimOWFb3DdzhtFMjblfmMJJyFjZgJzRJme0kaWA5xeH+MbSdqbjGxxMzIIUcK5ewPZl1rtxmpbOVKaCJnlMJcp0HQVV9vB9iZXlLFOZMkEgcFyZzIF1Yr+wTO33JTofuAW50I8yOIBXq1P8lYPc+G+f5b4PP4W9HOO/X7gFSfHRdIeI4dCfanGmNIBlaiAH1NsRHE/GsRXykTZvHZ3lS2f2kS/UqbSjRBWbjGHiuRJDuTr1RpR7ts1SXMqSj7eRRIBdMVhrxjmzXkBRPSTVJ2hoNBeTzK3luGlgEV12WW0kOVoe48M3H2YkXqNqRdgytoZ+xwbux8vMf3gEZWrimuc2e6wKTz+PMjm+qetOeNCYvv5qJkvvc7GHHJSKggjArzeu28e/az+yff1lJUOhUCgUCoVCodAPtzCgcA3P/d3mlhSOlTzc6z+TvSqlLdHNBlR3AL6P0gno9AnchIengZ0MkByB5EAgBwQChE/vzMkBnayCZMrIHRCra1cVIfxubkzFygdsi5d6D+mOQZ/sMGf3c2v0Ik0/ghPIHO/20/E1+o41iV6sMPxwFSFLmAOCxGXoZjR++8fu576tZ9mu9fL7JxWZpmPgBArbtXWqbpS6bzOtlaj5Bntji1xay2ENujRHJNyUx6m1AU6Uhmi0DSJRG0N1mW3mub94C5n+Jm1HIx/pvXUfTdc4vTJA29GIKV06DQPTUWlXIgjVZ7acJx3tMJqpUanHaNkaOzMllu+RaB6c5PI/mcL84K2k3rnK89UhKk6MQEC3q2IYDrrq0rUV1upxZNlnsL9GOtfCamnEje6VWhVFK4nflckYHQaTDapWFNPVGMg20BWXwb46R1bGkAwX01GRREBysEm7YWDOJ0nEO7xlyyyB5qP2d5jMV5it56l0ovTHWzS7OqrkocsuQSBYa8YpxFvsya3i7W/SHcte89zWd2VY+6WDBIZ2ze3frZMXyPb1kxfksoqwZAI1gCDYVD2TTr+G+rdHN3UcoVAoFAqFQqFQ6IdXGFC4hrF/9+1NtUs+cYluNkBOp173WJmzPgQgOQJh2bgRQWfEAznAV8FJ+yRn6Z2pAFKzvdoJvuajrGu0RgRqQ8Ko+lfqOygTY9ccy43IjN60zMczTzMYqfO+vuN8rT3DR5NrxITNTq1ETm4Rk7oc/uP9BEdP4p2/iJvQmfvENHIXcv/1EJGvP8tvfOFnicldZtQYk4pMVNJIqyZ5pUFWgp/IHGXWjTOqNJhSWjxQ2se7Z04RK7RJv2MVPdchE+3wnslTvGf6FK4rUdxIMRkrkzda/G8zj3BTvje9//TyAM/PjrBlYJ2fGzuMG8hImke5FkeNOfybm7+GLPnML+a5WMyzfahEcSXDYjvNe+56hvUPmwz+2CLmx6qslFNYrkLXU+jfusGeoRV29hdxPBlJCnC6Cu1qhNVSmtp6HHyB50soqosmu9S7Ed6y6xwXi3n8QLDSSHJ6eYCmpTN3Ypjl+T5m+tb4yd3HyEfbtE2drq2gRRz8pEu9GWW9G+fte08T+IJ618ByFdJGhw8OHmN9NcU3lnfQp7V4x/AZ2qbORLxC3THwPYnL79aveW7jn38KT4PKgdymrjujEhD9m8Ov2qbz/luQR01IOHhZh9RzG5vat2KGsxNCoVAoFAqFQqE3gzCg8AZ4pTWEC/7kyOveR+Z4mfgi2H0ea28dIvunh4gsyagVBXPURbgCXwMCkDsSbkxgJ4NevYWBLv3HHPQaJC+24YWlja/Ks5e+s0Dg+g0q//v4wyy5EZqOwZ2RRT6eXGLVbdH0Df64fCdbtRJfq+0D4MLv3sal+/dy4WMK1phN7qSN2L8Lads0Tp/L/5J7HIBW4HDK7vDz2UOcswb5TH0Plq8SEzZREfCwOcVgpMFPZZ7GbOk4voTryrxz8DRN1+DJ0hTdqkF/rsGZxgCPL0zz2eVbmWv1sdhMk4hZ7Nu6yEI1wx+cv5uHL2wjGusiyT6eJ3G2M8hUpsy+6UUGcnWW6ikI4PzpEbq+wn1TZ8noJjf2LyOJAFkEJFSLlqWz3Epxem2AkXSNTiXCYL7OXTvPE3gScl1hYmydgXiT0VyN02sDyJJP3TbYObLKcLTOTYOL7B5epesoJKdq/PhNxzBdjaZr8PzcMNlUm+5GBHs9Cl0JaSHCuWI/kvDxfYmGaVCuxnF8mT84fzdv2X2OUinFs5VRnlif5q6pizy2OE1C6eIHAgK4Ml2C3ioQL8qec1g/AP6dNyD0awceAOofuY2+P3r14o2ND9/G4jvAW4yirOgkTup45y9u6prWHzqyqXahUCgUCoVCoVDoh1sYUHiDvEiA1DRff/8zsxiVgEAKUNvBlX0CyKaEbArciEBywUt6CA/0miA+pxA5YwBgJ6C27SW5Fy954LwqBULAZTuPjcyvDX2DP67eyqOWiiwEBblFXO6yTfU50xhAawYEeoCi+KRzLSIpi/kPQW1ngvVbs1x6zx8zqfbGfLQzxKPmDDfoOlmlTVy22KlVOW6N8Wy3nxuNBTqeyk26hyhrvGf4FJOFMgB3JGfZqCYYHi9zZ2GOmeQav7brYVTJ4/39xxmMNfACwcnlQeJGF111Gc1XaVWjOF0FWfZ5eHEbiuT3UiK6GvV6FKH6xEcaHFqZYFu0yIVKH/OtbK/mQjnFo5e24vuCOwtzCBFQaiYYHd9gJFFDFgFDQxWi03UcT6ZmRYgpNu1ahAulPpqOwXIjycf6H+Pw0jh126CzHkVVPE7VBllvx1gy08hllXo7AnIAPigNGX1bvXfcIiCfbRDRbTS9lx5hdVUU4TM8WGVLch1ddul6vVoRZ2v99KVbSDZXAkcA/njhStAo8thZlLbg8nsjlD5+4JrXW/FXDvbWJL0OOymQbAlfBV8NcI3r9wGuCmCFQqFQKBQKhUKhH23hKg+vYOqLn2Arrz4lHEA4gtK9AxSEwJude11jxYpdErORXr0EwyB3MsDVBeu3BQhP4OlgpwJSJ1QaW3z8iI/wBHJTQm3YIKl4L33gC66dG28nA5xA5g9X38o/H/gWT6xPk5I7lL0yA0qdbcYqf1DbxZ70Cl/cP87g9Bob9Tg3jiyxPb7Kk+Ut3H7zHKt2L8XjjG2yQ4uyZOfYcOLAIn+3sYMHtn6dlq+QVxr8TfkA09Hew/F7z/4Eu26c5wuXbuC+sbMcrY3RTSmMF8pUzQj/ofA0e5/4OI/oW3FcmYeU3exLLXOpmiObaiNLPqX5Pqb2XGAxmsZbjcJAh5+dPsIfHr+bXaOrTMU3qDsRzlQKJPQuxUaC3zt9D32JNhHF4a7pCzx6YjuJQpNGI8KDc7vYWSgyV81xZ/9F7n/idmIjTVrVKGrUpt000CMOW1PrZPqatNoGFTPC5/b+Gc9aI2zJbzBfzVAYr1AqpYiNbFApJXE8GS/r4peiCAH6uowbDWitx5BjLg+d3MWv3vIwh2pTPHV0GyU5yl+++/f57cV386HRZ/j7je2ktQ6K5DGerlKINHB9mUfSOZBklP4+lj40jTkckNt+M8lLFo0hneFHbaozOq3xAPfXe8EDJw5KB9KzHoO//8x3Vip5Fa0RkGxBIAVEphtkH9lcsRCxbzvBsXB5h1AoFAqFQqFQ6M0gnKHwClJnN/emVXLBlwUor//NrHryMsLrFcoTsSjJc3UQIGyBYgrMmS5uxsWNgWwJJEtCdHvbGlMR8revUtkdXDX9/bsJRcEfsbB8lff1HUcVLlOJ3iyBI60pAKxAZUit8cnsE7gpl9/c8iC7BldJqyZb9RLvzJ/kk5kT/D+DvYJ7O7Qoj3YknEBGl3rF+n5p+O8BiEsGhnC4J32OdyVOUOokWCyn2ZYokY52qDkRLlVzPLy6jbVmnLFUjW+YKeyaTgD0JdoMRer0qU101cX1JFbX0ih1mVo3wlunZklO1XBaGsVuisATLDeSfP3iDp6cn6TrKLRsjZ/bcoSIbrN0tsClSpYjK2MI3eOGwjKy4jOWrfL23BkcT+aJtWm27lzm4PAlhocqZJMmQdB7nT/fylKrxnCavaKHM2qMD8bXeP78KM1KjEojSjrbpvjkMNgSVkdDLaoEUQ9cQbC7iQhAirhIsodUU3myNs2xlRG0wTYkHY50pqjbBvNWjqhic66S59TGAGOxCqudFE8uTBJZkZHjMextQ8RXPBRTUNsqYQ7odFMSkhtQeLrB8GMuajtAtnpFPEe+USf2hcObCibIWybxogFerBe4ai8liJ1d39S1bA1EN9UuFAqFQqFQKBQK/fALZyi8gv7/79soI8O4S8uv2i5/zKfTJ+FmopuZSX5NXrlCrOiz+k6H2o/NkLj/KVLxGyjvNXCSPrFzOuZIb9UHTw/QR1qoTyZpj/i4uuC3t3yZz6ZvZ0V++VtkOZPBq1YJXBe/rXC2XeCfZZ7la+1JxiNlTF/jPw8cA+BGbYG4ZABxvvyOT/HPTv082YjJx3JPsuwmeW/8HDIK5502M2qMZ7o2z1tb2RdZoOb1HiTvizoAfLo+xLtj5/lafR9/W97FaKxKvWtwIDbPobVJXF9mT/8Ky+00iVSdM8UCv7r807xl31m8QDDfyLHQzrJkpqm1IozmauRjbdr9GpII+PbyJDvyJRqJJl86s4+9E8ss1NOk4x1GEjVajs75pQKn0oO0TAN9qE3H1BnqqzGVK7PUTuM6MsPROn+5eAt7+ld56uIkkhzQcVWK5RSS7BFPdpBFgCJ87tk2y6Be52yzwH+uTHMwOsuP7z9OzYlwtlKgaRrY0x00zcOu61BwiJ/RaM04iJMJiAQICbyVKHcfPMW3L08SBOAvR5EGLeatHPcWznHRzPPs8gi7B1dp2r2pJ1PxDU4en2D7F0rYB7agHp0l3myiVw9gFtTeNfutEvZIGmG56F87Sv4lM1Wuv57DC9dLoZ/FDwwSCB/JEgRKgF6W8S5c2lT/sH5CKBQKhUKhUCj05hHOUHgVa/dde7WEl0o+OouVFXSzr1wEbzOEH6CUNOxELyyhnF8mkEG4Ak8Dkg52xsdPu8iyTyCB3BHUdga8JeLzx6NP4kwPvmy/XrV65ef4RRVZBDxl5am4cTJKm/PtfrpBLwgQEd9ZcnDO6eOm/kVuz11iRIE9WpURJU7Nd/ECwYLb4vnuCO9PnGKfVuZGY6k3XtCr8B+TuvjAz2QOc2/2DI9e2kqjY3B/8RZ0xWWhnWGu3ocqeexKrRL4Aln2SSgWlqfStHRyepuNTpxkzOJ9AyfwEezIlGh0DYaSDZZbKe7uu8BYoQKAZauMJGqsmQkcX6a/r8FsLY9TjJJPtrhlYh4/EDRtnanEBoEnOFfrZ3Epx6ETWynk63imwuJSDq8r49R1dueLCBGgKy6ztTxfvbyLuh1h0cpy2NzCkplGlzxqjSieJ/CbKq4jg9IrnOlGQdjf+TVTLkRQTEFWbWM3dBxTw0t67BwucrGV53y7n4lImWTM4nw5jxtIbHTj9KtNIkWJwFDRzxfxm00AtLpN+myT2BcO483OoR6dRVjdV0x7uR5rzyhuDEQA+oaEF/c2HY2Qt0y+rjFDoVAoFAqFQqHQD6cwoPAq7v7F69dQ8MoVFAuE9/oe4F4kAlBbAk8XKCPDeOvrpM730hq8aIA2r6O0BOqainMiTfKyR+ZMgJf0OP7CNPZ3/tFjrzJALzDR8VQm1ArHmyP8YnqRPxl7hHOOxwPtKAtur7jkYxY8Z47x+8NP8X/0PUfT93Be2M2IEmeHFmXejaMKj8c743ylPcPj5jQAspBYcFts11aZc5IsuFkApvs3GE7VuSUzT0LtHe9ArMFUosyXz+1ly8A6qurx6NIWzqwX2Na3xmonSVS1UWWPL67cQEyxOVMtsLyUZTq5QVR1+NzFA7RtDUn43DS8wGw5T6urMX9yiI9OHGKtnCQ62nv4Prk+QABstGIcLY3y0RsPcXfhAvGsiZqxsGyVWM5kZqJIpq9JLG+y1EqTiXaYr2bYaMRwXZlSI0HRSvJQaRfztSxNV2fnUBFdd4kPtpCXDGTdQ3R6dRNi8710mECAnfXwjIDZVj8EIJq9SUIdV8XyFEpmkmdqY5hdDQHkIy0u1Pp4ujrB0BMdRMcmaLa+c16PnyN45js1C/xmkyDWm9Ug79j6mq/D0s06dtpH7gjMSQepIzPwlH3dfpJh0N7+yik3oVAoFAqFQqFQ6EdPmPLwKn5n8FnewQ3Xbdec8miPyEw/9PrHSsw26OTSyF2o3T5C6lGbwmdOIMaGmP2nfTjJgEhRInfKJXq5gbAcCAL0eh83vKc3O+JXs3P82d/8LEMfPI0yUMAtlr4zwEveWD/fHWbEqLHhtemTY3yrPcXbY2c4bI2SkJa4XTe42zgNgC5UslJAVNL4bpavcoOxwAFdo+53uL85hIfERxIALb7Z3sGhyhSa7LFQzeB5EsVkkl8e+TsA/nz9DiKSzUC2wYVSH05TZ8/MIorwKJkJNMnjvQPP86kTb8HzJAhAM1zyg3WeXJ6ksR5HT1mossfFSh+piEVjNcGB3XPMTcGnL9xJ4AssS2WxkkON2xQSLX5h4gmebk6xxSjx/tgy95+4CSNmoyoepqVxYTVPJGrTmU/g7LDYaMZQFA8hwGprbB8rcuTkNJmhOkEgOL48TF+yl1KBCEgsChqGjl6VUJvgJMHe0kGdM0CSCKSAUwuDbJku0nFUamaEihlhMt2baSGJgHdNnCYud/nMiVt5z46TLLSzBEqvVkcwOYxSTuAuLRM4Vz/sS/t24D9/HuitIPJayFsmMcddhCOw+z2ELTH4eID2jaPX7evcvhPjq0+/pvFCoVAoFAqFQqHQD7dwhsL3QKAEeFEfZXL8de/Df+4MiQUXve6DEIhYBL/dhuUSgdQLBrS32DTGFQJZJlhdw5udI/rI1RX1/3DfXwDgbZRfPoYWYLkqhnAY08uUvN7p3xe5TF4O2Kmv8qnKLSy5nav6vRhM+Lqp4wU+5502lq+y4Sao+REALjkSO/VVHtzYC8Csk+Jsa5BL1RxVK8pwqo7vC55YmeLP1+/gd5fuY6md5nh1hOXFHLeNz6MmuqybMU4sDaPLLuvtGHdFz+OuRejPNVDmDbprUdZXU6QiFhMTa9iWiq66pCIWKxtpMDxyepubBxZotCLoEYfRfJV4ziQRs7i8nuHDiWV+uf/vGVaqHO7GCNoKuupSrsQZytYRUkAq2iE2Vac4nwMgptuk4ybpTJuziwMgAkxLIxsz6Uu2sT0Zz5LxXYnaPgdf9/H0gE4hQKtCOtXGzvmoU00Ku9eQFZ+MbtKwdNqVCIbqokg+ZSvGDcklDq1Ncrgygax6HFkfY6WVRJ8tIZom4tIyQffaswbKN6SRM6nXdQ2iqaD6KG0JyZQJdJ/U4aVNdfXV8FYSCoVCoVAoFAq92YRPAa/i/ypv5fJvHbxuu4kv+UiWRKC+sQkf+kNHaA3JSE4AUu/UeI0Gfcdg8IkArajSHgqY/2AS+5YZgF7Q4SXuMHr9Ate9eudCUDji8dz5MX7jufdT96I83x3ivzX6OWpOERUqZ+0CcdnispsEYMFtXbWLO406q57JjBrjrRGLcW2DtNTh0Y7E8e4ofiD4y8lH+JP6AGe7Q8y3eukObiBxYSXPULZOJtphOrrOYi1NSutwZ/4i/+Wev+LJi9PEIjZdR+GW8ctMJcq02gafPPMRfubub/OzY0fJHFgnrGve+QAAIABJREFUPVoj0dem3jFoWDp9uSZvHZhl7fAAkuyRzra50MhzpDjG3pFlDK2XrDGUbJCNdvj3+7/C75R3M6PGyMttbtXb3LX/LM0Xgg+r1SReV6bRMTgwsIQUdxjO1DEUlyAQpCIWMyMlpqZL7BwoYrkKSd3CchRGR8rEkx3isyrIAbFlQfIiNHY7NE7lCAwPazVGy9Jx2ipLn9qK93QGPdmlVElyam2AQrSJj2AkUWOxliYe7VJczaD9aRa/WiNIRPFNE2/96lUX5J0zyLu2YfUJAuv6Kzlcy9odOaIXNCQbtKpE7KJ63aKkL1L/9vqzGEKhUCgUCoVCodCPljCg8CoeXNnN7redv2477RtHkRxBoKkI9eWpAa9F/zMtoqsd/ETkymeJyxaeKkjMA1Nt7IzP2gEdae/2a+7jmstHBgGRv32O2EWVD255jstW78275auMaGX+pj3Imc4w/zx9kobfy8EfU65eNaLmuyQkmbrf4bPNQRadLLN2gYLc4vn2CE9bkzxmgYdE11cpNRKkox0U4RP4gpzRZrmSIirZbM2tc6o4yLlWgbIXR1Y8DM1hJFVHl10ikk021aY/1uJ/nLuBQ7Up/EBQr0exbYXGSoLKcpqUYfHwyja23zOHY2qoikfb1lBkn1Org/TF27RtjUtrORbLacpenKzS4uumzi4tghl4mK6GZyokoxbD2To4Eq1SnKajMzFY5sKlApar4HgSM6k1Lh4dY60ZZ76WxQ8Eaa1DIdGiWE3QrMSw+gKQA9woyF2IXFZxUx5IASQdHFcGOWBjv8CcspnoqzDeX6Ev3sb1JUa0CmUrRl+8TT7WRmooJJ/fwO9Y2IUEYtdWxP5dV50b7/R5RLuD5Lw8yLQpkoynC5xkgNwRSDYk5/3Xvp9QKBQKhUKhUCj0phEGFF5F7F8a/O74lzbVduSbLvM/kaXykQNvbNCnTuDGVHxdYfVXDyLvnEF64jjJzz1F7mQbbyWKUZLpZgIu/GwGyTD4VPXqVIuVD1+7GF/Q7TL+uUVuic/xi/lHMCSHvNJkWl3nBn2Jf5E9wl80ZnhfzLxS6BF6KzdseG2W3AgpKcKxbowb9EX2GQtk5RbnnH6SisVsp4Dlq/zV8k18ZXUPrivheDJVK0Iq3Sv46HsSf724n9NrA0R0m7IV47Q5hK67lC72kdZM6raBE8hEVIeOq5JPt9gaX2MkUSOfa6JpLpF+E+SAUjNOuRJHEj7v2nMSQ3FpWToZo8PHdh7iUrGPciXOaL6K68osdHuBlGfNCZ60fFKSxvlyHjzB2nqS4ViNdKEJPviBwHIVhkcqFC/niGkOHU9F39Lg3rHzRDSHuGYzV+8FK4ZzdWTdI5ADkrk20h1V6u9uozZBK8tINZWRgSqpWIdYysJJeiT72rRsnYxuElEciu0kv/X4+9mVXsXxZC4W8ww8GeCdvwi+h/ytY/jPnSE4durl5zeiM/S11dd12YkDOxFegJN1Mcdd1BYk7n9qk313Xb9RKBQKhUKhUCgU+pETBhRehf/cGZq+vKm22teP4CR9JPeNrfYAvRUjVu5OYA75LLyv7zsbnjqBXpbojDqoTYHWEBQ/diN/9W/feVX/7R86+8KOxMv27a2U+M1P/2M+eOiTfLm8n88Wb2Xe6eOINc6nqwcYUOoAPG7OXOnzoBnnW51B2kFv9kVa6jCp+owqdfrlFoZw2BFZRhIBn9u4jbnFPA1L567xOZYXcqyvJWmbOidXB5kqbKBKPjv7i5iWxqViH3OtPoaSDVKjdQp6kwuVPv7u0nY8X6LYSGAoLp956iDv63+OlqXj+xJ7B1eYmFhjMNHkQ7ufYaWVYs2KU+8YSJJPsZngkfUZEAGBL3A8maFcnQcv7WJArVN1ozzbmWTd61JItBBdifGhMk9emKY/3iI20ObYhXFuy89TaUVB81kppziyNE5Ut3ng1F5W19Ks1pM4noTT7aW73DM9i5f0aJs6jfU4riPTua2NPWzjJ13W6nGKyxmECMiO1vADwdb0Ohmtg+PLbE2v85Y9Z5lv5eiLtBHLBvG/fslqI0EAt+xh9dcOIg7sov5ztyHt3o64eQ/emVm8C5de8/Um7d3O0r0JWuMgRV2kjsTQ5y9sqm9wcB/Bs6df85ihUCgUCoVCoVDoh18YULiOpzqTm24bSODEBHIm84bGVOpd1HZAIEN70kGemb6ybfiJXo6+Gwl6yxCmQK+5fHzhzitt7khfBEBOJF5+jI7N6NfKyGdibI2uMRkr0/QN0rJJSu7w3livmOOo9p2ijqavs+4m8ILe5VLzI5yxNR5o7uOik6fopnimPcmzlVEen5tGmArbsuuokocUdTHiNk5bRddcZpf7mUiWsTyViO4Q+LAtUSJjmOiqy4nqMDHdxrYUplMbxHSbQqRJYbTKA2v7cBwZ3xdUrSgTiQopvcOc2YftyrQcnahu47oyVldlvR3DMxWEFNC0dIbidaK6zeONGbZHVnmwtJtzToq35s+jFEzykRaq3isGCVAYqLEtWiQe6SLrHtlUGyECtqQ3oKoROBJ7Cyt0HZWpoQ0cT+b4+hDQW6mBFzIGRvNV4ukOE2PrdCsR8oN1UhGLbLSDIvmsmkliSpflaoqd8RXOVAo0HZ2LlRwTX7FefoE8/TyFox3kUo34ko3o2nDszOu+3jrDCQIBnhHgmwpyR+CV1jbV1xw0rlpBJBQKhUKhUCgUCr15iOAH4GEgKbLBreLe7/dhXFPrp28j/vnNTf1e/I2DSC6MfXn9NS/Zdy3Vj95OdScYG4LURY/YF3pvquV8ntUPbcXKga8EyF2BbMHzv/r7V/q+c/JWpGTyZcX7Xur8nx3gkzc9hiR8htQahnAoe3F26Us4gcKal+BIa4rtkVViUpcJdYNvtnbSDRTGtDLfrm+h46k8dWESdVEnvq9MEAhql9MofRb5TJOmpWN1NEbzVTZaMRTZQ1M81qsJ3j1zioV2lhOXhxnsrzGaqPHs4gi7h1ZpOzprrTh+IFBkj8pSmkjexPcF/akWQ/E6fiA4OjfO1uE16l0D25UJAkHXUfA8CUN3GE7VOTM3RDLXRpZ8XF+i1egVYBzLVrF9mRuzizxbGWV+vp8tU0UunB9kZmaFtVac+8bO8mRpinqnV1ciE+2wUk6RiFnUVpJMbClRMyN4gUB5Yf8ArXqEd+48zXwriyE7nFgaRlU9sok2jY7BweFL6JLLSifFmplAV1zGYlVWO0mKzQS1hTS5ZyWyf3roDV9H17Pxidup7vEJpAB9Q2bgkIv+0JF/8HF/GBwO/p5GUHn5VJ8fUT/I9+JQKPTm9Wa6F4f34VAo9IPo1e7D4QyF69hsMAEgWgxwYgHdoeT3ZOzsySbxywJz0Key4zupF976Or4KXiTAV8FJBEje1X2DbvdVgwkAg99QeXBlN+PaBjcbC+zUijiBzLzTK+r4UGUvGcXEQ+KcNchFp5+yE+NUY5DT5hDlboyWq5N9TGfgqd4B3D18ESXf4Z6pC8giYGe+xB2TcyTULpIIMFSX9UoSVfX41tIWVlpJFNVjtZTmciNDOtFhvpYlojg02wYJo0utHkPNWPi+oNvUGYg1OLfRT9mKYURtfATr5QSN2Qz1epQgEAQBBMC/HPs6kubRahq0OzozuXUU1cO2FM7PDxBRHDx6xRaF7lFuRxmdWidjmGRjJkfLY0RUByECWuUora6GprlkYyYTW0pcXs3hB4JczCSiOQSBIKo5SKrPuXo/aa3DZKzMjWOLyLJPIdpka26dxxemWesm8APBQKxBQrVoujpdT8F2FWLzMvlDG9+T6+jVCFXDygkQgOZjbAgiT5zdVF9lcOAf9uBCoVAoFAqFQqHQD7QwoLAJl//97Ztql/uvh3DjAY3xN7bSwxXHz9J/pEmgBXSGXLht75VNww+WiC0I9JrAzbo0dtu848x7r2y333FT74dr1FF4Ufob54i+b4V/9fCHeMfXf5n3PPZLAKw4ab5Su4Fbk3PIwueble10fYULVoFL7Rwp1eKBh29l4/+doHtPkf7H11h6u0TS6PJ8dQgh4PDqGG1bpdaN8GxxhIxuko52aFk6iIA9gyvcOLCEJAI03eWDu49jKC6G4tIfb1Fs99I1luf6UFQPSQoQAqbG1zi70U/b1Ck1EnTWolxcynPz1GXG963wb27+GgC7h1YZTDT5YvUAb5s5TyplMpkvk9PbjPdXuHvrBXZMrXBmdpivze7kqZUJ7t12jupKClnyuSszy73951Alj7plcPPAIgCNVgRZ9ll8epiNVoxCvs6ufJGa2VuVI6I5xDQbr64xlShzqZElqVhkNJO3jZ3ndHGAlGbxgekTzNezbE+UsD2F7YkSR+fGiak27fUoQ//l29+TWS6vRtq3A/M9N5C85CM3JaSmwvBXV/CbzU31X/zw1D/o8YVCoVAoFAqFQqEfbGHKwyb84eUn+OT4nddvCCjjo2zcM0LuoQvXnSGwGfY7bmL1DhW1KciecTG++vRVY81+YgQn7aFVZKKrguiPF3ly7xf5rfWdHH7bAF65sqlxxE27kRbXEIqCM9FPN6NhZWSsPoEbAScekLwEhYcW8HNJ/Od6OfvBwX3M/lON/dvmyRstOp7KfCOH5SoMxRtYnoIkAqKKjSZ5WJ7CsdlxRkfK+IFgpZRGSAH7JxZZbScZjDVYMxNsS6/xfHmQ9XICyjracJs7x+ZYaGU4f2kABCRzbQLgo1sO86mn38bwUAVJBCyVMqRSJgmjy1o9znC2ztzlfgBu336RMxsFOl2V90yf4sGLu7A7Km/ddp5ja8PUFtKkRutkYybbUms8dGQvUtIh8AXbR4ss1VNMZKqcPjKBl3bREjZD2TqD0Qamq1Fs92YdSCLADwRN0yAV6zCWrLIrscqja1uxPZlKM0Yq1iFtdKh0oowkatyYXuRb/+vtSN869oavm82Qt05hzuRYuUuBiTbZr0ZJ/cXmZ+S8GbyZptnCD/69OBQKvTm9me7F4X04FAr9IApTHt6gSTW+6bbu5UWsjKC7d+x7MrZs+6hNgfDByly94oR7eRE3FiBZEl4kAAH25wsA/J/50xR/etsr7leoV8+ikC6XwHVxl1cQTx7H+OrTZD53hJEvLTPxuWW2/t5l+v7sCO7S8pVggpzLsnxPjKnJEj9dOErdiZDVTG7Nz3PXwEWK7QTnlwoUmwmatkFa67BmJtDiNm1bRZU9hgo1AFbbSUqVJKvtJFUzQteXua0wT39fgyBrY1UMSlaCjquiVFSE4qPIHq3lJH+9uB8cQUR1el+moVKvRbkht4QkBSxXUgjVR9I90lqHWqVXdPFiK08mYYKAtqeRiliQcDAtjUKkyZPLvYKc6VQbSfHpuCozuXXOl/LMHFhAjnjYDR3Hk7lQ6yNvtCgtZXBcGdNWqTUjWC2NjVocy1N5pjbGTGqN6dQGiuIxkqhhyC6uJ7HUTPPF+X3/04IJYv8u/HQMteXiqwHueoTkXOc19Q+FQqFQKBQKhUJvbmFAYRO6gfOa2jsJcBIyykDhDY8tP/IsRjkgseDhGbwshSH/jEAvS0i2oHnQxIkL/lXpBgBarxLTCBz76g/60gjDuLqN6+Jeutz7s7xC4LpXttU/chsXfn0b0i01YqrNnyzdyWyljzO1Ab58fg8PXdrJSKLG+GCZe0fOMxKrcbg4xo5MiUK6ya0DC1TaUdZqcZLJDmZXQ5Z9omqvkOKx4gi65DIcrzM5tMHtuy/QdRVG4rX/n737DJPrOg88/z83V05dXZ270ciBIAkSYJAsK1m2VlZYSQ4z4/XYu/Y4ju2d9XrG88wzOzPr3WfSeu1Zez32IwdpdqzoKHtk05IoWWISAZIAASI0Ujc6VnV15XDj2Q8FU6JJk91AgwE8v0/dVfeec6pu4aDvW+e8L+GIy2ipTs+1+I6jp/iusbOUJmvMl/NUmkmMQh+xYbHaT/PD+x5jKN3BMENk3cKPdJLZHlGo8dzyCGnLhZbJqZUx/FBn50QFGQlOV0Z4z9Q5NFdjo5JGhoKF1TxnVkcRAs6em2Ao18KsGiwt5en7Bp3Aws726XRtWrU4u0rr7J9ZYSTf5GJliJLT4szGKHsSZaQULLUzeNEgkeT6lTyjP7P5G/qb1dybYv2uJJc+bBNmAmLLOuLRk5s6Vz+4l43D25MnRFEURVEURVGUNy4VUNiEf7F2bEvHx1clnWGd3uHJbem/+MQGqatdvJTAf9cRhG0//9zQQ5eJLPALAVHNRvMlj/3rY3zXufeRurr5PsKzcwRLy694nDAtxD0Hqd4hiKb6jKRbdHyLjW6Mbt/mWi1LzPEZzTbRhGSjE+epjUl2xit899QZWoHNai3FupuguZEgqMYYSnaI2x7+egxTCzmYWcH3dZ6tj+FFBksbmUFpRdPlUGoZ2ddp9hxitseI3aQd2kyla4ShRr9pE0mBOd7BEBE1P8HubIVIDhIPFq0Wvq8TeDp+x+TCQonEWItkzKXRc9BFhKZHtKoJ5lrDyIIHviDydEaKDRzL57t3nsZZM6i14gRjLshBVYcDqRVmhjbIZzrYCY9ICsbjDbxQJwg0niqP44U6T9UnScX6VDbSrDTT1FdTDD2pEVxduIFPx43rDQn0vsBYN8nNha98wnXtXRmGHlm9hSNTFEVRFEVRFOWNQAUUNuHMP9i1peMLH3uM7qigvtPclv67U2nEuasMnXKpHLFZ+9F7EEfvACBYXSN9WZK4bIKEXkmQvFCH/zVL8be3v/Rf88NH2PObFxi6s8zhyUWKTpuE6VFKtknF+wylOrTbDi3XpubGuX/sKu8bOc3vX7wXWwQ8cX4WpODixhDJXJcjhy/R9U2qzQTmUA8/0lnpZ0jEXNzQYDTWIJfqcnWuxH25q3x+6RDvPHyWnfkqdxWXSep9rnQKXKoVSKd6vP3gefKZDv5ygrPrJf5s/iDPrI3znt3n0HIunz59D75nMFqqMzJew0l6dNs2I8kW/bkMLc9mT6mCZoVc2igwPVpFSwSYcZ+Vcpa+Z3K8OoWXjvDaFpoRYaU84imX47Vp1lopRhItfNdgbmmYr83PckdhhZFcCy8wyDh91ropSvE2pcIgV8Ou/+KT+/g3y0Nqh/dt+3XTDw62v+j7d9N//zHaYxr+nW0iU7L7E1USn3ti022lTq4SXryy7WNUFEVRFEVRFOWNRQUUNiE8O8eF39jaKgWnCr1hgTFz87kU7C8MAgPmF09gtCHSoT0dRxgGALmPP8bEl1pYw93BCX6APHHmBVsUXoqWSGx+EJpO9UcfYPXbJL3Qot23ObkwQcrs8x1DZ2m4DkGosbyeZao0SAS5P7PKo0s7+K9X7iUIND6/dIhiqYFhhnR6NlIKTi2OU6mlKKQ7WFbA5eUhTldGGE83abk2kdQoV9NoaZ/5foGM3Wepm3m+CsS6n+IHRx5lPN2kXk7xlaf3U1nLIHXJh2ZO0W7G2DtU5i+euJNUos8791wgnepRqaW4e2iJmO1RyLfRkISJiJ5n4oU6kavj9q//7GuEoYYMNKQEP9TZfdc1jIpJKd/E65p0NmJ4kY5phJy8MEUi1SeR6mMYEV+5uJvVjTRDyQ6rzRR93+Dc2jCRFFyeH0b7+jMveKujU5sr27gV0flL6NkM195XxE9ouDkJVxLofUH43IVNt+O/595XfSWFoiiKoiiKoiivTyqgsEn/7l2f3tLxpcebBDFJMJLdlv6jVgstlcJqS4IENKd1tD3fLNunXVzE65lEhkT0XPRdO9BzuZdvs9PZVN/CMAjfdie94mDbgKmFdJoO2rLDk6tTrHgZRhNNgkhDrtmU4i3Gkk0utoq0ywl0TTKU7lCtJ6k34xworSKEJJ/o4vdMIin40MRJ3jE5R9QyaVWSFO02jXaMy60C8YQL6zahFEwnNwilxno9iSYkJ2vjnO5N4gYGpfEa2bEmdtJFhIK80UH2dC5Ui1gbGp2excMX95BPdImkIKZ5TGQaAINcBnZIrZrE0QMS+R6HJ5bouhZIQdQ00WsGbtNmox2n3o9h7mpxz9A17ttzBc0JeUfxPLoW4WT7+L7BaKrFRLaOboQcnlgCYCjZwQsMbCugspEmeXabSoy+An2oQLRrklhF4scFfi4if1oy/d82n7dBL+RpTm3PqhtFURRFURRFUd74VEBhk7432djS8fL4aYZPRKy8Jfn8cvOb5T6wl3g5QEQQX4to7/lmsCKs1Rj7UxMtEPT2jRBl4nh37tiWfpd/5hiXP2rS3emxc98yx8uT7JlcI3dwnX2FMp8+fQ9eZNC5miFKB6x1U1zaKGBoESNTG+ScHksrOYq5FiP5JinTxe1YjCUbHN1zBdMMebiyl7ofw8y5aLGAp9YmMM2Q+bUCQkjMsQ4PX97NQ2cOMBpvMFZo8OW5PVxcLdIObTq+xQcmnmV3oUIUapgNjUv9Ik6+T6dn85b3PIvj+EyXqmSsHmOFBhUvScuzabRjXK3m2bNjkBdgox9nIlvn/PowoRS8/cB5UmMt5LBLvtTk3vEFdC0iCDS+vjzL1UaeqGvw0NoBDuTWuGN0mX7botaPsdZKoeuStm9TcDosrWcZzzRoriXZ9R89xv7jo9tyjV5JMDXMtfekCeLQLwiELyg8torx9Nym2+jfvYPCxx575QMVRVEURVEURXlTUAGFLYi+/e4tHZ99cgU/Be3dmW3p37myQX3WxM1JpCaI9BdWfEj+yQnyz0UESR2t1Se0t+Hyajqtgx479q0wNr6BqYW0ug535pbIOT2uNPNYToCGHBzeNCjG2gghuVbP0uo5JEyXZLbHj858nWsLQwzbLd576AzPVUqcuDJF3HF5sHCZfYk1/KaFWBsknSylW8QTfb5j8jxSCqJIQ7dDTldG2ejEicU9WHE43Ryj2kpwqjVOwvA4PLGEO+oT1zykhLjj8djiDK5r4gYGlh6y3krQDSzuyC3juwalTIt3l85ixnyuXSkymagTBDrdjsPJ8hizuQ30aw5+qPO1M3sYTTQRAjo9m3ysi5Pr4wYGmpAstzOYMZ/Kcpb6aoqY7ZG3uyy1M+QzHaaTGzjLJvLpMzd/fTZBmBat2QS9UkSvKOiOR9jrGsHlq5tepYKmY9XdWztQRVEURVEURVHeUFRAYZPu+rc/yaX/Sbzygd8iuLqAvQFuWt+WMYRzl3FqEUEqJHAgsfTC5eoyCMh+Y5nExSa92Txmy0fcc/CG+9NzOZZ+4T7slEvHs0hZLg3X4c6xJcpuCiEkXqBz59gS/dAgOdMgSoasdtIEgc6R0iKdapyLG0OMpFuc7Y2BhLKb4un1cXQh2TuxxndOnOOhlf187Jm3MDmzTlT06PQs3MBAF5LPX7gDb8MhCjQcx6e2lsbUQ+K2h+YKWp5NPtWh6Tn89cVdXKnnEU5IzuxQSHdwfQPDGFQxWK1mmG/mkFLw7PIYq/000tW5VsnxqSv3gBRoCZ+vXt5FNtnlrqlrtDsOV2p5/JJP91KGvbMrPLs8xsHRFXQ9wtJC8qkOq7UUj1zbwUY7znSxhplySQ53aDTiPL08QaWWYr2e5Ivn9zHzy8/e1GfhpRgjpZfc5iIO7KR8FGKrGmFcEiUDRp7YWnAgevAO5PHT2zVURVEURVEURVFuAyqgsEml//Qop975/275vOLJHu0Jgfu+o9syjtzJDab+G0SmIDIHgQpjfOz554OrC0Snz2H9xZOIR0/Snkki7j10Q31d/Kf78O9uM5prkrJd9mXWcIyAZ5bG+WDhadJWn42VDGvdFI4esLtQQdgh7xo5z1CqQye0SBXbvHX8CgvrOe6Oz1OaqJE3O3x48hmEkFxYGeYLCwdYq6cQGxbvHz/FkR0LFDIdlq4MEUpBLOYxOVvh0PSgrOXMTJl8oktlNYOfD5lfKZCyXJKmyx2Ty7Q6DjLQ+Oz8EfxQx11MMpvb4ODoCgBx00fXI0wzZDJWAwkyHKyAmC2tY1ghfs1mdTVLtZ8gEXdptxz2zKwS39lgbnkYr27z9PkZDCNkuZ1mZa7IobEV0vE+b5++yFSiRhjodDs2hfxgxYaMBGIhxu4feY6o1brJT8IL6Qf2sPzhnYh08kXPLb0rx9BTkFySSA0ypyzML57YUvt/O3GkoiiKoiiKoiiKCihsQVJztnyO9tWnSSxLGtPbk8wufO4CyWcGCf6C+CCg4O0qoWdfeluF1Qpp7nzxTeYrMUZKBKMuiZhLPzBouTadwMYNDPaVyiz5Oa428hTH63ihjiYiUqaLYYW0Q5udmXUyZg/PMyj3k3gti7jmEjN9nqlNcK2fp15L4Dg+thkQdzyiRMgTtR1YWsjqcg4z65KN9WluJFicGyaINDLxHh1vsHohlumjdzSsKw73Fa5yrZVlvp5j32gZJ+Xi+gatroMoukQI+qFJ2NNZbycIQ41+z+KRtVlyI01iCY96OUWlk2As3wArolRqkLO7BJFG1DVoejbFZAddjxC+hh4PkFKw0UggYyEZq89wos35xjDP1UoU8y0sO6DRjlHKtIi6BmNfD5Hu9m8dCNMOXgZks/2Cx/VikcgCBAQOhLbErssttf13fbYURVEURVEURXlzUwGFLdjz8Z9g/wljy+flfu8x6ocCtDv3b8s43F0lhn/9USJTo//+Y7g5k85bXzrxo9H2acxqcP/hLfXRvWsKBDQacXQhuaOwwlI3w3ozwZVans8tHaHrWuzKrrMzs87O5Dot3+bQ+DJ/eOYuHr82w1ovDcCVep5kocunyvdRaSWptBN0Apsjswv8/V3HiZk+cdtjfLLKU1emeHp5As0KCQONpedK7Jle5fvf8hjnT05RbSaoN+OYeshMYYPEzgbawSZ/fOXwoIrEaoqWZxNFgvFMAyEGN88T8TpvLVxCcwZbH6SEKBR0PZNmO8aB4VWEHVLbSDIab6I5IeX1NB3fJhPrg4B6O8619SyWHSACASs2Qkh2ltbR4wHHVyapdBO4gcGVeGuGAAAgAElEQVRaOYPrG+wYqhJ4OotPj7HrEz7On33jJq78381YrDL5UIOwVnv+Mf8993L+F3cSOiA1QXMX7PpMn+wnNp9Y0X3fUerfuT2fW0VRFEVRFEVRbi8qoLAFO37xMX5l9PiNnWxKehNJ0G4+n4IIBzfJsdUu64cMjHaINEBLpdCcF66iMNbbOFVJ9VBiS310RgyGCi10M6RcT/LXV3fyYOEys8Uq+USXnm/SWY9j6wGPXpnli9f2stJJU3PjxJMubs+kF5iM5pr0XIvJbJ0zlRE8T0dKwVI3Q8tz+NPFO+gHBmvVDCvrGUaH6xydmOfumWvEky7meIf92VUudopEGZ89pQphoNHzTUqxFknHJYoEkRTYjo+T71PvxghDDUsLmcrXmC5V+cr8Lj575W5kwyIT62OaIbJr0FpNEXYNnjy/g1yujRXzebY8SjzhYtoBM6kqCdNDiwfEbI9k3OVwaRl7oo023uO902eZSNS5a3KRmOUTRhrVVgIEhFKw0kwT9Q12fqZ1S7cNBItLyBMvTPJY22URZQOcMgQxsDcE2jee21K7q/cZ5L6+sJ1DVRRFURRFURTlNqECClv0F12b/vuPbfm8A/9qiWvv0um9/56bHoPe9pBvuQu92mL6dy7iLDVJfOkscu80YsfkC44NL1yi+LsniEwwdkxvuo/cuQ5+oDM5VCeKNHYUq3z60hH+5fTnMbSISAoSQ10WO1kOja8QRBrN3iCYkXRchCZpuA73FhY4NLJCGGmMpFoEnkGvZzG3NMzCRo5GJ8Z4skHY04nFPZaX83ztmX08dWWKzmKKwDeIax5NzyGb7+CFOlGg0XEtANbODOMvJhhJtxhKdsgmu0xm69w7vcBUYoOLq0UWnhon8HXuGF7m0KF5Op6JJiR7di8zPrNOLNsnO9Sm1XHweibdK2kysT5+3yBt9DmcW+ItOy/RuJijfSbP8YUp3GtJAk/n85cO8eUz+7hQLVJvxrm7uIgQEsvxOVBcI257HPil1Vc/oeGxO2gcCLGWTIy+JF6OmPrdi0jf21Ib0//yMYKl5Vs3TkVRFEVRFEVR3rBUQGGLHuvswqr7Wz4vWFoGMVh6frN70vVyjSCm0z5UAtdFLiwjPQ9paIj6i5P9Sd/DbEPtvtHNjzdh4voG/cAgneyxJ11mZ77KZ2tHuXh5BEsPKaVbmFpIKdZkONXmo7PP8Gu7P4WUAiFACMlca5hIChruoHykaQfoekTUN5BysPVAExLEoEykMCKwI5y4x8SeMmNDdf58/iB+pNNsxnD0gGS2RzrW52orT+RIZMHjhyYeRRMSXUjOLZe42sjzlcVdpJM9whEXv2dyoTbMhbUiCcvH8w2EkPzA1BP0ajEaixlijo8d94liEaEUJDM95lrD/NmlQzy1Mok50SGIS3YMV5GWZLjYpNcalLhMOS5hoHF6Y5ShVAevEueJU7uoPFUimL92U9d7q7RD+6jemUTrC6QOIoLEYpdwrbyldrrj8Vs0QkVRFEVRFEVRbgcqoLBFnzj+ANrXnr6hc0cfkZSPaLTf/tL5DjYrWFrGzZt0hnWE4yCDAOm6NHfEKb93x0uekzvdxEsJ5v/NAy/aFvFSjC+doFeNsdFK0PdMnqxM8exTO/ixoa8xM1MmbffpBwZDTpu1XpqljQyXukP8ytq72Z9f496ZedKWy0Ijy7DTJmb6LLay3DNxjVyqS3a4RTY5KHt5cWOI/bPLXL5UolRs8L13H0fXI64tDLFWTzGdq1FwOsimxXPLIwgh6V8PdtjDXUwn4N8+952sNVP8TbrBhOXR7dq8Y3yOH7v7ayRzXdZWs2STPZarg4COH+k80ZzFTHqI9OCbe9MMGN+xTrWepNu1Ob00StwZJFHcVyqz545rrLZS2MNdypU0+aEWuWKLytMlZkarrKxlWa5mkJpk/y+vs+Ofbz5fwXbpjyfpjAlSVzQKzw4qO8gnt1amsvehYyS/cPIWjVBRFEVRFEVRlNuBCihs0cxnbvzcxOeeYNfHFukW9Rsu5fg3ukWN7qjAnx2h/tG7aX3//dT2C7yUAMCYmUKY1vPHa4tl+gWB2Ra03ncn3nfe+4p97PhshFuOc6C0yoPDVxjZX+YXrn6Yq4tDrHcT7MuW+dqZPax1k+wpVVjrpomk4GR5jFMrY1y4OIplhLQDi0bPodlxOLU2hqWHtDoOXc/E9wwysT5tz2Z8ukr5XJGHFvaRsD2cbJ8dxSrVXpyGG+PwoatYdoChRTRaccJIIww0wlDj4PAqvY5FpZZienhj8ALk4L348+VDuH0Ts2xiaBH5TIdCqsOV5SFqbpxYzGO02KDTtWkvpVlazrNvbA0ZCsKGhWMGmHrIybPTLNaztFsO4cUk0tNpdRyarTix/XUuXykRT7mM/77F3n/8FOHc5Zu6xjfCmJ1h6e0mmg+FM310T5L/na0HNWJ//A2ifv8WjFBRFEVRFEVRlNuFCihskfWXx3Hfd/SGzw/mr9EdFmwcTN3UOArP9YlMaO6I0RvSaMxqBAmJ5oO+e5YoHUcfHvrmCcN5AOyaxGoEtCZNhG2/bB/WV58l85xOJAVz7WFq7ThTiQ00MyJlu9h6gJXy0IVkqZnmwvwI5+rDFBOd59vwAh1bC6lVkwTXEkSRYCzZ4Nj0PGPpJrbjU2klqXVjDMU6yLyHEBIhJGGg0fUtuq5FwRm0mU90sc2AsGfg+oOKG2Hf4GylRCbTZcdwlY1uDADdCNGEJO90ScRd/EJAuZai3oxTridJpXts9OP0XZOE6RF2DcyhHsVSg8VGBhkJ8hN1ep7JdLaGnvKJIsHMaJVwog+BIAp1NH3Qj7FhEJ7K4PzZN5BBcFPX90a1DwzjD/sEcYn1zCXSpypbbiN66123YGSKoiiKoiiKotxutl4DUaE7ZPDyt+Ivz67B+r0Rxa/NEFy+ekNt6A8/xQRHMFoeXjqFNATCFyBg4SMjjH2tS/1glt7QDEZfYrUkhTMBmhdhVfsMf2OOje+5Gy2A1Kcef8k+pO8x/OuP8uzUA+w+No+mSRY6ecaLdTY6cdadBKVsi6TlMpXqMW8GrJSz7B4vk0n0CHydRiPOSX2MO2cX6U2ZtH2LpxcncGyf5uogqPKeI8/yjZVpLlaHMOyARjPO6FAD2/GZSNZ5ojLDvJ0jY/c5OjTPH564h+J4ncpylsmpdfJOl4Y32MaRMF30ZEQYaQxl2jxRmWF/bpV+aOCHOp1ajD0zq8QMn5lkFZ2IP5o7yrzIYSY9Al8nCDWCSMOwBispAFaNFKlkj/a5HN3DPUaKDdb0QVnMMNBpLGTY+0uniVovzmHxatFLwzSnDEa+GJE7WSWsN6De2FIbnY/eR+JzT9yiESqKoiiKoiiKcjtRKxRuwNCT1Zs6f/SzcxgdDXcqf1Pt6A8/hb5aQwugPRNitsRgv7yA2t4YoSVoz0RsHJL0ChqJizVixy/T2p2i98Ae4qs+vbxG9G13v2w/w8cjFhsZ+n2TmWSVkUST/vVKCWv1FGfPT/DU0gSOETA2XGe9G0dKgWUHyLpFZSlLzu6yM71OzzOREsJIIz3SojS1wbVOjtF0k1K6RczxEQLSdp/hVJtqP0Hk6VTqSS6Vh3hyfRpCQWUxi1k1MPWQC5Ui7yqdpx8YzFWLjMcblOJNNloJYobPPal5fnzyq3zPzqdBwFIjQ8NzWO5lKJgdCAVRqOF3LAq5Nn6oU0q1EZpEu5AgijTWVrNMZuvIiT6ub7C2kSYRd4nFPISA0qPiNQ0mAMiRAkEScg9dIHzuwg21kXl8cZtHpSiKoiiKoijK7UqtULgBN3qz9vz5lQo7/lmF8k8/yPBXbm4swfIqTm0CBKQWJEJKIlPDywjMpiQ5r2E1JWY3Ijw7h55Ok/zM4+jpNP7hWZIrIZW7YuRj9+J8Y27wrfbfkvzsE6Qv7GfuFwTHK1PszZZ5x8wcG16cuOOxd3+Zvak13MigFThU3QSWFhDPeZw2Rum5Fo9cmcVvWhw9eJlz0TAJ26N8rog73sH1DX5s99f4ldPvxN2IYWZczi2MkEj36TQdckMtcvEeGavHSKyFF+p0PZNWOFjhkIy5fOLMfQznWnTbNjHd49nqNP1qjAvrMT7uW6Qsl7mTk6R3NAgjjavzRfbfucbHnnkL8ckWUSQgDo4R0Oo6NPoOb98xx8NyNx/Ze4q/Xt1JuZMkkejT6dlkUl0sIyT1SynEIzeWpHM79T50jMV3aEx8OSCsDnJI6Ok0YbO56TbCdxyBh5+6VUNUFEVRFEVRFOU2o1YovIb8JIh7D6EXbnylgr5zGj+uYa/r5E/VKXzxCv3iIJeCU4/wUtAdEbQmNPTds/Qe3MviP3+Qaz92CPPMPOnHrtIvSvyETuM79uG+9ygcu+NF/UQnz7L73/dp9m3O14dZdxPAoFxiyWnx1MYkq/00F+pFyp0kLc+hG1gIIUnF+9iOz8jUBpdqBUw9BCA+00QIuKu0hCYkR8YXEa7G+FCdRLqPuF5OslZNsljNkrH6ZI0u67UUh4qr3Hlwnsvzw/R9gyNT16g0kuhmyIOpi/zczi+RHW3ywOE59mQrlNtJdh1eJBPrk0900eyQk9UxLMensx5nNNsEIbGNgB1DVRrtGI8vz+A3bP78ykFaPYf9+TUMPcQvxyj9zz7p915CPPLMDV+77SJMi6W3acTWNJIPn3v+8a0EE8S9h9BVMEFRFEVRFEVRlC1QAYXX0OhjPVo7ElDI3XAbjbuKBDEw2hAmLML1KoVTksLpPsmFLpEF3dGIyIIwlyAyBF5a0h2L6N23C392BGddoPkShKA1ZbD0zpdOGBmdPEvi0xmWrhWYjNWYb+YIpSCme6y3Eyw0czR7Dh3XotxOstDMYRkhYSTotm3SlsvefIX7RhYYTTQx9ZB+3eGp1Qku9Yex9QAZCzG0CMsIiCKNTKYLgYbvGmhIHl7dTRQIvEjnbYU5cDU6bYcL1SJDmTZhoJPX25giJBvv8UD2MkdSC2RigwBFq2+zuJrDsELGkw32lcpYawaX54c5MrnISiPNtXqWMNBpNWLECj16HYtOPcZXL+ym/UyB2T/wCS9eueFrtlWvVOZTizlIW5K8JpFSvuyxf5fI0m/oPEVRFEVRFEVR3rzEjd6AbKe0yMv7xLte62FsiTFSIlhdu+l2Lv7K/aQvagz/2qM3dL6WSDD/c3diN6A5GzH6iCT5JyeerzKw9M8exE9KnIqgNyKxq4IgAdGBNl7VQe9qpK5qaL4kcAbP9YcjsmcFsfWIzFOrBFfmX/jax8d47pdGyeY7vGfqHGebI3R8i3IriaFF/OzeL/NQ9SALrRz1boxdhXWW22k+NHmKj5+9D69hkx9tkLB8yo0klhWQjfV56/AlvrBwgHolSXG0QavrYBgh3atpjLEub5u5REz3+OLVvXieQT7TYaOeBCEp5lqkLZeE6bLczjCT2cDSAh6/ugMAv2OiOYOVEVHLxBnq0V+PMb5jnWorgbsWxxjq4Tg+xWSHa5UcyUQfLzCITmQYedLDfOj4TVzpGxO+/QjWYg1Mg/Ds3Iue13fP0tk7hNQhcalJdPrcS7Ty8jofuY/EH6hEjC/lCfklmnJDvNbjeLW8EediRVFuf2+muVjNw4qivB693DysVijcoO0IJgDEl27uEkSdDrF1id6XoEHgCLTdg5to7dA+nIpECwSRCZEl6Q9JkBD4OsRCwsRgW0RsPSKMgX1/lfH9a3Te1Wb5Az6X/+E4xujIC/oMlpaZ/pxG+2yOk7VxWt43a164vsEd9iIr3TReqNNZj9PwHDJOn5LZIJfqQigYTzdp9W0MI6KQ6NIPDC52irTaMRAQRgLfM5BSECVCTDNkoZ3jzsQ1NE0S9g26rkUUCBzHZzJV50olT8t3qLdjXGtlyVtdpoc3EJrESnlYdkA61QNjUJJSS/qEUuBYPlrOQ9cl7WaMes8hDDTq81k6a4nXLJgAoPkR4cUrLxlMAHCncnRGdVLPrCCWtv6ZNGZnVDBBURRFURRFUZQbolYo3ATt8D6iU1v/RvhvW/3ZB0kthaTONW7oG2ZhGLQ/dA+xioe53qVyXx6zI2nu0IivDq6vlxY0DgRoXQ17QyO8o42/GkcmA/A19vzEkxjTk8x//wTJt5V5/K7PvaCPX96Y5Qs/9e1oX31hAkL9wB7O/nyaYqmBrkW0eg4T2Tpd32JvtkzVjbMvtcbFThGAyViNv5zfz+HSMs+WR/F9nbvGl6i7MfZl1vjrpZ3U6wnGhutMpOr0A5Oi0+bLF/aQTA3yH9S7MYJIIwh0RrJNGj2HpO0BYOohfqgzkmiy1M6wtpEmk+rScy3cnsnu8TLnz48j4gFCl9iOT289jplxGck36QcGlfkc+3+9QXjm/I1czleNsWOaxpER4qvuDeVykA/eiblSf9EKFOWb3kzfisEbdy5WFOX29maai9U8rCjK65FaoXCLbEcwAWDkVx+lMaPTOJi9ofNlEJA616ByOMb8BwtEJjR2akgN4msBVisiO+eTOWNg1TV0D7ymjYhAmBF2Waf9PfcRFjNMf/Ia+Z/7ZpDp31V3s/NLP8wue5W/+uTvsv75PYi7Dz7/fPjcBfb8j8fJ/e8xVpdz9HsWlh7S6ts8fGk3Ld/hYqdIzuqy1M7w8NJubNPnXHUY93wG3zV48sk9xA2PhU6OqUydiVKNSiPJ04sTaCJiwqkBoGsRXqgzma1jGQG6HjGT2sA2A44OzVPvOXQ8i8W1HM+VR5hM1dk1UqF7fAgpBVIKLpcLaEkf6epkMx2GUh1iQ11G8k16nx4h97459vzkN173wQQ9m2H9rWMkr7YRjzyDMK0tnS/uPoh49KQKJiiKoiiKoiiKcsNUQOF1wuhCa+rGL4fW7pJcDonMwWqEzKWI4RMeTrkLQPWgSeZqQPFkwMhjPfJPGkhTIqoWXj6itlujcneSzqERqNb4Um+QpO+fFuYwL8f4F//5h3jLqQ9z4p7P8KOf/jx6afiFA3j8FKWHDaK6xUS8TjbewzRDCk6HjNmn5TukLBc/1AfJFmN9wvE+2WyHKBGSsfpkrD5nV0tcu1xERoIw0Dm1OI4pQqLuYPvD8soggWW9nsDzdGpuHE1IjiavkHJcdC1idLhOKtbnnsw87yhewB0KuXtskWS6h9+2kFIg7JBsrE+tG8O/lKL72RHyv/PYDb//rybt0D7WP3iA0AFODbZCSN/bUhvrR9K3YGSKoiiKoiiKoryZqC0PN8mYnSG4fHVb2lr92QcZ/XoDeeLMTbWjH9wLqxXCneP4aQuz7bPwXUl2/P4aVDYIa4Nv/L3vOsrGfpP2VIRV1xAB2DUYeaRG7Y4M+g+UeeTwH/Izy0f50h8cJbEsCeLwj/7xn/Lj2SV+bPEBFj+QflE+CWPHNM27Rlh5q8DZ0aLTcHjn/vNU3QT90ODiapF43MXzDNyOxQN7LlPtJ4gQxAwfSwtoeDGqnTi11TQfvucEf3jqbr5t3xxfP7Gf6b2rNPs2pWSbK+sFfvXuT/G/zX2AcjWN7fi8e+Y8i90sDS+GqYVcXBnGiXl0KnHu3LfA+fIwnE5ROB2+sfIHCEHnw8cQEcTW+hiVFuHc5S0303//MZzPf+MWDPD282ZaZgtv7LlYUZTb15tpLlbzsKIor0dqy8MtVPm20W1ra/TrDRp7Uui5Gy8jCQyW6xsGWtdHGhrtqTjpS5KgmCKaGXv+uPj5MqmFQeUDqUuMPnhZ2DicpZ/T6H5+hJ1f/mH+r9HHuf+Dp6gdgORKyL//q/cD8JsTj7H0PTtftNw+uDJP/I+eYPwrEbbpQyRY6aVZ6yYZjTcZyTcZTbWwrYBktsdCK0cvMCk4HU6fmmauWqTl2gSRhujpfFv6ArGUS97qImMhPd+k07NZaycJA43PVo9RXk+TTPYJQ41eaOHoAaYW0vJswqZJp+GgdXWeWx4h8ycJZj+++IYKJmipFK3vu4/mtI7RDRGPnkReW0bY9iuf/C38d99D4vFXr+SloiiKoiiKoii3r02vUBBC6MBxYElK+d1CiB3Ap4ACcAL4H6SUnhDCBj4B3ANUge+TUl59ubbfyNFYzXGI+v1ta6/6ow+QnXMxHjuDdN1ta1dzHNrvvRPNlzh/9i3fTguBuPcQqw+k6BfAT0U4FY3uZEDmnIHuDqpC1N/W59I7f5cHTn4E1zd4//Rp/nXxDCdcj4/+xU8z/iVB5rFrBEvLL+rbGCmx+oFZEBC8t05zI4EwInaOV4gZPkvNNG8ZvcIjKzuo1xMIXRK2TWZn17i6WuDu6WsYWkTTc7hazdOrxvjo0eM8tLCPoWSH2VSVhy/tZijbpt6OMTO0wdX1PEPpDkvLeYyyya5P1uHiAlGns23v6aul//5jhJYgtASJFY/I0jA6wdYSMWo6jb93lMx/ffzWDfQ29Hr7VuxWzsPwxp6LFUW5fb2Z5mI1DyuK8nr0cvPwVgIK/wS4F0hfnzw/A/yhlPJTQoj/DJyUUv6GEOIngcNSyh8XQnw/8N9LKb/v5dpWk+cLlX/qQUpPNJHHT29vw0KgxeN/501174PHqO8y0F3oliSxssBLQ7wsMbqSWDXk6vdFfPLbf4v7Hf3583Y9/MOETROcEKFL9v/i6ksGFv6GXiwS7BmncnccLwXOg+u4vkm3ZWPYAbouScX77MuvMVcv0vdMJrN15spFxnIN1popbNMn7bisfXUcLxuRWNIwupLEaojUBInFLvLJZ7f3/btF9F07CC++cNWAlkrh3reH5rSFlxXEVyOcekgvb+ClBflzLsaXTmy6j/oPPkD2E2+MHBGvJ6/DP2Jv2TwMai5WFOX16c00F6t5WFGU16OXm4eNzTQghJgA3gf8H8A/EUII4J3A379+yMeBfwX8BvDB6z8DfA74NSGEkK+HZA1vEKED/WKMrS1m3wQpX/Yb+tTJVYzeMJoXkVg16QzrmF2wGxLNl+huyP7/s87Pfemn2P/TZ/jY5FfRhcaR6QVOXJ0i6hjY+T5Xf3CG5NI0qQUX/StPvfj1VSqISoXhRwAhWP75BwjjIKc8wo04fjwkDDSO96aI2R71tRSdnoX1TJJVkRpUqdBhNQbTf9EEKZEnzqCn08gwJOp0eMN82I7dgSg3nv9VGAbyyH5aU3H8mIbZlSTWBttSgpiGlxYMn2ijX1oh3GQX/e8+poIJtwE1DyuKorz21FysKIryQpsKKAC/AvwCkLr+ewGoSymD678vAuPXfx4HrgFIKQMhROP68evbMuLXofJPP8jwrz26be2N/vJjVH/kfmKH9hE9NwfRZm8db05wdQHz6gIASdsmPTWO6PaRrTbekV0svc2hcKaIXY8ofyTF+7vvonv/Lmr7TPRhiUxFuD0TOevT3Smp9EyS9zxI4YyPl9bJPrny4jKFUjL2H1783gnTer5ywdDfeu5bt5l86//IYbO5XW/Fq0Y7e5Wg1QIGCT5X3z3IyeFlBJnLIemvX8HfM87GAQckjP7VGuGFS5sLJlxP4vhGyhWhvCw1DyuKorz21FysKIryLV4xKaMQ4ruBspRy8+urN0EI8Y+EEMeFEMd9ti9XwGtB799coFnce+iFD0iJ5oNfjKPvnb2ptm+UdF3CucuE5XVEKonmhYQxSW2vzsYBA2/HMCIew/7Ck4z+P99g5ydr7PyUS+Er9uAu39fQOxr9omT1mMnyu0IWPzSOdnjf5vp/mTKIYmrw/7SeTqMXiwCEbz+CfmAP2uF9CMPAmJnCGClhzEwhjt4B2mCLxt/0/61lL1+Q2FC8eisq9VwOrViA+w/T+IH76e4pYnYksY2I7MUQsxMB0CtZ9IYEhdNdwguXNt1+58PH6Izor3yg8rp3q+bh623fNnOxoijKraT+JlYURXmxzaxQeAvwASHEfwc4QBr4VSArhDCuR2QngKXrxy8Bk8CiEMIAMgwS0byAlPK3gN+CwX6xm30hr6XCxx5j7WcepPSfbmyVwkvlSsj93mNUf+QB9Emb7Nm5mx3iDZO+R7C0jFhaZtf5PGF1A2FaaDMTeLMl2FVC+BFB3CB2sUL+6wsUTh6kM5Vk7T4IHYlfCHCWTDQPlv+NQPvig9Tv9tj3f7cRC8tIz3vFxJb6/t10Z7N0iwaRAd3vLdEfDTEaGmZrN0FCEiQsoliEs3yMyBokk/SGA7SujvzBe7HXddydfZwLDxKZALuwGuBlQGoSIQEpkJrE6AoiGyJDkj0PoQ1SB6MLQ1+8gux0CZtNjNERgpXVLb+vmuNQ/ug+/KSgMxEhdcieNSg9XH4+aGDMTFF5706qhyXxFRCPntx0+52P3EfiD54gseWRKa9Tt2QehttrLlYURbnF1N/EiqIof8srBhSklL8I/CKAEOLtwM9LKf+BEOKzwEcZZLX9h8CfXD/lT6///tj157/8Ztgrpvnb/xK9jCC0IWfb21rx4UaJWAwALZ8lnLuMNgfcf5heyUF3JbI+yAUQOSZmJ0Tv6wSpCOTghr0zDrsyDc7vzLB3xwpXPzqN5uUxemDXJIkVH2eljbQMuhNx9F6EFkg0N6S6N4bdjJAaxNdDkDqpBY3qYUlkC5CQOy3w0wZSgJsbBADsVRM/E6F5AhGCZkR42UE7UTxC8w3cYoC1oeMWQzAj0CSer0EgMJs63VGBn5TofQEF0N82Q3Kxj3FxEAwxJsYJFpde8F4ZIyWC1bUXvYf67lmwTNaP5nGzgsgAp6IhIsg/14NqbXBcaZj2oRH6ecHMn3s451YIXtTaS+t96Bh2fbNHK28Eah5WFEV57am5WFEU5cU2m0PhpfxT4FNCiF8CngZ++/rjvw38FyHERWAD+P6bG+IbQ/E3tj/p3eTvX+bST+wgPLof69LqDX0TflM0/fn8DfruWa5+dITInMZqwcQfxwb5EB4/RQwwpicJrgcUxCPPYALTlYOs35NGRBrNHSA1WGmm0V3B+XPjMBKAkBgNAy8jqDwoMOo59D6IUL7qlxgAABedSURBVNCf9BA9A6NlEWQi7LKOVYf6Ph2jLTC6IMLBKgK7JmhPS0QERhtiFUG/AMNPBXSHdXSXQZDjpAUywmwH1HfZOPWQ8JKGmwW9bxBfk4S2wEuB7kF3NEJzBcGwREQCLxvR3RGSPh3HOLSTyBBIDXIXRukNGfhJQfFEi9V7UsTLM3hJDTcriJcj4mWPxQccwus7LCa/2COI69hfPoU+OQbdHt7+KdqTe/DjgtARDD/dR3/4qU0HE2o/9AC531MJGN9E1DysKIry2lNzsaIob1qbLht5K90uJXL0A3sIn7uw7e2u/C8PIgIY+dXtS/x4I8K3H2HlAQcAdyjC6AhyZwdbBbykIHPFQ0QS67lFhGnSuH+C9uhgD7+fArMF/esZFkNHEqRD0CRaR0fzBOGYi3XVRvMEvYmA9EiL1nwGvS/Q+wJ30sOomDh7GrjnMjDTxe8biI6Bs6LTHwlBgN7VMDqC/pTH8FdN/AREpsDoSaQAuynxUgLNB6sVIXVBd0ijMymRGhi9wbYHEQkiQ5K+DFoAveKgjKY77uFcs4BBQKM/GiI8wcijko39OkYf/OSgL2ddEMRB8yG1GJE+30Kv1InyKaJT59AcB220hD+W49q743jZCHtdY+RJD+fJS4S12qauTfPv3Q9A+pOPb/NVf3N7vZUqu9Vul7lYUZTby5tpLlbzsKIor0c3XTZS2Zz5Dw0xcQsCClKD3qi8ZQGLzbLWWkjdIXQkdlUDCUY/IjJA8wV+chA8aH3nTkILgtjg23vdk5htQZCA5DUJAvoFgdE1EMFga0JkSYyYT79oYNZ0EJLmegK7ruGOexgLFghJmIzo9Sz04HqFB1dHJgK8nEDaEQQCoyMwWxAlPZo7LMwW+EmQ4vrNfQBuZhBQkJqG5oOXBashkNf/mfSLEqlLjI7ATwicWkT6akR3WMPL6xjdQZuaB1pPIA3oDusYvcHr0VwxWC3Rkxg9SC8ExC5VEa0OweoaBuOIdBomRqgfzNGc0REhZM8J7HqEc3KB6Hr1h1eiJRJ4acHQb6qVCYqiKIqiKIqivHpUQGEbzfz+IsG3bBPYLmP/4VHa33s/tTvz5JYzhNe3FrzawrNzTD7ksPLW1OAGeiyi4enkzgfEV3oESZPabpP2TITZ1IhMib0hCOKC3IWATkkntAS5ix5OTaef1Rj+o/OE1Q20O/fj52O0x7XBioIlk+buiNAerKAJkhI8DS3rwYozWEVwJkGsBZFl4OYkkRMiezpuPkLzNHQ9ItIlQgrCA23ay3HQoDchMQo94o8kqe8BoyuQOsQq0C8AAqy6RnxVUt8n6Y5BZGkkliNS10LiZQEiRHclgSPg0iAQ4dQ8rEqP1u4UTtVHCyL0boBerhPMXyP6llKYtbdO0h7TQICzLimc9rG/8OTz73VkGMjg5Tc6CNNi5afvZfRrTRVMUBRFURRFURTlVacCCtsouLpA4wfuJ/P/bf+y8+RnHqf8Uw8S7puGx09te/ubpV9aopCbxc0ZuLnBjXRj1sBq6uiuJIxBmAkAA6mBXROEFoPyhRKyl32kJoitulh1nbC6AYBWriGH4uT/8hLhzlH8pInRswhtaGMiAWfJxE/raL6gX4wQEUSmhtlmUAC1YSIiAZokiEs0eT2ZYk/gN22IRxgNHb2h4QqHyAJpSJwNQRDjeuUHQILVAqcW4azrRAboLnTGNLy2wGpKdE+SPLVCMJpDr7bxRzJYV8oEi0uktINoixVku4OWyyKdwfYI6Xvou2cRnk9tj4aXi4gvaxSeqSO6Lt8ahpLRK29Fan7kCOP/f3t3GhvHed9x/PvMzN48dnlT1G0rkRRbh+3Ekhy3aVobSdAEaJEGCQokQAMUKPoiLQoUMQoEyMu+aNIGCIoEaFAUKJrLKRK4bRLHOWpYsmQ3imRZh3VREimRFC+J3OUud2aevtghQ8myrbWO5e78PsBCO7NL6vkPyZ/Av555nv8ar2srSRERERERkbtFDYW77F40E5b0fX0/V/9iL/m2R0n87K5vR39bgqlpkj+ZJum4FA6twz83XFuQ8cIlvLVD5PPtXHS7SMyDCSExb6l0GRZ6DMlrML/Go/fAJEF7msTpywSAWygw9olNlLsM1acexF0wbPjvObqOLWITLt2vQWLsGsGZ87iFAvO/swXrGNzFEGtC0hMLTO1sg9ChNGjIjlsykyEja1Kk5hy8EqTGPExoyI1YpncFJK67FNeGuBXD3MYQ60F63KHSF5AZccldCfHKloGDFcKEQ+bSdcpD7Sy2u1SzDm2XylTXdtcuyvgkievzhPNFANzRSXAcgmKRsFjEGxyg+vRjXF+foJI3eOXajAunakhNW8IjJ950nU3Cw1beYqbLnh1M7sjR880D3N25MCIiIiIiIrdPDYUm079/lpGnCgxUdpM8ffmWWxPeF2GAf24YAP/CpdqfI6MwAkMdO7n6SA6narG1ZRUorfPpPO/Qfq7I7I5uFrod+i04164TzMzQ/53jmHwn1TUFZrZmGf29dtKTFm/BkpwPwc3jnIFgZobMDw+9aTjd0ameaItNb+0Q205mKW3Ok75cYnpHB8ligLcQYl0PtwJB0lAaMPQdD1nodug5PIc7PQ9Ts8uLITrpNE6+E39snNRwjkx/L0EhB0dPL9++EABcv748Fn9snPDJ3Tgb+lgYSHP5gw49h2trNzhBbe2F/kMhHa9PE5w4fcvLe6ttQt3t72F2Rxft336ZHq29KCIiIiIiDaaGQpMJj5xgXWkzIx8foDu3nuSPG9RQeBtm/xH6Fx/CnZpj8sk1tW0SMwGzD3okihlmttbWDigNZWgrbcCcOosp5AlGrmCGL9I7s4WgM4M7XYRrcxhjsO252/rf+KVfxP2RUQCy/maC0+foKW+B6WuEU9MkP/A+/LYE1jGkZxxyP3iVjkInwdT0m/6OsFwmHCvXnheLhOeKOO3thFEz4WZuvhPTVWBiS5pK3tRqX1NibjpLYg4W2yE1C7mRBYKTZ277mnpDaxj9gx7WPH9VsxJERERERGRVUEOhCQWnzzH4lXNM/9leEp/eQ/u3V99/V9tXj+ED3QmP9qFO5kZTVNssC10ufsaSHTdc2ePiPtJF/yudtB29gpNJE1QXsRcvQ7F4wy/OnhnAG+i/rRkZxvNwCgWCq1ex2RTug5tumAlg9h9habmEZLSI5tJaDrdj5e4Lbr6TcNNaJh/toFIwLHZaktdqu0m4FfCzlvwvMnQdL5EYncYfvojb30cwPnHbf9/8nzxO/sVh+r+2X80EERERERFZNdRQaGJd3zrAhS/vo73RA3kb4bkLuG/4dHd3YYf6orMFHD9kfgME7ZZSr0ubH2BtbSHC8H2bcUpVwmMnlz+Pf2UMd8tmnNk0Ybm8fH5p/Qa3o4Ngbg5vaA0L2wcJUg65Ux1M7soTpKEvl8YUy5jiAliLLS1grcUYQ1ip3PIWg7dj9+0EoNyWoJpzub4ZwlSIqRocv7bVp7MImaKh76VJ7IVR/GJtjYV6mgmlP3qctu8d5O33exAREREREbn/1FBocj1HA2Y+t5fuwzOER0++8wfcZ0tbHwZT0xDNAugpbeL6rj6S0y7lQZ/FTkPpoTVU21yKAy5+DrJXLAtP78MtQ+9vipj9R7DZFP7j20icHGV+70a8+YCLT6ToPDMEBrITPmGp9velxxcIunIUh2q/4M892MHU+wq4i7DYYWm7BAt9htyoXd7KMvHTV5fH7RYKtXUU9uzAPT4M6waZ39KJuxDi+JYre1NYF3qP+LiLIYk5l0qyNjuh/VLA4C/ncS5fJRifqGtWgVsoUHxiC8UBl/RsSO77B+/Wl0JEREREROSuUkOhyeWePUjmyd2c+1SBwo49dL84urxI4moVnDlP7sx5Orq7MJkMixt7Ka1JA+BngRCCJLiLMLc5xM/m8J/aR2oG0lMh3RfStB0YxvZ3kR1LMv2wofA6WAOJU6MkHAOVCtee3kZ2zOL40PHCSTr+N0l161ouPp2mNAgDB6skZ6sUh9K4N++o0J2n/MQWKp0Oqd6tVHMOkzsMbtkjUYTsuGVuA4SuIffjo2x4KYXJdxKOTRCWy1h4V7cnnPvrreRGoecbB+7wKouIiIiIiNxbaii0AOfFw6y3uxj+RAa3soa2BjUUbmdtAJNI/nZ3hGjGQqIjR/7sGOHMLJk921ns9Ag9Q+61EtmJHKEbUskbqu2GapvBvzgKYYDr+4SJLpwKOH5IqT+BfWwjJrQk5qr4KYOfMSTmLaaQhzDEqfjYBFQzlumtCdJTHuUuQ7mQpmfhYdzpecLhS5Te20O5y8WpWrLPHyUsl1no2Ue5G7JjIZ1nS3ilLOWCQ8fm9QTH34DZa3d07eb3bmTDl9RIEBERERGR5mCW7ltvpA7TZR83v9/oYbSEykffz/jjCdb9tEji/Dj+lbFGD+kdGc9bvjWiXsGHHsFZDLCOobg2jZ8y9ByYAM8Fa5l+tJsgaUiULOnJKgDT21NYA50XfIp9LomSZbHdEKQN1gETQmrGUm0zdJ2o4C74mP1HAAif3I0JLebAaxAGeGuHlneUeDe8jeuZf2iA3Eunl7eqlNXjoH2B63baNHoc94uyWERWozhlsXJYRFajt8thzVBoMan/eYXE9n1ceSJH5+AGcs+uzobCypkKt91MMAZuaoB5Lx3DVhdx02nyJ7KYTAZ/ZHS5SVFIuBg/JMylMCeHCefmGJh9COsYvNEpwj3rwIBbsSTmQ6rtLottho7hCqFnSPzqyA3jc148XNs2Mqzd0HAnzQT27KDUmST93CHt3iAiIiIiIk1HDYUWNPgP+wFwHtrKxWf2kZ62pK7ZVbW95FIzob4PevNsmqXPE5bLsGL3h6UmwMqFKpc+2r56DAAfyD17GSf9250jUis+t/cWMyfC+fn6x770sb+7G0JIHBsmePkoyXf9mURERERERBpLDYUWFh47ydra785Un36M2c/updRnyJ8NSM1USY7OEhRyuOfHCK5ebexg7zF32xaCE6drz/OdBCvWO1i5DeVK1vfBcSG8af5AnbcJue95AJtJYqoB/Oow8O4WbBQREREREVlN1FCIicRPXyUPdO3cxuUPFwgfcEm+t5/FTkNPV5rMxQJUfeyly4Tlcm1a/9zcLW8zaEZmdu6GmQgrLW8ReSs3NxPq5A0OMLurl7bvrp7ZISIiIiIiIneDGgoxEx45wcCRW5z3PJzNG5j+1G6CFIQeVAqGIG1JzhpMAF7ZkihaQtfQeXYB6zmkzowT9Odxyj7B66fuf0G3aeXilMFNuzHcrcUQjefhbNlE8YECC90uhX97Gf/KGG3fXZ3rWIiIiIiIiNwJNRQEqE3vD944S/6Ns296zbz/YfxcgnJ3glKfQyVvKA5l8RbAe+96ZrdakjMO/evfDw74aYfUrE/y0Bu1WQ4xMffHjzG31mHdD0ZID19s9HBERERERETuKTUU5B3ZV17DBXLR42Y9Nx0vL2y4cxt+T5aF3gShB6nZEIC218ehXGmKLS3fird5I5X1XTiLAfPrMrR/52XavvsybdQWexQREREREWl1aijIPRMeOYEHtEfHxvMofvxRru8aIEwYciP9hEmX5MQ84dkLENp3t/vDfeIWCtDbRWlLF1wtk7owhX/+wnJ9IiIiIiIicaKGgtw31vfJ/ufBG865/HbHA2fnNoJ8GkKwniExvUC1kMH95a8xieTbNhu8TRvwz1+47bHc6vM5uRy2UsFpb4eeAvPbe8BAanKRxOnLBOMTMDNDKrotRDMRREREREQkztRQkFUjPHICZ+UxtYYDsPzLv9vfR7imF5tyufZAFj9rSM+ETD3kUjgxSGayigktC71JvFJIerLM1MM5uo4vYD1DNetRybtUc4bUtRA/49B9YBw8d3lbyWBmBmZmyJw+tzwWbfMoIiIiIiJyIzUUpKkE4xMwPgFA54qdGHPfZ3mrS7dQwHtwLc65UYKpafovrcEfvYyhtr5D6ubPeb8GLyIiIiIi0kLUUJCWsbSjRDAzA6/MLDcK/NHLjRuUiIiIiIhIi3Le+S0iIiIiIiIiIjdSQ0FERERERERE6qaGgoiIiIiIiIjUTQ0FEREREREREambGgoiIiIiIiIiUjc1FERERERERESkbmooiIiIiIiIiEjd1FAQERERERERkbqpoSAiIiIiIiIidVNDQURERERERETqpoaCiIiIiIiIiNRNDQURERERERERqZsaCiIiIiIiIiJSNzUURERERERERKRuaiiIiIiIiIiISN2MtbbRY8AYMwecavQ4GqQHmGz0IBpEtcdPs9W9wVrb2+hB3C/K4qb63rxb4lo3qPZmqj02Wawcbqrvy7tJtcdPs9X9ljns3e+RvIVT1trHGj2IRjDGvKra4yeutce17iaiLI6ZuNYNqj2utTcB5XAMqfb41d5KdeuWBxERERERERGpmxoKIiIiIiIiIlK31dJQ+GajB9BAqj2e4lp7XOtuFnH++sS19rjWDapdVqc4f21UezzFtfaWqXtVLMooIiIiIiIiIs1ltcxQEBEREREREZEmooaCiIiIiIiIiNSt4Q0FY8xHjDGnjDFnjDFfbPR47jZjzLeMMRPGmGMrznUZY543xpyO/ixE540x5mvRtThqjHmkcSO/M8aYdcaYXxhjjhtjXjfGfCE6H4fa08aYQ8aYI1HtX47ObzLGHIxq/I4xJhmdT0XHZ6LXNzZy/HfKGOMaYw4bY56LjmNRdzNTDrdmFkF8szjuOQzK4makLG69LIL45jAoi+OSww1tKBhjXODrwEeB7cBnjDHbGzmme+BfgY/cdO6LwAvW2i3AC9Ex1K7Dlujx58A/36cx3gs+8DfW2u3AHuAvo69tHGqvAB+21u4EdgEfMcbsAf4e+Kq19kFgBvh89P7PAzPR+a9G72tmXwBOrDiOS91NSTnc0lkE8c3iuOcwKIubirK4ZbMI4pvDoCyORw5baxv2APYCP1lx/AzwTCPHdI/q3AgcW3F8ChiMng8Cp6Ln3wA+c6v3NfsD+CHwVNxqB7LAr4HHgUnAi84vf+8DPwH2Rs+96H2m0WN/l/WupfaP4oeB5wATh7qb+aEcjkcWragndlkctxyOalAWN9lDWdz6WbSiltjlcFRHrLI4Tjnc6FsehoBLK45HonOtrt9aeyV6Pgb0R89b8npE03Z2AweJSe3RFKffABPA88BZYNZa60dvWVnfcu3R69eA7vs74rvmH4G/BcLouJt41N3MWupnrw6xyKKV4pbFMc5hUBY3o5b52atTy2fRSnHLYYh1FscmhxvdUIg9W2tFtezencaYNuBZ4K+stddXvtbKtVtrA2vtLmrdyQ8AWxs8pHvOGPOHwIS19v8aPRaRerRyFi2JYxbHMYdBWSzNq1WzaEkccxjimcVxy+FGNxRGgXUrjtdG51rduDFmECD6cyI631LXwxiToBac/26t/UF0Oha1L7HWzgK/oDatKW+M8aKXVta3XHv0eicwdZ+Hejc8AXzCGDMMfJvaFK9/ovXrbnYt+bN3G2KTRXHP4pjlMCiLm1XL/ezdplhkUdxzGGKXxbHK4UY3FF4BtkQrXiaBTwM/avCY7ocfAZ+Lnn+O2r1US+c/G63uuge4tmIqVFMxxhjgX4AT1tqvrHgpDrX3GmPy0fMMtfvkTlAL0U9Gb7u59qVr8kng51GnuqlYa5+x1q611m6k9rP8c2vtn9LidbcA5XCLZhHEN4vjmsOgLG5iyuIWzCKIbw5DfLM4djnc6EUcgI8Bb1C7n+bvGj2ee1DffwBXgCq1e2U+T+2emBeA08DPgK7ovYbaCr9ngdeAxxo9/juo+4PUpm4dBX4TPT4Wk9p3AIej2o8BX4rObwYOAWeA7wGp6Hw6Oj4Tvb650TXchWvwIeC5uNXdrA/lcGtmUVRPLLNYObx8HZTFTfRQFrdeFkW1xDKHo1pin8VxyGETFSEiIiIiIiIictsafcuDiIiIiIiIiDQhNRREREREREREpG5qKIiIiIiIiIhI3dRQEBEREREREZG6qaEgIiIiIiIiInVTQ0FERERERERE6qaGgoiIiIiIiIjU7f8BlKkQV+upiaAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model.load_state_dict(torch.load('best_metric_model.pth'))\n", + "model.eval()\n", + "with torch.no_grad():\n", + " for i, val_data in enumerate(val_loader):\n", + " roi_size = (160, 160, 160)\n", + " sw_batch_size = 4\n", + " val_outputs = sliding_window_inference(val_data['image'], roi_size, sw_batch_size, model, device)\n", + " # plot the slice [:, :, 100]\n", + " plt.figure('check', (18, 6))\n", + " plt.subplot(1, 3, 1)\n", + " plt.title('image ' + str(i))\n", + " plt.imshow(val_data['image'][0, 0, :, :, 100])\n", + " plt.subplot(1, 3, 2)\n", + " plt.title('label ' + str(i))\n", + " plt.imshow(val_data['label'][0, 0, :, :, 100])\n", + " plt.subplot(1, 3, 3)\n", + " plt.title('output ' + str(i))\n", + " plt.imshow(torch.argmax(val_outputs, dim=1).detach().cpu()[0, :, :, 100])\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/multi_gpu_test.ipynb b/examples/multi_gpu_test.ipynb index 8f1827f15b..b12437a803 100644 --- a/examples/multi_gpu_test.ipynb +++ b/examples/multi_gpu_test.ipynb @@ -24,9 +24,6 @@ "from ignite.engine.engine import Events\n", "from ignite.handlers import ModelCheckpoint\n", "\n", - "# assumes the framework is found here, change as necessary\n", - "sys.path.append(\"..\")\n", - "\n", "import monai\n", "\n", "\n", @@ -53,7 +50,7 @@ "net = monai.networks.nets.UNet(\n", " dimensions=2,\n", " in_channels=1,\n", - " num_classes=1,\n", + " out_channels=1,\n", " channels=(16, 32, 64, 128, 256),\n", " strides=(2, 2, 2, 2),\n", " num_res_units=2,\n", diff --git a/examples/nifti_read_example.ipynb b/examples/nifti_read_example.ipynb index 339fac0a19..f50f838156 100644 --- a/examples/nifti_read_example.ipynb +++ b/examples/nifti_read_example.ipynb @@ -37,13 +37,11 @@ "from torch.utils.data import DataLoader\n", "import monai.transforms.compose as transforms\n", "\n", - "sys.path.append('..') # assumes this is where MONAI is\n", - "\n", "import monai\n", "\n", "from monai.transforms.utils import rescale_array\n", "from monai.data.nifti_reader import NiftiDataset\n", - "from monai.transforms import AddChannel, Transpose, Rescale, ToTensor, UniformRandomPatch\n", + "from monai.transforms import AddChannel, Transpose, Rescale, ToTensor, RandUniformPatch\n", "from monai.data.grid_dataset import GridPatchDataset\n", "\n", "monai.config.print_config()" @@ -137,13 +135,13 @@ "imtrans=transforms.Compose([\n", " Rescale(),\n", " AddChannel(),\n", - " UniformRandomPatch((64, 64, 64)),\n", + " RandUniformPatch((64, 64, 64)),\n", " ToTensor()\n", "]) \n", "\n", "segtrans=transforms.Compose([\n", " AddChannel(),\n", - " UniformRandomPatch((64, 64, 64)),\n", + " RandUniformPatch((64, 64, 64)),\n", " ToTensor()\n", "]) \n", " \n", diff --git a/examples/unet_inference_3d.py b/examples/segmentation_3d/unet_evaluation_array.py similarity index 50% rename from examples/unet_inference_3d.py rename to examples/segmentation_3d/unet_evaluation_array.py index aa84a6560d..305a513c1e 100644 --- a/examples/unet_inference_3d.py +++ b/examples/segmentation_3d/unet_evaluation_array.py @@ -13,31 +13,34 @@ import sys import tempfile from glob import glob - +import logging import nibabel as nib import numpy as np import torch -import torchvision.transforms as transforms from ignite.engine import Engine from torch.utils.data import DataLoader from monai import config from monai.handlers.checkpoint_loader import CheckpointLoader from monai.handlers.segmentation_saver import SegmentationSaver +import monai.transforms.compose as transforms from monai.data.nifti_reader import NiftiDataset -from monai.transforms import AddChannel, Rescale, ToTensor +from monai.transforms import AddChannel, Rescale from monai.networks.nets.unet import UNet from monai.networks.utils import predict_segmentation from monai.data.synthetic import create_test_image_3d from monai.utils.sliding_window_inference import sliding_window_inference +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.mean_dice import MeanDice -sys.path.append("..") # assumes the framework is found here, change as necessary config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) tempdir = tempfile.mkdtemp() # tempdir = './temp' -for i in range(50): - im, seg = create_test_image_3d(256, 256, 256) +print('generating synthetic data to {} (this may take a while)'.format(tempdir)) +for i in range(5): + im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i)) @@ -47,39 +50,59 @@ images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz'))) segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz'))) -imtrans = transforms.Compose([Rescale(), AddChannel(), ToTensor()]) -segtrans = transforms.Compose([AddChannel(), ToTensor()]) + +# Define transforms for image and segmentation +imtrans = transforms.Compose([Rescale(), AddChannel()]) +segtrans = transforms.Compose([AddChannel()]) ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans, image_only=False) -device = torch.device("cpu:0") -roi_size = (64, 64, 64) -sw_batch_size = 4 +device = torch.device("cuda:0") net = UNet( dimensions=3, in_channels=1, - num_classes=1, + out_channels=1, channels=(16, 32, 64, 128, 256), strides=(2, 2, 2, 2), num_res_units=2, ) net.to(device) +# define sliding window size and batch size for windows inference +roi_size = (96, 96, 96) +sw_batch_size = 4 + -def _sliding_window_processor(_engine, batch): +def _sliding_window_processor(engine, batch): net.eval() img, seg, meta_data = batch with torch.no_grad(): - seg_probs = sliding_window_inference(img, roi_size, sw_batch_size, lambda x: net(x)[0], device) - return predict_segmentation(seg_probs) + seg_probs = sliding_window_inference(img, roi_size, sw_batch_size, net, device) + return seg_probs, seg.to(device) + +evaluator = Engine(_sliding_window_processor) -infer_engine = Engine(_sliding_window_processor) +# add evaluation metric to the evaluator engine +MeanDice(add_sigmoid=True, to_onehot_y=False).attach(evaluator, 'Mean_Dice') + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None # no need to print loss value, so disable per iteration output +) +val_stats_handler.attach(evaluator) -# checkpoint_handler = ModelCheckpoint('./', 'net', n_saved=10, save_interval=3, require_empty=False) -# infer_engine.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={'net': net}) +# for the arrary data format, assume the 3rd item of batch data is the meta_data +file_saver = SegmentationSaver( + output_path='tempdir', output_ext='.nii.gz', output_postfix='seg', name='evaluator', + batch_transform=lambda x: x[2], output_transform=lambda output: predict_segmentation(output[0])) +file_saver.attach(evaluator) -SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg').attach(infer_engine) -CheckpointLoader(load_path='./net_checkpoint_9.pth', load_dict={'net': net}).attach(infer_engine) +# the model was trained by "unet_training_array" exmple +ckpt_saver = CheckpointLoader(load_path='./runs/net_checkpoint_50.pth', load_dict={'net': net}) +ckpt_saver.attach(evaluator) +# sliding window inferene need to input 1 image in every iteration loader = DataLoader(ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available()) -state = infer_engine.run(loader) +state = evaluator.run(loader) diff --git a/examples/segmentation_3d/unet_evaluation_dict.py b/examples/segmentation_3d/unet_evaluation_dict.py new file mode 100644 index 0000000000..5a906af138 --- /dev/null +++ b/examples/segmentation_3d/unet_evaluation_dict.py @@ -0,0 +1,113 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import tempfile +from glob import glob +import logging +import nibabel as nib +import numpy as np +import torch +from ignite.engine import Engine +from torch.utils.data import DataLoader + +import monai +from monai.data.utils import list_data_collate +from monai.utils.sliding_window_inference import sliding_window_inference +from monai.data.synthetic import create_test_image_3d +from monai.networks.utils import predict_segmentation +from monai.networks.nets.unet import UNet +from monai.transforms.composables import LoadNiftid, AsChannelFirstd, Rescaled +import monai.transforms.compose as transforms +from monai.handlers.segmentation_saver import SegmentationSaver +from monai.handlers.checkpoint_loader import CheckpointLoader +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.mean_dice import MeanDice +from monai import config + +config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +tempdir = tempfile.mkdtemp() +# tempdir = './temp' +print('generating synthetic data to {} (this may take a while)'.format(tempdir)) +for i in range(5): + im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1) + + n = nib.Nifti1Image(im, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i)) + + n = nib.Nifti1Image(seg, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i)) + +images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz'))) +segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz'))) +val_files = [{'img': img, 'seg': seg} for img, seg in zip(images, segs)] + +# Define transforms for image and segmentation +val_transforms = transforms.Compose([ + LoadNiftid(keys=['img', 'seg']), + AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1), + Rescaled(keys=['img', 'seg']) +]) +val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) + +device = torch.device("cuda:0") +net = UNet( + dimensions=3, + in_channels=1, + out_channels=1, + channels=(16, 32, 64, 128, 256), + strides=(2, 2, 2, 2), + num_res_units=2, +) +net.to(device) + +# define sliding window size and batch size for windows inference +roi_size = (96, 96, 96) +sw_batch_size = 4 + + +def _sliding_window_processor(engine, batch): + net.eval() + with torch.no_grad(): + seg_probs = sliding_window_inference(batch['img'], roi_size, sw_batch_size, net, device) + return seg_probs, batch['seg'].to(device) + + +evaluator = Engine(_sliding_window_processor) + +# add evaluation metric to the evaluator engine +MeanDice(add_sigmoid=True, to_onehot_y=False).attach(evaluator, 'Mean_Dice') + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None # no need to print loss value, so disable per iteration output +) +val_stats_handler.attach(evaluator) + +# convert the necessary metadata from batch data +SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg', name='evaluator', + batch_transform=lambda batch: {'filename_or_obj': batch['img.filename_or_obj'], + 'original_affine': batch['img.original_affine'], + 'affine': batch['img.affine'], + }, + output_transform=lambda output: predict_segmentation(output[0])).attach(evaluator) +# the model was trained by "unet_training_dict" exmple +CheckpointLoader(load_path='./runs/net_checkpoint_50.pth', load_dict={'net': net}).attach(evaluator) + +# sliding window inferene need to input 1 image in every iteration +val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate, + pin_memory=torch.cuda.is_available()) +state = evaluator.run(val_loader) diff --git a/examples/segmentation_3d/unet_training_array.py b/examples/segmentation_3d/unet_training_array.py new file mode 100644 index 0000000000..fd700b23f3 --- /dev/null +++ b/examples/segmentation_3d/unet_training_array.py @@ -0,0 +1,166 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import tempfile +from glob import glob +import logging +import nibabel as nib +import numpy as np +import torch +from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator +from ignite.handlers import ModelCheckpoint, EarlyStopping +from torch.utils.data import DataLoader + +import monai +import monai.transforms.compose as transforms + +from monai.data.nifti_reader import NiftiDataset +from monai.transforms import AddChannel, Rescale, RandUniformPatch, Resize +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler, TensorBoardImageHandler +from monai.handlers.mean_dice import MeanDice +from monai.data.synthetic import create_test_image_3d +from monai.handlers.utils import stopping_fn_from_metric +from monai.networks.utils import predict_segmentation + +monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# Create a temporary directory and 40 random image, mask paris +tempdir = tempfile.mkdtemp() +print('generating synthetic data to {} (this may take a while)'.format(tempdir)) +for i in range(40): + im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1) + + n = nib.Nifti1Image(im, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i)) + + n = nib.Nifti1Image(seg, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i)) + +images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz'))) +segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz'))) + +# Define transforms for image and segmentation +train_imtrans = transforms.Compose([ + Rescale(), + AddChannel(), + RandUniformPatch((96, 96, 96)) +]) +train_segtrans = transforms.Compose([ + AddChannel(), + RandUniformPatch((96, 96, 96)) +]) +val_imtrans = transforms.Compose([ + Rescale(), + AddChannel(), + Resize((96, 96, 96)) +]) +val_segtrans = transforms.Compose([ + AddChannel(), + Resize((96, 96, 96)) +]) + +# Define nifti dataset, dataloader +check_ds = NiftiDataset(images, segs, transform=train_imtrans, seg_transform=train_segtrans) +check_loader = DataLoader(check_ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available()) +im, seg = monai.utils.misc.first(check_loader) +print(im.shape, seg.shape) + +# create a training data loader +train_ds = NiftiDataset(images[:20], segs[:20], transform=train_imtrans, seg_transform=train_segtrans) +train_loader = DataLoader(train_ds, batch_size=5, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available()) +# create a validation data loader +val_ds = NiftiDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans) +val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available()) + +# Create UNet, DiceLoss and Adam optimizer +net = monai.networks.nets.UNet( + dimensions=3, + in_channels=1, + out_channels=1, + channels=(16, 32, 64, 128, 256), + strides=(2, 2, 2, 2), + num_res_units=2, +) +loss = monai.losses.DiceLoss(do_sigmoid=True) +lr = 1e-3 +opt = torch.optim.Adam(net.parameters(), lr) +device = torch.device("cuda:0") + +# ignite trainer expects batch=(img, seg) and returns output=loss at every iteration, +# user can add output_transform to return other values, like: y_pred, y, etc. +trainer = create_supervised_trainer(net, opt, loss, device, False) + +# adding checkpoint handler to save models (network params and optimizer stats) during training +checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False) +trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, + handler=checkpoint_handler, + to_save={'net': net, 'opt': opt}) + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't set metrics for trainer here, so just print loss, user can also customize print functions +# and can use output_transform to convert engine.state.output if it's not a loss value +train_stats_handler = StatsHandler(name='trainer') +train_stats_handler.attach(trainer) + +# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler +train_tensorboard_stats_handler = TensorBoardStatsHandler() +train_tensorboard_stats_handler.attach(trainer) + +validation_every_n_epochs = 1 +# Set parameters for validation +metric_name = 'Mean_Dice' +# add evaluation metric to the evaluator engine +val_metrics = {metric_name: MeanDice(add_sigmoid=True, to_onehot_y=False)} + +# ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values +evaluator = create_supervised_evaluator(net, val_metrics, device, True) + + +@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) +def run_validation(engine): + evaluator.run(val_loader) + + +# Add early stopping handler to evaluator +early_stopper = EarlyStopping(patience=4, + score_function=stopping_fn_from_metric(metric_name), + trainer=trainer) +evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) + +# Add stats event handler to print validation stats via evaluator +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None, # no need to print loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_stats_handler.attach(evaluator) + +# add handler to record metrics to TensorBoard at every validation epoch +val_tensorboard_stats_handler = TensorBoardStatsHandler( + output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_tensorboard_stats_handler.attach(evaluator) + +# add handler to draw the first image and the corresponding label and model output in the last batch +# here we draw the 3D output as GIF format along Depth axis, at every validation epoch +val_tensorboard_image_handler = TensorBoardImageHandler( + batch_transform=lambda batch: (batch[0], batch[1]), + output_transform=lambda output: predict_segmentation(output[0]), + global_iter_transform=lambda x: trainer.state.epoch +) +evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=val_tensorboard_image_handler) + +train_epochs = 30 +state = trainer.run(train_loader, train_epochs) diff --git a/examples/segmentation_3d/unet_training_dict.py b/examples/segmentation_3d/unet_training_dict.py new file mode 100644 index 0000000000..d68f24a857 --- /dev/null +++ b/examples/segmentation_3d/unet_training_dict.py @@ -0,0 +1,172 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import tempfile +from glob import glob +import logging +import nibabel as nib +import numpy as np +import torch +from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator, _prepare_batch +from ignite.handlers import ModelCheckpoint, EarlyStopping +from torch.utils.data import DataLoader + +import monai +import monai.transforms.compose as transforms +from monai.transforms.composables import \ + LoadNiftid, AsChannelFirstd, Rescaled, RandCropByPosNegLabeld, RandRotate90d +from monai.handlers.stats_handler import StatsHandler +from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler, TensorBoardImageHandler +from monai.handlers.mean_dice import MeanDice +from monai.data.synthetic import create_test_image_3d +from monai.handlers.utils import stopping_fn_from_metric +from monai.data.utils import list_data_collate +from monai.networks.utils import predict_segmentation + +monai.config.print_config() +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# Create a temporary directory and 40 random image, mask paris +tempdir = tempfile.mkdtemp() +print('generating synthetic data to {} (this may take a while)'.format(tempdir)) +for i in range(40): + im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1) + + n = nib.Nifti1Image(im, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'img%i.nii.gz' % i)) + + n = nib.Nifti1Image(seg, np.eye(4)) + nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i)) + +images = sorted(glob(os.path.join(tempdir, 'img*.nii.gz'))) +segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz'))) +train_files = [{'img': img, 'seg': seg} for img, seg in zip(images[:20], segs[:20])] +val_files = [{'img': img, 'seg': seg} for img, seg in zip(images[-20:], segs[-20:])] + +# Define transforms for image and segmentation +train_transforms = transforms.Compose([ + LoadNiftid(keys=['img', 'seg']), + AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1), + Rescaled(keys=['img', 'seg']), + RandCropByPosNegLabeld(keys=['img', 'seg'], label_key='seg', size=[96, 96, 96], pos=1, neg=1, num_samples=4), + RandRotate90d(keys=['img', 'seg'], prob=0.8, spatial_axes=[0, 2]) +]) +val_transforms = transforms.Compose([ + LoadNiftid(keys=['img', 'seg']), + AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1), + Rescaled(keys=['img', 'seg']) +]) + +# Define dataset, dataloader +check_ds = monai.data.Dataset(data=train_files, transform=train_transforms) +# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training +check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate, + pin_memory=torch.cuda.is_available()) +check_data = monai.utils.misc.first(check_loader) +print(check_data['img'].shape, check_data['seg'].shape) + +# create a training data loader +train_ds = monai.data.Dataset(data=train_files, transform=train_transforms) +# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training +train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, + collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()) +# create a validation data loader +val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) +val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, collate_fn=list_data_collate, + pin_memory=torch.cuda.is_available()) + +# Create UNet, DiceLoss and Adam optimizer +net = monai.networks.nets.UNet( + dimensions=3, + in_channels=1, + out_channels=1, + channels=(16, 32, 64, 128, 256), + strides=(2, 2, 2, 2), + num_res_units=2, +) +loss = monai.losses.DiceLoss(do_sigmoid=True) +lr = 1e-3 +opt = torch.optim.Adam(net.parameters(), lr) +device = torch.device("cuda:0") + +# ignite trainer expects batch=(img, seg) and returns output=loss at every iteration, +# user can add output_transform to return other values, like: y_pred, y, etc. +def prepare_batch(batch, device=None, non_blocking=False): + return _prepare_batch((batch['img'], batch['seg']), device, non_blocking) + + +trainer = create_supervised_trainer(net, opt, loss, device, False, prepare_batch=prepare_batch) + +# adding checkpoint handler to save models (network params and optimizer stats) during training +checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False) +trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, + handler=checkpoint_handler, + to_save={'net': net, 'opt': opt}) + +# StatsHandler prints loss at every iteration and print metrics at every epoch, +# we don't set metrics for trainer here, so just print loss, user can also customize print functions +# and can use output_transform to convert engine.state.output if it's not loss value +train_stats_handler = StatsHandler(name='trainer') +train_stats_handler.attach(trainer) + +# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler +train_tensorboard_stats_handler = TensorBoardStatsHandler() +train_tensorboard_stats_handler.attach(trainer) + +validation_every_n_iters = 5 +# Set parameters for validation +metric_name = 'Mean_Dice' +# add evaluation metric to the evaluator engine +val_metrics = {metric_name: MeanDice(add_sigmoid=True, to_onehot_y=False)} + +# ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, +# user can add output_transform to return other values +evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch) + + +@trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters)) +def run_validation(engine): + evaluator.run(val_loader) + + +# Add early stopping handler to evaluator +early_stopper = EarlyStopping(patience=4, + score_function=stopping_fn_from_metric(metric_name), + trainer=trainer) +evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) + +# Add stats event handler to print validation stats via evaluator +val_stats_handler = StatsHandler( + name='evaluator', + output_transform=lambda x: None, # no need to print loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer +val_stats_handler.attach(evaluator) + +# add handler to record metrics to TensorBoard at every validation epoch +val_tensorboard_stats_handler = TensorBoardStatsHandler( + output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output + global_epoch_transform=lambda x: trainer.state.iteration) # fetch global iteration number from trainer +val_tensorboard_stats_handler.attach(evaluator) + +# add handler to draw the first image and the corresponding label and model output in the last batch +# here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations. +val_tensorboard_image_handler = TensorBoardImageHandler( + batch_transform=lambda batch: (batch['img'], batch['seg']), + output_transform=lambda output: predict_segmentation(output[0]), + global_iter_transform=lambda x: trainer.state.epoch +) +evaluator.add_event_handler( + event_name=Events.ITERATION_COMPLETED(every=2), handler=val_tensorboard_image_handler) + +train_epochs = 5 +state = trainer.run(train_loader, train_epochs) diff --git a/examples/transform_speed.ipynb b/examples/transform_speed.ipynb new file mode 100644 index 0000000000..6d486d1850 --- /dev/null +++ b/examples/transform_speed.ipynb @@ -0,0 +1,370 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data loading pipeline examples\n", + "\n", + "The purpose of this notebook is to illustrate reading Nifti files and test speed of different methods." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 0.0.1\n", + "Python version: 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n", + "Numpy version: 1.18.1\n", + "Pytorch version: 1.4.0\n", + "Ignite version: 0.3.0\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import os\n", + "import sys\n", + "from glob import glob\n", + "import tempfile\n", + "\n", + "import numpy as np\n", + "import nibabel as nib\n", + "\n", + "\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torch.multiprocessing import Pool, Process, set_start_method\n", + "try:\n", + " set_start_method('spawn')\n", + "except RuntimeError:\n", + " pass\n", + "\n", + "import monai\n", + "from monai.transforms.compose import Compose\n", + "from monai.data.nifti_reader import NiftiDataset\n", + "from monai.transforms import (AddChannel, Rescale, ToTensor, \n", + " RandUniformPatch, Rotate, RandAffine)\n", + "\n", + "monai.config.print_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0. Preparing input data (nifti images)\n", + "\n", + "Create a number of test Nifti files, 3d single channel images with spatial size (256, 256, 256) voxels." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "tempdir = tempfile.mkdtemp()\n", + "\n", + "for i in range(5):\n", + " im, seg = monai.data.synthetic.create_test_image_3d(256,256,256)\n", + " \n", + " n = nib.Nifti1Image(im, np.eye(4))\n", + " nib.save(n, os.path.join(tempdir, 'im%i.nii.gz'%i))\n", + " \n", + " n = nib.Nifti1Image(seg, np.eye(4))\n", + " nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz'%i))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# prepare list of image names and segmentation names\n", + "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n", + "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Test image loading with minimal preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([3, 1, 256, 256, 256]) torch.Size([3, 1, 256, 256, 256])\n" + ] + } + ], + "source": [ + "imtrans = Compose([\n", + " AddChannel(),\n", + " ToTensor()\n", + "]) \n", + "\n", + "segtrans = Compose([\n", + " AddChannel(),\n", + " ToTensor()\n", + "]) \n", + " \n", + "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n", + "loader = DataLoader(ds, batch_size=3, num_workers=8)\n", + "\n", + "im, seg = monai.utils.misc.first(loader)\n", + "print(im.shape, seg.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.11 s ± 207 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%timeit data = next(iter(loader))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Test image-patch loading with CPU multi-processing:\n", + "\n", + "- rotate (256, 256, 256)-voxel in the plane axes=(1, 2)\n", + "- extract random (64, 64, 64) patches\n", + "- implemented in MONAI using ` scipy.ndimage.rotate`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([3, 1, 64, 64, 64]) torch.Size([3, 1, 64, 64, 64])\n" + ] + } + ], + "source": [ + "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n", + "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))\n", + "\n", + "imtrans = Compose([\n", + " Rescale(),\n", + " AddChannel(),\n", + " Rotate(angle=45.),\n", + " RandUniformPatch((64, 64, 64)),\n", + " ToTensor()\n", + "]) \n", + "\n", + "segtrans = Compose([\n", + " AddChannel(),\n", + " Rotate(angle=45.),\n", + " RandUniformPatch((64, 64, 64)),\n", + " ToTensor()\n", + "]) \n", + " \n", + "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n", + "loader = DataLoader(ds, batch_size=3, num_workers=8, pin_memory=torch.cuda.is_available())\n", + "\n", + "im, seg = monai.utils.misc.first(loader)\n", + "print(im.shape, seg.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.3 s ± 175 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n" + ] + } + ], + "source": [ + "%timeit -n 3 data = next(iter(loader))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(the above results were based on a 2.9 GHz 6-Core Intel Core i9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Test image-patch loading with preprocessing on GPU:\n", + "\n", + "- random rotate (256, 256, 256)-voxel in the plane axes=(1, 2)\n", + "- extract random (64, 64, 64) patches\n", + "- implemented in MONAI using native pytorch resampling" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([3, 1, 64, 64, 64]) torch.Size([3, 1, 64, 64, 64])\n" + ] + } + ], + "source": [ + "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n", + "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))\n", + "\n", + "# same parameter with different interpolation mode for image and segmentation\n", + "rand_affine_img = RandAffine(prob=1.0, rotate_range=np.pi/4, translate_range=(96, 96, 96),\n", + " spatial_size=(64, 64, 64), mode='bilinear',\n", + " as_tensor_output=True, device=torch.device('cuda:0'))\n", + "rand_affine_seg = RandAffine(prob=1.0, rotate_range=np.pi/4, translate_range=(96, 96, 96),\n", + " spatial_size=(64, 64, 64), mode='nearest',\n", + " as_tensor_output=True, device=torch.device('cuda:0'))\n", + " \n", + "imtrans = Compose([\n", + " Rescale(),\n", + " AddChannel(),\n", + " rand_affine_img,\n", + "]) \n", + "\n", + "segtrans = Compose([\n", + " AddChannel(),\n", + " rand_affine_seg,\n", + "]) \n", + " \n", + "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n", + "loader = DataLoader(ds, batch_size=3, num_workers=0)\n", + "\n", + "im, seg = monai.utils.misc.first(loader)\n", + "\n", + "print(im.shape, seg.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.42 s ± 1.72 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n" + ] + } + ], + "source": [ + "%timeit -n 3 data = next(iter(loader))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TITAN Xp COLLECTORS EDITION\n", + "|===========================================================================|\n", + "| PyTorch CUDA memory summary, device ID 0 |\n", + "|---------------------------------------------------------------------------|\n", + "| CUDA OOMs: 0 | cudaMalloc retries: 0 |\n", + "|===========================================================================|\n", + "| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |\n", + "|---------------------------------------------------------------------------|\n", + "| Allocated memory | 6144 KB | 156672 KB | 16680 MB | 16674 MB |\n", + "|---------------------------------------------------------------------------|\n", + "| Active memory | 6144 KB | 156672 KB | 16680 MB | 16674 MB |\n", + "|---------------------------------------------------------------------------|\n", + "| GPU reserved memory | 225280 KB | 225280 KB | 225280 KB | 0 B |\n", + "|---------------------------------------------------------------------------|\n", + "| Non-releasable memory | 14336 KB | 77824 KB | 11219 MB | 11205 MB |\n", + "|---------------------------------------------------------------------------|\n", + "| Allocations | 2 | 14 | 2222 | 2220 |\n", + "|---------------------------------------------------------------------------|\n", + "| Active allocs | 2 | 14 | 2222 | 2220 |\n", + "|---------------------------------------------------------------------------|\n", + "| GPU reserved segments | 8 | 8 | 8 | 0 |\n", + "|---------------------------------------------------------------------------|\n", + "| Non-releasable allocs | 1 | 6 | 1460 | 1459 |\n", + "|===========================================================================|\n", + "\n" + ] + } + ], + "source": [ + "print(torch.cuda.get_device_name(0))\n", + "print(torch.cuda.memory_summary(0, abbreviated=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "!rm -rf {tempdir}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/transforms_demo_2d.ipynb b/examples/transforms_demo_2d.ipynb new file mode 100644 index 0000000000..9ced3e86b2 --- /dev/null +++ b/examples/transforms_demo_2d.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2D image transformation demo\n", + "\n", + "This demo shows how to apply 2D transforms in MONAI.\n", + "Main features:\n", + " - Random elastic transforms implemented in native Pytorch\n", + " - Easy-to-use interfaces that are designed and implemented in the pythonic way\n", + " \n", + "Find out more in MONAI's wiki page: https://github.com/Project-MONAI/MONAI/wiki" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Before running this demo\n", + "**please download the GLAS (gland segmentation in histology images) challenge data from:**\n", + "https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/\n", + "\n", + "The dataset used in this competition is provided for research purposes only. Commercial uses are not allowed.\n", + "\n", + "If you intend to publish research work that uses this dataset, you must cite our review paper to be published after the competition\n", + "\n", + "K. Sirinukunwattana, J. P. W. Pluim, H. Chen, X Qi, P. Heng, Y. Guo, L. Wang, B. J. Matuszewski, E. Bruni, U. Sanchez, A. Böhm, O. Ronneberger, B. Ben Cheikh, D. Racoceanu, P. Kainz, M. Pfeiffer, M. Urschler, D. R. J. Snead, N. M. Rajpoot, \"Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest\" http://arxiv.org/abs/1603.00275 [Preprint]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 0.0.1\n", + "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]\n", + "Numpy version: 1.18.1\n", + "Pytorch version: 1.4.0\n", + "Ignite version: 0.3.0\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import sys\n", + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "\n", + "import monai\n", + "from monai.transforms import Affine, Rand2DElastic\n", + "\n", + "monai.config.print_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "img_name = './Warwick QU Dataset (Released 2016_07_08)/train_22.bmp'\n", + "seg_name = './Warwick QU Dataset (Released 2016_07_08)/train_22_anno.bmp'\n", + "im = np.array(Image.open(img_name))\n", + "seg = np.array(Image.open(seg_name))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(522, 775, 3) (522, 775)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(1,2)\n", + "axes[0].imshow(im)\n", + "axes[1].imshow(seg)\n", + "print(im.shape, seg.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Affine transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 300, 400) (1, 300, 400)\n" + ] + } + ], + "source": [ + "from monai.transforms import Affine\n", + "\n", + "# MONAI transforms always take channel-first data: [channel x H x W]\n", + "im_data = np.moveaxis(im, -1, 0) # make them channel first\n", + "seg_data = np.expand_dims(seg, 0) # make a channel for the segmentation\n", + "\n", + "# create an Affine transform\n", + "affine = Affine(rotate_params=np.pi/4, scale_params=(1.2, 1.2), translate_params=(200, 40), \n", + " padding_mode='zeros', device=torch.device('cuda:0'))\n", + "# convert both image and segmentation using different interpolation mode\n", + "new_img = affine(im_data, (300, 400), mode='bilinear')\n", + "new_seg = affine(seg_data, (300, 400), mode='nearest')\n", + "print(new_img.shape, new_seg.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(1,2)\n", + "axes[0].imshow(np.moveaxis(new_img.astype(int), 0, -1))\n", + "axes[1].imshow(new_seg[0].astype(int))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Elastic deformation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 224, 224) (1, 224, 224)\n" + ] + } + ], + "source": [ + "from monai.transforms import Rand2DElastic\n", + "\n", + "# create an elsatic deformation transform\n", + "deform = Rand2DElastic(prob=1.0, spacing=(30, 30), magnitude_range=(5, 6),\n", + " rotate_range=(np.pi/4,), scale_range=(0.2, 0.2), translate_range=(100, 100), \n", + " padding_mode='zeros', device=torch.device('cuda:0'))\n", + "# transform both image and segmentation using different interpolation mode\n", + "deform.set_random_state(seed=123)\n", + "new_img = deform(im_data, (224, 224), mode='bilinear')\n", + "deform.set_random_state(seed=123)\n", + "new_seg = deform(seg_data, (224, 224), mode='nearest')\n", + "print(new_img.shape, new_seg.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(1,2)\n", + "axes[0].imshow(np.moveaxis(new_img.astype(int), 0, -1))\n", + "axes[1].imshow(new_seg[0].astype(int))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/unet_segmentation_3d.ipynb b/examples/unet_segmentation_3d.ipynb index bfba0a70bf..00dceb150a 100644 --- a/examples/unet_segmentation_3d.ipynb +++ b/examples/unet_segmentation_3d.ipynb @@ -39,14 +39,11 @@ "from ignite.handlers import ModelCheckpoint, EarlyStopping\n", "from torch.utils.data import DataLoader\n", "\n", - "# assumes the framework is found here, change as necessary\n", - "sys.path.append(\"..\")\n", - "\n", "import monai\n", "import monai.transforms.compose as transforms\n", "\n", "from monai.data.nifti_reader import NiftiDataset\n", - "from monai.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch)\n", + "from monai.transforms import (AddChannel, Rescale, ToTensor, RandUniformPatch)\n", "from monai.handlers.stats_handler import StatsHandler\n", "from monai.handlers.mean_dice import MeanDice\n", "from monai.visualize import img2tensorboard\n", @@ -110,12 +107,12 @@ "imtrans = transforms.Compose([\n", " Rescale(), \n", " AddChannel(), \n", - " UniformRandomPatch((96, 96, 96)), \n", + " RandUniformPatch((96, 96, 96)), \n", " ToTensor()\n", "])\n", "segtrans = transforms.Compose([\n", " AddChannel(), \n", - " UniformRandomPatch((96, 96, 96)), \n", + " RandUniformPatch((96, 96, 96)), \n", " ToTensor()\n", "])\n", "\n", @@ -145,7 +142,7 @@ "net = monai.networks.nets.UNet(\n", " dimensions=3,\n", " in_channels=1,\n", - " num_classes=1,\n", + " out_channels=1,\n", " channels=(16, 32, 64, 128, 256),\n", " strides=(2, 2, 2, 2),\n", " num_res_units=2,\n", @@ -197,7 +194,7 @@ "trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,\n", " handler=checkpoint_handler,\n", " to_save={'net': net, 'opt': opt})\n", - "train_stats_handler = StatsHandler()\n", + "train_stats_handler = StatsHandler(output_transform=lambda x: x[1])\n", "train_stats_handler.attach(trainer)\n", "\n", "writer = SummaryWriter()\n", @@ -260,7 +257,7 @@ "\n", "# Add stats event handler to print validation stats via evaluator\n", "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "val_stats_handler = StatsHandler()\n", + "val_stats_handler = StatsHandler(lambda x: None)\n", "val_stats_handler.attach(evaluator)\n", "\n", "# Add early stopping handler to evaluator.\n", @@ -496,7 +493,7 @@ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", "\n", "train_ds = NiftiDataset(images[:20], segs[:20], transform=imtrans, seg_transform=segtrans)\n", - "train_loader = DataLoader(train_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available())\n", + "train_loader = DataLoader(train_ds, batch_size=5, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available())\n", "\n", "train_epochs = 30\n", "state = trainer.run(train_loader, train_epochs)" @@ -571,7 +568,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/unet_segmentation_3d.py b/examples/unet_segmentation_3d.py deleted file mode 100644 index 0e68eb67c8..0000000000 --- a/examples/unet_segmentation_3d.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright 2020 MONAI Consortium -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# http://www.apache.org/licenses/LICENSE-2.0 -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import tempfile -from glob import glob -import logging - -import nibabel as nib -import numpy as np -import torch -from torch.utils.tensorboard import SummaryWriter -from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator -from ignite.handlers import ModelCheckpoint, EarlyStopping -from torch.utils.data import DataLoader - -# assumes the framework is found here, change as necessary -sys.path.append("..") - -import monai -import monai.transforms.compose as transforms - -from monai.data.nifti_reader import NiftiDataset -from monai.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch) -from monai.handlers.stats_handler import StatsHandler -from monai.handlers.mean_dice import MeanDice -from monai.visualize import img2tensorboard -from monai.data.synthetic import create_test_image_3d -from monai.handlers.utils import stopping_fn_from_metric - -monai.config.print_config() - -# Create a temporary directory and 50 random image, mask paris -tempdir = tempfile.mkdtemp() - -for i in range(50): - im, seg = create_test_image_3d(128, 128, 128) - - n = nib.Nifti1Image(im, np.eye(4)) - nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i)) - - n = nib.Nifti1Image(seg, np.eye(4)) - nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i)) - -images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz'))) -segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz'))) - -# Define transforms for image and segmentation -imtrans = transforms.Compose([ - Rescale(), - AddChannel(), - UniformRandomPatch((96, 96, 96)), - ToTensor() -]) -segtrans = transforms.Compose([ - AddChannel(), - UniformRandomPatch((96, 96, 96)), - ToTensor() -]) - -# Define nifti dataset, dataloader. -ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans) -loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available()) -im, seg = monai.utils.misc.first(loader) -print(im.shape, seg.shape) - -lr = 1e-5 - -# Create UNet, DiceLoss and Adam optimizer. -net = monai.networks.nets.UNet( - dimensions=3, - in_channels=1, - num_classes=1, - channels=(16, 32, 64, 128, 256), - strides=(2, 2, 2, 2), - num_res_units=2, -) - -loss = monai.losses.DiceLoss(do_sigmoid=True) -opt = torch.optim.Adam(net.parameters(), lr) - -# Since network outputs logits and segmentation, we need a custom function. -def _loss_fn(i, j): - return loss(i[0], j) - -# Create trainer -device = torch.device("cuda:0") -trainer = create_supervised_trainer(net, opt, _loss_fn, device, False, - output_transform=lambda x, y, y_pred, loss: [y_pred, loss.item(), y]) - -# adding checkpoint handler to save models (network params and optimizer stats) during training -checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False) -trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, - handler=checkpoint_handler, - to_save={'net': net, 'opt': opt}) -train_stats_handler = StatsHandler() -train_stats_handler.attach(trainer) - -@trainer.on(Events.EPOCH_COMPLETED) -def log_training_loss(engine): - # log loss to tensorboard with second item of engine.state.output, loss.item() from output_transform - writer.add_scalar('Loss/train', engine.state.output[1], engine.state.epoch) - - # tensor of ones to use where for converting labels to zero and ones - ones = torch.ones(engine.state.batch[1][0].shape, dtype=torch.int32) - first_output_tensor = engine.state.output[0][1][0].detach().cpu() - # log model output to tensorboard, as three dimensional tensor with no channels dimension - img2tensorboard.add_animated_gif_no_channels(writer, "first_output_final_batch", first_output_tensor, 64, - 255, engine.state.epoch) - # get label tensor and convert to single class - first_label_tensor = torch.where(engine.state.batch[1][0] > 0, ones, engine.state.batch[1][0]) - # log label tensor to tensorboard, there is a channel dimension when getting label from batch - img2tensorboard.add_animated_gif(writer, "first_label_final_batch", first_label_tensor, 64, - 255, engine.state.epoch) - second_output_tensor = engine.state.output[0][1][1].detach().cpu() - img2tensorboard.add_animated_gif_no_channels(writer, "second_output_final_batch", second_output_tensor, 64, - 255, engine.state.epoch) - second_label_tensor = torch.where(engine.state.batch[1][1] > 0, ones, engine.state.batch[1][1]) - img2tensorboard.add_animated_gif(writer, "second_label_final_batch", second_label_tensor, 64, - 255, engine.state.epoch) - third_output_tensor = engine.state.output[0][1][2].detach().cpu() - img2tensorboard.add_animated_gif_no_channels(writer, "third_output_final_batch", third_output_tensor, 64, - 255, engine.state.epoch) - third_label_tensor = torch.where(engine.state.batch[1][2] > 0, ones, engine.state.batch[1][2]) - img2tensorboard.add_animated_gif(writer, "third_label_final_batch", third_label_tensor, 64, - 255, engine.state.epoch) - engine.logger.info("Epoch[%s] Loss: %s", engine.state.epoch, engine.state.output[1]) - -writer = SummaryWriter() - -# Set parameters for validation -validation_every_n_epochs = 1 -metric_name = 'Mean_Dice' - -# add evaluation metric to the evaluator engine -val_metrics = {metric_name: MeanDice(add_sigmoid=True)} -evaluator = create_supervised_evaluator(net, val_metrics, device, True, - output_transform=lambda x, y, y_pred: (y_pred[0], y)) - -# Add stats event handler to print validation stats via evaluator -logging.basicConfig(stream=sys.stdout, level=logging.INFO) -val_stats_handler = StatsHandler() -val_stats_handler.attach(evaluator) - -# Add early stopping handler to evaluator. -early_stopper = EarlyStopping(patience=4, - score_function=stopping_fn_from_metric(metric_name), - trainer=trainer) -evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) - -# create a validation data loader -val_ds = NiftiDataset(images[-20:], segs[-20:], transform=imtrans, seg_transform=segtrans) -val_loader = DataLoader(ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available()) - - -@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) -def run_validation(engine): - evaluator.run(val_loader) - -@evaluator.on(Events.EPOCH_COMPLETED) -def log_metrics_to_tensorboard(engine): - for name, value in engine.state.metrics.items(): - writer.add_scalar('Metrics/{name}', value, trainer.state.epoch) - -# create a training data loader -logging.basicConfig(stream=sys.stdout, level=logging.INFO) - -train_ds = NiftiDataset(images[:20], segs[:20], transform=imtrans, seg_transform=segtrans) -train_loader = DataLoader(train_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available()) - -train_epochs = 30 -state = trainer.run(train_loader, train_epochs) diff --git a/monai/__init__.py b/monai/__init__.py index d101b7d6dc..b2917fa8c9 100644 --- a/monai/__init__.py +++ b/monai/__init__.py @@ -15,8 +15,7 @@ from .utils.module import load_submodules __copyright__ = "(c) 2020 MONAI Consortium" -__version__tuple__ = (0, 0, 1) -__version__ = "%i.%i.%i" % __version__tuple__ +__version__ = "0.0.1" __basedir__ = os.path.dirname(__file__) diff --git a/monai/data/dataset.py b/monai/data/dataset.py new file mode 100644 index 0000000000..8e5bb7b0a6 --- /dev/null +++ b/monai/data/dataset.py @@ -0,0 +1,46 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from monai.utils.module import export + + +@export("monai.data") +class Dataset(torch.utils.data.Dataset): + """ + Generic dataset to handle dictionary format data, it can operate transforms for specific fields. + For example, typical input data can be a list of dictionaries:: + + [{ { { + 'img': 'image1.nii.gz', 'img': 'image2.nii.gz', 'img': 'image3.nii.gz', + 'seg': 'label1.nii.gz', 'seg': 'label2.nii.gz', 'seg': 'label3.nii.gz', + 'extra': 123 'extra': 456 'extra': 789 + }, }, }] + """ + + def __init__(self, data, transform=None): + """ + Args: + data (Iterable): input data to load and transform to generate dataset for model. + transform (Callable, optional): transforms to excute operations on input data. + """ + self.data = data + self.transform = transform + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + data = self.data[index] + if self.transform is not None: + data = self.transform(data) + + return data diff --git a/monai/data/nifti_reader.py b/monai/data/nifti_reader.py index 287c97fdde..01a2b86045 100644 --- a/monai/data/nifti_reader.py +++ b/monai/data/nifti_reader.py @@ -9,14 +9,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import nibabel as nib - +import numpy as np from torch.utils.data import Dataset from torch.utils.data._utils.collate import np_str_obj_array_pattern -from monai.utils.module import export +from monai.data.utils import correct_nifti_header_if_necessary from monai.transforms.compose import Randomizable +from monai.utils.module import export def load_nifti(filename_or_obj, as_closest_canonical=False, image_only=True, dtype=None): @@ -39,6 +39,7 @@ def load_nifti(filename_or_obj, as_closest_canonical=False, image_only=True, dty """ img = nib.load(filename_or_obj) + img = correct_nifti_header_if_necessary(img) header = dict(img.header) header['filename_or_obj'] = filename_or_obj @@ -107,6 +108,7 @@ def __getitem__(self, index): else: img, meta_data = load_nifti(self.image_files[index], as_closest_canonical=self.as_closest_canonical, image_only=self.image_only, dtype=self.dtype) + target = None if self.seg_files is not None: target = load_nifti(self.seg_files[index]) elif self.labels is not None: diff --git a/monai/data/synthetic.py b/monai/data/synthetic.py index a51d730357..1c49454c8e 100644 --- a/monai/data/synthetic.py +++ b/monai/data/synthetic.py @@ -14,12 +14,13 @@ from monai.transforms.utils import rescale_array -def create_test_image_2d(width, height, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5): +def create_test_image_2d(width, height, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5, channel_dim=None): """ - Return a noisy 2D image with `numObj' circles and a 2D mask image. The maximum radius of the circles is given as - `radMax'. The mask will have `numSegClasses' number of classes for segmentations labeled sequentially from 1, plus a - background class represented as 0. If `noiseMax' is greater than 0 then noise will be added to the image taken from - the uniform distribution on range [0,noiseMax). + Return a noisy 2D image with `num_obj` circles and a 2D mask image. The maximum radius of the circles is given as + `rad_max`. The mask will have `num_seg_classes` number of classes for segmentations labeled sequentially from 1, plus a + background class represented as 0. If `noise_max` is greater than 0 then noise will be added to the image taken from + the uniform distribution on range `[0,noise_max)`. If `channel_dim` is None, will create an image without channel + dimension, otherwise create an image with channel dimension as first dim or last dim. """ image = np.zeros((width, height)) @@ -40,14 +41,21 @@ def create_test_image_2d(width, height, num_objs=12, rad_max=30, noise_max=0.0, norm = np.random.uniform(0, num_seg_classes * noise_max, size=image.shape) noisyimage = rescale_array(np.maximum(image, norm)) + if channel_dim is not None: + assert isinstance(channel_dim, int) and channel_dim in (-1, 0, 2), 'invalid channel dim.' + noisyimage, labels = noisyimage[None], labels[None] \ + if channel_dim == 0 else (noisyimage[..., None], labels[..., None]) + return noisyimage, labels -def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5): +def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, + noise_max=0.0, num_seg_classes=5, channel_dim=None): """ Return a noisy 3D image and segmentation. - See also: create_test_image_2d + See also: + :py:meth:`~create_test_image_2d` """ image = np.zeros((width, height, depth)) @@ -69,4 +77,9 @@ def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_ma norm = np.random.uniform(0, num_seg_classes * noise_max, size=image.shape) noisyimage = rescale_array(np.maximum(image, norm)) + if channel_dim is not None: + assert isinstance(channel_dim, int) and channel_dim in (-1, 0, 3), 'invalid channel dim.' + noisyimage, labels = (noisyimage[None], labels[None]) \ + if channel_dim == 0 else (noisyimage[..., None], labels[..., None]) + return noisyimage, labels diff --git a/monai/data/utils.py b/monai/data/utils.py index 1e7de42141..a19916ac7c 100644 --- a/monai/data/utils.py +++ b/monai/data/utils.py @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import warnings import math from itertools import starmap, product from torch.utils.data._utils.collate import default_collate @@ -120,21 +121,21 @@ def dense_patch_slices(image_size, patch_size, scan_interval): def iter_patch(arr, patch_size, start_pos=(), copy_back=True, pad_mode="wrap", **pad_opts): """ - Yield successive patches from `arr' of size `patchSize'. The iteration can start from position `startPos' in `arr' - but drawing from a padded array extended by the `patchSize' in each dimension (so these coordinates can be negative - to start in the padded region). If `copyBack' is True the values from each patch are written back to `arr'. + Yield successive patches from `arr` of size `patch_size`. The iteration can start from position `start_pos` in `arr` + but drawing from a padded array extended by the `patch_size` in each dimension (so these coordinates can be negative + to start in the padded region). If `copy_back` is True the values from each patch are written back to `arr`. Args: arr (np.ndarray): array to iterate over patch_size (tuple of int or None): size of patches to generate slices for, 0 or None selects whole dimension start_pos (tuple of it, optional): starting position in the array, default is 0 for each dimension copy_back (bool): if True data from the yielded patches is copied back to `arr` once the generator completes - pad_mode (str, optional): padding mode, see numpy.pad - pad_opts (dict, optional): padding options, see numpy.pad + pad_mode (str, optional): padding mode, see `numpy.pad` + pad_opts (dict, optional): padding options, see `numpy.pad` Yields: Patches of array data from `arr` which are views into a padded array which can be modified, if `copy_back` is - True these changes will be reflected in `arr` once the iteration completes + True these changes will be reflected in `arr` once the iteration completes. """ # ensure patchSize and startPos are the right length patch_size = get_valid_patch_size(arr.shape, patch_size) @@ -191,3 +192,64 @@ def list_data_collate(batch): elem = batch[0] data = [i for k in batch for i in k] if isinstance(elem, list) else batch return default_collate(data) + + +def correct_nifti_header_if_necessary(img_nii): + """ + check nifti object header's format, update the header if needed. + in the updated image pixdim matches the affine. + + Args: + img (nifti image object) + """ + dim = img_nii.header['dim'][0] + if dim >= 5: + return img_nii # do nothing for high-dimensional array + # check that affine matches zooms + pixdim = np.asarray(img_nii.header.get_zooms())[:dim] + norm_affine = np.sqrt(np.sum(np.square(img_nii.affine[:dim, :dim]), 0)) + if np.allclose(pixdim, norm_affine): + return img_nii + if hasattr(img_nii, 'get_sform'): + return rectify_header_sform_qform(img_nii) + return img_nii + + +def rectify_header_sform_qform(img_nii): + """ + Look at the sform and qform of the nifti object and correct it if any + incompatibilities with pixel dimensions + + Adapted from https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/io/misc_io.py + """ + d = img_nii.header['dim'][0] + pixdim = np.asarray(img_nii.header.get_zooms())[:d] + sform, qform = img_nii.get_sform(), img_nii.get_qform() + norm_sform = np.sqrt(np.sum(np.square(sform[:d, :d]), 0)) + norm_qform = np.sqrt(np.sum(np.square(qform[:d, :d]), 0)) + sform_mismatch = not np.allclose(norm_sform, pixdim) + qform_mismatch = not np.allclose(norm_qform, pixdim) + + if img_nii.header['sform_code'] != 0: + if not sform_mismatch: + return img_nii + if not qform_mismatch: + img_nii.set_sform(img_nii.get_qform()) + return img_nii + if img_nii.header['qform_code'] != 0: + if not qform_mismatch: + return img_nii + if not sform_mismatch: + img_nii.set_qform(img_nii.get_sform()) + return img_nii + + norm_affine = np.sqrt(np.sum(np.square(img_nii.affine[:, :3]), 0)) + to_divide = np.tile(np.expand_dims(np.append(norm_affine, 1), axis=1), [1, 4]) + pixdim = np.append(pixdim, [1.] * (4 - len(pixdim))) + to_multiply = np.tile(np.expand_dims(pixdim, axis=1), [1, 4]) + affine = img_nii.affine / to_divide.T * to_multiply.T + warnings.warn('Modifying image affine from {} to {}'.format(img_nii.affine, affine)) + + img_nii.set_sform(affine) + img_nii.set_qform(affine) + return img_nii diff --git a/monai/engine/multi_gpu_supervised_trainer.py b/monai/engine/multi_gpu_supervised_trainer.py index 12d7605d0e..ea9fb2044d 100644 --- a/monai/engine/multi_gpu_supervised_trainer.py +++ b/monai/engine/multi_gpu_supervised_trainer.py @@ -53,13 +53,14 @@ def _default_eval_transform(x, y, y_pred): def create_multigpu_supervised_trainer(net, optimizer, loss_fn, devices=None, non_blocking=False, prepare_batch=_prepare_batch, output_transform=_default_transform): """ - ***Derived from `create_supervised_trainer` in Ignite. + Derived from `create_supervised_trainer` in Ignite. Factory function for creating a trainer for supervised models. + Args: net (`torch.nn.Module`): the network to train. optimizer (`torch.optim.Optimizer`): the optimizer to use. - loss_fn (torch.nn loss function): the loss function to use. + loss_fn (`torch.nn` loss function): the loss function to use. devices (list, optional): device(s) type specification (default: None). Applies to both model and batches. None is all devices used, empty list is CPU only. non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously @@ -68,10 +69,13 @@ def create_multigpu_supervised_trainer(net, optimizer, loss_fn, devices=None, no tuple of tensors `(batch_x, batch_y)`. output_transform (callable, optional): function that receives 'x', 'y', 'y_pred', 'loss' and returns value to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`. - Note: `engine.state.output` for this engine is defind by `output_transform` parameter and is the loss - of the processed batch by default. + Returns: Engine: a trainer engine with supervised update function. + + Note: + `engine.state.output` for this engine is defind by `output_transform` parameter and is the loss + of the processed batch by default. """ devices = get_devices_spec(devices) @@ -86,9 +90,10 @@ def create_multigpu_supervised_trainer(net, optimizer, loss_fn, devices=None, no def create_multigpu_supervised_evaluator(net, metrics=None, devices=None, non_blocking=False, prepare_batch=_prepare_batch, output_transform=_default_eval_transform): """ - ***Derived from `create_supervised_evaluator` in Ignite. + Derived from `create_supervised_evaluator` in Ignite. Factory function for creating an evaluator for supervised models. + Args: net (`torch.nn.Module`): the model to train. metrics (dict of str - :class:`~ignite.metrics.Metric`): a map of metric names to Metrics. @@ -101,8 +106,11 @@ def create_multigpu_supervised_evaluator(net, metrics=None, devices=None, non_bl output_transform (callable, optional): function that receives 'x', 'y', 'y_pred' and returns value to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)` which fits output expected by metrics. If you change it you should use `output_transform` in metrics. - Note: `engine.state.output` for this engine is defind by `output_transform` parameter and is + + Note: + `engine.state.output` for this engine is defind by `output_transform` parameter and is a tuple of `(batch_pred, batch_y)` by default. + Returns: Engine: an evaluator engine with supervised inference function. """ diff --git a/monai/handlers/checkpoint_loader.py b/monai/handlers/checkpoint_loader.py index bbf1323a17..82d1d67e04 100644 --- a/monai/handlers/checkpoint_loader.py +++ b/monai/handlers/checkpoint_loader.py @@ -26,8 +26,9 @@ class CheckpointLoader: Args: load_path (string): the file path of checkpoint, it should be a PyTorch pth file. - load_dict (dict): target objects that load checkpoint to. examples: - {'network': net, 'optimizer': optimizer, 'engine', engine} + load_dict (dict): target objects that load checkpoint to. examples:: + + {'network': net, 'optimizer': optimizer, 'engine', engine} """ diff --git a/monai/handlers/classification_saver.py b/monai/handlers/classification_saver.py new file mode 100644 index 0000000000..501dce816f --- /dev/null +++ b/monai/handlers/classification_saver.py @@ -0,0 +1,95 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import csv +import numpy as np +import torch +from ignite.engine import Events +import logging + + +class ClassificationSaver: + """ + Event handler triggered on completing every iteration to save the classification predictions as CSV file. + """ + + def __init__(self, output_dir='./', overwrite=True, + batch_transform=lambda x: x, output_transform=lambda x: x, name=None): + """ + Args: + output_dir (str): output CSV file directory. + overwrite (bool): whether to overwriting existing CSV file content. If we are not overwriting, + then we check if the results have been previously saved, and load them to the prediction_dict. + batch_transform (Callable): a callable that is used to transform the + ignite.engine.batch into expected format to extract the meta_data dictionary. + output_transform (Callable): a callable that is used to transform the + ignite.engine.output into the form expected model prediction data. + The first dimension of this transform's output will be treated as the + batch dimension. Each item in the batch will be saved individually. + name (str): identifier of logging.logger to use, defaulting to `engine.logger`. + + """ + self.output_dir = output_dir + self._prediction_dict = {} + self._preds_filepath = os.path.join(output_dir, 'predictions.csv') + self.overwrite = overwrite + self.batch_transform = batch_transform + self.output_transform = output_transform + + self.logger = None if name is None else logging.getLogger(name) + + def attach(self, engine): + if self.logger is None: + self.logger = engine.logger + if not engine.has_event_handler(self, Events.ITERATION_COMPLETED): + engine.add_event_handler(Events.ITERATION_COMPLETED, self) + if not engine.has_event_handler(self.finalize, Events.COMPLETED): + engine.add_event_handler(Events.COMPLETED, self.finalize) + + def finalize(self, _engine=None): + """ + Writes the prediction dict to a csv + + """ + if not self.overwrite and os.path.exists(self._preds_filepath): + with open(self._preds_filepath, 'r') as f: + reader = csv.reader(f) + for row in reader: + self._prediction_dict[row[0]] = np.array(row[1:]).astype(np.float32) + + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + with open(self._preds_filepath, 'w') as f: + for k, v in sorted(self._prediction_dict.items()): + f.write(k) + for result in v.flatten(): + f.write("," + str(result)) + f.write("\n") + self.logger.info('saved classification predictions into: {}'.format(self._preds_filepath)) + + def __call__(self, engine): + """ + This method assumes self.batch_transform will extract Metadata from the input batch. + Metadata should have the following keys: + + - ``'filename_or_obj'`` -- save the prediction corresponding to file name. + + """ + meta_data = self.batch_transform(engine.state.batch) + filenames = meta_data['filename_or_obj'] + + engine_output = self.output_transform(engine.state.output) + for batch_id, filename in enumerate(filenames): # save a batch of files + output = engine_output[batch_id] + if isinstance(output, torch.Tensor): + output = output.detach().cpu().numpy() + self._prediction_dict[filename] = output.astype(np.float32) diff --git a/monai/handlers/segmentation_saver.py b/monai/handlers/segmentation_saver.py index a87e517f81..98eb972d3a 100644 --- a/monai/handlers/segmentation_saver.py +++ b/monai/handlers/segmentation_saver.py @@ -10,10 +10,10 @@ # limitations under the License. import os - +import numpy as np import torch from ignite.engine import Events - +import logging from monai.data.nifti_writer import write_nifti @@ -23,13 +23,15 @@ class SegmentationSaver: """ def __init__(self, output_path='./', dtype='float32', output_postfix='seg', output_ext='.nii.gz', - output_transform=lambda x: x, name=None): + batch_transform=lambda x: x, output_transform=lambda x: x, name=None): """ Args: output_path (str): output image directory. dtype (str): to convert the image to save to this datatype. output_postfix (str): a string appended to all output file names. output_ext (str): output file extension name. + batch_transform (Callable): a callable that is used to transform the + ignite.engine.batch into expected format to extract the meta_data dictionary. output_transform (Callable): a callable that is used to transform the ignite.engine.output into the form expected nifti image data. The first dimension of this transform's output will be treated as the @@ -40,6 +42,7 @@ def __init__(self, output_path='./', dtype='float32', output_postfix='seg', outp self.dtype = dtype self.output_postfix = output_postfix self.output_ext = output_ext + self.batch_transform = batch_transform self.output_transform = output_transform self.logger = None if name is None else logging.getLogger(name) @@ -47,7 +50,8 @@ def __init__(self, output_path='./', dtype='float32', output_postfix='seg', outp def attach(self, engine): if self.logger is None: self.logger = engine.logger - return engine.add_event_handler(Events.ITERATION_COMPLETED, self) + if not engine.has_event_handler(self, Events.ITERATION_COMPLETED): + engine.add_event_handler(Events.ITERATION_COMPLETED, self) @staticmethod def _create_file_basename(postfix, input_file_name, folder_path, data_root_dir=""): @@ -88,24 +92,30 @@ def _create_file_basename(postfix, input_file_name, folder_path, data_root_dir=" def __call__(self, engine): """ - This method assumes: - - 3rd output of engine.state.batch is a meta data dict, and have the keys: - 'filename_or_obj' -- for output file name creation - and optionally 'original_affine', 'affine' for data orientation handling. - - output file datatype from `engine.state.output.dtype`. + This method assumes self.batch_transform will extract Metadata from the input batch. + Metadata should have the following keys: + + - ``'filename_or_obj'`` -- for output file name creation + - ``'original_affine'`` (optional) for data orientation handling + - ``'affine'`` (optional) for data output affine. + + output file datatype is determined from ``engine.state.output.dtype``. """ - meta_data = engine.state.batch[2] # assuming 3rd output of input dataset is a meta data dict + meta_data = self.batch_transform(engine.state.batch) filenames = meta_data['filename_or_obj'] original_affine = meta_data.get('original_affine', None) affine = meta_data.get('affine', None) + engine_output = self.output_transform(engine.state.output) for batch_id, filename in enumerate(filenames): # save a batch of files seg_output = engine_output[batch_id] - _affine = affine[batch_id] - _original_affine = original_affine[batch_id] + affine_ = affine[batch_id] + original_affine_ = original_affine[batch_id] if isinstance(seg_output, torch.Tensor): seg_output = seg_output.detach().cpu().numpy() output_filename = self._create_file_basename(self.output_postfix, filename, self.output_path) output_filename = '{}{}'.format(output_filename, self.output_ext) - write_nifti(seg_output, _affine, output_filename, _original_affine, dtype=seg_output.dtype) + # change output to "channel last" format and write to nifti format file + to_save = np.moveaxis(seg_output, 0, -1) + write_nifti(to_save, affine_, output_filename, original_affine_, dtype=seg_output.dtype) self.logger.info('saved: {}'.format(output_filename)) diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py index b1ba3563d5..1dd4cf4e8a 100644 --- a/monai/handlers/stats_handler.py +++ b/monai/handlers/stats_handler.py @@ -9,38 +9,64 @@ # See the License for the specific language governing permissions and # limitations under the License. +import warnings import logging - +import torch from ignite.engine import Engine, Events +from monai.utils.misc import is_scalar -KEY_VAL_FORMAT = '{}: {:.4f} ' +DEFAULT_KEY_VAL_FORMAT = '{}: {:.4f} ' +DEFAULT_TAG = 'Loss' class StatsHandler(object): """StatsHandler defines a set of Ignite Event-handlers for all the log printing logics. It's can be used for any Ignite Engine(trainer, validator and evaluator). - And it can support logging for epoch level and iteration level with pre-defined StatsLoggers. - By default, this class logs the dictionary of `engine.state.metrics`. + And it can support logging for epoch level and iteration level with pre-defined loggers. + + Default behaviors: + - When EPOCH_COMPLETED, logs ``engine.state.metrics`` using ``self.logger``. + - When ITERATION_COMPELTED, logs + ``self.output_transform(engine.state.output)`` using ``self.logger``. + """ def __init__(self, epoch_print_logger=None, iteration_print_logger=None, - name=None): + output_transform=lambda x: x, + global_epoch_transform=lambda x: x, + name=None, + tag_name=DEFAULT_TAG, + key_var_format=DEFAULT_KEY_VAL_FORMAT): """ + Args: epoch_print_logger (Callable): customized callable printer for epoch level logging. - must accept parameter "engine", use default printer if None. + must accept parameter "engine", use default printer if None. iteration_print_logger (Callable): custimized callable printer for iteration level logging. - must accept parameter "engine", use default printer if None. - name (str): identifier of logging.logger to use, defaulting to `engine.logger`. + must accept parameter "engine", use default printer if None. + output_transform (Callable): a callable that is used to transform the + ``ignite.engine.output`` into a scalar to print, or a dictionary of {key: scalar}. + in the latter case, the output string will be formated as key: value. + by default this value logging happens when every iteration completed. + global_epoch_transform (Callable): a callable that is used to customize global epoch number. + For example, in evaluation, the evaluator engine might want to print synced epoch number + with the trainer engine. + name (str): identifier of logging.logger to use, defaulting to ``engine.logger``. + tag_name (string): when iteration output is a scalar, tag_name is used to print + tag_name: scalar_value to logger. Defaults to ``'Loss'``. + key_var_format (string): a formatting string to control the output string format of key: value. """ self.epoch_print_logger = epoch_print_logger self.iteration_print_logger = iteration_print_logger - + self.output_transform = output_transform + self.global_epoch_transform = global_epoch_transform self.logger = None if name is None else logging.getLogger(name) + self.tag_name = tag_name + self.key_var_format = key_var_format def attach(self, engine: Engine): """Register a set of Ignite Event-Handlers to a specified Ignite engine. @@ -108,39 +134,60 @@ def _default_epoch_print(self, engine: Engine): prints_dict = engine.state.metrics if not prints_dict: return - current_epoch = engine.state.epoch + current_epoch = self.global_epoch_transform(engine.state.epoch) out_str = "Epoch[{}] Metrics -- ".format(current_epoch) for name in sorted(prints_dict): value = prints_dict[name] - out_str += KEY_VAL_FORMAT.format(name, value) + out_str += self.key_var_format.format(name, value) self.logger.info(out_str) def _default_iteration_print(self, engine: Engine): """Execute iteration log operation based on Ignite engine.state data. - print the values from ignite state.logs dict. + Print the values from ignite state.logs dict. + Default behaivor is to print loss from output[1], skip if output[1] is not loss. Args: engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. """ - prints_dict = engine.state.metrics - if not prints_dict: - return + loss = self.output_transform(engine.state.output) + if loss is None: + return # no printing if the output is empty + + out_str = '' + if isinstance(loss, dict): # print dictionary items + for name in sorted(loss): + value = loss[name] + if not is_scalar(value): + warnings.warn('ignoring non-scalar output in StatsHandler,' + ' make sure `output_transform(engine.state.output)` returns' + ' a scalar or dictionary of key and scalar pairs to avoid this warning.' + ' {}:{}'.format(name, type(value))) + continue # not printing multi dimensional output + out_str += self.key_var_format.format(name, value.item() if torch.is_tensor(value) else value) + else: + if is_scalar(loss): # not printing multi dimensional output + out_str += self.key_var_format.format(self.tag_name, loss.item() if torch.is_tensor(loss) else loss) + else: + warnings.warn('ignoring non-scalar output in StatsHandler,' + ' make sure `output_transform(engine.state.output)` returns' + ' a scalar or a dictionary of key and scalar pairs to avoid this warning.' + ' {}'.format(type(loss))) + + if not out_str: + return # no value to print + num_iterations = engine.state.epoch_length current_iteration = (engine.state.iteration - 1) % num_iterations + 1 current_epoch = engine.state.epoch num_epochs = engine.state.max_epochs - out_str = "Epoch: {}/{}, Iter: {}/{} -- ".format( + base_str = "Epoch: {}/{}, Iter: {}/{} --".format( current_epoch, num_epochs, current_iteration, num_iterations) - for name in sorted(prints_dict): - value = prints_dict[name] - out_str += KEY_VAL_FORMAT.format(name, value) - - self.logger.info(out_str) + self.logger.info(' '.join([base_str, out_str])) diff --git a/monai/handlers/tensorboard_handlers.py b/monai/handlers/tensorboard_handlers.py new file mode 100644 index 0000000000..fb5eb34e4e --- /dev/null +++ b/monai/handlers/tensorboard_handlers.py @@ -0,0 +1,258 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import warnings +import torch +from torch.utils.tensorboard import SummaryWriter +from ignite.engine import Engine, Events +from monai.visualize import img2tensorboard +from monai.utils.misc import is_scalar +from monai.transforms.utils import rescale_array + +DEFAULT_TAG = 'Loss' + + +class TensorBoardStatsHandler(object): + """TensorBoardStatsHandler defines a set of Ignite Event-handlers for all the TensorBoard logics. + It's can be used for any Ignite Engine(trainer, validator and evaluator). + And it can support both epoch level and iteration level with pre-defined TensorBoard event writer. + The expected data source is ignite ``engine.state.output`` and ``engine.state.metrics``. + + Default behaviors: + - When EPOCH_COMPLETED, write each dictionary item in + ``engine.state.metrics`` to TensorBoard. + - When ITERATION_COMPELTED, write each dictionary item in + ``self.output_transform(engine.state.output)`` to TensorBoard. + """ + + def __init__(self, + summary_writer=None, + epoch_event_writer=None, + iteration_event_writer=None, + output_transform=lambda x: x, + global_epoch_transform=lambda x: x, + tag_name=DEFAULT_TAG): + """ + Args: + summary_writer (SummaryWriter): user can specify TensorBoard SummaryWriter, + default to create a new writer. + epoch_event_writer (Callable): customized callable TensorBoard writer for epoch level. + must accept parameter "engine" and "summary_writer", use default event writer if None. + iteration_event_writer (Callable): custimized callable TensorBoard writer for iteration level. + must accept parameter "engine" and "summary_writer", use default event writer if None. + output_transform (Callable): a callable that is used to transform the + ``ignite.engine.output`` into a scalar to plot, or a dictionary of {key: scalar}. + in the latter case, the output string will be formated as key: value. + by default this value plotting happens when every iteration completed. + global_epoch_transform (Callable): a callable that is used to customize global epoch number. + For example, in evaluation, the evaluator engine might want to use trainer engines epoch number + when plotting epoch vs metric curves. + tag_name (string): when iteration output is a scalar, tag_name is used to plot, defaults to ``'Loss'``. + """ + self._writer = SummaryWriter() if summary_writer is None else summary_writer + self.epoch_event_writer = epoch_event_writer + self.iteration_event_writer = iteration_event_writer + self.output_transform = output_transform + self.global_epoch_transform = global_epoch_transform + self.tag_name = tag_name + + def attach(self, engine: Engine): + """Register a set of Ignite Event-Handlers to a specified Ignite engine. + + Args: + engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. + + """ + if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED): + engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed) + if not engine.has_event_handler(self.epoch_completed, Events.EPOCH_COMPLETED): + engine.add_event_handler(Events.EPOCH_COMPLETED, self.epoch_completed) + + def epoch_completed(self, engine: Engine): + """handler for train or validation/evaluation epoch completed Event. + Write epoch level events, default values are from ignite state.metrics dict. + + Args: + engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. + + """ + if self.epoch_event_writer is not None: + self.epoch_event_writer(engine, self._writer) + else: + self._default_epoch_writer(engine, self._writer) + + def iteration_completed(self, engine: Engine): + """handler for train or validation/evaluation iteration completed Event. + Write iteration level events, default values are from ignite state.logs dict. + + Args: + engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. + + """ + if self.iteration_event_writer is not None: + self.iteration_event_writer(engine, self._writer) + else: + self._default_iteration_writer(engine, self._writer) + + def _default_epoch_writer(self, engine: Engine, writer: SummaryWriter): + """Execute epoch level event write operation based on Ignite engine.state data. + Default is to write the values from ignite state.metrics dict. + + Args: + engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. + writer (SummaryWriter): TensorBoard writer, created in TensorBoardHandler. + + """ + current_epoch = self.global_epoch_transform(engine.state.epoch) + summary_dict = engine.state.metrics + for name, value in summary_dict.items(): + writer.add_scalar(name, value, current_epoch) + writer.flush() + + def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter): + """Execute iteration level event write operation based on Ignite engine.state data. + Default is to write the loss value of current iteration. + + Args: + engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator. + writer (SummaryWriter): TensorBoard writer, created in TensorBoardHandler. + + """ + loss = self.output_transform(engine.state.output) + if loss is None: + return # do nothing if output is empty + if isinstance(loss, dict): + for name in sorted(loss): + value = loss[name] + if not is_scalar(value): + warnings.warn('ignoring non-scalar output in TensorBoardStatsHandler,' + ' make sure `output_transform(engine.state.output)` returns' + ' a scalar or dictionary of key and scalar pairs to avoid this warning.' + ' {}:{}'.format(name, type(value))) + continue # not plot multi dimensional output + writer.add_scalar(name, value.item() if torch.is_tensor(value) else value, engine.state.iteration) + elif is_scalar(loss): # not printing multi dimensional output + writer.add_scalar(self.tag_name, loss.item() if torch.is_tensor(loss) else loss, engine.state.iteration) + else: + warnings.warn('ignoring non-scalar output in TensorBoardStatsHandler,' + ' make sure `output_transform(engine.state.output)` returns' + ' a scalar or a dictionary of key and scalar pairs to avoid this warning.' + ' {}'.format(type(loss))) + writer.flush() + + +class TensorBoardImageHandler(object): + """TensorBoardImageHandler is an ignite Event handler that can visualise images, labels and outputs as 2D/3D images. + 2D output (shape in Batch, channel, H, W) will be shown as simple image using the first element in the batch, + for 3D to ND output (shape in Batch, channel, H, W, D) input, each of ``self.max_channels`` number of images' + last three dimensions will be shown as animated GIF along the last axis (typically Depth). + + It's can be used for any Ignite Engine (trainer, validator and evaluator). + User can easily added it to engine for any expected Event, for example: ``EPOCH_COMPLETED``, + ``ITERATION_COMPLETED``. The expected data source is ignite's ``engine.state.batch`` and ``engine.state.output``. + + Default behavior: + - Show y_pred as images (GIF for 3D) on TensorBoard when Event triggered, + - need to use ``batch_transform`` and ``output_transform`` to specify + how many images to show and show which channel. + - Expects ``batch_transform(engine.state.batch)`` to return data + format: (image[N, channel, ...], label[N, channel, ...]). + - Expects ``output_transform(engine.state.output)`` to return a torch + tensor in format (y_pred[N, channel, ...], loss). + + """ + + def __init__(self, + summary_writer=None, + batch_transform=lambda x: x, + output_transform=lambda x: x, + global_iter_transform=lambda x: x, + max_channels=1, + max_frames=64): + """ + Args: + summary_writer (SummaryWriter): user can specify TensorBoard SummaryWriter, + default to create a new writer. + batch_transform (Callable): a callable that is used to transform the + ``ignite.engine.batch`` into expected format to extract several label data. + output_transform (Callable): a callable that is used to transform the + ``ignite.engine.output`` into expected format to extract several output data. + global_iter_transform (Callable): a callable that is used to customize global step number for TensorBoard. + For example, in evaluation, the evaluator engine needs to know current epoch from trainer. + max_channels (int): number of channels to plot. + max_frames (int): number of frames for 2D-t plot. + """ + self._writer = SummaryWriter() if summary_writer is None else summary_writer + self.batch_transform = batch_transform + self.output_transform = output_transform + self.global_iter_transform = global_iter_transform + + self.max_frames = max_frames + self.max_channels = max_channels + + def __call__(self, engine): + step = self.global_iter_transform(engine.state.iteration) + + show_images = self.batch_transform(engine.state.batch)[0] + if torch.is_tensor(show_images): + show_images = show_images.detach().cpu().numpy() + if show_images is not None: + if not isinstance(show_images, np.ndarray): + raise ValueError('output_transform(engine.state.output)[0] must be an ndarray or tensor.') + self._add_2_or_3_d(show_images, step, 'input_0') + + show_labels = self.batch_transform(engine.state.batch)[1] + if torch.is_tensor(show_labels): + show_labels = show_labels.detach().cpu().numpy() + if show_labels is not None: + if not isinstance(show_labels, np.ndarray): + raise ValueError('batch_transform(engine.state.batch)[1] must be an ndarray or tensor.') + self._add_2_or_3_d(show_labels, step, 'input_1') + + show_outputs = self.output_transform(engine.state.output) + if torch.is_tensor(show_outputs): + show_outputs = show_outputs.detach().cpu().numpy() + if show_outputs is not None: + if not isinstance(show_outputs, np.ndarray): + raise ValueError('output_transform(engine.state.output) must be an ndarray or tensor.') + self._add_2_or_3_d(show_outputs, step, 'output') + + self._writer.flush() + + def _add_2_or_3_d(self, data, step, tag='output'): + # for i, d in enumerate(data): # go through a batch of images + d = data[0] # show the first element in a batch + + if d.ndim == 2: + d = rescale_array(d, 0, 1) + dataformats = 'HW' + self._writer.add_image('{}_{}'.format(tag, dataformats), d, step, dataformats=dataformats) + return + + if d.ndim == 3: + if d.shape[0] == 3 and self.max_channels == 3: # RGB + dataformats = 'CHW' + self._writer.add_image('{}_{}'.format(tag, dataformats), d, step, dataformats=dataformats) + return + for j, d2 in enumerate(d[:self.max_channels]): + d2 = rescale_array(d2, 0, 1) + dataformats = 'HW' + self._writer.add_image('{}_{}_{}'.format(tag, dataformats, j), d2, step, dataformats=dataformats) + return + + if d.ndim >= 4: + spatial = d.shape[-3:] + for j, d3 in enumerate(d.reshape([-1] + list(spatial))[:self.max_channels]): + d3 = rescale_array(d3, 0, 255) + img2tensorboard.add_animated_gif( + self._writer, '{}_HWD_{}'.format(tag, j), d3[None], self.max_frames, 1.0, step) + return diff --git a/monai/handlers/utils.py b/monai/handlers/utils.py index 377d4d0073..1cd849d18e 100644 --- a/monai/handlers/utils.py +++ b/monai/handlers/utils.py @@ -12,13 +12,17 @@ def stopping_fn_from_metric(metric_name): """Returns a stopping function for ignite.handlers.EarlyStopping using the given metric name.""" + def stopping_fn(engine): return engine.state.metrics[metric_name] + return stopping_fn def stopping_fn_from_loss(): """Returns a stopping function for ignite.handlers.EarlyStopping using the loss value.""" + def stopping_fn(engine): return -engine.state.output + return stopping_fn diff --git a/monai/losses/dice.py b/monai/losses/dice.py index 46792a4714..10dc15ad77 100644 --- a/monai/losses/dice.py +++ b/monai/losses/dice.py @@ -23,11 +23,11 @@ @alias("dice", "Dice") class DiceLoss(_Loss): """ - Multiclass dice loss. Input logits 'pred' (BNHW[D] where N is number of classes) is compared with ground truth - `ground' (B1HW[D]). Axis N of `pred' is expected to have logit predictions for each class rather than being image - channels, while the same axis of `ground' should be 1. If the N channel of `pred' is 1 binary dice loss will be - calculated. The `smooth' parameter is a value added to the intersection and union components of the inter-over-union - calculation to smooth results and prevent divide-by-0, this value should be small. The `include_background' class + Multiclass dice loss. Input logits `pred` (BNHW[D] where N is number of classes) is compared with ground truth + `ground' (B1HW[D]). Axis N of `pred` is expected to have logit predictions for each class rather than being image + channels, while the same axis of `ground` should be 1. If the N channel of `pred` is 1 binary dice loss will be + calculated. The `smooth` parameter is a value added to the intersection and union components of the inter-over-union + calculation to smooth results and prevent divide-by-0, this value should be small. The `include_background` class attribute can be set to False for an instance of DiceLoss to exclude the first category (channel index 0) which is by convention assumed to be background. If the non-background segmentations are small compared to the total image size they can get overwhelmed by the signal from the background so excluding it in such cases helps convergence. @@ -78,7 +78,7 @@ def forward(self, pred, ground, smooth=1e-5): intersection = psum * tsum sums = psum + tsum - score = 2.0 * (intersection.sum(2) + smooth) / (sums.sum(2) + smooth) + score = (2.0 * intersection.sum(2) + smooth) / (sums.sum(2) + smooth) return 1 - score.mean() @@ -86,6 +86,7 @@ def forward(self, pred, ground, smooth=1e-5): class GeneralizedDiceLoss(_Loss): """ Compute the generalised Dice loss defined in: + Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. DLMIA 2017. @@ -159,5 +160,5 @@ def forward(self, pred, ground, smooth=1e-5): b[infs] = 0.0 b[infs] = torch.max(b) - score = 2.0 * (intersection.sum(2) * w) / (sums.sum(2) * w + smooth) + score = (2.0 * intersection.sum(2) * w + smooth) / (sums.sum(2) * w + smooth) return 1 - score.mean() diff --git a/monai/metrics/compute_meandice.py b/monai/metrics/compute_meandice.py index 37bf95c646..d88ec32490 100644 --- a/monai/metrics/compute_meandice.py +++ b/monai/metrics/compute_meandice.py @@ -44,10 +44,10 @@ def compute_meandice(y_pred, Dice scores per batch and per class (shape: [batch_size, n_classes]). Note: - This method provide two options to convert `y_pred` into a binary matrix: - (1) when `mutually_exclusive` is True, it uses a combination of argmax and to_onehot, - (2) when `mutually_exclusive` is False, it uses a threshold `logit_thresh` - (optionally with a sigmoid function before thresholding). + This method provides two options to convert `y_pred` into a binary matrix + (1) when `mutually_exclusive` is True, it uses a combination of ``argmax`` and ``to_onehot``, + (2) when `mutually_exclusive` is False, it uses a threshold ``logit_thresh`` + (optionally with a ``sigmoid`` function before thresholding). """ n_classes = y_pred.shape[1] diff --git a/monai/networks/layers/convutils.py b/monai/networks/layers/convutils.py index 009d828e95..96781448f7 100644 --- a/monai/networks/layers/convutils.py +++ b/monai/networks/layers/convutils.py @@ -26,8 +26,8 @@ def same_padding(kernel_size, dilation=1): def calculate_out_shape(in_shape, kernel_size, stride, padding): """ - Calculate the output tensor shape when applying a convolution to a tensor of shape `inShape' with kernel size - 'kernel_size', stride value `stride', and input padding value `padding'. All arguments can be scalars or multiple + Calculate the output tensor shape when applying a convolution to a tensor of shape `inShape` with kernel size + `kernel_size`, stride value `stride`, and input padding value `padding`. All arguments can be scalars or multiple values, return value is a scalar if all inputs are scalars. """ in_shape = np.atleast_1d(in_shape) @@ -35,3 +35,25 @@ def calculate_out_shape(in_shape, kernel_size, stride, padding): out_shape = tuple(int(s) for s in out_shape) return tuple(out_shape) if len(out_shape) > 1 else out_shape[0] + + +def gaussian_1d(sigma, truncated=4.): + """ + one dimensional gaussian kernel. + + Args: + sigma: std of the kernel + truncated: tail length + + Returns: + 1D numpy array + """ + if sigma <= 0: + raise ValueError('sigma must be positive') + + tail = int(sigma * truncated + .5) + sigma2 = sigma * sigma + x = np.arange(-tail, tail + 1) + out = np.exp(-.5 / sigma2 * x ** 2) + out /= out.sum() + return out diff --git a/monai/networks/layers/factories.py b/monai/networks/layers/factories.py index b295453bbd..139de92655 100644 --- a/monai/networks/layers/factories.py +++ b/monai/networks/layers/factories.py @@ -9,6 +9,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +handles spatial 1D, 2D, 3D network components with a factory pattern. +""" + from torch import nn as nn diff --git a/monai/networks/layers/simplelayers.py b/monai/networks/layers/simplelayers.py index 716c9291b3..c41ff93a0f 100644 --- a/monai/networks/layers/simplelayers.py +++ b/monai/networks/layers/simplelayers.py @@ -11,6 +11,9 @@ import torch import torch.nn as nn +import torch.nn.functional as F + +from monai.networks.layers.convutils import gaussian_1d, same_padding class SkipConnection(nn.Module): @@ -30,3 +33,47 @@ class Flatten(nn.Module): def forward(self, x): return x.view(x.size(0), -1) + + +class GaussianFilter: + + def __init__(self, spatial_dims, sigma, truncated=4., device=None): + """ + Args: + spatial_dims (int): number of spatial dimensions of the input image. + must have shape (Batch, channels, H[, W, ...]). + sigma (float): std. + truncated (float): spreads how many stds. + device (torch.device): device on which the tensor will be allocated. + """ + self.kernel = torch.nn.Parameter(torch.tensor(gaussian_1d(sigma, truncated)), False) + self.spatial_dims = spatial_dims + self.conv_n = [F.conv1d, F.conv2d, F.conv3d][spatial_dims - 1] + self.padding = same_padding(self.kernel.size()[0]) + self.device = device + + self.kernel = self.kernel.to(self.device) + + def __call__(self, x): + """ + Args: + x (tensor): in shape [Batch, chns, H, W, D]. + """ + if not torch.is_tensor(x): + x = torch.Tensor(x) + chns = x.shape[1] + sp_dim = self.spatial_dims + x = x.to(self.device) + + def _conv(input_, d): + if d < 0: + return input_ + s = [1] * (sp_dim + 2) + s[d + 2] = -1 + kernel = self.kernel.reshape(s).float() + kernel = kernel.repeat([chns, 1] + [1] * sp_dim) + padding = [0] * sp_dim + padding[d] = self.padding + return self.conv_n(input=_conv(input_, d - 1), weight=kernel, padding=padding, groups=chns) + + return _conv(x, sp_dim - 1) diff --git a/monai/networks/nets/densenet3d.py b/monai/networks/nets/densenet3d.py index f5493f6c04..78fab167c4 100644 --- a/monai/networks/nets/densenet3d.py +++ b/monai/networks/nets/densenet3d.py @@ -146,7 +146,6 @@ def __init__(self, OrderedDict([ ('relu', nn.ReLU(inplace=True)), ('norm', get_avgpooling_type(spatial_dims, is_adaptive=True)(1)), - ('relu', nn.ReLU(inplace=True)), ('flatten', nn.Flatten(1)), ('class', nn.Linear(in_channels, out_channels)), ])) diff --git a/monai/networks/nets/unet.py b/monai/networks/nets/unet.py index b0d42612eb..ad9b3ddbf4 100644 --- a/monai/networks/nets/unet.py +++ b/monai/networks/nets/unet.py @@ -13,7 +13,6 @@ from monai.networks.blocks.convolutions import Convolution, ResidualUnit from monai.networks.layers.simplelayers import SkipConnection -from monai.networks.utils import predict_segmentation from monai.utils import export from monai.utils.aliases import alias @@ -22,13 +21,13 @@ @alias("Unet", "unet") class UNet(nn.Module): - def __init__(self, dimensions, in_channels, num_classes, channels, strides, kernel_size=3, up_kernel_size=3, + def __init__(self, dimensions, in_channels, out_channels, channels, strides, kernel_size=3, up_kernel_size=3, num_res_units=0, instance_norm=True, dropout=0): super().__init__() assert len(channels) == (len(strides) + 1) self.dimensions = dimensions self.in_channels = in_channels - self.num_classes = num_classes + self.out_channels = out_channels self.channels = channels self.strides = strides self.kernel_size = kernel_size @@ -58,7 +57,7 @@ def _create_block(inc, outc, channels, strides, is_top): return nn.Sequential(down, SkipConnection(subblock), up) - self.model = _create_block(in_channels, num_classes, self.channels, self.strides, True) + self.model = _create_block(in_channels, out_channels, self.channels, self.strides, True) def _get_down_layer(self, in_channels, out_channels, strides, is_top): if self.num_res_units > 0: @@ -98,4 +97,4 @@ def _get_up_layer(self, in_channels, out_channels, strides, is_top): def forward(self, x): x = self.model(x) - return x, predict_segmentation(x) + return x diff --git a/monai/networks/utils.py b/monai/networks/utils.py index 5a22884846..628e4ea762 100644 --- a/monai/networks/utils.py +++ b/monai/networks/utils.py @@ -9,7 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -Utilities and types for defining networks, these depend on Pytorch. +Utilities and types for defining networks, these depend on PyTorch. """ import torch @@ -18,10 +18,11 @@ def one_hot(labels, num_classes): """ - For a tensor `labels' of dimensions B1[spatial_dims], return a tensor of dimensions BN[spatial_dims] - for `num_classes' N number of classes. + For a tensor `labels` of dimensions B1[spatial_dims], return a tensor of dimensions `BN[spatial_dims]` + for `num_classes` N number of classes. Example: + For every value v = labels[b,1,h,w], the value in the result at [b,v,h,w] will be 1 and all others 0. Note that this will include the background label, thus a binary mask should be treated as having 2 classes. """ @@ -47,11 +48,10 @@ def slice_channels(tensor, *slicevals): def predict_segmentation(logits): """ - Given the logits from a network, computing the segmentation by thresholding all values above 0 if `logits' has one - channel, or computing the argmax along the channel axis otherwise. + Given the logits from a network, computing the segmentation by thresholding all values above 0 if `logits` has one + channel, or computing the `argmax` along the channel axis otherwise, logits has shape `BCHW[D]` """ - # generate prediction outputs, logits has shape BCHW[D] if logits.shape[1] == 1: - return (logits[:, 0] >= 0).int() # for binary segmentation threshold on channel 0 + return (logits >= 0).int() # for binary segmentation threshold on channel 0 else: - return logits.max(1)[1] # take the index of the max value along dimension 1 + return logits.argmax(1).unsqueeze(1) # take the index of the max value along dimension 1 diff --git a/monai/transforms/adaptors.py b/monai/transforms/adaptors.py new file mode 100644 index 0000000000..183085e5db --- /dev/null +++ b/monai/transforms/adaptors.py @@ -0,0 +1,244 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +How to use the adaptor function +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The key to using 'adaptor' lies in understanding the function that want to +adapt. The 'inputs' and 'outputs' parameters take either strings, lists/tuples +of strings or a dictionary mapping strings, depending on call signature of the +function being called. + +The adaptor function is written to minimise the cognitive load on the caller. +There should be a minimal number of cases where the caller has to set anything +on the input parameter, and for functions that return a single value, it is +only necessary to name the dictionary keyword to which that value is assigned. + +Use of `outputs` +---------------- + +`outputs` can take either a string, a list/tuple of string or a dict of string +to string, depending on what the transform being adapted returns: + + - If the transform returns a single argument, then outputs can be supplied a + string that indicates what key to assign the return value to in the + dictionary + - If the transform returns a list/tuple of values, then outputs can be supplied + a list/tuple of the same length. The strings in outputs map the return value + at the corresponding position to a key in the dictionary + - If the transform returns a dictionary of values, then outputs must be supplied + a dictionary that maps keys in the function's return dictionary to the + dictionary being passed between functions + +Note, the caller is free to use a more complex way of specifying the outputs +parameter than is required. The following are synonymous and will be treated +identically: + +.. code-block:: python + + # single argument + adaptor(MyTransform(), 'image') + adaptor(MyTransform(), ['image']) + adaptor(MyTransform(), {'image': 'image'}) + + # multiple arguments + adaptor(MyTransform(), ['image', 'label']) + adaptor(MyTransform(), {'image': 'image', 'label': 'label'}) + +Use of `inputs` +--------------- + +`inputs` can usually be omitted when using `adaptor`. It is only required when a +the function's parameter names do not match the names in the dictionary that is +used to chain transform calls. + +.. code-block:: python + + class MyTransform1: + ... + def __call__(image): + return '''do stuff to image''' + + class MyTransform2: + ... + def __call__(img): + return '''do stuff to image''' + + d = {'image': i} + + Compose([ + adaptor(MyTransform1(), 'image'), + adaptor(MyTransform2(), 'image', {'img':'image'}) + ]) + +Inputs: + +- dictionary in: None | Name maps +- params in (match): None | Name list | Name maps +- params in (mismatch): Name maps +- params & `**kwargs` (match) : None | Name maps +- params & `**kwargs` (mismatch) : Name maps + +Outputs: + +- dictionary out: None | Name maps +- list/tuple out: list/tuple +- variable out: string + +""" + +import monai + + +@monai.utils.export('monai.transforms') +def adaptor(function, outputs, inputs=None): + + def must_be_types_or_none(variable_name, variable, types): + if variable is not None: + if not isinstance(variable, types): + raise ValueError( + "'{}' must be None or {} but is {}".format( + variable_name, types, type(variable))) + + def must_be_types(variable_name, variable, types): + if not isinstance(variable, types): + raise ValueError( + "'{}' must be one of {} but is {}".format( + variable_name, types, type(variable))) + + def map_names(ditems, input_map): + return {input_map(k, k): v for k, v in ditems.items()} + + def map_only_names(ditems, input_map): + return {v: ditems[k] for k, v in input_map.items()} + + def _inner(ditems): + + sig = FunctionSignature(function) + + if sig.found_kwargs: + must_be_types_or_none('inputs', inputs, (dict,)) + # we just forward all arguments unless we have been provided an input map + if inputs is None: + dinputs = dict(ditems) + else: + # dict + dinputs = map_names(ditems, inputs) + + else: + # no **kwargs + # select only items from the method signature + dinputs = dict((k, v) for k, v in ditems.items() if k in sig.non_var_parameters) + must_be_types_or_none('inputs', inputs, (str, list, tuple, dict)) + if inputs is None: + pass + elif isinstance(inputs, str): + if len(sig.non_var_parameters) != 1: + raise ValueError("if 'inputs' is a string, function may only have a single non-variadic parameter") + dinputs = {inputs: ditems[inputs]} + elif isinstance(inputs, (list, tuple)): + dinputs = dict((k, dinputs[k]) for k in inputs) + else: + # dict + dinputs = map_only_names(ditems, inputs) + + ret = function(**dinputs) + + # now the mapping back to the output dictionary depends on outputs and what was returned from the function + op = outputs + if isinstance(ret, dict): + must_be_types_or_none('outputs', op, (dict,)) + if op is not None: + ret = {v: ret[k] for k, v in op.items()} + elif isinstance(ret, (list, tuple)): + if len(ret) == 1: + must_be_types('outputs', op, (str, list, tuple)) + else: + must_be_types('outputs', op, (list, tuple)) + + if isinstance(op, str): + op = [op] + + if len(ret) != len(outputs): + raise ValueError("'outputs' must have the same length as the number of elements that were returned") + + ret = dict((k, v) for k, v in zip(op, ret)) + else: + must_be_types('outputs', op, (str, list, tuple)) + if isinstance(op, (list, tuple)): + if len(op) != 1: + raise ValueError("'outputs' must be of length one if it is a list or tuple") + op = op[0] + ret = {op: ret} + + ditems = dict(ditems) + for k, v in ret.items(): + ditems[k] = v + + return ditems + + return _inner + + +@monai.utils.export('monai.transforms') +def apply_alias(fn, name_map): + + def _inner(data): + + # map names + pre_call = dict(data) + for _from, _to in name_map.items(): + pre_call[_to] = pre_call.pop(_from) + + # execute + post_call = fn(pre_call) + + # map names back + for _from, _to in name_map.items(): + post_call[_from] = post_call.pop(_to) + + return post_call + + return _inner + + +@monai.utils.export('monai.transforms') +def to_kwargs(fn): + def _inner(data): + return fn(**data) + + return _inner + + +class FunctionSignature: + def __init__(self, function): + import inspect + sfn = inspect.signature(function) + self.found_args = False + self.found_kwargs = False + self.defaults = {} + self.non_var_parameters = set() + for p in sfn.parameters.values(): + if p.kind is inspect.Parameter.VAR_POSITIONAL: + self.found_args = True + if p.kind is inspect.Parameter.VAR_KEYWORD: + self.found_kwargs = True + else: + self.non_var_parameters.add(p.name) + self.defaults[p.name] = p.default is not p.empty + + def __repr__(self): + s = " image_threshold`` to select + the negative sample(background) center. so the crop center will only exist on valid image area. + image_threshold (int or float): if enabled image_key, use ``image > image_threshold`` to determine + the valid image content area. """ - def __init__(self, keys, label_key, size, pos=1, neg=1, num_samples=1): + def __init__(self, keys, label_key, size, pos=1, neg=1, num_samples=1, image_key=None, image_threshold=0): MapTransform.__init__(self, keys) assert isinstance(label_key, str), 'label_key must be a string.' assert isinstance(size, (list, tuple)), 'size must be list or tuple.' @@ -181,15 +465,19 @@ def __init__(self, keys, label_key, size, pos=1, neg=1, num_samples=1): self.size = size self.pos_ratio = float(pos) / (float(pos) + float(neg)) self.num_samples = num_samples + self.image_key = image_key + self.image_threshold = image_threshold self.centers = None - def randomize(self, label): - self.centers = generate_pos_neg_label_crop_centers(label, self.size, self.num_samples, self.pos_ratio, self.R) + def randomize(self, label, image): + self.centers = generate_pos_neg_label_crop_centers(label, self.size, self.num_samples, self.pos_ratio, + image, self.image_threshold, self.R) def __call__(self, data): d = dict(data) label = d[self.label_key] - self.randomize(label) + image = d[self.image_key] if self.image_key else None + self.randomize(label, image) results = [dict() for _ in range(self.num_samples)] for key in data.keys(): if key in self.keys: @@ -204,17 +492,473 @@ def __call__(self, data): return results -# if __name__ == "__main__": -# import numpy as np -# data = { -# 'img': np.array((1, 2, 3, 4)).reshape((1, 2, 2)), -# 'seg': np.array((1, 2, 3, 4)).reshape((1, 2, 2)), -# 'affine': 3, -# 'dtype': 4, -# 'unused': 5, -# } -# rotator = RandRotate90d(keys=['img', 'seg'], prob=0.8) -# # rotator.set_random_state(1234) -# data_result = rotator(data) -# print(data_result.keys()) -# print(data_result['img'], data_result['seg']) +@export +@alias('RandAffineD', 'RandAffineDict') +class RandAffined(Randomizable, MapTransform): + """ + A dictionary-based wrapper of :py:class:`monai.transforms.transforms.RandAffine`. + """ + + def __init__(self, keys, + spatial_size, prob=0.1, + rotate_range=None, shear_range=None, translate_range=None, scale_range=None, + mode='bilinear', padding_mode='zeros', as_tensor_output=True, device=None): + """ + Args: + keys (Hashable items): keys of the corresponding items to be transformed. + spatial_size (list or tuple of int): output image spatial size. + if ``data`` component has two spatial dimensions, ``spatial_size`` should have 2 elements [h, w]. + if ``data`` component has three spatial dimensions, ``spatial_size`` should have 3 elements [h, w, d]. + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``. + if mode is a tuple of interpolation mode strings, each string corresponds to a key in ``keys``. + this is useful to set different modes for different data items. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. + Defaults to ``'zeros'``. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + + See also: + - :py:class:`monai.transforms.compose.MapTransform` + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + """ + MapTransform.__init__(self, keys) + default_mode = 'bilinear' if isinstance(mode, (tuple, list)) else mode + self.rand_affine = RandAffine(prob=prob, + rotate_range=rotate_range, shear_range=shear_range, + translate_range=translate_range, scale_range=scale_range, + spatial_size=spatial_size, + mode=default_mode, padding_mode=padding_mode, + as_tensor_output=as_tensor_output, device=device) + self.mode = mode + + def set_random_state(self, seed=None, state=None): + self.rand_affine.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self): + self.rand_affine.randomize() + + def __call__(self, data): + d = dict(data) + self.randomize() + + spatial_size = self.rand_affine.spatial_size + if self.rand_affine.do_transform: + grid = self.rand_affine.rand_affine_grid(spatial_size=spatial_size) + else: + grid = create_grid(spatial_size) + + if isinstance(self.mode, (tuple, list)): + for key, m in zip(self.keys, self.mode): + d[key] = self.rand_affine.resampler(d[key], grid, mode=m) + return d + + for key in self.keys: # same interpolation mode + d[key] = self.rand_affine.resampler(d[key], grid, self.rand_affine.mode) + return d + + +@export +@alias('Rand2DElasticD', 'Rand2DElasticDict') +class Rand2DElasticd(Randomizable, MapTransform): + """ + A dictionary-based wrapper of :py:class:`monai.transforms.transforms.Rand2DElastic`. + """ + + def __init__(self, keys, + spatial_size, spacing, magnitude_range, prob=0.1, + rotate_range=None, shear_range=None, translate_range=None, scale_range=None, + mode='bilinear', padding_mode='zeros', as_tensor_output=False, device=None): + """ + Args: + keys (Hashable items): keys of the corresponding items to be transformed. + spatial_size (2 ints): specifying output image spatial size [h, w]. + spacing (2 ints): distance in between the control points. + magnitude_range (2 ints): the random offsets will be generated from + ``uniform[magnitude[0], magnitude[1])``. + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid, + otherwise returns a ``spatial_size`` centered area extracted from the input image. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``. + if mode is a tuple of interpolation mode strings, each string corresponds to a key in ``keys``. + this is useful to set different modes for different data items. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. + Defaults to ``'zeros'``. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + See also: + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + - :py:class:`Affine` for the affine transformation parameters configurations. + """ + MapTransform.__init__(self, keys) + default_mode = 'bilinear' if isinstance(mode, (tuple, list)) else mode + self.rand_2d_elastic = Rand2DElastic(spacing=spacing, magnitude_range=magnitude_range, prob=prob, + rotate_range=rotate_range, shear_range=shear_range, + translate_range=translate_range, scale_range=scale_range, + spatial_size=spatial_size, + mode=default_mode, padding_mode=padding_mode, + as_tensor_output=as_tensor_output, device=device) + self.mode = mode + + def set_random_state(self, seed=None, state=None): + self.rand_2d_elastic.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self, spatial_size): + self.rand_2d_elastic.randomize(spatial_size) + + def __call__(self, data): + d = dict(data) + spatial_size = self.rand_2d_elastic.spatial_size + self.randomize(spatial_size) + + if self.rand_2d_elastic.do_transform: + grid = self.rand_2d_elastic.deform_grid(spatial_size) + grid = self.rand_2d_elastic.rand_affine_grid(grid=grid) + grid = torch.nn.functional.interpolate(grid[None], spatial_size, mode='bicubic', align_corners=False)[0] + else: + grid = create_grid(spatial_size) + + if isinstance(self.mode, (tuple, list)): + for key, m in zip(self.keys, self.mode): + d[key] = self.rand_2d_elastic.resampler(d[key], grid, mode=m) + return d + + for key in self.keys: # same interpolation mode + d[key] = self.rand_2d_elastic.resampler(d[key], grid, mode=self.rand_2d_elastic.mode) + return d + + +@export +@alias('Rand3DElasticD', 'Rand3DElasticDict') +class Rand3DElasticd(Randomizable, MapTransform): + """ + A dictionary-based wrapper of :py:class:`monai.transforms.transforms.Rand3DElastic`. + """ + + def __init__(self, keys, + spatial_size, sigma_range, magnitude_range, prob=0.1, + rotate_range=None, shear_range=None, translate_range=None, scale_range=None, + mode='bilinear', padding_mode='zeros', as_tensor_output=False, device=None): + """ + Args: + keys (Hashable items): keys of the corresponding items to be transformed. + spatial_size (3 ints): specifying output image spatial size [h, w, d]. + sigma_range (2 ints): a Gaussian kernel with standard deviation sampled + from ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid. + magnitude_range (2 ints): the random offsets on the grid will be generated from + ``uniform[magnitude[0], magnitude[1])``. + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid, + otherwise returns a ``spatial_size`` centered area extracted from the input image. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``. + if mode is a tuple of interpolation mode strings, each string corresponds to a key in ``keys``. + this is useful to set different modes for different data items. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. + Defaults to ``'zeros'``. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + See also: + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + - :py:class:`Affine` for the affine transformation parameters configurations. + """ + MapTransform.__init__(self, keys) + default_mode = 'bilinear' if isinstance(mode, (tuple, list)) else mode + self.rand_3d_elastic = Rand3DElastic(sigma_range=sigma_range, magnitude_range=magnitude_range, prob=prob, + rotate_range=rotate_range, shear_range=shear_range, + translate_range=translate_range, scale_range=scale_range, + spatial_size=spatial_size, + mode=default_mode, padding_mode=padding_mode, + as_tensor_output=as_tensor_output, device=device) + self.mode = mode + + def set_random_state(self, seed=None, state=None): + self.rand_3d_elastic.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self, grid_size): + self.rand_3d_elastic.randomize(grid_size) + + def __call__(self, data): + d = dict(data) + spatial_size = self.rand_3d_elastic.spatial_size + self.randomize(spatial_size) + grid = create_grid(spatial_size) + if self.rand_3d_elastic.do_transform: + device = self.rand_3d_elastic.device + grid = torch.tensor(grid).to(device) + gaussian = GaussianFilter(spatial_dims=3, sigma=self.rand_3d_elastic.sigma, truncated=3., device=device) + grid[:3] += gaussian(self.rand_3d_elastic.rand_offset[None])[0] * self.rand_3d_elastic.magnitude + grid = self.rand_3d_elastic.rand_affine_grid(grid=grid) + + if isinstance(self.mode, (tuple, list)): + for key, m in zip(self.keys, self.mode): + d[key] = self.rand_3d_elastic.resampler(d[key], grid, mode=m) + return d + + for key in self.keys: # same interpolation mode + d[key] = self.rand_3d_elastic.resampler(d[key], grid, mode=self.rand_3d_elastic.mode) + return d + + +@export +@alias('FlipD', 'FlipDict') +class Flipd(MapTransform): + """Dictionary-based wrapper of Flip. + + See `numpy.flip` for additional details. + https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html + + Args: + keys (dict): Keys to pick data for transformation. + spatial_axis (None, int or tuple of ints): Spatial axes along which to flip over. Default is None. + """ + + def __init__(self, keys, spatial_axis=None): + MapTransform.__init__(self, keys) + self.flipper = Flip(spatial_axis=spatial_axis) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.flipper(d[key]) + return d + + +@export +@alias('RandFlipD', 'RandFlipDict') +class RandFlipd(Randomizable, MapTransform): + """Dict-based wrapper of RandFlip. + + See `numpy.flip` for additional details. + https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html + + Args: + prob (float): Probability of flipping. + spatial_axis (None, int or tuple of ints): Spatial axes along which to flip over. Default is None. + """ + + def __init__(self, keys, prob=0.1, spatial_axis=None): + MapTransform.__init__(self, keys) + self.spatial_axis = spatial_axis + self.prob = prob + + self._do_transform = False + self.flipper = Flip(spatial_axis=spatial_axis) + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + + def __call__(self, data): + self.randomize() + d = dict(data) + if not self._do_transform: + return d + for key in self.keys: + d[key] = self.flipper(d[key]) + return d + + +@export +@alias('RotateD', 'RotateDict') +class Rotated(MapTransform): + """Dictionary-based wrapper of Rotate. + + Args: + keys (dict): Keys to pick data for transformation. + angle (float): Rotation angle in degrees. + spatial_axes (tuple of 2 ints): Spatial axes of rotation. Default: (0, 1). + This is the first two axis in spatial dimensions. + reshape (bool): If true, output shape is made same as input. Default: True. + order (int): Order of spline interpolation. Range 0-5. Default: 1. This is + different from scipy where default interpolation is 3. + mode (str): Points outside boundary filled according to this mode. Options are + 'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'. + cval (scalar): Values to fill outside boundary. Default: 0. + prefiter (bool): Apply spline_filter before interpolation. Default: True. + """ + + def __init__(self, keys, angle, spatial_axes=(0, 1), reshape=True, order=1, + mode='constant', cval=0, prefilter=True): + MapTransform.__init__(self, keys) + self.rotator = Rotate(angle=angle, spatial_axes=spatial_axes, reshape=reshape, + order=order, mode=mode, cval=cval, prefilter=prefilter) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.rotator(d[key]) + return d + + +@export +@alias('RandRotateD', 'RandRotateDict') +class RandRotated(Randomizable, MapTransform): + """Randomly rotates the input arrays. + + Args: + prob (float): Probability of rotation. + degrees (tuple of float or float): Range of rotation in degrees. If single number, + angle is picked from (-degrees, degrees). + spatial_axes (tuple of 2 ints): Spatial axes of rotation. Default: (0, 1). + This is the first two axis in spatial dimensions. + reshape (bool): If true, output shape is made same as input. Default: True. + order (int): Order of spline interpolation. Range 0-5. Default: 1. This is + different from scipy where default interpolation is 3. + mode (str): Points outside boundary filled according to this mode. Options are + 'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'. + cval (scalar): Value to fill outside boundary. Default: 0. + prefiter (bool): Apply spline_filter before interpolation. Default: True. + """ + def __init__(self, keys, degrees, prob=0.1, spatial_axes=(0, 1), reshape=True, order=1, + mode='constant', cval=0, prefilter=True): + MapTransform.__init__(self, keys) + self.prob = prob + self.degrees = degrees + self.reshape = reshape + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.spatial_axes = spatial_axes + + if not hasattr(self.degrees, '__iter__'): + self.degrees = (-self.degrees, self.degrees) + assert len(self.degrees) == 2, "degrees should be a number or pair of numbers." + + self._do_transform = False + self.angle = None + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + self.angle = self.R.uniform(low=self.degrees[0], high=self.degrees[1]) + + def __call__(self, data): + self.randomize() + d = dict(data) + if not self._do_transform: + return d + rotator = Rotate(self.angle, self.spatial_axes, self.reshape, self.order, + self.mode, self.cval, self.prefilter) + for key in self.keys: + d[key] = rotator(d[key]) + return d + + +@export +@alias('ZoomD', 'ZoomDict') +class Zoomd(MapTransform): + """Dictionary-based wrapper of Zoom transform. + + Args: + zoom (float or sequence): The zoom factor along the spatial axes. + If a float, zoom is the same for each spatial axis. + If a sequence, zoom should contain one value for each spatial axis. + order (int): order of interpolation. Default=3. + mode (str): Determines how input is extended beyond boundaries. Default is 'constant'. + cval (scalar, optional): Value to fill past edges. Default is 0. + use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes + 'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found. + keep_size (bool): Should keep original size (pad if needed). + """ + + def __init__(self, keys, zoom, order=3, mode='constant', cval=0, + prefilter=True, use_gpu=False, keep_size=False): + MapTransform.__init__(self, keys) + self.zoomer = Zoom(zoom=zoom, order=order, mode=mode, cval=cval, + prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size) + + def __call__(self, data): + d = dict(data) + for key in self.keys: + d[key] = self.zoomer(d[key]) + return d + + +@export +@alias('RandZoomD', 'RandZoomDict') +class RandZoomd(Randomizable, MapTransform): + """Dict-based wrapper of RandZoom. + + Args: + keys (dict): Keys to pick data for transformation. + prob (float): Probability of zooming. + min_zoom (float or sequence): Min zoom factor. Can be float or sequence same size as image. + If a float, min_zoom is the same for each spatial axis. + If a sequence, min_zoom should contain one value for each spatial axis. + max_zoom (float or sequence): Max zoom factor. Can be float or sequence same size as image. + If a float, max_zoom is the same for each spatial axis. + If a sequence, max_zoom should contain one value for each spatial axis. + order (int): order of interpolation. Default=3. + mode ('reflect', 'constant', 'nearest', 'mirror', 'wrap'): Determines how input is + extended beyond boundaries. Default: 'constant'. + cval (scalar, optional): Value to fill past edges. Default is 0. + use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes + 'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found. + keep_size (bool): Should keep original size (pad if needed). + """ + + def __init__(self, keys, prob=0.1, min_zoom=0.9, + max_zoom=1.1, order=3, mode='constant', + cval=0, prefilter=True, use_gpu=False, keep_size=False): + MapTransform.__init__(self, keys) + if hasattr(min_zoom, '__iter__') and \ + hasattr(max_zoom, '__iter__'): + assert len(min_zoom) == len(max_zoom), "min_zoom and max_zoom must have same length." + self.min_zoom = min_zoom + self.max_zoom = max_zoom + self.prob = prob + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.use_gpu = use_gpu + self.keep_size = keep_size + + self._do_transform = False + self._zoom = None + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + if hasattr(self.min_zoom, '__iter__'): + self._zoom = (self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)) + else: + self._zoom = self.R.uniform(self.min_zoom, self.max_zoom) + + def __call__(self, data): + self.randomize() + d = dict(data) + if not self._do_transform: + return d + zoomer = Zoom(self._zoom, self.order, self.mode, self.cval, self.prefilter, self.use_gpu, self.keep_size) + for key in self.keys: + d[key] = zoomer(d[key]) + return d + + +@export +@alias('DeleteKeysD', 'DeleteKeysDict') +class DeleteKeysd(MapTransform): + """ + Delete specified keys from data dictionary to release memory. + It will remove the key-values and copy the others to construct a new dictionary. + """ + + def __init__(self, keys): + """ + Args: + keys (hashable items): keys of the corresponding items to be transformed. + See also: :py:class:`monai.transforms.compose.MapTransform` + """ + MapTransform.__init__(self, keys) + + def __call__(self, data): + return {key: val for key, val in data.items() if key not in self.keys} diff --git a/monai/transforms/compose.py b/monai/transforms/compose.py index d6e5e4aa29..77099a898b 100644 --- a/monai/transforms/compose.py +++ b/monai/transforms/compose.py @@ -8,11 +8,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +""" +A collection of generic interfaces for MONAI transforms. +""" import warnings +from typing import Hashable import numpy as np +from monai.utils.misc import ensure_tuple + class Transform: """ @@ -21,19 +27,27 @@ class Transform: It could be stateful and may modify ``data`` in place, the implementation should be aware of: - - thread safety when mutating its own states. - When used from a multi-process context, transform's instance variables are read-only. - - ``data`` content unused by this transform may still be used in the - subsequent transforms in a composed transform. - see also: `monai.transforms.compose.Compose`. - - storing too much information in ``data`` may not scale. + + #. thread safety when mutating its own states. + When used from a multi-process context, transform's instance variables are read-only. + #. ``data`` content unused by this transform may still be used in the + subsequent transforms in a composed transform. + #. storing too much information in ``data`` may not scale. + + See Also + + :py:class:`monai.transforms.compose.Compose` """ def __call__(self, data): """ ``data`` is an element which often comes from an iteration over an - iterable, such as``torch.utils.data.Dataset``. This method should + iterable, such as :py:class:`torch.utils.data.Dataset`. This method should return an updated version of ``data``. + To simplify the input validations, most of the transforms assume that + + - ``data`` component is a "channel-first" array, + - the channel dimension is not omitted even if number of channels is one. """ raise NotImplementedError @@ -48,7 +62,7 @@ class Randomizable: def set_random_state(self, seed=None, state=None): """ Set the random state locally, to control the randomness, the derived - classes should use `self.R` instead of `np.random` to introduce random + classes should use :py:attr:`self.R` instead of `np.random` to introduce random factors. Args: @@ -57,8 +71,6 @@ def set_random_state(self, seed=None, state=None): Returns: a Randomizable instance. - Note: - thread safety """ if seed is not None: _seed = id(seed) if not isinstance(seed, int) else seed @@ -76,63 +88,70 @@ def set_random_state(self, seed=None, state=None): def randomize(self): """ - all self.R calls happen here so that we have a better chance to identify errors of sync the random state. + Within this method, :py:attr:`self.R` should be used, instead of `np.random`, to introduce random factors. + + all :py:attr:`self.R` calls happen here so that we have a better chance to + identify errors of sync the random state. """ raise NotImplementedError class Compose(Randomizable): """ - `Compose` provides the ability to chain a series of calls together in a + ``Compose`` provides the ability to chain a series of calls together in a sequence. Each transform in the sequence must take a single argument and return a single value, so that the transforms can be called in a chain. - `Compose` can be used in two ways: - 1. With a series of transforms that accept and return a single ndarray / - / tensor / tensor-like parameter - 2. With a series of transforms that accept and return a dictionary that - contains one or more parameters. Such transforms must have pass-through - semantics; unused values in the dictionary must be copied to the return - dictionary. It is required that the dictionary is copied between input - and output of each transform. + ``Compose`` can be used in two ways: + + #. With a series of transforms that accept and return a single + ndarray / tensor / tensor-like parameter. + #. With a series of transforms that accept and return a dictionary that + contains one or more parameters. Such transforms must have pass-through + semantics; unused values in the dictionary must be copied to the return + dictionary. It is required that the dictionary is copied between input + and output of each transform. + If some transform generates a list batch of data in the transform chain, every item in the list is still a dictionary, and all the following transforms will apply to every item of the list, for example: - (1) transformA normalizes the intensity of 'img' field in the dict data. - (2) transformB crops out a list batch of images on 'img' and 'seg' field. - And constructs a list of dict data, other fields are copied: - { [{ { - 'img': [1, 2], 'img': [1], 'img': [2], - 'seg': [1, 2], 'seg': [1], 'seg': [2], - 'extra': 123, ---> 'extra': 123, 'extra': 123, - 'shape': 'CHWD' 'shape': 'CHWD' 'shape': 'CHWD' - } }, }] - (3) transformC then randomly rotates or flips 'img' and 'seg' fields of - every dictionary item in the list. + + #. transformA normalizes the intensity of 'img' field in the dict data. + #. transformB crops out a list batch of images on 'img' and 'seg' field. + And constructs a list of dict data, other fields are copied:: + + { [{ { + 'img': [1, 2], 'img': [1], 'img': [2], + 'seg': [1, 2], 'seg': [1], 'seg': [2], + 'extra': 123, --> 'extra': 123, 'extra': 123, + 'shape': 'CHWD' 'shape': 'CHWD' 'shape': 'CHWD' + } }, }] + + #. transformC then randomly rotates or flips 'img' and 'seg' fields of + every dictionary item in the list. + When using the pass-through dictionary operation, you can make use of - `monai.data.transforms.adaptor` to wrap transforms that don't conform + :class:`monai.transforms.adaptors.adaptor` to wrap transforms that don't conform to the requirements. This approach allows you to use transforms from otherwise incompatible libraries with minimal additional work. Note: - In many cases, Compose is not the best way to create pre-processing - pipelines. Pre-processing is often not a strictly sequential series of - operations, and much of the complexity arises when a not-sequential - set of functions must be called as if it were a sequence. - - Example: images and labels - Images typically require some kind of normalisation that labels do not. - Both are then typically augmented through the use of random rotations, - flips, and deformations. - Compose can be used with a series of transforms that take a dictionary - that contains 'image' and 'label' entries. This might require wrapping - `torchvision` transforms before passing them to compose. - Alternatively, one can create a class with a __call__ function that - calls your pre-processing functions taking into account that not all of - them are called on the labels - - TODO: example / links to alternative approaches + In many cases, Compose is not the best way to create pre-processing + pipelines. Pre-processing is often not a strictly sequential series of + operations, and much of the complexity arises when a not-sequential + set of functions must be called as if it were a sequence. + + Example: images and labels + Images typically require some kind of normalisation that labels do not. + Both are then typically augmented through the use of random rotations, + flips, and deformations. + Compose can be used with a series of transforms that take a dictionary + that contains 'image' and 'label' entries. This might require wrapping + `torchvision` transforms before passing them to compose. + Alternatively, one can create a class with a `__call__` function that + calls your pre-processing functions taking into account that not all of + them are called on the labels. """ def __init__(self, transforms=None): @@ -169,3 +188,33 @@ def __call__(self, input_): else: input_ = transform(input_) return input_ + + +class MapTransform(Transform): + """ + A subclass of :py:class:`monai.transforms.compose.Transform` with an assumption + that the ``data`` input of ``self.__call__`` is a MutableMapping such as ``dict``. + + The ``keys`` parameter will be used to get and set the actual data + item to transform. That is, the callable of this transform should + follow the pattern: + + .. code-block:: python + + def __call__(self, data): + for key in self.keys: + if key in data: + # update output data with some_transform_function(data[key]). + else: + # do nothing or some exceptions handling. + return data + + """ + + def __init__(self, keys): + self.keys = ensure_tuple(keys) + if not self.keys: + raise ValueError('keys unspecified') + for key in self.keys: + if not isinstance(key, Hashable): + raise ValueError('keys should be a hashable or a sequence of hashables, got {}'.format(type(key))) diff --git a/monai/transforms/transforms.py b/monai/transforms/transforms.py index dc6f571106..a98d57ad9f 100644 --- a/monai/transforms/transforms.py +++ b/monai/transforms/transforms.py @@ -14,20 +14,249 @@ """ import numpy as np +import scipy.ndimage +import pydicom +import nibabel as nib import torch +from torch.utils.data._utils.collate import np_str_obj_array_pattern +from skimage.transform import resize import monai -from monai.data.utils import get_random_patch, get_valid_patch_size +from monai.data.utils import get_random_patch, get_valid_patch_size, correct_nifti_header_if_necessary +from monai.networks.layers.simplelayers import GaussianFilter from monai.transforms.compose import Randomizable -from monai.transforms.utils import rescale_array +from monai.transforms.utils import (create_control_grid, create_grid, create_rotate, create_scale, create_shear, + create_translate, rescale_array) +from monai.utils.misc import ensure_tuple +from monai.utils.medical_image_converter import dictify_dicom export = monai.utils.export("monai.transforms") +@export +class Spacing: + """ + Resample input image into the specified `pixdim`. + """ + + def __init__(self, pixdim, keep_shape=False): + """ + Args: + pixdim (sequence of floats): output voxel spacing. + keep_shape (bool): whether to maintain the original spatial shape + after resampling. Defaults to False. + """ + self.pixdim = pixdim + self.keep_shape = keep_shape + self.original_pixdim = pixdim + + def __call__(self, data_array, original_affine=None, original_pixdim=None, interp_order=1): + """ + Args: + data_array (ndarray): in shape (num_channels, H[, W, ...]). + original_affine (4x4 matrix): original affine. + original_pixdim (sequence of floats): original voxel spacing. + interp_order (int): The order of the spline interpolation, default is 3. + The order has to be in the range 0-5. + https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html + Returns: + resampled array (in spacing: `self.pixdim`), original pixdim, current pixdim. + """ + if original_affine is None and original_pixdim is None: + raise ValueError('please provide either original_affine or original_pixdim.') + spatial_rank = data_array.ndim - 1 + if original_affine is not None: + affine = np.array(original_affine, dtype=np.float64, copy=True) + if not affine.shape == (4, 4): + raise ValueError('`original_affine` must be 4 x 4.') + original_pixdim = np.sqrt(np.sum(np.square(affine[:spatial_rank, :spatial_rank]), 1)) + + inp_d = np.asarray(original_pixdim)[:spatial_rank] + if inp_d.size < spatial_rank: + inp_d = np.append(inp_d, [1.] * (inp_d.size - spatial_rank)) + out_d = np.asarray(self.pixdim)[:spatial_rank] + if out_d.size < spatial_rank: + out_d = np.append(out_d, [1.] * (out_d.size - spatial_rank)) + + self.original_pixdim, self.pixdim = inp_d, out_d + scale = inp_d / out_d + if not np.isfinite(scale).all(): + raise ValueError('Unknown pixdims: source {}, target {}'.format(inp_d, out_d)) + zoom_ = monai.transforms.Zoom(scale, order=interp_order, mode='nearest', keep_size=self.keep_shape) + return zoom_(data_array), self.original_pixdim, self.pixdim + + +@export +class Orientation: + """ + Change the input image's orientation into the specified based on `axcodes`. + """ + + def __init__(self, axcodes, labels=None): + """ + Args: + axcodes (N elements sequence): for spatial ND input's orientation. + e.g. axcodes='RAS' represents 3D orientation: + (Left, Right), (Posterior, Anterior), (Inferior, Superior). + default orientation labels options are: 'L' and 'R' for the first dimension, + 'P' and 'A' for the second, 'I' and 'S' for the third. + labels : optional, None or sequence of (2,) sequences + (2,) sequences are labels for (beginning, end) of output axis. + + See Also: `nibabel.orientations.ornt2axcodes`. + """ + self.axcodes = axcodes + self.labels = labels + + def __call__(self, data_array, original_affine=None, original_axcodes=None): + """ + if `original_affine` is provided, the orientation is computed from the affine. + + Args: + data_array (ndarray): in shape (num_channels, H[, W, ...]). + original_affine (4x4 matrix): original affine. + original_axcodes (N elements sequence): for spatial ND input's orientation. + Returns: + data_array (reoriented in `self.axcodes`), original axcodes, current axcodes. + """ + if original_affine is None and original_axcodes is None: + raise ValueError('please provide either original_affine or original_axcodes.') + spatial_rank = len(data_array.shape) - 1 + if original_affine is not None: + affine = np.array(original_affine, dtype=np.float64, copy=True) + if not affine.shape == (4, 4): + raise ValueError('`original_affine` must be 4 x 4.') + original_axcodes = nib.aff2axcodes(original_affine, labels=self.labels) + original_axcodes = original_axcodes[:spatial_rank] + self.axcodes = self.axcodes[:spatial_rank] + src = nib.orientations.axcodes2ornt(original_axcodes, labels=self.labels) + dst = nib.orientations.axcodes2ornt(self.axcodes) + spatial_ornt = nib.orientations.ornt_transform(src, dst) + spatial_ornt[:, 0] += 1 # skip channel dim + ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt]) + data_array = nib.orientations.apply_orientation(data_array, ornt) + return data_array, original_axcodes, self.axcodes + + +@export +class LoadDICOM: + def __init__(self, image_only=False, dtype=np.float32): + self.image_only = image_only + self.dtype = dtype + + def __call__(self, filename): + dataset = pydicom.dcmread(filename) + + data = dataset.pixel_array.astype(self.dtype) + + if self.image_only: + return data + header = dictify_dicom(dataset) + compatible_meta = dict() + for meta_key in header: + meta_datum = header[meta_key] + if type(meta_datum).__name__ == 'ndarray' \ + and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None: + continue + compatible_meta[meta_key] = meta_datum + return data, compatible_meta + + +@export +class LoadNifti: + """ + Load Nifti format file from provided path. + """ + + def __init__(self, as_closest_canonical=False, image_only=False, dtype=np.float32): + """ + Args: + as_closest_canonical (bool): if True, load the image as closest to canonical axis format. + image_only (bool): if True return only the image volume, other return image volume and header dict. + dtype (np.dtype, optional): if not None convert the loaded image to this data type. + + Note: + The loaded image volume if `image_only` is True, or a tuple containing the volume and the Nifti + header in dict format otherwise. + header['original_affine'] stores the original affine loaded from `filename_or_obj`. + header['affine'] stores the affine after the optional `as_closest_canonical` transform. + """ + self.as_closest_canonical = as_closest_canonical + self.image_only = image_only + self.dtype = dtype + + def __call__(self, filename): + """ + Args: + filename (str or file): path to file or file-like object. + """ + img = nib.load(filename) + img = correct_nifti_header_if_necessary(img) + + header = dict(img.header) + header['filename_or_obj'] = filename + header['original_affine'] = img.affine + header['affine'] = img.affine + header['as_closest_canonical'] = self.as_closest_canonical + + if self.as_closest_canonical: + img = nib.as_closest_canonical(img) + header['affine'] = img.affine + + data = np.array(img.get_fdata(dtype=self.dtype)) + img.uncache() + + if self.image_only: + return data + compatible_meta = dict() + for meta_key in header: + meta_datum = header[meta_key] + if type(meta_datum).__name__ == 'ndarray' \ + and np_str_obj_array_pattern.search(meta_datum.dtype.str) is not None: + continue + compatible_meta[meta_key] = meta_datum + return data, compatible_meta + + +@export +class AsChannelFirst: + """ + Change the channel dimension of the image to the first dimension. + + Most of the image transformations in ``monai.transforms`` + assumes the input image is in the channel-first format, which has the shape + (num_channels, spatial_dim_1[, spatial_dim_2, ...]). + + This transform could be used to convert, for example, a channel-last image array in shape + (spatial_dim_1[, spatial_dim_2, ...], num_channels) into the channel-first format, + so that the multidimensional image array can be correctly interpreted by the other + transforms. + + Args: + channel_dim (int): which dimension of input image is the channel, default is the last dimension. + """ + + def __init__(self, channel_dim=-1): + assert isinstance(channel_dim, int) and channel_dim >= -1, 'invalid channel dimension.' + self.channel_dim = channel_dim + + def __call__(self, img): + return np.moveaxis(img, self.channel_dim, 0) + + @export class AddChannel: """ Adds a 1-length channel dimension to the input image. + + Most of the image transformations in ``monai.transforms`` + assumes the input image is in the channel-first format, which has the shape + (num_channels, spatial_dim_1[, spatial_dim_2, ...]). + + This transform could be used, for example, to convert a (spatial_dim_1[, spatial_dim_2, ...]) + spatial image into the channel-first format so that the + multidimensional image array can be correctly interpreted by the other + transforms. """ def __call__(self, img): @@ -62,22 +291,221 @@ def __call__(self, img): return rescale_array(img, self.minv, self.maxv, self.dtype) +@export +class GaussianNoise(Randomizable): + """Add gaussian noise to image. + + Args: + mean (float or array of floats): Mean or “centre” of the distribution. + std (float): Standard deviation (spread) of distribution. + """ + + def __init__(self, mean=0.0, std=0.1): + self.mean = mean + self.std = std + + def __call__(self, img): + return img + self.R.normal(self.mean, self.R.uniform(0, self.std), size=img.shape) + + @export class Flip: - """Reverses the order of elements along the given axis. Preserves shape. - Uses np.flip in practice. See numpy.flip for additional details. + """Reverses the order of elements along the given spatial axis. Preserves shape. + Uses ``np.flip`` in practice. See numpy.flip for additional details. + https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html + + Args: + spatial_axis (None, int or tuple of ints): spatial axes along which to flip over. Default is None. + """ + + def __init__(self, spatial_axis=None): + self.spatial_axis = spatial_axis + + def __call__(self, img): + """ + Args: + img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]), + """ + flipped = list() + for channel in img: + flipped.append( + np.flip(channel, self.spatial_axis) + ) + return np.stack(flipped) + + +@export +class Resize: + """ + Resize the input image to given resolution. Uses skimage.transform.resize underneath. + For additional details, see https://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.resize. Args: - axes (None, int or tuple of ints): Axes along which to flip over. Default is None. + output_spatial_shape (tuple or list): expected shape of spatial dimensions after resize operation. + order (int): Order of spline interpolation. Default=1. + mode (str): Points outside boundaries are filled according to given mode. + Options are 'constant', 'edge', 'symmetric', 'reflect', 'wrap'. + cval (float): Used with mode 'constant', the value outside image boundaries. + clip (bool): Wheter to clip range of output values after interpolation. Default: True. + preserve_range (bool): Whether to keep original range of values. Default is True. + If False, input is converted according to conventions of img_as_float. See + https://scikit-image.org/docs/dev/user_guide/data_types.html. + anti_aliasing (bool): Whether to apply a gaussian filter to image before down-scaling. + Default is True. + anti_aliasing_sigma (float, tuple of floats): Standard deviation for gaussian filtering. + """ + + def __init__(self, output_spatial_shape, order=1, mode='reflect', cval=0, + clip=True, preserve_range=True, anti_aliasing=True, anti_aliasing_sigma=None): + assert isinstance(order, int), "order must be integer." + self.output_spatial_shape = output_spatial_shape + self.order = order + self.mode = mode + self.cval = cval + self.clip = clip + self.preserve_range = preserve_range + self.anti_aliasing = anti_aliasing + self.anti_aliasing_sigma = anti_aliasing_sigma + + def __call__(self, img): + """ + Args: + img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]), + """ + resized = list() + for channel in img: + resized.append( + resize(channel, self.output_spatial_shape, order=self.order, + mode=self.mode, cval=self.cval, + clip=self.clip, preserve_range=self.preserve_range, + anti_aliasing=self.anti_aliasing, + anti_aliasing_sigma=self.anti_aliasing_sigma) + ) + return np.stack(resized).astype(np.float32) + + +@export +class Rotate: """ + Rotates an input image by given angle. Uses scipy.ndimage.rotate. For more details, see + https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.rotate.html - def __init__(self, axis=None): - assert axis is None or isinstance(axis, (int, list, tuple)), \ - "axis must be None, int or tuple of ints." - self.axis = axis + Args: + angle (float): Rotation angle in degrees. + spatial_axes (tuple of 2 ints): Spatial axes of rotation. Default: (0, 1). + This is the first two axis in spatial dimensions. + reshape (bool): If true, output shape is made same as input. Default: True. + order (int): Order of spline interpolation. Range 0-5. Default: 1. This is + different from scipy where default interpolation is 3. + mode (str): Points outside boundary filled according to this mode. Options are + 'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'. + cval (scalar): Values to fill outside boundary. Default: 0. + prefiter (bool): Apply spline_filter before interpolation. Default: True. + """ + + def __init__(self, angle, spatial_axes=(0, 1), reshape=True, order=1, mode='constant', cval=0, prefilter=True): + self.angle = angle + self.reshape = reshape + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.spatial_axes = spatial_axes + + def __call__(self, img): + """ + Args: + img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]), + """ + rotated = list() + for channel in img: + rotated.append( + scipy.ndimage.rotate(channel, self.angle, self.spatial_axes, reshape=self.reshape, + order=self.order, mode=self.mode, cval=self.cval, prefilter=self.prefilter) + ) + return np.stack(rotated).astype(np.float32) + + +@export +class Zoom: + """ Zooms a nd image. Uses scipy.ndimage.zoom or cupyx.scipy.ndimage.zoom in case of gpu. + For details, please see https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html. + + Args: + zoom (float or sequence): The zoom factor along the spatial axes. + If a float, zoom is the same for each spatial axis. + If a sequence, zoom should contain one value for each spatial axis. + order (int): order of interpolation. Default=3. + mode (str): Determines how input is extended beyond boundaries. Default is 'constant'. + cval (scalar, optional): Value to fill past edges. Default is 0. + use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes + 'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found. + keep_size (bool): Should keep original size (pad if needed). + """ + + def __init__(self, zoom, order=3, mode='constant', cval=0, prefilter=True, use_gpu=False, keep_size=False): + assert isinstance(order, int), "Order must be integer." + self.zoom = zoom + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.use_gpu = use_gpu + self.keep_size = keep_size + + if self.use_gpu: + try: + from cupyx.scipy.ndimage import zoom as zoom_gpu + + self._zoom = zoom_gpu + except ImportError: + print('For GPU zoom, please install cupy. Defaulting to cpu.') + self._zoom = scipy.ndimage.zoom + self.use_gpu = False + else: + self._zoom = scipy.ndimage.zoom def __call__(self, img): - return np.flip(img, self.axis) + """ + Args: + img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]), + """ + zoomed = list() + if self.use_gpu: + import cupy + for channel in cupy.array(img): + zoom_channel = self._zoom(channel, + zoom=self.zoom, + order=self.order, + mode=self.mode, + cval=self.cval, + prefilter=self.prefilter) + zoomed.append(cupy.asnumpy(zoom_channel)) + else: + for channel in img: + zoomed.append( + self._zoom(channel, + zoom=self.zoom, + order=self.order, + mode=self.mode, + cval=self.cval, + prefilter=self.prefilter)) + zoomed = np.stack(zoomed).astype(np.float32) + + if not self.keep_size or np.allclose(img.shape, zoomed.shape): + return zoomed + + pad_vec = [[0, 0]] * len(img.shape) + slice_vec = [slice(None)] * len(img.shape) + for idx, (od, zd) in enumerate(zip(img.shape, zoomed.shape)): + diff = od - zd + half = abs(diff) // 2 + if diff > 0: # need padding + pad_vec[idx] = [half, diff - half] + elif diff < 0: # need slicing + slice_vec[idx] = slice(half, half + od) + zoomed = np.pad(zoomed, pad_vec, mode='constant') + return zoomed[tuple(slice_vec)] @export @@ -91,13 +519,16 @@ def __call__(self, img): @export -class UniformRandomPatch(Randomizable): +class RandUniformPatch(Randomizable): """ Selects a patch of the given size chosen at a uniformly random position in the image. + + Args: + patch_spatial_size (tuple or list): Expected patch size of spatial dimensions. """ - def __init__(self, patch_size): - self.patch_size = (None,) + tuple(patch_size) + def __init__(self, patch_spatial_size): + self.patch_spatial_size = (None,) + tuple(patch_spatial_size) self._slices = None @@ -105,31 +536,29 @@ def randomize(self, image_shape, patch_shape): self._slices = get_random_patch(image_shape, patch_shape, self.R) def __call__(self, img): - patch_size = get_valid_patch_size(img.shape, self.patch_size) - self.randomize(img.shape, patch_size) + patch_spatial_size = get_valid_patch_size(img.shape, self.patch_spatial_size) + self.randomize(img.shape, patch_spatial_size) return img[self._slices] @export -class IntensityNormalizer: +class NormalizeIntensity: """Normalize input based on provided args, using calculated mean and std if not provided (shape of subtrahend and divisor must match. if 0, entire volume uses same subtrahend and - divisor, otherwise the shape can have dimension 1 for channels). - Current implementation can only support 'channel_last' format data. + divisor, otherwise the shape can have dimension 1 for channels). + Current implementation can only support 'channel_last' format data. Args: subtrahend (ndarray): the amount to subtract by (usually the mean) divisor (ndarray): the amount to divide by (usually the standard deviation) - dtype: output data format """ - def __init__(self, subtrahend=None, divisor=None, dtype=np.float32): + def __init__(self, subtrahend=None, divisor=None): if subtrahend is not None or divisor is not None: assert isinstance(subtrahend, np.ndarray) and isinstance(divisor, np.ndarray), \ 'subtrahend and divisor must be set in pair and in numpy array.' self.subtrahend = subtrahend self.divisor = divisor - self.dtype = dtype def __call__(self, img): if self.subtrahend is not None and self.divisor is not None: @@ -139,13 +568,40 @@ def __call__(self, img): img -= np.mean(img) img /= np.std(img) - if self.dtype != img.dtype: - img = img.astype(self.dtype) return img @export -class ImageEndPadder: +class ScaleIntensityRange: + """Apply specific intensity scaling to the whole numpy array. + Scaling from [a_min, a_max] to [b_min, b_max] with clip option. + + Args: + a_min (int or float): intensity original range min. + a_max (int or float): intensity original range max. + b_min (int or float): intensity target range min. + b_max (int or float): intensity target range max. + clip (bool): whether to perform clip after scaling. + """ + + def __init__(self, a_min, a_max, b_min, b_max, clip=False): + self.a_min = a_min + self.a_max = a_max + self.b_min = b_min + self.b_max = b_max + self.clip = clip + + def __call__(self, img): + img = (img - self.a_min) / (self.a_max - self.a_min) + img = img * (self.b_max - self.b_min) + self.b_min + if self.clip: + img = np.clip(img, self.b_min, self.b_max) + + return img + + +@export +class PadImageEnd: """Performs padding by appending to the end of the data all on one side for each dimension. Uses np.pad so in practice, a mode needs to be provided. See numpy.lib.arraypad.pad for additional details. @@ -153,15 +609,13 @@ class ImageEndPadder: Args: out_size (list): the size of region of interest at the end of the operation. mode (string): a portion from numpy.lib.arraypad.pad is copied below. - dtype: output data format. """ - def __init__(self, out_size, mode, dtype=np.float32): - assert out_size is not None and isinstance(out_size, (list, tuple)), 'out_size must be list or tuple' + def __init__(self, out_size, mode): + assert isinstance(out_size, (list, tuple)), 'out_size must be list or tuple.' self.out_size = out_size - assert isinstance(mode, str), 'mode must be str' + assert isinstance(mode, str), 'mode must be str.' self.mode = mode - self.dtype = dtype def _determine_data_pad_width(self, data_shape): return [(0, max(self.out_size[i] - data_shape[i], 0)) for i in range(len(self.out_size))] @@ -179,39 +633,49 @@ class Rotate90: Rotate an array by 90 degrees in the plane specified by `axes`. """ - def __init__(self, k=1, axes=(1, 2)): + def __init__(self, k=1, spatial_axes=(0, 1)): """ Args: k (int): number of times to rotate by 90 degrees. - axes (2 ints): defines the plane to rotate with 2 axes. + spatial_axes (2 ints): defines the plane to rotate with 2 spatial axes. + Default: (0, 1), this is the first two axis in spatial dimensions. """ self.k = k - self.plane_axes = axes + self.spatial_axes = spatial_axes def __call__(self, img): - return np.rot90(img, self.k, self.plane_axes) + """ + Args: + img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]), + """ + rotated = list() + for channel in img: + rotated.append( + np.rot90(channel, self.k, self.spatial_axes) + ) + return np.stack(rotated) @export class RandRotate90(Randomizable): """ With probability `prob`, input arrays are rotated by 90 degrees - in the plane specified by `axes`. + in the plane specified by `spatial_axes`. """ - def __init__(self, prob=0.1, max_k=3, axes=(1, 2)): + def __init__(self, prob=0.1, max_k=3, spatial_axes=(0, 1)): """ Args: prob (float): probability of rotating. (Default 0.1, with 10% probability it returns a rotated array) max_k (int): number of rotations will be sampled from `np.random.randint(max_k) + 1`. (Default 3) - axes (2 ints): defines the plane to rotate with 2 axes. - (Default (1, 2)) + spatial_axes (2 ints): defines the plane to rotate with 2 spatial axes. + Default: (0, 1), this is the first two axis in spatial dimensions. """ self.prob = min(max(prob, 0.0), 1.0) self.max_k = max_k - self.axes = axes + self.spatial_axes = spatial_axes self._do_transform = False self._rand_k = 0 @@ -224,15 +688,15 @@ def __call__(self, img): self.randomize() if not self._do_transform: return img - rotator = Rotate90(self._rand_k, self.axes) + rotator = Rotate90(self._rand_k, self.spatial_axes) return rotator(img) @export class SpatialCrop: """General purpose cropper to produce sub-volume region of interest (ROI). - It can support to crop 1, 2 or 3 dimensions spatial data. - Either a center and size must be provided, or alternatively if center and size + It can support to crop ND spatial (channel-first) data. + Either a spatial center and size must be provided, or alternatively if center and size are not provided, the start and end coordinates of the ROI must be provided. The sub-volume must sit the within original image. @@ -248,44 +712,669 @@ def __init__(self, roi_center=None, roi_size=None, roi_start=None, roi_end=None) roi_end (list or tuple): voxel coordinates for end of the crop ROI. """ if roi_center is not None and roi_size is not None: - assert isinstance(roi_center, (list, tuple)), 'roi_center must be list or tuple.' - assert isinstance(roi_size, (list, tuple)), 'roi_size must be list or tuple.' - assert all(x > 0 for x in roi_center), 'all elements of roi_center must be positive.' - assert all(x > 0 for x in roi_size), 'all elements of roi_size must be positive.' roi_center = np.asarray(roi_center, dtype=np.uint16) roi_size = np.asarray(roi_size, dtype=np.uint16) self.roi_start = np.subtract(roi_center, np.floor_divide(roi_size, 2)) self.roi_end = np.add(self.roi_start, roi_size) else: assert roi_start is not None and roi_end is not None, 'roi_start and roi_end must be provided.' - assert isinstance(roi_start, (list, tuple)), 'roi_start must be list or tuple.' - assert isinstance(roi_end, (list, tuple)), 'roi_end must be list or tuple.' - assert all(x >= 0 for x in roi_start), 'all elements of roi_start must be greater than or equal to 0.' - assert all(x > 0 for x in roi_end), 'all elements of roi_end must be positive.' - self.roi_start = roi_start - self.roi_end = roi_end + self.roi_start = np.asarray(roi_start, dtype=np.uint16) + self.roi_end = np.asarray(roi_end, dtype=np.uint16) + + assert np.all(self.roi_start >= 0), 'all elements of roi_start must be greater than or equal to 0.' + assert np.all(self.roi_end > 0), 'all elements of roi_end must be positive.' + assert np.all(self.roi_end >= self.roi_start), 'invalid roi range.' def __call__(self, img): max_end = img.shape[1:] - assert (np.subtract(max_end, self.roi_start) >= 0).all(), 'roi start out of image space.' - assert (np.subtract(max_end, self.roi_end) >= 0).all(), 'roi end out of image space.' - assert (np.subtract(self.roi_end, self.roi_start) >= 0).all(), 'invalid roi range.' - if len(self.roi_start) == 1: - data = img[:, self.roi_start[0]:self.roi_end[0]].copy() - elif len(self.roi_start) == 2: - data = img[:, self.roi_start[0]:self.roi_end[0], self.roi_start[1]:self.roi_end[1]].copy() - elif len(self.roi_start) == 3: - data = img[:, self.roi_start[0]:self.roi_end[0], self.roi_start[1]:self.roi_end[1], - self.roi_start[2]:self.roi_end[2]].copy() - else: - raise ValueError('unsupported image shape.') + sd = min(len(self.roi_start), len(max_end)) + assert np.all(max_end[:sd] >= self.roi_start[:sd]), 'roi start out of image space.' + assert np.all(max_end[:sd] >= self.roi_end[:sd]), 'roi end out of image space.' + + slices = [slice(None)] + [slice(s, e) for s, e in zip(self.roi_start[:sd], self.roi_end[:sd])] + data = img[tuple(slices)].copy() return data -# if __name__ == "__main__": -# img = np.array((1, 2, 3, 4)).reshape((1, 2, 2)) -# rotator = RandRotate90(prob=0.0, max_k=3, axes=(1, 2)) -# # rotator.set_random_state(1234) -# img_result = rotator(img) -# print(type(img)) -# print(img_result) +@export +class RandRotate(Randomizable): + """Randomly rotates the input arrays. + + Args: + prob (float): Probability of rotation. + degrees (tuple of float or float): Range of rotation in degrees. If single number, + angle is picked from (-degrees, degrees). + spatial_axes (tuple of 2 ints): Spatial axes of rotation. Default: (0, 1). + This is the first two axis in spatial dimensions. + reshape (bool): If true, output shape is made same as input. Default: True. + order (int): Order of spline interpolation. Range 0-5. Default: 1. This is + different from scipy where default interpolation is 3. + mode (str): Points outside boundary filled according to this mode. Options are + 'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'. + cval (scalar): Value to fill outside boundary. Default: 0. + prefiter (bool): Apply spline_filter before interpolation. Default: True. + """ + + def __init__(self, degrees, prob=0.1, spatial_axes=(0, 1), reshape=True, order=1, + mode='constant', cval=0, prefilter=True): + self.prob = prob + self.degrees = degrees + self.reshape = reshape + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.spatial_axes = spatial_axes + + if not hasattr(self.degrees, '__iter__'): + self.degrees = (-self.degrees, self.degrees) + assert len(self.degrees) == 2, "degrees should be a number or pair of numbers." + + self._do_transform = False + self.angle = None + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + self.angle = self.R.uniform(low=self.degrees[0], high=self.degrees[1]) + + def __call__(self, img): + self.randomize() + if not self._do_transform: + return img + rotator = Rotate(self.angle, self.spatial_axes, self.reshape, self.order, + self.mode, self.cval, self.prefilter) + return rotator(img) + + +@export +class RandFlip(Randomizable): + """Randomly flips the image along axes. Preserves shape. + See numpy.flip for additional details. + https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html + + Args: + prob (float): Probability of flipping. + spatial_axis (None, int or tuple of ints): Spatial axes along which to flip over. Default is None. + """ + + def __init__(self, prob=0.1, spatial_axis=None): + self.prob = prob + self.flipper = Flip(spatial_axis=spatial_axis) + self._do_transform = False + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + + def __call__(self, img): + self.randomize() + if not self._do_transform: + return img + return self.flipper(img) + + +@export +class RandZoom(Randomizable): + """Randomly zooms input arrays with given probability within given zoom range. + + Args: + prob (float): Probability of zooming. + min_zoom (float or sequence): Min zoom factor. Can be float or sequence same size as image. + If a float, min_zoom is the same for each spatial axis. + If a sequence, min_zoom should contain one value for each spatial axis. + max_zoom (float or sequence): Max zoom factor. Can be float or sequence same size as image. + If a float, max_zoom is the same for each spatial axis. + If a sequence, max_zoom should contain one value for each spatial axis. + order (int): order of interpolation. Default=3. + mode ('reflect', 'constant', 'nearest', 'mirror', 'wrap'): Determines how input is + extended beyond boundaries. Default: 'constant'. + cval (scalar, optional): Value to fill past edges. Default is 0. + use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes + 'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found. + keep_size (bool): Should keep original size (pad if needed). + """ + + def __init__(self, prob=0.1, min_zoom=0.9, max_zoom=1.1, order=3, + mode='constant', cval=0, prefilter=True, + use_gpu=False, keep_size=False): + if hasattr(min_zoom, '__iter__') and \ + hasattr(max_zoom, '__iter__'): + assert len(min_zoom) == len(max_zoom), "min_zoom and max_zoom must have same length." + self.min_zoom = min_zoom + self.max_zoom = max_zoom + self.prob = prob + self.order = order + self.mode = mode + self.cval = cval + self.prefilter = prefilter + self.use_gpu = use_gpu + self.keep_size = keep_size + + self._do_transform = False + self._zoom = None + + def randomize(self): + self._do_transform = self.R.random_sample() < self.prob + if hasattr(self.min_zoom, '__iter__'): + self._zoom = (self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)) + else: + self._zoom = self.R.uniform(self.min_zoom, self.max_zoom) + + def __call__(self, img): + self.randomize() + if not self._do_transform: + return img + zoomer = Zoom(self._zoom, self.order, self.mode, self.cval, self.prefilter, self.use_gpu, self.keep_size) + return zoomer(img) + + +class AffineGrid: + """ + Affine transforms on the coordinates. + """ + + def __init__(self, + rotate_params=None, + shear_params=None, + translate_params=None, + scale_params=None, + as_tensor_output=True, + device=None): + self.rotate_params = rotate_params + self.shear_params = shear_params + self.translate_params = translate_params + self.scale_params = scale_params + + self.as_tensor_output = as_tensor_output + self.device = device + + def __call__(self, spatial_size=None, grid=None): + """ + Args: + spatial_size (list or tuple of int): output grid size. + grid (ndarray): grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. + """ + if grid is None: + if spatial_size is not None: + grid = create_grid(spatial_size) + else: + raise ValueError('Either specify a grid or a spatial size to create a grid from.') + + spatial_dims = len(grid.shape) - 1 + affine = np.eye(spatial_dims + 1) + if self.rotate_params: + affine = affine @ create_rotate(spatial_dims, self.rotate_params) + if self.shear_params: + affine = affine @ create_shear(spatial_dims, self.shear_params) + if self.translate_params: + affine = affine @ create_translate(spatial_dims, self.translate_params) + if self.scale_params: + affine = affine @ create_scale(spatial_dims, self.scale_params) + affine = torch.tensor(affine, device=self.device) + + grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.clone().detach() + if self.device: + grid = grid.to(self.device) + grid = (affine.float() @ grid.reshape((grid.shape[0], -1)).float()).reshape([-1] + list(grid.shape[1:])) + if self.as_tensor_output: + return grid + return grid.cpu().numpy() + + +class RandAffineGrid(Randomizable): + """ + generate randomised affine grid + """ + + def __init__(self, + rotate_range=None, + shear_range=None, + translate_range=None, + scale_range=None, + as_tensor_output=True, + device=None): + """ + Args: + rotate_range (a sequence of positive floats): rotate_range[0] with be used to generate the 1st rotation + parameter from `uniform[-rotate_range[0], rotate_range[0])`. Similarly, `rotate_range[2]` and + `rotate_range[3]` are used in 3D affine for the range of 2nd and 3rd axes. + shear_range (a sequence of positive floats): shear_range[0] with be used to generate the 1st shearing + parameter from `uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` to + `shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to + N-th parameter. + translate_range (a sequence of positive floats): translate_range[0] with be used to generate the 1st + shift parameter from `uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]` + to `translate_range[N]` controls the range of the uniform distribution used to generate + the 2nd to N-th parameter. + scale_range (a sequence of positive floats): scaling_range[0] with be used to generate the 1st scaling + factor from `uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` to + `scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to + N-th parameter. + + See also: + - :py:meth:`monai.transforms.utils.create_rotate` + - :py:meth:`monai.transforms.utils.create_shear` + - :py:meth:`monai.transforms.utils.create_translate` + - :py:meth:`monai.transforms.utils.create_scale` + """ + self.rotate_range = ensure_tuple(rotate_range) + self.shear_range = ensure_tuple(shear_range) + self.translate_range = ensure_tuple(translate_range) + self.scale_range = ensure_tuple(scale_range) + + self.rotate_params = None + self.shear_params = None + self.translate_params = None + self.scale_params = None + + self.as_tensor_output = as_tensor_output + self.device = device + + def randomize(self): + if self.rotate_range: + self.rotate_params = [self.R.uniform(-f, f) for f in self.rotate_range if f is not None] + if self.shear_range: + self.shear_params = [self.R.uniform(-f, f) for f in self.shear_range if f is not None] + if self.translate_range: + self.translate_params = [self.R.uniform(-f, f) for f in self.translate_range if f is not None] + if self.scale_range: + self.scale_params = [self.R.uniform(-f, f) + 1.0 for f in self.scale_range if f is not None] + + def __call__(self, spatial_size=None, grid=None): + """ + Returns: + a 2D (3xHxW) or 3D (4xHxWxD) grid. + """ + self.randomize() + affine_grid = AffineGrid(rotate_params=self.rotate_params, shear_params=self.shear_params, + translate_params=self.translate_params, scale_params=self.scale_params, + as_tensor_output=self.as_tensor_output, device=self.device) + return affine_grid(spatial_size, grid) + + +class RandDeformGrid(Randomizable): + """ + generate random deformation grid + """ + + def __init__(self, spacing, magnitude_range, as_tensor_output=True, device=None): + """ + Args: + spacing (2 or 3 ints): spacing of the grid in 2D or 3D. + e.g., spacing=(1, 1) indicates pixel-wise deformation in 2D, + spacing=(1, 1, 1) indicates voxel-wise deformation in 3D, + spacing=(2, 2) indicates deformation field defined on every other pixel in 2D. + magnitude_range (2 ints): the random offsets will be generated from + `uniform[magnitude[0], magnitude[1])`. + as_tensor_output (bool): whether to output tensor instead of numpy array. + defaults to True. + device (torch device): device to store the output grid data. + """ + self.spacing = spacing + self.magnitude = magnitude_range + + self.rand_mag = 1.0 + self.as_tensor_output = as_tensor_output + self.random_offset = 0.0 + self.device = device + + def randomize(self, grid_size): + self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))) + self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1]) + + def __call__(self, spatial_size): + control_grid = create_control_grid(spatial_size, self.spacing) + self.randomize(control_grid.shape[1:]) + control_grid[:len(spatial_size)] += self.rand_mag * self.random_offset + if self.as_tensor_output: + control_grid = torch.tensor(control_grid, device=self.device) + return control_grid + + +class Resample: + + def __init__(self, padding_mode='zeros', as_tensor_output=False, device=None): + """ + computes output image using values from `img`, locations from `grid` using pytorch. + supports spatially 2D or 3D (num_channels, H, W[, D]). + + Args: + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. Defaults to 'zeros'. + as_tensor_output(bool): whether to return a torch tensor. Defaults to False. + device (torch.device): device on which the tensor will be allocated. + """ + self.padding_mode = padding_mode + self.as_tensor_output = as_tensor_output + self.device = device + + def __call__(self, img, grid, mode='bilinear'): + """ + Args: + img (ndarray or tensor): shape must be (num_channels, H, W[, D]). + grid (ndarray or tensor): shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'. + """ + if not torch.is_tensor(img): + img = torch.tensor(img) + grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.clone().detach() + if self.device: + img = img.to(self.device) + grid = grid.to(self.device) + + for i, dim in enumerate(img.shape[1:]): + grid[i] = 2. * grid[i] / (dim - 1.) + grid = grid[:-1] / grid[-1:] + grid = grid[range(img.ndim - 2, -1, -1)] + grid = grid.permute(list(range(grid.ndim))[1:] + [0]) + out = torch.nn.functional.grid_sample(img[None].float(), + grid[None].float(), + mode=mode, + padding_mode=self.padding_mode, + align_corners=False)[0] + if not self.as_tensor_output: + return out.cpu().numpy() + return out + + +@export +class Affine: + """ + transform ``img`` given the affine parameters. + """ + + def __init__(self, + rotate_params=None, + shear_params=None, + translate_params=None, + scale_params=None, + spatial_size=None, + mode='bilinear', + padding_mode='zeros', + as_tensor_output=False, + device=None): + """ + The affines are applied in rotate, shear, translate, scale order. + + Args: + rotate_params (float, list of floats): a rotation angle in radians, + a scalar for 2D image, a tuple of 3 floats for 3D. Defaults to no rotation. + shear_params (list of floats): + a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing. + translate_params (list of floats): + a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in pixel/voxel + relative to the center of the input image. Defaults to no translation. + scale_params (list of floats): + a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Defaults to no scaling. + spatial_size (list or tuple of int): output image spatial size. + if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. + if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. Defaults to 'zeros'. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + """ + self.affine_grid = AffineGrid(rotate_params=rotate_params, + shear_params=shear_params, + translate_params=translate_params, + scale_params=scale_params, + as_tensor_output=True, + device=device) + self.resampler = Resample(padding_mode=padding_mode, as_tensor_output=as_tensor_output, device=device) + self.spatial_size = spatial_size + self.mode = mode + + def __call__(self, img, spatial_size=None, mode=None): + """ + Args: + img (ndarray or tensor): shape must be (num_channels, H, W[, D]), + spatial_size (list or tuple of int): output image spatial size. + if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. + if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'. + """ + spatial_size = spatial_size or self.spatial_size + mode = mode or self.mode + grid = self.affine_grid(spatial_size=spatial_size) + return self.resampler(img=img, grid=grid, mode=mode) + + +@export +class RandAffine(Randomizable): + """ + Random affine transform. + """ + + def __init__(self, + prob=0.1, + rotate_range=None, + shear_range=None, + translate_range=None, + scale_range=None, + spatial_size=None, + mode='bilinear', + padding_mode='zeros', + as_tensor_output=True, + device=None): + """ + Args: + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid. + spatial_size (list or tuple of int): output image spatial size. + if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. + if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. Defaults to 'zeros'. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + + See also: + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + - :py:class:`Affine` for the affine transformation parameters configurations. + """ + + self.rand_affine_grid = RandAffineGrid(rotate_range=rotate_range, shear_range=shear_range, + translate_range=translate_range, scale_range=scale_range, + as_tensor_output=True, device=device) + self.resampler = Resample(padding_mode=padding_mode, as_tensor_output=as_tensor_output, device=device) + + self.spatial_size = spatial_size + self.mode = mode + + self.do_transform = False + self.prob = prob + + def set_random_state(self, seed=None, state=None): + self.rand_affine_grid.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self): + self.do_transform = self.R.rand() < self.prob + self.rand_affine_grid.randomize() + + def __call__(self, img, spatial_size=None, mode=None): + """ + Args: + img (ndarray or tensor): shape must be (num_channels, H, W[, D]), + spatial_size (list or tuple of int): output image spatial size. + if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w]. + if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'. + """ + self.randomize() + spatial_size = spatial_size or self.spatial_size + mode = mode or self.mode + if self.do_transform: + grid = self.rand_affine_grid(spatial_size=spatial_size) + else: + grid = create_grid(spatial_size) + return self.resampler(img=img, grid=grid, mode=mode) + + +@export +class Rand2DElastic(Randomizable): + """ + Random elastic deformation and affine in 2D + """ + + def __init__(self, + spacing, + magnitude_range, + prob=0.1, + rotate_range=None, + shear_range=None, + translate_range=None, + scale_range=None, + spatial_size=None, + mode='bilinear', + padding_mode='zeros', + as_tensor_output=False, + device=None): + """ + Args: + spacing (2 ints): distance in between the control points. + magnitude_range (2 ints): the random offsets will be generated from + ``uniform[magnitude[0], magnitude[1])``. + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid, + otherwise returns a ``spatial_size`` centered area extracted from the input image. + spatial_size (2 ints): specifying output image spatial size [h, w]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. + Defaults to ``'zeros'``. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + + See also: + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + - :py:class:`Affine` for the affine transformation parameters configurations. + """ + self.deform_grid = RandDeformGrid(spacing=spacing, magnitude_range=magnitude_range, + as_tensor_output=True, device=device) + self.rand_affine_grid = RandAffineGrid(rotate_range=rotate_range, shear_range=shear_range, + translate_range=translate_range, scale_range=scale_range, + as_tensor_output=True, device=device) + self.resampler = Resample(padding_mode=padding_mode, as_tensor_output=as_tensor_output, device=device) + + self.spatial_size = spatial_size + self.mode = mode + self.prob = prob + self.do_transform = False + + def set_random_state(self, seed=None, state=None): + self.deform_grid.set_random_state(seed, state) + self.rand_affine_grid.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self, spatial_size): + self.do_transform = self.R.rand() < self.prob + self.deform_grid.randomize(spatial_size) + self.rand_affine_grid.randomize() + + def __call__(self, img, spatial_size=None, mode=None): + """ + Args: + img (ndarray or tensor): shape must be (num_channels, H, W), + spatial_size (2 ints): specifying output image spatial size [h, w]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``self.mode``. + """ + spatial_size = spatial_size or self.spatial_size + self.randomize(spatial_size) + mode = mode or self.mode + if self.do_transform: + grid = self.deform_grid(spatial_size=spatial_size) + grid = self.rand_affine_grid(grid=grid) + grid = torch.nn.functional.interpolate(grid[None], spatial_size, mode='bicubic', align_corners=False)[0] + else: + grid = create_grid(spatial_size) + return self.resampler(img, grid, mode) + + +@export +class Rand3DElastic(Randomizable): + """ + Random elastic deformation and affine in 3D + """ + + def __init__(self, + sigma_range, + magnitude_range, + prob=0.1, + rotate_range=None, + shear_range=None, + translate_range=None, + scale_range=None, + spatial_size=None, + mode='bilinear', + padding_mode='zeros', + as_tensor_output=False, + device=None): + """ + Args: + sigma_range (2 ints): a Gaussian kernel with standard deviation sampled + from ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid. + magnitude_range (2 ints): the random offsets on the grid will be generated from + ``uniform[magnitude[0], magnitude[1])``. + prob (float): probability of returning a randomized affine grid. + defaults to 0.1, with 10% chance returns a randomized grid, + otherwise returns a ``spatial_size`` centered area extracted from the input image. + spatial_size (3 ints): specifying output image spatial size [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``. + padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices. + Defaults to ``'zeros'``. + as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies + whether to convert it back to numpy arrays. + device (torch.device): device on which the tensor will be allocated. + + See also: + - :py:class:`RandAffineGrid` for the random affine paramters configurations. + - :py:class:`Affine` for the affine transformation parameters configurations. + """ + self.rand_affine_grid = RandAffineGrid(rotate_range, shear_range, translate_range, scale_range, True, device) + self.resampler = Resample(padding_mode=padding_mode, as_tensor_output=as_tensor_output, device=device) + + self.sigma_range = sigma_range + self.magnitude_range = magnitude_range + self.spatial_size = spatial_size + self.mode = mode + self.device = device + + self.prob = prob + self.do_transform = False + self.rand_offset = None + self.magnitude = 1.0 + self.sigma = 1.0 + + def set_random_state(self, seed=None, state=None): + self.rand_affine_grid.set_random_state(seed, state) + Randomizable.set_random_state(self, seed, state) + return self + + def randomize(self, grid_size): + self.do_transform = self.R.rand() < self.prob + if self.do_transform: + self.rand_offset = self.R.uniform(-1., 1., [3] + list(grid_size)) + self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1]) + self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1]) + self.rand_affine_grid.randomize() + + def __call__(self, img, spatial_size=None, mode=None): + """ + Args: + img (ndarray or tensor): shape must be (num_channels, H, W, D), + spatial_size (3 ints): specifying spatial 3D output image spatial size [h, w, d]. + mode ('nearest'|'bilinear'): interpolation order. Defaults to 'self.mode'. + """ + spatial_size = spatial_size or self.spatial_size + mode = mode or self.mode + self.randomize(spatial_size) + grid = create_grid(spatial_size) + if self.do_transform: + grid = torch.tensor(grid).to(self.device) + gaussian = GaussianFilter(3, self.sigma, 3., device=self.device) + grid[:3] += gaussian(self.rand_offset[None])[0] * self.magnitude + grid = self.rand_affine_grid(grid=grid) + return self.resampler(img, grid, mode) diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index f477a24754..f7a2f24501 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -9,42 +9,44 @@ # See the License for the specific language governing permissions and # limitations under the License. - +import warnings import random import numpy as np +from monai.utils.misc import ensure_tuple + def rand_choice(prob=0.5): - """Returns True if a randomly chosen number is less than or equal to `prob', by default this is a 50/50 chance.""" + """Returns True if a randomly chosen number is less than or equal to `prob`, by default this is a 50/50 chance.""" return random.random() <= prob def img_bounds(img): - """Returns the minimum and maximum indices of non-zero lines in axis 0 of `img', followed by that for axis 1.""" + """Returns the minimum and maximum indices of non-zero lines in axis 0 of `img`, followed by that for axis 1.""" ax0 = np.any(img, axis=0) ax1 = np.any(img, axis=1) return np.concatenate((np.where(ax0)[0][[0, -1]], np.where(ax1)[0][[0, -1]])) def in_bounds(x, y, margin, maxx, maxy): - """Returns True if (x,y) is within the rectangle (margin,margin,maxx-margin,maxy-margin).""" + """Returns True if (x,y) is within the rectangle (margin, margin, maxx-margin, maxy-margin).""" return margin <= x < (maxx - margin) and margin <= y < (maxy - margin) def is_empty(img): - """Returns True if `img' is empty, that is its maximum value is not greater than its minimum.""" + """Returns True if `img` is empty, that is its maximum value is not greater than its minimum.""" return not (img.max() > img.min()) # use > instead of <= so that an image full of NaNs will result in True def ensure_tuple_size(tup, dim): - """Returns a copy of `tup' with `dim' values by either shortened or padded with zeros as necessary.""" + """Returns a copy of `tup` with `dim` values by either shortened or padded with zeros as necessary.""" tup = tuple(tup) + (0,) * dim return tup[:dim] def zero_margins(img, margin): - """Returns True if the values within `margin' indices of the edges of `img' in dimensions 1 and 2 are 0.""" + """Returns True if the values within `margin` indices of the edges of `img` in dimensions 1 and 2 are 0.""" if np.any(img[:, :, :margin]) or np.any(img[:, :, -margin:]): return False @@ -55,7 +57,7 @@ def zero_margins(img, margin): def rescale_array(arr, minv=0.0, maxv=1.0, dtype=np.float32): - """Rescale the values of numpy array `arr' to be from `minv' to `maxv'.""" + """Rescale the values of numpy array `arr` to be from `minv` to `maxv`.""" if dtype is not None: arr = arr.astype(dtype) @@ -70,7 +72,7 @@ def rescale_array(arr, minv=0.0, maxv=1.0, dtype=np.float32): def rescale_instance_array(arr, minv=0.0, maxv=1.0, dtype=np.float32): - """Rescale each array slice along the first dimension of `arr' independently.""" + """Rescale each array slice along the first dimension of `arr` independently.""" out = np.zeros(arr.shape, dtype) for i in range(arr.shape[0]): out[i] = rescale_array(arr[i], minv, maxv, dtype) @@ -79,24 +81,27 @@ def rescale_instance_array(arr, minv=0.0, maxv=1.0, dtype=np.float32): def rescale_array_int_max(arr, dtype=np.uint16): - """Rescale the array `arr' to be between the minimum and maximum values of the type `dtype'.""" + """Rescale the array `arr` to be between the minimum and maximum values of the type `dtype`.""" info = np.iinfo(dtype) return rescale_array(arr, info.min, info.max).astype(dtype) def copypaste_arrays(src, dest, srccenter, destcenter, dims): """ - Calculate the slices to copy a sliced area of array `src' into array `dest'. The area has dimensions `dims' (use 0 - or None to copy everything in that dimension), the source area is centered at `srccenter' index in `src' and copied - into area centered at `destcenter' in `dest'. The dimensions of the copied area will be clipped to fit within the + Calculate the slices to copy a sliced area of array `src` into array `dest`. The area has dimensions `dims` (use 0 + or None to copy everything in that dimension), the source area is centered at `srccenter` index in `src` and copied + into area centered at `destcenter` in `dest`. The dimensions of the copied area will be clipped to fit within the source and destination arrays so a smaller area may be copied than expected. Return value is the tuples of slice - objects indexing the copied area in `src', and those indexing the copy area in `dest'. + objects indexing the copied area in `src`, and those indexing the copy area in `dest`. + + Example - Example: - src=np.random.randint(0,10,(6,6)) - dest=np.zeros_like(src) - srcslices,destslices=copypasteArrays(src,dest,(3,2),(2,1),(3,4)) - dest[destslices]=src[srcslices] + .. code-block:: python + + src = np.random.randint(0,10,(6,6)) + dest = np.zeros_like(src) + srcslices, destslices = copypaste_arrays(src, dest, (3, 2),(2, 1),(3, 4)) + dest[destslices] = src[srcslices] print(src) print(dest) @@ -112,6 +117,7 @@ def copypaste_arrays(src, dest, srccenter, destcenter, dims): [4 7 1 8 0 0] [0 0 0 0 0 0] [0 0 0 0 0 0]] + """ srcslices = [slice(None)] * src.ndim destslices = [slice(None)] * dest.ndim @@ -131,10 +137,10 @@ def copypaste_arrays(src, dest, srccenter, destcenter, dims): def resize_center(img, *resize_dims, fill_value=0): """ - Resize `img' by cropping or expanding the image from the center. The `resizeDims' values are the output dimensions - (or None to use original dimension of `img'). If a dimension is smaller than that of `img' then the result will be - cropped and if larger padded with zeros, in both cases this is done relative to the center of `img'. The result is - a new image with the specified dimensions and values from `img' copied into its center. + Resize `img` by cropping or expanding the image from the center. The `resize_dims` values are the output dimensions + (or None to use original dimension of `img`). If a dimension is smaller than that of `img` then the result will be + cropped and if larger padded with zeros, in both cases this is done relative to the center of `img`. The result is + a new image with the specified dimensions and values from `img` copied into its center. """ resize_dims = tuple(resize_dims[i] or img.shape[i] for i in range(len(resize_dims))) @@ -150,7 +156,7 @@ def resize_center(img, *resize_dims, fill_value=0): def one_hot(labels, num_classes): """ - Converts label image `labels' to a one-hot vector with `num_classes' number of channels as last dimension. + Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension. """ labels = labels % num_classes y = np.eye(num_classes) @@ -159,14 +165,20 @@ def one_hot(labels, num_classes): return onehot.reshape(tuple(labels.shape) + (num_classes,)).astype(labels.dtype) -def generate_pos_neg_label_crop_centers(label, size, num_samples, pos_ratio, rand_state=np.random): +def generate_pos_neg_label_crop_centers(label, size, num_samples, pos_ratio, image=None, + image_threshold=0, rand_state=np.random): """Generate valid sample locations based on image with option for specifying foreground ratio Valid: samples sitting entirely within image, expected input shape: [C, H, W, D] or [C, H, W] + Args: label (numpy.ndarray): use the label data to get the foreground/background information. size (list or tuple): size of the ROIs to be sampled. num_samples (int): total sample centers to be generated. pos_ratio (float): ratio of total locations generated that have center being foreground. + image (numpy.ndarray): if image is not None, use ``label = 0 & image > image_threshold`` + to select background. so the crop center will only exist on valid image area. + image_threshold (int or float): if enabled image_key, use ``image > image_threshold`` to + determine the valid image content area. rand_state (random.RandomState): numpy randomState object to align with other modules. """ max_size = label.shape[1:] @@ -183,16 +195,24 @@ def generate_pos_neg_label_crop_centers(label, size, num_samples, pos_ratio, ran valid_end[i] += 1 # Prepare fg/bg indices - label_flat = label.ravel() - fg_indicies = np.where(label_flat > 0)[0] - bg_indicies = np.where(label_flat == 0)[0] + label_flat = np.any(label, axis=0).ravel() # in case label has multiple dimensions + fg_indicies = np.nonzero(label_flat)[0] + if image is not None: + img_flat = np.any(image > image_threshold, axis=0).ravel() + bg_indicies = np.nonzero(np.logical_and(img_flat, ~label_flat))[0] + else: + bg_indicies = np.nonzero(~label_flat)[0] + + if not len(fg_indicies) or not len(bg_indicies): + if not len(fg_indicies) and not len(bg_indicies): + raise ValueError('no sampling location available.') + warnings.warn('N foreground {}, N background {}, unable to generate class balanced samples.'.format( + len(fg_indicies), len(bg_indicies))) + pos_ratio = 0 if not len(fg_indicies) else 1 centers = [] for _ in range(num_samples): - if rand_state.rand() < pos_ratio: - indicies_to_use = fg_indicies - else: - indicies_to_use = bg_indicies + indicies_to_use = fg_indicies if rand_state.rand() < pos_ratio else bg_indicies random_int = rand_state.randint(len(indicies_to_use)) center = np.unravel_index(indicies_to_use[random_int], label.shape) center = center[1:] @@ -208,3 +228,137 @@ def generate_pos_neg_label_crop_centers(label, size, num_samples, pos_ratio, ran centers.append(center_ori) return centers + + +def create_grid(spatial_size, spacing=None, homogeneous=True, dtype=float): + """ + compute a `spatial_size` mesh. + + Args: + spatial_size (sequence of ints): spatial size of the grid. + spacing (sequence of ints): same len as ``spatial_size``, defaults to 1.0 (dense grid). + homogeneous (bool): whether to make homogeneous coordinates. + dtype (type): output grid data type. + """ + spacing = spacing or tuple(1.0 for _ in spatial_size) + ranges = [np.linspace(-(d - 1.) / 2. * s, (d - 1.) / 2. * s, int(d)) for d, s in zip(spatial_size, spacing)] + coords = np.asarray(np.meshgrid(*ranges, indexing='ij'), dtype=dtype) + if not homogeneous: + return coords + return np.concatenate([coords, np.ones_like(coords[0:1, ...])]) + + +def create_control_grid(spatial_shape, spacing, homogeneous=True, dtype=float): + """ + control grid with two additional point in each direction + """ + grid_shape = [] + for d, s in zip(spatial_shape, spacing): + d = int(d) + if d % 2 == 0: + grid_shape.append(np.ceil((d - 1.) / (2. * s) + 0.5) * 2. + 2.) + else: + grid_shape.append(np.ceil((d - 1.) / (2. * s)) * 2. + 3.) + return create_grid(grid_shape, spacing, homogeneous, dtype) + + +def create_rotate(spatial_dims, radians): + """ + create a 2D or 3D rotation matrix + + Args: + spatial_dims (2|3): spatial rank + radians (float or a sequence of floats): rotation radians + when spatial_dims == 3, the `radians` sequence corresponds to + rotation in the 1st, 2nd, and 3rd dim respectively. + """ + radians = ensure_tuple(radians) + if spatial_dims == 2: + if len(radians) >= 1: + sin_, cos_ = np.sin(radians[0]), np.cos(radians[0]) + return np.array([[cos_, -sin_, 0.], [sin_, cos_, 0.], [0., 0., 1.]]) + + if spatial_dims == 3: + affine = None + if len(radians) >= 1: + sin_, cos_ = np.sin(radians[0]), np.cos(radians[0]) + affine = np.array([ + [1., 0., 0., 0.], + [0., cos_, -sin_, 0.], + [0., sin_, cos_, 0.], + [0., 0., 0., 1.], + ]) + if len(radians) >= 2: + sin_, cos_ = np.sin(radians[1]), np.cos(radians[1]) + affine = affine @ np.array([ + [cos_, 0.0, sin_, 0.], + [0., 1., 0., 0.], + [-sin_, 0., cos_, 0.], + [0., 0., 0., 1.], + ]) + if len(radians) >= 3: + sin_, cos_ = np.sin(radians[2]), np.cos(radians[2]) + affine = affine @ np.array([ + [cos_, -sin_, 0., 0.], + [sin_, cos_, 0., 0.], + [0., 0., 1., 0.], + [0., 0., 0., 1.], + ]) + return affine + + raise ValueError('create_rotate got spatial_dims={}, radians={}.'.format(spatial_dims, radians)) + + +def create_shear(spatial_dims, coefs): + """ + create a shearing matrix + Args: + spatial_dims (int): spatial rank + coefs (floats): shearing factors, defaults to 0. + """ + coefs = list(ensure_tuple(coefs)) + if spatial_dims == 2: + while len(coefs) < 2: + coefs.append(0.0) + return np.array([ + [1, coefs[0], 0.], + [coefs[1], 1., 0.], + [0., 0., 1.], + ]) + if spatial_dims == 3: + while len(coefs) < 6: + coefs.append(0.0) + return np.array([ + [1., coefs[0], coefs[1], 0.], + [coefs[2], 1., coefs[3], 0.], + [coefs[4], coefs[5], 1., 0.], + [0., 0., 0., 1.], + ]) + raise NotImplementedError + + +def create_scale(spatial_dims, scaling_factor): + """ + create a scaling matrix + Args: + spatial_dims (int): spatial rank + scaling_factor (floats): scaling factors, defaults to 1. + """ + scaling_factor = list(ensure_tuple(scaling_factor)) + while len(scaling_factor) < spatial_dims: + scaling_factor.append(1.) + return np.diag(scaling_factor[:spatial_dims] + [1.]) + + +def create_translate(spatial_dims, shift): + """ + create a translation matrix + Args: + spatial_dims (int): spatial rank + shift (floats): translate factors, defaults to 0. + """ + shift = ensure_tuple(shift) + affine = np.eye(spatial_dims + 1) + for i, a in enumerate(shift[:spatial_dims]): + affine[i, spatial_dims] = a + return affine diff --git a/monai/utils/medical_image_converter.py b/monai/utils/medical_image_converter.py index e9b9968bc7..1ebd463f4d 100644 --- a/monai/utils/medical_image_converter.py +++ b/monai/utils/medical_image_converter.py @@ -12,6 +12,7 @@ import os import argparse import numpy as np +import pydicom import SimpleITK as Sitk ALLOWED_SRC_FORMATS = ['.nii', '.nii.gz', '.mhd', '.mha', '.dcm'] @@ -34,6 +35,38 @@ def contain_dicom(path): return False +# Convert values from DICOM files to standard Python types +def convert_value(value): # https://github.com/pydicom/pydicom/issues/319 + t = type(value) + if t in (list, int, float): + pass + elif t == str: + value = value.encode() + elif t == pydicom.valuerep.MultiValue: + value = np.array(value) + elif t == pydicom.valuerep.PersonName3: + value = str(value) + elif t == pydicom.valuerep.DSfloat: + value = float(value) + elif t == pydicom.valuerep.IS: + value = int(value) + else: + value = repr(value) + return value + +# Convert a DICOM file into a dictionary +def dictify_dicom(dataset): + dictionary = dict() + for element in dataset: + if element.tag == (0x7fe0, 0x0010): # skip pixel array tags + continue + if element.VR == 'SQ': # recursive call for sequences + dictionary[element.tag] = [dictify_dicom(item) for item in element] + else: + dictionary[element.tag] = convert_value(element.value) + return dictionary + + def get_dicom_dir_list(source_dir): dicom_dir_list = [] diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 775b8f2ebd..d1cbed9f87 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -11,10 +11,13 @@ import itertools +import numpy as np +import torch + def zip_with(op, *vals, mapfunc=map): """ - Map `op`, using `mapfunc`, to each tuple derived from zipping the iterables in `vals'. + Map `op`, using `mapfunc`, to each tuple derived from zipping the iterables in `vals`. """ return mapfunc(op, zip(*vals)) @@ -40,3 +43,15 @@ def ensure_tuple(vals): vals = (vals,) return tuple(vals) + + +def is_scalar_tensor(val): + if torch.is_tensor(val) and val.ndim == 0: + return True + return False + + +def is_scalar(val): + if torch.is_tensor(val) and val.ndim == 0: + return True + return np.isscalar(val) diff --git a/monai/utils/sliding_window_inference.py b/monai/utils/sliding_window_inference.py index 2efc7a7481..7eff40bd36 100644 --- a/monai/utils/sliding_window_inference.py +++ b/monai/utils/sliding_window_inference.py @@ -10,9 +10,8 @@ # limitations under the License. import torch - -from monai.transforms.transforms import ImageEndPadder -from monai.transforms.transforms import ToTensor +from ignite.utils import convert_tensor +from monai.transforms.transforms import PadImageEnd from monai.data.utils import dense_patch_slices @@ -48,8 +47,8 @@ def sliding_window_inference(inputs, roi_size, sw_batch_size, predictor, device) original_image_size = [image_size[i] for i in range(num_spatial_dims)] # in case that image size is smaller than roi size image_size = tuple(max(image_size[i], roi_size[i]) for i in range(num_spatial_dims)) - inputs = ImageEndPadder(roi_size, 'constant')(inputs) # in np array - inputs = ToTensor()(inputs) + inputs = PadImageEnd(roi_size, 'constant')(inputs) # in np array + inputs = convert_tensor(torch.from_numpy(inputs), device, False) # TODO: interval from user's specification scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims) diff --git a/monai/visualize/img2tensorboard.py b/monai/visualize/img2tensorboard.py index 82211a8ecd..7498ce1c85 100644 --- a/monai/visualize/img2tensorboard.py +++ b/monai/visualize/img2tensorboard.py @@ -20,15 +20,14 @@ def _image3_animated_gif(imp, scale_factor=1): Function to actually create the animated gif. Args: imp: tuple of tag and a list of image tensors - scale_factor: amount to multiply values by (if the image data is between 0 and 1, using 255 for this value will - scale it to displayable range) + scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will + scale it to displayable range """ - # x=numpy.random.randint(0,256,[10,10,10],numpy.uint8) (tag, ims) = imp ims = [ - (np.asarray((ims[i, :, :])) * scale_factor).astype(np.uint8) - for i in range(ims.shape[0]) + (np.asarray((ims[:, :, i])) * scale_factor).astype(np.uint8) + for i in range(ims.shape[2]) ] ims = [GifImage.fromarray(im) for im in ims] img_str = b'' @@ -49,8 +48,8 @@ def _image3_animated_gif(imp, scale_factor=1): def make_animated_gif_summary(tag, tensor, max_out=3, - animation_axes=(1,), - image_axes=(2, 3), + animation_axes=(3,), + image_axes=(1, 2), other_indices=None, scale_factor=1): """ @@ -58,13 +57,13 @@ def make_animated_gif_summary(tag, Args: tag: Data identifier - tensor: tensor for the image, expected to be in CDHW format + tensor: tensor for the image, expected to be in CHWD format max_out: maximum number of slices to animate through animation_axes: axis to animate on (not currently used) image_axes: axes of image (not currently used) other_indices: (not currently used) - scale_factor: amount to multiply values by (if the image data is between 0 and 1, using 255 for this value will - scale it to displayable range) + scale_factor: amount to multiply values by. + if the image data is between 0 and 1, using 255 for this value will scale it to displayable range """ if max_out == 1: @@ -101,8 +100,8 @@ def add_animated_gif(writer, tag, image_tensor, max_out, scale_factor, global_st tag: Data identifier image_tensor: tensor for the image to add, expected to be in CDHW format max_out: maximum number of slices to animate through - scale_factor: amount to multiply values by (if the image data is between 0 and 1, using 255 for this value will - scale it to displayable range) + scale_factor: amount to multiply values by. If the image data is between 0 and 1, using 255 for this value will + scale it to displayable range global_step: Global step value to record """ writer._get_file_writer().add_summary(make_animated_gif_summary(tag, image_tensor, max_out=max_out, @@ -119,8 +118,8 @@ def add_animated_gif_no_channels(writer, tag, image_tensor, max_out, scale_facto tag: Data identifier image_tensor: tensor for the image to add, expected to be in DHW format max_out: maximum number of slices to animate through - scale_factor: amount to multiply values by (if the image data is between 0 and 1, using 255 for this value will - scale it to displayable range) + scale_factor: amount to multiply values by. If the image data is between 0 and 1, using 255 for this value will + scale it to displayable range global_step: Global step value to record """ writer._get_file_writer().add_summary(make_animated_gif_summary(tag, image_tensor.unsqueeze(0), diff --git a/requirements.txt b/requirements.txt index 4959052d8c..791434792b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,12 @@ torch>=1.4 pytorch-ignite==0.3.0 numpy -jupyter +pydicom +nibabel +tensorboard pillow +scipy +scikit-image coverage -nibabel parameterized -tensorboard SimpleITK diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000..864d1ef8ed --- /dev/null +++ b/setup.py @@ -0,0 +1,57 @@ +import os +import io +import re +from setuptools import setup, find_packages + +# inspired by https://github.com/pytorch/ignite/blob/master/setup.py +def read(*names, **kwargs): + print(os.path.join(os.path.dirname(__file__), *names)) + with io.open( + os.path.join(os.path.dirname(__file__), *names), + encoding=kwargs.get("encoding", "utf8") + ) as fp: + return fp.read() + +def find_version(*file_paths): + version_file = read(*file_paths) + version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", + version_file, re.M) + if version_match: + return version_match.group(1) + raise RuntimeError("Unable to find version string.") + +readme = read("README.md") +VERSION = find_version("monai", "__init__.py") +requirements = [ + "torch", + "pytorch-ignite", + "numpy", + "pillow", + "coverage", + "pydicom", + "nibabel", + "parameterized", + "tensorboard", + "scikit-image", + "scipy", + "SimpleITK" +] + +if __name__ == '__main__': + setup( + # Metadata + name="monai", + version=VERSION, + author="MONAI Consortium", + url="https://github.com/Project-MONAI/MONAI", + author_email="monai.miccai2019@gmail.com", + description="AI Toolkit for Healthcare Imaging", + long_description_content_type="text/markdown", + long_description=readme, + license="Apache License 2.0", + + # Package info + packages=find_packages(exclude=('docs', 'examples', 'tests')), + zip_safe=True, + install_requires=requirements + ) diff --git a/tests/integration_determinism.py b/tests/integration_determinism.py new file mode 100644 index 0000000000..5e31d10fab --- /dev/null +++ b/tests/integration_determinism.py @@ -0,0 +1,88 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import numpy as np +import torch +from torch.utils.data import DataLoader, Dataset +from monai.transforms import AddChannel, Rescale, RandUniformPatch, RandRotate90 +import monai.transforms.compose as transforms +from monai.data.synthetic import create_test_image_2d +from monai.losses.dice import DiceLoss +from monai.networks.nets.unet import UNet + + +def run_test(batch_size=64, train_steps=100, device=torch.device("cuda:0")): + + class _TestBatch(Dataset): + + def __init__(self, transforms): + self.transforms = transforms + + def __getitem__(self, _unused_id): + im, seg = create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1) + seed = np.random.randint(2147483647) + self.transforms.set_random_state(seed=seed) + im = self.transforms(im) + self.transforms.set_random_state(seed=seed) + seg = self.transforms(seg) + return im, seg + + def __len__(self): + return train_steps + + net = UNet( + dimensions=2, + in_channels=1, + out_channels=1, + channels=(4, 8, 16, 32), + strides=(2, 2, 2), + num_res_units=2, + ).to(device) + + loss = DiceLoss(do_sigmoid=True) + opt = torch.optim.Adam(net.parameters(), 1e-2) + train_transforms = transforms.Compose([ + AddChannel(), + Rescale(), + RandUniformPatch((96, 96)), + RandRotate90() + ]) + + src = DataLoader(_TestBatch(train_transforms), batch_size=batch_size) + + net.train() + epoch_loss = 0 + step = 0 + for img, seg in src: + step += 1 + opt.zero_grad() + output = net(img.to(device)) + step_loss = loss(output, seg.to(device)) + step_loss.backward() + opt.step() + epoch_loss += step_loss.item() + epoch_loss /= step + + print('Loss:', epoch_loss) + result = np.allclose(epoch_loss, 0.578675) + if result is False: + print('Loss value is wrong, expect to be 0.578675.') + return result + + +if __name__ == "__main__": + np.random.seed(0) + torch.manual_seed(0) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + sys.exit(0 if run_test() is True else 1) diff --git a/tests/integration_sliding_window.py b/tests/integration_sliding_window.py index 32abbfff5c..91fd2994be 100644 --- a/tests/integration_sliding_window.py +++ b/tests/integration_sliding_window.py @@ -28,7 +28,7 @@ from tests.utils import make_nifti_image -def run_test(batch_size=2, device=torch.device("cpu:0")): +def run_test(batch_size=2, device=torch.device("cuda:0")): im, seg = create_test_image_3d(25, 28, 63, rad_max=10, noise_max=1, num_objs=4, num_seg_classes=1) input_shape = im.shape @@ -40,11 +40,11 @@ def run_test(batch_size=2, device=torch.device("cpu:0")): net = UNet( dimensions=3, in_channels=1, - num_classes=1, + out_channels=1, channels=(4, 8, 16, 32), strides=(2, 2, 2), num_res_units=2, - ) + ).to(device) roi_size = (16, 32, 48) sw_batch_size = batch_size @@ -52,29 +52,32 @@ def _sliding_window_processor(_engine, batch): net.eval() img, seg, meta_data = batch with torch.no_grad(): - seg_probs = sliding_window_inference(img, roi_size, sw_batch_size, lambda x: net(x)[0], device) + seg_probs = sliding_window_inference(img, roi_size, sw_batch_size, net, device) return predict_segmentation(seg_probs) infer_engine = Engine(_sliding_window_processor) with tempfile.TemporaryDirectory() as temp_dir: - SegmentationSaver(output_path=temp_dir, output_ext='.nii.gz', output_postfix='seg').attach(infer_engine) + SegmentationSaver(output_path=temp_dir, output_ext='.nii.gz', output_postfix='seg', + batch_transform=lambda x: x[2]).attach(infer_engine) infer_engine.run(loader) basename = os.path.basename(img_name)[:-len('.nii.gz')] saved_name = os.path.join(temp_dir, basename, '{}_seg.nii.gz'.format(basename)) - testing_shape = nib.load(saved_name).get_fdata().shape + # get spatial dimensions shape, the saved nifti image format: HWDC + testing_shape = nib.load(saved_name).get_fdata().shape[:-1] if os.path.exists(img_name): os.remove(img_name) if os.path.exists(seg_name): os.remove(seg_name) - - return testing_shape == input_shape + if testing_shape != input_shape: + print('testing shape: {} does not match input shape: {}.'.format(testing_shape, input_shape)) + return False + return True if __name__ == "__main__": result = run_test() - sys.exit(0 if result else 1) diff --git a/tests/integration_unet2d.py b/tests/integration_unet2d.py index 1fd9074c66..819be91e4d 100644 --- a/tests/integration_unet2d.py +++ b/tests/integration_unet2d.py @@ -35,28 +35,26 @@ def __len__(self): net = UNet( dimensions=2, in_channels=1, - num_classes=1, + out_channels=1, channels=(4, 8, 16, 32), strides=(2, 2, 2), num_res_units=2, - ) + ).to(device) loss = DiceLoss(do_sigmoid=True) opt = torch.optim.Adam(net.parameters(), 1e-4) src = DataLoader(_TestBatch(), batch_size=batch_size) - def loss_fn(pred, grnd): - return loss(pred[0], grnd) - - trainer = create_supervised_trainer(net, opt, loss_fn, device, False) + trainer = create_supervised_trainer(net, opt, loss, device, False) trainer.run(src, 1) - - return trainer.state.output + loss = trainer.state.output + print('Loss:', loss) + if loss >= 1: + print('Loss value is wrong, expect to be < 1.') + return loss if __name__ == "__main__": result = run_test() - print(result) - sys.exit(0 if result < 1 else 1) diff --git a/tests/test_adaptors.py b/tests/test_adaptors.py new file mode 100644 index 0000000000..d089290d04 --- /dev/null +++ b/tests/test_adaptors.py @@ -0,0 +1,164 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import itertools + + +from monai.transforms.adaptors import adaptor, apply_alias, to_kwargs, FunctionSignature + + +class TestAdaptors(unittest.TestCase): + + def test_function_signature(self): + + def foo(image, label=None, *a, **kw): + pass + + f = FunctionSignature(foo) + + def test_single_in_single_out(self): + def foo(image): + return image * 2 + + it = itertools.product( + ['image', ['image']], + [None, 'image', ['image'], {'image': 'image'}] + ) + for i in it: + d = {'image': 2} + dres = adaptor(foo, i[0], i[1])(d) + self.assertEqual(dres['image'], 4) + + d = {'image': 2} + dres = adaptor(foo, 'image')(d) + self.assertEqual(dres['image'], 4) + + d = {'image': 2} + dres = adaptor(foo, 'image', 'image')(d) + self.assertEqual(dres['image'], 4) + + d = {'image': 2} + dres = adaptor(foo, 'image', {'image': 'image'})(d) + self.assertEqual(dres['image'], 4) + + d = {'img': 2} + dres = adaptor(foo, 'img', {'img': 'image'})(d) + self.assertEqual(dres['img'], 4) + + d = {'img': 2} + dres = adaptor(foo, ['img'], {'img': 'image'})(d) + self.assertEqual(dres['img'], 4) + + def test_multi_in_single_out(self): + def foo(image, label): + return image * label + + it = itertools.product( + ['image', ['image']], + [None, ['image', 'label'], {'image': 'image', 'label': 'label'}] + ) + + for i in it: + d = {'image': 2, 'label': 3} + dres = adaptor(foo, i[0], i[1])(d) + self.assertEqual(dres['image'], 6) + self.assertEqual(dres['label'], 3) + + it = itertools.product( + ['newimage', ['newimage']], + [None, ['image', 'label'], {'image': 'image', 'label': 'label'}] + ) + + for i in it: + d = {'image': 2, 'label': 3} + dres = adaptor(foo, i[0], i[1])(d) + self.assertEqual(dres['image'], 2) + self.assertEqual(dres['label'], 3) + self.assertEqual(dres['newimage'], 6) + + it = itertools.product( + ['img', ['img']], + [{'img': 'image', 'lbl': 'label'}] + ) + + for i in it: + d = {'img': 2, 'lbl': 3} + dres = adaptor(foo, i[0], i[1])(d) + self.assertEqual(dres['img'], 6) + self.assertEqual(dres['lbl'], 3) + + def test_default_arg_single_out(self): + def foo(a, b=2): + return a * b + + d = {'a': 5} + dres = adaptor(foo, 'c')(d) + self.assertEqual(dres['c'], 10) + + d = {'b': 5} + with self.assertRaises(TypeError): + dres = adaptor(foo, 'c')(d) + + def test_multi_out(self): + def foo(a, b): + return a * b, a / b + + d = {'a': 3, 'b': 4} + dres = adaptor(foo, ['c', 'd'])(d) + self.assertEqual(dres['c'], 12) + self.assertEqual(dres['d'], 3 / 4) + + def test_dict_out(self): + def foo(a): + return {'a': a * 2} + + d = {'a': 2} + dres = adaptor(foo, {'a': 'a'})(d) + self.assertEqual(dres['a'], 4) + + d = {'b': 2} + dres = adaptor(foo, {'a': 'b'}, {'b': 'a'})(d) + self.assertEqual(dres['b'], 4) + + +class TestApplyAlias(unittest.TestCase): + + def test_apply_alias(self): + + def foo(d): + d['x'] *= 2 + return d + + d = {'a': 1, 'b': 3} + result = apply_alias(foo, {'b': 'x'})(d) + self.assertDictEqual({'a': 1, 'b': 6}, result) + + +class TestToKwargs(unittest.TestCase): + + def test_to_kwargs(self): + + def foo(**kwargs): + results = {k: v * 2 for k, v in kwargs.items()} + return results + + def compose_like(fn, data): + data = fn(data) + return data + + d = {'a': 1, 'b': 2} + + actual = compose_like(to_kwargs(foo), d) + self.assertDictEqual(actual, {'a': 2, 'b': 4}) + + with self.assertRaises(TypeError): + actual = compose_like(foo, d) diff --git a/tests/test_add_channeld.py b/tests/test_add_channeld.py new file mode 100644 index 0000000000..a2940ffffb --- /dev/null +++ b/tests/test_add_channeld.py @@ -0,0 +1,37 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np +from parameterized import parameterized +from monai.transforms.composables import AddChanneld + +TEST_CASE_1 = [ + {'keys': ['img', 'seg']}, + { + 'img': np.array([[0, 1], [1, 2]]), + 'seg': np.array([[0, 1], [1, 2]]) + }, + (1, 2, 2), +] + + +class TestAddChanneld(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_shape(self, input_param, input_data, expected_shape): + result = AddChanneld(**input_param)(input_data) + self.assertEqual(result['img'].shape, expected_shape) + self.assertEqual(result['seg'].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_affine.py b/tests/test_affine.py new file mode 100644 index 0000000000..e179be1fc1 --- /dev/null +++ b/tests/test_affine.py @@ -0,0 +1,64 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import Affine + +TEST_CASES = [ + [ + dict(padding_mode='zeros', as_tensor_output=False, device=None), + {'img': np.arange(4).reshape((1, 2, 2)), 'spatial_size': (4, 4)}, + np.array([[[0., 0., 0., 0.], [0., 0., 0.25, 0.], [0., 0.5, 0.75, 0.], [0., 0., 0., 0.]]]) + ], + [ + dict(rotate_params=[np.pi / 2], padding_mode='zeros', as_tensor_output=False, device=None), + {'img': np.arange(4).reshape((1, 2, 2)), 'spatial_size': (4, 4)}, + np.array([[[0., 0., 0., 0.], [0., 0.5, 0., 0.], [0., 0.75, 0.25, 0.], [0., 0., 0., 0.]]]) + ], + [ + dict(padding_mode='zeros', as_tensor_output=False, device=None), + {'img': np.arange(8).reshape((1, 2, 2, 2)), 'spatial_size': (4, 4, 4)}, + np.array([[[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0., 0.125, 0.], [0., 0.25, 0.375, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0.5, 0.625, 0.], [0., 0.75, 0.875, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]]]) + ], + [ + dict(rotate_params=[np.pi / 2], padding_mode='zeros', as_tensor_output=False, device=None), + {'img': np.arange(8).reshape((1, 2, 2, 2)), 'spatial_size': (4, 4, 4)}, + np.array([[[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0.25, 0., 0.], [0., 0.375, 0.125, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0.75, 0.5, 0.], [0., 0.875, 0.625, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]]]) + ], +] + + +class TestAffine(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_affine(self, input_param, input_data, expected_val): + g = Affine(**input_param) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_affine_grid.py b/tests/test_affine_grid.py new file mode 100644 index 0000000000..759f1f10af --- /dev/null +++ b/tests/test_affine_grid.py @@ -0,0 +1,75 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import AffineGrid + +TEST_CASES = [ + [{'as_tensor_output': False, 'device': torch.device('cpu:0')}, {'spatial_size': (2, 2)}, + np.array([[[-0.5, -0.5], [0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]], [[1., 1.], [1., 1.]]])], + [{'as_tensor_output': True, 'device': None}, {'spatial_size': (2, 2)}, + torch.tensor([[[-0.5, -0.5], [0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]], [[1., 1.], [1., 1.]]])], + [{'as_tensor_output': False, 'device': None}, {'grid': np.ones((3, 3, 3))}, + np.ones((3, 3, 3))], + [{'as_tensor_output': True, 'device': torch.device('cpu:0')}, {'grid': np.ones((3, 3, 3))}, + torch.ones((3, 3, 3))], + [{'as_tensor_output': False, 'device': None}, {'grid': torch.ones((3, 3, 3))}, + np.ones((3, 3, 3))], + [{'as_tensor_output': True, 'device': torch.device('cpu:0')}, {'grid': torch.ones((3, 3, 3))}, + torch.ones((3, 3, 3))], + [{'rotate_params': (1., 1.), 'scale_params': (-20, 10), 'as_tensor_output': True, 'device': torch.device('cpu:0')}, + {'grid': torch.ones((3, 3, 3))}, + torch.tensor([[[-19.2208, -19.2208, -19.2208], [-19.2208, -19.2208, -19.2208], [-19.2208, -19.2208, -19.2208]], + [[-11.4264, -11.4264, -11.4264], [-11.4264, -11.4264, -11.4264], [-11.4264, -11.4264, -11.4264]], + [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]])], + [ + { + 'rotate_params': (1., 1., 1.), 'scale_params': (-20, 10), 'as_tensor_output': True, 'device': + torch.device('cpu:0') + }, + {'grid': torch.ones((4, 3, 3, 3))}, + torch.tensor([[[[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]], + [[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]], + [[-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435], [-9.5435, -9.5435, -9.5435]]], + [[[-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, -20.2381]], + [[-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, -20.2381]], + [[-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, -20.2381], [-20.2381, -20.2381, + -20.2381]]], + [[[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]], + [[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]], + [[-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844], [-0.5844, -0.5844, -0.5844]]], + [[[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]], + [[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]], + [[1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000], [1.0000, 1.0000, 1.0000]]]]), + ], +] + + +class TestAffineGrid(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_affine_grid(self, input_param, input_data, expected_val): + g = AffineGrid(**input_param) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_as_channel_first.py b/tests/test_as_channel_first.py new file mode 100644 index 0000000000..ccd0f3765a --- /dev/null +++ b/tests/test_as_channel_first.py @@ -0,0 +1,49 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np +from parameterized import parameterized +from monai.transforms.transforms import AsChannelFirst + +TEST_CASE_1 = [ + { + 'channel_dim': -1 + }, + (4, 1, 2, 3) +] + +TEST_CASE_2 = [ + { + 'channel_dim': 3 + }, + (4, 1, 2, 3) +] + +TEST_CASE_3 = [ + { + 'channel_dim': 2 + }, + (3, 1, 2, 4) +] + + +class TestAsChannelFirst(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) + def test_shape(self, input_param, expected_shape): + test_data = np.random.randint(0, 2, size=[1, 2, 3, 4]) + result = AsChannelFirst(**input_param)(test_data) + self.assertTupleEqual(result.shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_as_channel_firstd.py b/tests/test_as_channel_firstd.py new file mode 100644 index 0000000000..6f9b450c4f --- /dev/null +++ b/tests/test_as_channel_firstd.py @@ -0,0 +1,58 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np +from parameterized import parameterized +from monai.transforms.composables import AsChannelFirstd + +TEST_CASE_1 = [ + { + 'keys': ['image', 'label', 'extra'], + 'channel_dim': -1 + }, + (4, 1, 2, 3) +] + +TEST_CASE_2 = [ + { + 'keys': ['image', 'label', 'extra'], + 'channel_dim': 3 + }, + (4, 1, 2, 3) +] + +TEST_CASE_3 = [ + { + 'keys': ['image', 'label', 'extra'], + 'channel_dim': 2 + }, + (3, 1, 2, 4) +] + + +class TestAsChannelFirstd(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) + def test_shape(self, input_param, expected_shape): + test_data = { + 'image': np.random.randint(0, 2, size=[1, 2, 3, 4]), + 'label': np.random.randint(0, 2, size=[1, 2, 3, 4]), + 'extra': np.random.randint(0, 2, size=[1, 2, 3, 4]) + } + result = AsChannelFirstd(**input_param)(test_data) + self.assertTupleEqual(result['image'].shape, expected_shape) + self.assertTupleEqual(result['label'].shape, expected_shape) + self.assertTupleEqual(result['extra'].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_create_grid_and_affine.py b/tests/test_create_grid_and_affine.py new file mode 100644 index 0000000000..7359485b2f --- /dev/null +++ b/tests/test_create_grid_and_affine.py @@ -0,0 +1,176 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms.utils import (create_control_grid, create_grid, create_rotate, create_scale, create_shear, + create_translate) + + +class TestCreateGrid(unittest.TestCase): + + def test_create_grid(self): + with self.assertRaisesRegex(TypeError, ''): + create_grid(None) + with self.assertRaisesRegex(TypeError, ''): + create_grid((1, 1), spacing=2.) + with self.assertRaisesRegex(TypeError, ''): + create_grid((1, 1), spacing=2.) + + g = create_grid((1, 1)) + expected = np.array([[[0.]], [[0.]], [[1.]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((1, 1), homogeneous=False) + expected = np.array([[[0.]], [[0.]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((1, 1), spacing=(1.2, 1.3)) + expected = np.array([[[0.]], [[0.]], [[1.]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((1, 1, 1), spacing=(1.2, 1.3, 1.0)) + expected = np.array([[[[0.]]], [[[0.]]], [[[0.]]], [[[1.]]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((1, 1, 1), spacing=(1.2, 1.3, 1.0), homogeneous=False) + expected = np.array([[[[0.]]], [[[0.]]], [[[0.]]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((1, 1, 1), spacing=(1.2, 1.3, 1.0), dtype=int) + np.testing.assert_equal(g.dtype, np.int64) + + g = create_grid((2, 2, 2)) + expected = np.array([[[[-0.5, -0.5], [-0.5, -0.5]], [[0.5, 0.5], [0.5, 0.5]]], + [[[-0.5, -0.5], [0.5, 0.5]], [[-0.5, -0.5], [0.5, 0.5]]], + [[[-0.5, 0.5], [-0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]]], + [[[1., 1.], [1., 1.]], [[1., 1.], [1., 1.]]]]) + np.testing.assert_allclose(g, expected) + + g = create_grid((2, 2, 2), spacing=(1.2, 1.3, 1.0)) + expected = np.array([[[[-0.6, -0.6], [-0.6, -0.6]], [[0.6, 0.6], [0.6, 0.6]]], + [[[-0.65, -0.65], [0.65, 0.65]], [[-0.65, -0.65], [0.65, 0.65]]], + [[[-0.5, 0.5], [-0.5, 0.5]], [[-0.5, 0.5], [-0.5, 0.5]]], + [[[1., 1.], [1., 1.]], [[1., 1.], [1., 1.]]]]) + np.testing.assert_allclose(g, expected) + + def test_create_control_grid(self): + with self.assertRaisesRegex(TypeError, ''): + create_control_grid(None, None) + with self.assertRaisesRegex(TypeError, ''): + create_control_grid((1, 1), 2.) + + g = create_control_grid((1., 1.), (1., 1.)) + expected = np.array([ + [[-1., -1., -1.], [0., 0., 0.], [1., 1., 1.]], + [[-1., 0., 1.], [-1., 0., 1.], [-1., 0., 1.]], + [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], + ]) + np.testing.assert_allclose(g, expected) + + g = create_control_grid((1., 1.), (2., 2.)) + expected = np.array([ + [[-2., -2., -2.], [0., 0., 0.], [2., 2., 2.]], + [[-2., 0., 2.], [-2., 0., 2.], [-2., 0., 2.]], + [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], + ]) + np.testing.assert_allclose(g, expected) + + g = create_control_grid((2., 2.), (1., 1.)) + expected = np.array([ + [[-1.5, -1.5, -1.5, -1.5], [-0.5, -0.5, -0.5, -0.5], [0.5, 0.5, 0.5, 0.5], [1.5, 1.5, 1.5, 1.5]], + [[-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5]], + [[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]], + ]) + np.testing.assert_allclose(g, expected) + + g = create_control_grid((2., 2.), (2., 2.)) + expected = np.array([ + [[-3., -3., -3., -3.], [-1., -1., -1., -1.], [1., 1., 1., 1.], [3., 3., 3., 3.]], + [[-3., -1., 1., 3.], [-3., -1., 1., 3.], [-3., -1., 1., 3.], [-3., -1., 1., 3.]], + [[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]], + ]) + np.testing.assert_allclose(g, expected) + + g = create_control_grid((1., 1., 1.), (2., 2., 2.), homogeneous=False) + expected = np.array([[[[-2., -2., -2.], [-2., -2., -2.], [-2., -2., -2.]], + [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[2., 2., 2.], [2., 2., 2.], [2., 2., 2.]]], + [[[-2., -2., -2.], [0., 0., 0.], [2., 2., 2.]], + [[-2., -2., -2.], [0., 0., 0.], [2., 2., 2.]], + [[-2., -2., -2.], [0., 0., 0.], [2., 2., 2.]]], + [[[-2., 0., 2.], [-2., 0., 2.], [-2., 0., 2.]], + [[-2., 0., 2.], [-2., 0., 2.], [-2., 0., 2.]], + [[-2., 0., 2.], [-2., 0., 2.], [-2., 0., 2.]]]]) + np.testing.assert_allclose(g, expected) + + +def test_assert(func, params, expected): + m = func(*params) + np.testing.assert_allclose(m, expected, atol=1e-7) + + +class TestCreateAffine(unittest.TestCase): + + def test_create_rotate(self): + with self.assertRaisesRegex(TypeError, ''): + create_rotate(2, None) + + with self.assertRaisesRegex(ValueError, ''): + create_rotate(5, 1) + + test_assert(create_rotate, (2, 1.1), + np.array([[0.45359612, -0.89120736, 0.], [0.89120736, 0.45359612, 0.], [0., 0., 1.]])) + test_assert( + create_rotate, (3, 1.1), + np.array([[1., 0., 0., 0.], [0., 0.45359612, -0.89120736, 0.], [0., 0.89120736, 0.45359612, 0.], + [0., 0., 0., 1.]])) + test_assert( + create_rotate, (3, (1.1, 1)), + np.array([[0.54030231, 0., 0.84147098, 0.], [0.74992513, 0.45359612, -0.48152139, 0.], + [-0.38168798, 0.89120736, 0.24507903, 0.], [0., 0., 0., 1.]])) + test_assert( + create_rotate, (3, (1, 1, 1.1)), + np.array([[0.24507903, -0.48152139, 0.84147098, 0.], [0.80270075, -0.38596121, -0.45464871, 0.], + [0.54369824, 0.78687425, 0.29192658, 0.], [0., 0., 0., 1.]])) + test_assert(create_rotate, (3, (0, 0, np.pi / 2)), + np.array([[0., -1., 0., 0.], [1., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + + def test_create_shear(self): + test_assert(create_shear, (2, 1.), np.array([[1., 1., 0.], [0., 1., 0.], [0., 0., 1.]])) + test_assert(create_shear, (2, (2., 3.)), np.array([[1., 2., 0.], [3., 1., 0.], [0., 0., 1.]])) + test_assert(create_shear, (3, 1.), + np.array([[1., 1., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + + def test_create_scale(self): + test_assert(create_scale, (2, 2), np.array([[2., 0., 0.], [0., 1., 0.], [0., 0., 1.]])) + test_assert(create_scale, (2, [2, 2, 2]), np.array([[2., 0., 0.], [0., 2., 0.], [0., 0., 1.]])) + test_assert(create_scale, (3, [1.5, 2.4]), + np.array([[1.5, 0., 0., 0.], [0., 2.4, 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + test_assert(create_scale, (3, 1.5), + np.array([[1.5, 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + test_assert(create_scale, (3, [1, 2, 3, 4, 5]), + np.array([[1., 0., 0., 0.], [0., 2., 0., 0.], [0., 0., 3., 0.], [0., 0., 0., 1.]])) + + def test_create_translate(self): + test_assert(create_translate, (2, 2), np.array([[1., 0., 2.], [0., 1., 0.], [0., 0., 1.]])) + test_assert(create_translate, (2, [2, 2, 2]), np.array([[1., 0., 2.], [0., 1., 2.], [0., 0., 1.]])) + test_assert(create_translate, (3, [1.5, 2.4]), + np.array([[1., 0., 0., 1.5], [0., 1., 0., 2.4], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + test_assert(create_translate, (3, 1.5), + np.array([[1., 0., 0., 1.5], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])) + test_assert(create_translate, (3, [1, 2, 3, 4, 5]), + np.array([[1., 0., 0., 1.], [0., 1., 0., 2.], [0., 0., 1., 3.], [0., 0., 0., 1.]])) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_dataset.py b/tests/test_dataset.py new file mode 100644 index 0000000000..6829812dbc --- /dev/null +++ b/tests/test_dataset.py @@ -0,0 +1,64 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import os +import shutil +import numpy as np +import tempfile +import nibabel as nib +from parameterized import parameterized +from monai.data.dataset import Dataset +from monai.transforms.composables import LoadNiftid + +TEST_CASE_1 = [ + (128, 128, 128) +] + + +class TestDataset(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_shape(self, expected_shape): + test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4)) + tempdir = tempfile.mkdtemp() + nib.save(test_image, os.path.join(tempdir, 'test_image1.nii.gz')) + nib.save(test_image, os.path.join(tempdir, 'test_label1.nii.gz')) + nib.save(test_image, os.path.join(tempdir, 'test_extra1.nii.gz')) + nib.save(test_image, os.path.join(tempdir, 'test_image2.nii.gz')) + nib.save(test_image, os.path.join(tempdir, 'test_label2.nii.gz')) + nib.save(test_image, os.path.join(tempdir, 'test_extra2.nii.gz')) + test_data = [ + { + 'image': os.path.join(tempdir, 'test_image1.nii.gz'), + 'label': os.path.join(tempdir, 'test_label1.nii.gz'), + 'extra': os.path.join(tempdir, 'test_extra1.nii.gz') + }, + { + 'image': os.path.join(tempdir, 'test_image2.nii.gz'), + 'label': os.path.join(tempdir, 'test_label2.nii.gz'), + 'extra': os.path.join(tempdir, 'test_extra2.nii.gz') + } + ] + dataset = Dataset(data=test_data, transform=LoadNiftid(keys=['image', 'label', 'extra'])) + data1 = dataset[0] + data2 = dataset[1] + shutil.rmtree(tempdir) + self.assertTupleEqual(data1['image'].shape, expected_shape) + self.assertTupleEqual(data1['label'].shape, expected_shape) + self.assertTupleEqual(data1['extra'].shape, expected_shape) + self.assertTupleEqual(data2['image'].shape, expected_shape) + self.assertTupleEqual(data2['label'].shape, expected_shape) + self.assertTupleEqual(data2['extra'].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_delete_keys.py b/tests/test_delete_keys.py new file mode 100644 index 0000000000..3bc1d0f11a --- /dev/null +++ b/tests/test_delete_keys.py @@ -0,0 +1,39 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import time +import sys +from parameterized import parameterized +from monai.transforms.composables import DeleteKeysd + +TEST_CASE_1 = [ + {'keys': [str(i) for i in range(30)]}, + 20, +] + + +class TestDeleteKeysd(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_memory(self, input_param, expected_key_size): + input_data = dict() + for i in range(50): + input_data[str(i)] = [time.time()] * 100000 + result = DeleteKeysd(**input_param)(input_data) + self.assertEqual(len(result.keys()), expected_key_size) + self.assertGreaterEqual( + sys.getsizeof(input_data) * float(expected_key_size) / len(input_data), + sys.getsizeof(result)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_dice_loss.py b/tests/test_dice_loss.py index a7ad9171b9..e937185f91 100644 --- a/tests/test_dice_loss.py +++ b/tests/test_dice_loss.py @@ -39,7 +39,7 @@ 'ground': torch.tensor([[[[1., 1.], [1., 1.]]], [[[1., 0.], [1., 0.]]]]), 'smooth': 1e-4, }, - 0.416636, + 0.416657, ] TEST_CASE_3 = [ # shape: (2, 2, 3), (2, 1, 3) @@ -64,7 +64,7 @@ 'ground': torch.tensor([[[1., 0., 0.]], [[1., 1., 0.]]]), 'smooth': 1e-4, }, - 0.435015, + 0.435050, ] TEST_CASE_5 = [ # shape: (2, 2, 3), (2, 1, 3) @@ -77,12 +77,12 @@ 'ground': torch.tensor([[[1., 0., 0.]], [[1., 1., 0.]]]), 'smooth': 1e-4, }, - 0.383678, + 0.383713, ] TEST_CASE_6 = [ # shape: (1, 1, 2, 2), (1, 1, 2, 2) { - 'include_background': False, + 'include_background': True, 'do_sigmoid': True, }, { diff --git a/tests/test_flip.py b/tests/test_flip.py index a70b9c92c5..050d66d8db 100644 --- a/tests/test_flip.py +++ b/tests/test_flip.py @@ -17,27 +17,30 @@ from monai.transforms import Flip from tests.utils import NumpyImageTestCase2D +INVALID_CASES = [("wrong_axis", ['s', 1], TypeError), + ("not_numbers", 's', TypeError)] -class FlipTest(NumpyImageTestCase2D): +VALID_CASES = [("no_axis", None), + ("one_axis", 1), + ("many_axis", [0, 1])] - @parameterized.expand([ - ("wrong_axis", ['s', 1], TypeError), - ("not_numbers", 's', AssertionError) - ]) - def test_invalid_inputs(self, _, axis, raises): + +class TestFlip(NumpyImageTestCase2D): + + @parameterized.expand(INVALID_CASES) + def test_invalid_inputs(self, _, spatial_axis, raises): with self.assertRaises(raises): - flip = Flip(axis) - flip(self.imt) - - @parameterized.expand([ - ("no_axis", None), - ("one_axis", 1), - ("many_axis", [0, 1, 2]) - ]) - def test_correct_results(self, _, axis): - flip = Flip(axis=axis) - expected = np.flip(self.imt, axis) - self.assertTrue(np.allclose(expected, flip(self.imt))) + flip = Flip(spatial_axis) + flip(self.imt[0]) + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, _, spatial_axis): + flip = Flip(spatial_axis=spatial_axis) + expected = list() + for channel in self.imt[0]: + expected.append(np.flip(channel, spatial_axis)) + expected = np.stack(expected) + self.assertTrue(np.allclose(expected, flip(self.imt[0]))) if __name__ == '__main__': diff --git a/tests/test_flipd.py b/tests/test_flipd.py new file mode 100644 index 0000000000..e2fcb6b915 --- /dev/null +++ b/tests/test_flipd.py @@ -0,0 +1,48 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms import Flipd +from tests.utils import NumpyImageTestCase2D + +INVALID_CASES = [("wrong_axis", ['s', 1], TypeError), + ("not_numbers", 's', TypeError)] + +VALID_CASES = [("no_axis", None), + ("one_axis", 1), + ("many_axis", [0, 1])] + + +class TestFlipd(NumpyImageTestCase2D): + + @parameterized.expand(INVALID_CASES) + def test_invalid_cases(self, _, spatial_axis, raises): + with self.assertRaises(raises): + flip = Flipd(keys='img', spatial_axis=spatial_axis) + flip({'img': self.imt[0]}) + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, _, spatial_axis): + flip = Flipd(keys='img', spatial_axis=spatial_axis) + expected = list() + for channel in self.imt[0]: + expected.append(np.flip(channel, spatial_axis)) + expected = np.stack(expected) + res = flip({'img': self.imt[0]}) + assert np.allclose(expected, res['img']) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_gaussian_filter.py b/tests/test_gaussian_filter.py new file mode 100644 index 0000000000..ade658e74c --- /dev/null +++ b/tests/test_gaussian_filter.py @@ -0,0 +1,58 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch + +from monai.networks.layers.simplelayers import GaussianFilter + + +class GaussianFilterTestCase(unittest.TestCase): + + def test_1d(self): + a = torch.ones(1, 8, 10) + g = GaussianFilter(1, 3, 3, torch.device('cpu:0')) + expected = np.array([[ + [ + 0.56658804, 0.69108766, 0.79392236, 0.86594427, 0.90267116, 0.9026711, 0.8659443, 0.7939224, 0.6910876, + 0.56658804 + ], + ]]) + expected = np.tile(expected, (1, 8, 1)) + np.testing.assert_allclose(g(a).cpu().numpy(), expected) + + def test_2d(self): + a = torch.ones(1, 1, 3, 3) + g = GaussianFilter(2, 3, 3, torch.device('cpu:0')) + expected = np.array([[[[0.13380532, 0.14087981, 0.13380532], [0.14087981, 0.14832835, 0.14087981], + [0.13380532, 0.14087981, 0.13380532]]]]) + + np.testing.assert_allclose(g(a).cpu().numpy(), expected) + + def test_3d(self): + a = torch.ones(1, 1, 4, 3, 4) + g = GaussianFilter(3, 3, 3, torch.device('cpu:0')) + expected = np.array( + [[[[[0.07294822, 0.08033235, 0.08033235, 0.07294822], [0.07680509, 0.08457965, 0.08457965, 0.07680509], + [0.07294822, 0.08033235, 0.08033235, 0.07294822]], + [[0.08033235, 0.08846395, 0.08846395, 0.08033235], [0.08457965, 0.09314119, 0.09314119, 0.08457966], + [0.08033235, 0.08846396, 0.08846396, 0.08033236]], + [[0.08033235, 0.08846395, 0.08846395, 0.08033235], [0.08457965, 0.09314119, 0.09314119, 0.08457966], + [0.08033235, 0.08846396, 0.08846396, 0.08033236]], + [[0.07294822, 0.08033235, 0.08033235, 0.07294822], [0.07680509, 0.08457965, 0.08457965, 0.07680509], + [0.07294822, 0.08033235, 0.08033235, 0.07294822]]]]],) + np.testing.assert_allclose(g(a).cpu().numpy(), expected) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_gaussian_noise.py b/tests/test_gaussian_noise.py new file mode 100644 index 0000000000..400ce4ad73 --- /dev/null +++ b/tests/test_gaussian_noise.py @@ -0,0 +1,38 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np + +from parameterized import parameterized + +from monai.transforms import GaussianNoise +from tests.utils import NumpyImageTestCase2D + + +class GaussianNoiseTest(NumpyImageTestCase2D): + + @parameterized.expand([ + ("test_zero_mean", 0, 0.1), + ("test_non_zero_mean", 1, 0.5) + ]) + def test_correct_results(self, _, mean, std): + seed = 42 + gaussian_fn = GaussianNoise(mean=mean, std=std) + gaussian_fn.set_random_state(seed) + noised = gaussian_fn(self.imt) + np.random.seed(seed) + expected = self.imt + np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape) + assert np.allclose(expected, noised) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_generalized_dice_loss.py b/tests/test_generalized_dice_loss.py index fe29bc2d11..b2ce96169e 100644 --- a/tests/test_generalized_dice_loss.py +++ b/tests/test_generalized_dice_loss.py @@ -39,7 +39,7 @@ 'ground': torch.tensor([[[[1., 1.], [1., 1.]]], [[[1., 0.], [1., 0.]]]]), 'smooth': 1e-4, }, - 0.41678, + 0.416597, ] TEST_CASE_2 = [ # shape: (2, 2, 3), (2, 1, 3) @@ -64,7 +64,7 @@ 'ground': torch.tensor([[[1., 0., 0.]], [[1., 1., 0.]]]), 'smooth': 1e-4, }, - 0.435111, + 0.435034, ] TEST_CASE_4 = [ # shape: (2, 2, 3), (2, 1, 3) @@ -77,7 +77,7 @@ 'ground': torch.tensor([[[1., 0., 0.]], [[1., 1., 0.]]]), 'smooth': 1e-4, }, - 0.383776, + 0.383699, ] TEST_CASE_5 = [ # shape: (2, 2, 3), (2, 1, 3) @@ -89,12 +89,12 @@ 'ground': torch.tensor([[[0., 0., 0.]], [[0., 0., 0.]]]), 'smooth': 1e-8, }, - 1.0, + 0.0, ] TEST_CASE_6 = [ # shape: (1, 1, 2, 2), (1, 1, 2, 2) { - 'include_background': False, + 'include_background': True, 'do_sigmoid': True, }, { diff --git a/tests/test_generate_pos_neg_label_crop_centers.py b/tests/test_generate_pos_neg_label_crop_centers.py index b327968fe8..29c94036fa 100644 --- a/tests/test_generate_pos_neg_label_crop_centers.py +++ b/tests/test_generate_pos_neg_label_crop_centers.py @@ -21,6 +21,8 @@ 'size': [2, 2, 2], 'num_samples': 2, 'pos_ratio': 1.0, + 'image': None, + 'image_threshold': 0, 'rand_state': np.random.RandomState() }, list, diff --git a/tests/test_handler_classification_saver.py b/tests/test_handler_classification_saver.py new file mode 100644 index 0000000000..3eea9d86ed --- /dev/null +++ b/tests/test_handler_classification_saver.py @@ -0,0 +1,55 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import csv +import shutil +import unittest +import numpy as np +import torch +from ignite.engine import Engine + +from monai.handlers.classification_saver import ClassificationSaver + + +class TestHandlerClassificationSaver(unittest.TestCase): + + def test_saved_content(self): + default_dir = os.path.join('.', 'tempdir') + shutil.rmtree(default_dir, ignore_errors=True) + + # set up engine + def _train_func(engine, batch): + return torch.zeros(8) + + engine = Engine(_train_func) + + # set up testing handler + saver = ClassificationSaver(output_dir=default_dir) + saver.attach(engine) + + data = [{'filename_or_obj': ['testfile' + str(i) for i in range(8)]}] + engine.run(data, epoch_length=2, max_epochs=1) + filepath = os.path.join(default_dir, 'predictions.csv') + self.assertTrue(os.path.exists(filepath)) + with open(filepath, 'r') as f: + reader = csv.reader(f) + i = 0 + for row in reader: + self.assertEqual(row[0], 'testfile' + str(i)) + self.assertEqual(np.array(row[1:]).astype(np.float32), 0.0) + i += 1 + self.assertEqual(i, 8) + shutil.rmtree(default_dir) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_handler_stats.py b/tests/test_handler_stats.py index 5bbe17d1c2..fdb0600e04 100644 --- a/tests/test_handler_stats.py +++ b/tests/test_handler_stats.py @@ -9,6 +9,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import torch import logging import re import unittest @@ -29,12 +30,12 @@ def test_metrics_print(self): # set up engine def _train_func(engine, batch): - pass + return torch.tensor(0.0) engine = Engine(_train_func) # set up dummy metric - @engine.on(Events.ITERATION_COMPLETED) + @engine.on(Events.EPOCH_COMPLETED) def _update_metric(engine): current_metric = engine.state.metrics.get(key_to_print, 0.1) engine.state.metrics[key_to_print] = current_metric + 0.1 @@ -49,12 +50,65 @@ def _update_metric(engine): output_str = log_stream.getvalue() grep = re.compile('.*{}.*'.format(key_to_handler)) has_key_word = re.compile('.*{}.*'.format(key_to_print)) - matched = [] for idx, line in enumerate(output_str.split('\n')): if grep.match(line): - self.assertTrue(has_key_word.match(line)) - matched.append(idx) - self.assertEqual(matched, [1, 2, 3, 5, 6, 7, 8, 10]) + if idx in [5, 10]: + self.assertTrue(has_key_word.match(line)) + + def test_loss_print(self): + log_stream = StringIO() + logging.basicConfig(stream=log_stream, level=logging.INFO) + key_to_handler = 'test_logging' + key_to_print = 'myLoss' + + # set up engine + def _train_func(engine, batch): + return torch.tensor(0.0) + + engine = Engine(_train_func) + + # set up testing handler + stats_handler = StatsHandler(name=key_to_handler, tag_name=key_to_print) + stats_handler.attach(engine) + + engine.run(range(3), max_epochs=2) + + # check logging output + output_str = log_stream.getvalue() + grep = re.compile('.*{}.*'.format(key_to_handler)) + has_key_word = re.compile('.*{}.*'.format(key_to_print)) + for idx, line in enumerate(output_str.split('\n')): + if grep.match(line): + if idx in [1, 2, 3, 6, 7, 8]: + self.assertTrue(has_key_word.match(line)) + + def test_loss_dict(self): + log_stream = StringIO() + logging.basicConfig(stream=log_stream, level=logging.INFO) + key_to_handler = 'test_logging' + key_to_print = 'myLoss1' + + # set up engine + def _train_func(engine, batch): + return torch.tensor(0.0) + + engine = Engine(_train_func) + + # set up testing handler + stats_handler = StatsHandler(name=key_to_handler, + output_transform=lambda x: {key_to_print: x}) + stats_handler.attach(engine) + + engine.run(range(3), max_epochs=2) + + # check logging output + output_str = log_stream.getvalue() + grep = re.compile('.*{}.*'.format(key_to_handler)) + has_key_word = re.compile('.*{}.*'.format(key_to_print)) + for idx, line in enumerate(output_str.split('\n')): + if grep.match(line): + if idx in [1, 2, 3, 6, 7, 8]: + self.assertTrue(has_key_word.match(line)) if __name__ == '__main__': diff --git a/tests/test_handler_tb_image.py b/tests/test_handler_tb_image.py new file mode 100644 index 0000000000..9bf55e162b --- /dev/null +++ b/tests/test_handler_tb_image.py @@ -0,0 +1,60 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os +import shutil +import unittest + +import numpy as np +import torch +from ignite.engine import Engine, Events +from parameterized import parameterized + +from monai.handlers.tensorboard_handlers import TensorBoardImageHandler + +TEST_CASES = [ + [[20, 20]], + [[2, 20, 20]], + [[3, 20, 20]], + [[20, 20, 20]], + [[2, 20, 20, 20]], + [[2, 2, 20, 20, 20]], +] + + +class TestHandlerTBImage(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_tb_image_shape(self, shape): + default_dir = os.path.join('.', 'runs') + shutil.rmtree(default_dir, ignore_errors=True) + + # set up engine + def _train_func(engine, batch): + return torch.zeros((1, 1, 10, 10)) + + engine = Engine(_train_func) + + # set up testing handler + stats_handler = TensorBoardImageHandler() + engine.add_event_handler(Events.ITERATION_COMPLETED, stats_handler) + + data = zip(np.random.normal(size=(10, 4, *shape)), np.random.normal(size=(10, 4, *shape))) + engine.run(data, epoch_length=10, max_epochs=1) + + self.assertTrue(os.path.exists(default_dir)) + self.assertTrue(len(glob.glob(default_dir)) > 0) + shutil.rmtree(default_dir) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_handler_tb_stats.py b/tests/test_handler_tb_stats.py new file mode 100644 index 0000000000..53a691701f --- /dev/null +++ b/tests/test_handler_tb_stats.py @@ -0,0 +1,81 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +import tempfile +import unittest +import glob + +from ignite.engine import Engine, Events +from torch.utils.tensorboard import SummaryWriter + +from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler + + +class TestHandlerTBStats(unittest.TestCase): + + def test_metrics_print(self): + default_dir = os.path.join('.', 'runs') + shutil.rmtree(default_dir, ignore_errors=True) + + # set up engine + def _train_func(engine, batch): + return batch + 1.0 + + engine = Engine(_train_func) + + # set up dummy metric + @engine.on(Events.EPOCH_COMPLETED) + def _update_metric(engine): + current_metric = engine.state.metrics.get('acc', 0.1) + engine.state.metrics['acc'] = current_metric + 0.1 + + # set up testing handler + stats_handler = TensorBoardStatsHandler() + stats_handler.attach(engine) + engine.run(range(3), max_epochs=2) + # check logging output + + self.assertTrue(os.path.exists(default_dir)) + shutil.rmtree(default_dir) + + def test_metrics_writer(self): + default_dir = os.path.join('.', 'runs') + shutil.rmtree(default_dir, ignore_errors=True) + with tempfile.TemporaryDirectory() as temp_dir: + + # set up engine + def _train_func(engine, batch): + return batch + 1.0 + + engine = Engine(_train_func) + + # set up dummy metric + @engine.on(Events.EPOCH_COMPLETED) + def _update_metric(engine): + current_metric = engine.state.metrics.get('acc', 0.1) + engine.state.metrics['acc'] = current_metric + 0.1 + + # set up testing handler + writer = SummaryWriter(log_dir=temp_dir) + stats_handler = TensorBoardStatsHandler( + writer, output_transform=lambda x: {'loss': x * 2.0}, + global_epoch_transform=lambda x: x * 3.0) + stats_handler.attach(engine) + engine.run(range(3), max_epochs=2) + # check logging output + self.assertTrue(len(glob.glob(temp_dir)) > 0) + self.assertTrue(not os.path.exists(default_dir)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_header_correct.py b/tests/test_header_correct.py new file mode 100644 index 0000000000..b4d38b6dbf --- /dev/null +++ b/tests/test_header_correct.py @@ -0,0 +1,36 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import nibabel as nib +import numpy as np + +from monai.data.utils import correct_nifti_header_if_necessary + + +class TestCorrection(unittest.TestCase): + + def test_correct(self): + test_img = nib.Nifti1Image(np.zeros((1, 2, 3)), np.eye(4)) + test_img.header.set_zooms((100, 100, 100)) + test_img = correct_nifti_header_if_necessary(test_img) + np.testing.assert_allclose( + test_img.affine, np.array([[100., 0., 0., 0.], [0., 100., 0., 0.], [0., 0., 100., 0.], [0., 0., 0., 1.]])) + + def test_affine(self): + test_img = nib.Nifti1Image(np.zeros((1, 2, 3)), np.eye(4) * 20.) + test_img = correct_nifti_header_if_necessary(test_img) + np.testing.assert_allclose( + test_img.affine, np.array([[20., 0., 0., 0.], [0., 20., 0., 0.], [0., 0., 20., 0.], [0., 0., 0., 20.]])) + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_load_dicom.py b/tests/test_load_dicom.py new file mode 100644 index 0000000000..a8861d8c1e --- /dev/null +++ b/tests/test_load_dicom.py @@ -0,0 +1,42 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import pydicom +from pydicom.data import get_testdata_files +from parameterized import parameterized +from monai.transforms.transforms import LoadDICOM + + +TEST_CASE_IMAGE_ONLY = [ + {'image_only': True} +] + +TEST_CASE_IMAGE_METADATA = [ + {'image_only': False} +] + + +class TestLoadDICOM(unittest.TestCase): + + @parameterized.expand([TEST_CASE_IMAGE_ONLY, TEST_CASE_IMAGE_METADATA]) + def test_shape(self, input_param): + filename = get_testdata_files('CT_small.dcm')[0] + dataset = pydicom.dcmread(filename) + expected_shape = dataset.pixel_array.shape + result = LoadDICOM(**input_param)(filename) + if isinstance(result, tuple): + result = result[0] + self.assertTupleEqual(result.shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_load_dicomd.py b/tests/test_load_dicomd.py new file mode 100644 index 0000000000..b0e26c52a0 --- /dev/null +++ b/tests/test_load_dicomd.py @@ -0,0 +1,42 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import pydicom +from pydicom.data import get_testdata_files +from parameterized import parameterized +from monai.transforms.composables import LoadDICOMd + + +KEYS = ['image', 'label', 'extra'] + +TEST_CASE_1 = [ + {'keys': KEYS} +] + + +class TestLoadDICOMd(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_shape(self, input_param): + filename = get_testdata_files('CT_small.dcm')[0] + dataset = pydicom.dcmread(filename) + expected_shape = dataset.pixel_array.shape + test_data = dict() + for key in KEYS: + test_data.update({key: filename}) + result = LoadDICOMd(**input_param)(test_data) + for key in KEYS: + self.assertTupleEqual(result[key].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_load_nifti.py b/tests/test_load_nifti.py new file mode 100644 index 0000000000..de0660ccb3 --- /dev/null +++ b/tests/test_load_nifti.py @@ -0,0 +1,54 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import os +import shutil +import numpy as np +import tempfile +import nibabel as nib +from parameterized import parameterized +from monai.transforms.transforms import LoadNifti + +TEST_CASE_IMAGE_ONLY = [ + { + 'as_closest_canonical': False, + 'image_only': True + }, + (128, 128, 128) +] + +TEST_CASE_IMAGE_METADATA = [ + { + 'as_closest_canonical': False, + 'image_only': False + }, + (128, 128, 128) +] + + +class TestLoadNifti(unittest.TestCase): + + @parameterized.expand([TEST_CASE_IMAGE_ONLY, TEST_CASE_IMAGE_METADATA]) + def test_shape(self, input_param, expected_shape): + test_image = np.random.randint(0, 2, size=[128, 128, 128]) + tempdir = tempfile.mkdtemp() + nib.save(nib.Nifti1Image(test_image, np.eye(4)), os.path.join(tempdir, 'test_image.nii.gz')) + test_data = os.path.join(tempdir, 'test_image.nii.gz') + result = LoadNifti(**input_param)(test_data) + shutil.rmtree(tempdir) + if isinstance(result, tuple): + result = result[0] + self.assertTupleEqual(result.shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_load_niftid.py b/tests/test_load_niftid.py new file mode 100644 index 0000000000..071972f03f --- /dev/null +++ b/tests/test_load_niftid.py @@ -0,0 +1,49 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import os +import shutil +import numpy as np +import tempfile +import nibabel as nib +from parameterized import parameterized +from monai.transforms.composables import LoadNiftid + +KEYS = ['image', 'label', 'extra'] + +TEST_CASE_1 = [ + { + 'keys': KEYS, + 'as_closest_canonical': False + }, + (128, 128, 128) +] + + +class TestLoadNiftid(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_shape(self, input_param, expected_shape): + test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]), np.eye(4)) + tempdir = tempfile.mkdtemp() + test_data = dict() + for key in KEYS: + nib.save(test_image, os.path.join(tempdir, key + '.nii.gz')) + test_data.update({key: os.path.join(tempdir, key + '.nii.gz')}) + result = LoadNiftid(**input_param)(test_data) + shutil.rmtree(tempdir) + for key in KEYS: + self.assertTupleEqual(result[key].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_map_transform.py b/tests/test_map_transform.py index bfddfa37b2..10878aa8f9 100644 --- a/tests/test_map_transform.py +++ b/tests/test_map_transform.py @@ -13,7 +13,7 @@ from parameterized import parameterized -from monai.transforms.composables import MapTransform +from monai.transforms.compose import MapTransform TEST_CASES = [ ['item', ('item',)], diff --git a/tests/test_intensity_normalizer.py b/tests/test_normalize_intensity.py similarity index 81% rename from tests/test_intensity_normalizer.py rename to tests/test_normalize_intensity.py index e83732c04c..680146cb87 100644 --- a/tests/test_intensity_normalizer.py +++ b/tests/test_normalize_intensity.py @@ -13,14 +13,14 @@ import numpy as np -from monai.transforms.transforms import IntensityNormalizer +from monai.transforms import NormalizeIntensity from tests.utils import NumpyImageTestCase2D -class IntensityNormTestCase(NumpyImageTestCase2D): +class TestNormalizeIntensity(NumpyImageTestCase2D): - def test_image_normalizer_default(self): - normalizer = IntensityNormalizer() + def test_image_normalize_intensity(self): + normalizer = NormalizeIntensity() normalised = normalizer(self.imt) expected = (self.imt - np.mean(self.imt)) / np.std(self.imt) self.assertTrue(np.allclose(normalised, expected)) diff --git a/tests/test_normalize_intensityd.py b/tests/test_normalize_intensityd.py new file mode 100644 index 0000000000..7a76c17070 --- /dev/null +++ b/tests/test_normalize_intensityd.py @@ -0,0 +1,31 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms import NormalizeIntensityd +from tests.utils import NumpyImageTestCase2D + + +class TestNormalizeIntensityd(NumpyImageTestCase2D): + + def test_image_normalize_intensityd(self): + key = 'img' + normalizer = NormalizeIntensityd(keys=[key]) + normalised = normalizer({key: self.imt}) + expected = (self.imt - np.mean(self.imt)) / np.std(self.imt) + self.assertTrue(np.allclose(normalised[key], expected)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_orientation.py b/tests/test_orientation.py new file mode 100644 index 0000000000..8cd1c55f79 --- /dev/null +++ b/tests/test_orientation.py @@ -0,0 +1,38 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms.transforms import Orientation + +TEST_CASES = [ + [{'axcodes': 'RAS'}, + np.ones((2, 10, 15, 20)), {'original_axcodes': 'ALS'}, (2, 15, 10, 20)], + [{'axcodes': 'AL'}, + np.ones((2, 10, 15)), {'original_axcodes': 'AR'}, (2, 10, 15)], + [{'axcodes': 'L'}, + np.ones((2, 10)), {'original_axcodes': 'R'}, (2, 10)], +] + + +class TestOrientationCase(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_ornt(self, init_param, img, data_param, expected_shape): + res = Orientation(**init_param)(img, **data_param) + np.testing.assert_allclose(res[0].shape, expected_shape) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_orientationd.py b/tests/test_orientationd.py new file mode 100644 index 0000000000..999f31efe2 --- /dev/null +++ b/tests/test_orientationd.py @@ -0,0 +1,55 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms.composables import Orientationd + + +class TestOrientationdCase(unittest.TestCase): + + def test_orntd(self): + data = {'seg': np.ones((2, 1, 2, 3)), 'affine': np.eye(4)} + ornt = Orientationd(keys='seg', affine_key='affine', axcodes='RAS') + res = ornt(data) + np.testing.assert_allclose(res['seg'].shape, (2, 1, 2, 3)) + self.assertEqual(res['orientation']['original_ornt'], ('R', 'A', 'S')) + self.assertEqual(res['orientation']['current_ornt'], 'RAS') + + def test_orntd_3d(self): + data = {'seg': np.ones((2, 1, 2, 3)), 'img': np.ones((2, 1, 2, 3)), 'affine': np.eye(4)} + ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='PLI') + res = ornt(data) + np.testing.assert_allclose(res['img'].shape, (2, 2, 1, 3)) + self.assertEqual(res['orientation']['original_ornt'], ('R', 'A', 'S')) + self.assertEqual(res['orientation']['current_ornt'], 'PLI') + + def test_orntd_2d(self): + data = {'seg': np.ones((2, 1, 3)), 'img': np.ones((2, 1, 3)), 'affine': np.eye(4)} + ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='PLI') + res = ornt(data) + np.testing.assert_allclose(res['img'].shape, (2, 3, 1)) + self.assertEqual(res['orientation']['original_ornt'], ('R', 'A')) + self.assertEqual(res['orientation']['current_ornt'], 'PL') + + def test_orntd_1d(self): + data = {'seg': np.ones((2, 3)), 'img': np.ones((2, 3)), 'affine': np.eye(4)} + ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='L') + res = ornt(data) + np.testing.assert_allclose(res['img'].shape, (2, 3)) + self.assertEqual(res['orientation']['original_ornt'], ('R',)) + self.assertEqual(res['orientation']['current_ornt'], 'L') + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_image_end_padder.py b/tests/test_pad_image_end.py similarity index 88% rename from tests/test_image_end_padder.py rename to tests/test_pad_image_end.py index 1d705a0ce1..3d7c3782f1 100644 --- a/tests/test_image_end_padder.py +++ b/tests/test_pad_image_end.py @@ -12,7 +12,7 @@ import unittest import numpy as np from parameterized import parameterized -from monai.transforms.transforms import ImageEndPadder +from monai.transforms import PadImageEnd TEST_CASE_1 = [ { @@ -23,11 +23,11 @@ np.zeros((1, 3, 16, 16, 8)), ] -class TestImageEndPadder(unittest.TestCase): +class TestPadImageEnd(unittest.TestCase): @parameterized.expand([TEST_CASE_1]) def test_image_end_pad_shape(self, input_param, input_data, expected_val): - padder = ImageEndPadder(**input_param) + padder = PadImageEnd(**input_param) result = padder(input_data) self.assertAlmostEqual(result.shape, expected_val.shape) diff --git a/tests/test_rand_affine.py b/tests/test_rand_affine.py new file mode 100644 index 0000000000..60c436cc6d --- /dev/null +++ b/tests/test_rand_affine.py @@ -0,0 +1,67 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import RandAffine + +TEST_CASES = [ + [ + dict(as_tensor_output=False, device=None), {'img': torch.ones((3, 3, 3)), 'spatial_size': (2, 2)}, + np.ones((3, 2, 2)) + ], + [ + dict(as_tensor_output=True, device=None), {'img': torch.ones((1, 3, 3, 3)), 'spatial_size': (2, 2, 2)}, + torch.ones((1, 2, 2, 2)) + ], + [ + dict(prob=0.9, + rotate_range=(np.pi / 2,), + shear_range=[1, 2], + translate_range=[2, 1], + as_tensor_output=True, + spatial_size=(2, 2, 2), + device=None), {'img': torch.ones((1, 3, 3, 3)), 'mode': 'bilinear'}, + torch.tensor([[[[0.0000, 0.6577], [0.9911, 1.0000]], [[0.7781, 1.0000], [1.0000, 0.4000]]]]) + ], + [ + dict(prob=0.9, + rotate_range=(np.pi / 2,), + shear_range=[1, 2], + translate_range=[2, 1], + scale_range=[.1, .2], + as_tensor_output=True, + device=None), {'img': torch.arange(64).reshape((1, 8, 8)), 'spatial_size': (3, 3)}, + torch.tensor([[[16.9127, 13.3079, 9.7031], [26.8129, 23.2081, 19.6033], [36.7131, 33.1083, 29.5035]]]) + ], +] + + +class TestRandAffine(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_affine(self, input_param, input_data, expected_val): + g = RandAffine(**input_param) + g.set_random_state(123) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_affine_grid.py b/tests/test_rand_affine_grid.py new file mode 100644 index 0000000000..b5c51e394e --- /dev/null +++ b/tests/test_rand_affine_grid.py @@ -0,0 +1,96 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import RandAffineGrid + +TEST_CASES = [ + [{'as_tensor_output': False, 'device': None}, {'grid': torch.ones((3, 3, 3))}, + np.ones((3, 3, 3))], + [{'rotate_range': (1, 2), 'translate_range': (3, 3, 3)}, {'grid': torch.arange(0, 27).reshape((3, 3, 3))}, + torch.tensor( + np.array([[[-32.81998, -33.910976, -35.001972], [-36.092968, -37.183964, -38.27496], + [-39.36596, -40.456955, -41.54795]], + [[2.1380205, 3.1015975, 4.0651755], [5.028752, 5.9923296, 6.955907], [7.919484, 8.883063, 9.84664]], + [[18., 19., 20.], [21., 22., 23.], [24., 25., 26.]]]))], + [{'translate_range': (3, 3, 3), 'as_tensor_output': False, 'device': torch.device('cpu:0')}, + {'spatial_size': (3, 3, 3)}, + np.array([[[[0.17881513, 0.17881513, 0.17881513], [0.17881513, 0.17881513, 0.17881513], + [0.17881513, 0.17881513, 0.17881513]], + [[1.1788151, 1.1788151, 1.1788151], [1.1788151, 1.1788151, 1.1788151], + [1.1788151, 1.1788151, 1.1788151]], + [[2.1788151, 2.1788151, 2.1788151], [2.1788151, 2.1788151, 2.1788151], + [2.1788151, 2.1788151, 2.1788151]]], + [[[-2.283164, -2.283164, -2.283164], [-1.283164, -1.283164, -1.283164], + [-0.28316402, -0.28316402, -0.28316402]], + [[-2.283164, -2.283164, -2.283164], [-1.283164, -1.283164, -1.283164], + [-0.28316402, -0.28316402, -0.28316402]], + [[-2.283164, -2.283164, -2.283164], [-1.283164, -1.283164, -1.283164], + [-0.28316402, -0.28316402, -0.28316402]]], + [[[-2.6388912, -1.6388912, -0.6388912], [-2.6388912, -1.6388912, -0.6388912], + [-2.6388912, -1.6388912, -0.6388912]], + [[-2.6388912, -1.6388912, -0.6388912], [-2.6388912, -1.6388912, -0.6388912], + [-2.6388912, -1.6388912, -0.6388912]], + [[-2.6388912, -1.6388912, -0.6388912], [-2.6388912, -1.6388912, -0.6388912], + [-2.6388912, -1.6388912, -0.6388912]]], + [[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], + [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]]])], + [{'rotate_range': (1., 1., 1.), 'shear_range': (0.1,), 'scale_range': (1.2,)}, + {'grid': torch.arange(0, 108).reshape((4, 3, 3, 3))}, + torch.tensor( + np.array([[[[-9.4201e+00, -8.1672e+00, -6.9143e+00], [-5.6614e+00, -4.4085e+00, -3.1556e+00], + [-1.9027e+00, -6.4980e-01, 6.0310e-01]], + [[1.8560e+00, 3.1089e+00, 4.3618e+00], [5.6147e+00, 6.8676e+00, 8.1205e+00], + [9.3734e+00, 1.0626e+01, 1.1879e+01]], + [[1.3132e+01, 1.4385e+01, 1.5638e+01], [1.6891e+01, 1.8144e+01, 1.9397e+01], + [2.0650e+01, 2.1902e+01, 2.3155e+01]]], + [[[9.9383e-02, -4.8845e-01, -1.0763e+00], [-1.6641e+00, -2.2519e+00, -2.8398e+00], + [-3.4276e+00, -4.0154e+00, -4.6032e+00]], + [[-5.1911e+00, -5.7789e+00, -6.3667e+00], [-6.9546e+00, -7.5424e+00, -8.1302e+00], + [-8.7180e+00, -9.3059e+00, -9.8937e+00]], + [[-1.0482e+01, -1.1069e+01, -1.1657e+01], [-1.2245e+01, -1.2833e+01, -1.3421e+01], + [-1.4009e+01, -1.4596e+01, -1.5184e+01]]], + [[[5.9635e+01, 6.1199e+01, 6.2764e+01], [6.4328e+01, 6.5892e+01, 6.7456e+01], + [6.9021e+01, 7.0585e+01, 7.2149e+01]], + [[7.3714e+01, 7.5278e+01, 7.6842e+01], [7.8407e+01, 7.9971e+01, 8.1535e+01], + [8.3099e+01, 8.4664e+01, 8.6228e+01]], + [[8.7792e+01, 8.9357e+01, 9.0921e+01], [9.2485e+01, 9.4049e+01, 9.5614e+01], + [9.7178e+01, 9.8742e+01, 1.0031e+02]]], + [[[8.1000e+01, 8.2000e+01, 8.3000e+01], [8.4000e+01, 8.5000e+01, 8.6000e+01], + [8.7000e+01, 8.8000e+01, 8.9000e+01]], + [[9.0000e+01, 9.1000e+01, 9.2000e+01], [9.3000e+01, 9.4000e+01, 9.5000e+01], + [9.6000e+01, 9.7000e+01, 9.8000e+01]], + [[9.9000e+01, 1.0000e+02, 1.0100e+02], [1.0200e+02, 1.0300e+02, 1.0400e+02], + [1.0500e+02, 1.0600e+02, 1.0700e+02]]]]))], +] + + +class TestRandAffineGrid(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_affine_grid(self, input_param, input_data, expected_val): + g = RandAffineGrid(**input_param) + g.set_random_state(123) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py new file mode 100644 index 0000000000..b07f7015e5 --- /dev/null +++ b/tests/test_rand_affined.py @@ -0,0 +1,90 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.composables import RandAffined + +TEST_CASES = [ + [ + dict(as_tensor_output=False, device=None, spatial_size=(2, 2), keys=('img', 'seg')), + {'img': torch.ones((3, 3, 3)), 'seg': torch.ones((3, 3, 3))}, + np.ones((3, 2, 2)) + ], + [ + dict(as_tensor_output=True, device=None, spatial_size=(2, 2, 2), keys=('img', 'seg')), + {'img': torch.ones((1, 3, 3, 3)), 'seg': torch.ones((1, 3, 3, 3))}, + torch.ones((1, 2, 2, 2)) + ], + [ + dict(prob=0.9, + rotate_range=(np.pi / 2,), + shear_range=[1, 2], + translate_range=[2, 1], + as_tensor_output=True, + spatial_size=(2, 2, 2), + device=None, + keys=('img', 'seg'), + mode='bilinear'), {'img': torch.ones((1, 3, 3, 3)), 'seg': torch.ones((1, 3, 3, 3))}, + torch.tensor([[[[0.0000, 0.6577], [0.9911, 1.0000]], [[0.7781, 1.0000], [1.0000, 0.4000]]]]) + ], + [ + dict(prob=0.9, + rotate_range=(np.pi / 2,), + shear_range=[1, 2], + translate_range=[2, 1], + scale_range=[.1, .2], + as_tensor_output=True, + spatial_size=(3, 3), + keys=('img', 'seg'), + device=None), {'img': torch.arange(64).reshape((1, 8, 8)), 'seg': torch.arange(64).reshape((1, 8, 8))}, + torch.tensor([[[16.9127, 13.3079, 9.7031], [26.8129, 23.2081, 19.6033], [36.7131, 33.1083, 29.5035]]]) + ], + [ + dict(prob=0.9, + mode=('bilinear', 'nearest'), + rotate_range=(np.pi / 2,), + shear_range=[1, 2], + translate_range=[2, 1], + scale_range=[.1, .2], + as_tensor_output=False, + spatial_size=(3, 3), + keys=('img', 'seg'), + device=torch.device('cpu:0')), + {'img': torch.arange(64).reshape((1, 8, 8)), 'seg': torch.arange(64).reshape((1, 8, 8))}, + {'img': np.array([[[16.9127, 13.3079, 9.7031], [26.8129, 23.2081, 19.6033], [36.7131, 33.1083, 29.5035]]]), + 'seg': np.array([[[19., 12., 12.], [27., 20., 21.], [35., 36., 29.]]])} + ], +] + + +class TestRandAffined(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_affined(self, input_param, input_data, expected_val): + g = RandAffined(**input_param).set_random_state(123) + res = g(input_data) + for key in res: + result = res[key] + expected = expected_val[key] if isinstance(expected_val, dict) else expected_val + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_crop_by_pos_neg_labeld.py b/tests/test_rand_crop_by_pos_neg_labeld.py index 021c582409..f83d737873 100644 --- a/tests/test_rand_crop_by_pos_neg_labeld.py +++ b/tests/test_rand_crop_by_pos_neg_labeld.py @@ -21,7 +21,9 @@ 'size': [2, 2, 2], 'pos': 1, 'neg': 1, - 'num_samples': 2 + 'num_samples': 2, + 'image_key': None, + 'image_threshold': 0 }, { 'image': np.random.randint(0, 2, size=[3, 3, 3, 3]), @@ -34,10 +36,32 @@ (3, 2, 2, 2), ] +TEST_CASE_2 = [ + { + 'keys': ['image', 'extral', 'label'], + 'label_key': 'label', + 'size': [2, 2, 2], + 'pos': 1, + 'neg': 1, + 'num_samples': 2, + 'image_key': None, + 'image_threshold': 0 + }, + { + 'image': np.zeros([3, 3, 3, 3]) - 1, + 'extral': np.zeros([3, 3, 3, 3]), + 'label': np.ones([3, 3, 3, 3]), + 'affine': np.eye(3), + 'shape': 'CHWD' + }, + list, + (3, 2, 2, 2), +] + class TestRandCropByPosNegLabeld(unittest.TestCase): - @parameterized.expand([TEST_CASE_1]) + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_type_shape(self, input_param, input_data, expected_type, expected_shape): result = RandCropByPosNegLabeld(**input_param)(input_data) self.assertIsInstance(result, expected_type) diff --git a/tests/test_rand_deform_grid.py b/tests/test_rand_deform_grid.py new file mode 100644 index 0000000000..390672ab98 --- /dev/null +++ b/tests/test_rand_deform_grid.py @@ -0,0 +1,94 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import RandDeformGrid + +TEST_CASES = [ + [ + dict(spacing=(1, 2), magnitude_range=(1., 2.), as_tensor_output=False, device=None), + {'spatial_size': (3, 3)}, + np.array([[[-3.45774551, -0.6608006, -1.62002671, -4.02259806, -2.77692349], + [1.21748926, -4.25845712, -1.57592837, 0.69985342, -2.16382767], + [-0.91158377, -0.12717178, 2.00258405, -0.85789449, -0.59616292], + [0.41676882, 3.96204313, 3.93633727, 2.34820726, 1.51855713], + [2.99011186, 4.00170105, 0.74339613, 3.57886072, 0.31633439]], + [[-4.85634965, -0.78197195, -1.91838077, 1.81192079, 2.84286669], + [-4.34323645, -5.75784424, -2.37875058, 1.06023016, 5.24536301], + [-4.23315172, -1.99617861, 0.92412057, 0.81899041, 4.38084451], + [-5.08141703, -4.31985211, -0.52488611, 2.77048576, 4.45464513], + [-4.01588556, 1.21238156, 0.55444352, 3.31421131, 7.00529793]], + [[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], + [1., 1., 1., 1., 1.]]]) + ], + [ + dict(spacing=(1, 2, 2), magnitude_range=(1., 3.), as_tensor_output=False, device=None), + {'spatial_size': (1, 2, 2)}, + np.array([[[[-2.81748977, 0.66968869, -0.52625642, -3.52173734], + [-1.96865364, 1.76472402, -5.06258324, -1.71805669], + [1.11934537, -2.45103851, -2.13654555, -1.15855539], + [1.49678424, -2.06960677, -1.74328475, -1.7271617]], + [[3.69301983, 3.66097025, 1.68091953, 0.6465273], [1.23445289, 2.49568333, -1.56671014, 1.96849393], + [-2.09916271, -1.06768069, 1.51861453, -2.39180117], + [-0.23449363, -1.44269211, -0.42794076, -4.68520972]], + [[-1.96578162, -0.17168741, 2.55269525, 0.70931081], + [1.00476444, 2.15217619, -0.47246061, 1.4748298], [-0.34829048, -1.89234811, 0.34558185, 1.9606272], + [1.56684302, 0.98019418, 5.00513708, 1.69126978]]], + [[[-1.36146598, 0.7469491, -5.16647064, -4.73906938], + [1.91920577, -2.33606298, -0.95030633, 0.7901769], [2.49116076, 3.93791246, 3.50390686, 2.79030531], + [1.70638302, 4.33070564, 3.52613304, 0.77965554]], + [[-0.62725323, -1.64857887, -2.92384357, -3.39022706], + [-3.00611521, -0.66597021, -0.21577072, -2.39146379], + [2.94568388, -0.83686357, -2.55435186, 2.74064119], [2.3247117, 2.78900974, 1.59788581, + 0.31140512]], + [[-0.89856598, -4.15325814, -0.21934502, -1.64845891], + [-1.52694693, -2.81794479, -2.22623861, -3.0299247], + [4.49410486, 1.27529645, 2.92559679, -1.12171559], [3.30307684, 4.97189727, 2.43914751, + 4.7262225]]], + [[[-4.81571068, -3.28263239, 1.635167, 2.36520831], [-1.92511521, -4.311247, 2.19242556, 7.34990574], + [-3.04122716, -0.94284154, 1.30058968, -0.11719455], + [-2.28657395, -3.68766906, 0.28400757, 5.08072864]], + [[-4.2308508, -0.16084264, 2.69545963, 3.4666492], + [-5.29514976, -1.55660775, 4.28031473, -0.39019547], + [-3.4617024, -1.92430221, 1.20214712, + 4.25261228], [-0.30683774, -1.4524049, 2.35996724, 3.83663135]], + [[-2.20587965, -1.94408353, -0.66964855, 1.15838178], + [-4.26637632, -0.46145396, 2.27393031, + 3.5415298], [-3.91902371, 2.02343374, 3.54278271, 2.40735681], + [-4.3785335, -0.78200288, 3.12162619, 3.55709275]]], + [[[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]], + [[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]], + [[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]]]]) + ], +] + + +class TestRandDeformGrid(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_deform_grid(self, input_param, input_data, expected_val): + g = RandDeformGrid(**input_param) + g.set_random_state(123) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_elastic_2d.py b/tests/test_rand_elastic_2d.py new file mode 100644 index 0000000000..d01fd5c556 --- /dev/null +++ b/tests/test_rand_elastic_2d.py @@ -0,0 +1,65 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import Rand2DElastic + +TEST_CASES = [ + [{'spacing': (.3, .3), 'magnitude_range': (1., 2.), 'prob': 0.0, 'as_tensor_output': False, 'device': None}, + {'img': torch.ones((3, 3, 3)), 'spatial_size': (2, 2)}, + np.ones((3, 2, 2))], + [ + {'spacing': (.3, .3), 'magnitude_range': (1., 2.), 'prob': 0.9, 'as_tensor_output': False, 'device': None}, + {'img': torch.ones((3, 3, 3)), 'spatial_size': (2, 2), 'mode': 'bilinear'}, + np.array([[[0., 0.], [0., 0.04970419]], [[0., 0.], [0., 0.04970419]], [[0., 0.], [0., 0.04970419]]]), + ], + [ + { + 'spacing': (1., 1.), 'magnitude_range': (1., 1.), 'scale_range': [1.2, 2.2], 'prob': 0.9, 'padding_mode': + 'border', 'as_tensor_output': True, 'device': None, 'spatial_size': (2, 2) + }, + {'img': torch.arange(27).reshape((3, 3, 3))}, + torch.tensor([[[1.6605, 1.0083], [6.0000, 6.2224]], [[10.6605, 10.0084], [15.0000, 15.2224]], + [[19.6605, 19.0083], [24.0000, 24.2224]]]), + ], + [ + { + 'spacing': (.3, .3), 'magnitude_range': (.1, .2), 'translate_range': [-.01, .01], + 'scale_range': [0.01, 0.02], 'prob': 0.9, 'as_tensor_output': False, 'device': None, 'spatial_size': (2, 2), + }, + {'img': torch.arange(27).reshape((3, 3, 3))}, + np.array([[[0.2001334, 1.2563337], [5.2274017, 7.90148]], [[8.675412, 6.9098353], [13.019891, 16.850012]], + [[17.15069, 12.563337], [20.81238, 25.798544]]]) + ], +] + + +class TestRand2DElastic(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_2d_elastic(self, input_param, input_data, expected_val): + g = Rand2DElastic(**input_param) + g.set_random_state(123) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_elastic_3d.py b/tests/test_rand_elastic_3d.py new file mode 100644 index 0000000000..065d260de7 --- /dev/null +++ b/tests/test_rand_elastic_3d.py @@ -0,0 +1,54 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import Rand3DElastic + +TEST_CASES = [ + [{'magnitude_range': (.3, 2.3), 'sigma_range': (1., 20.), 'prob': 0.0, 'as_tensor_output': False, 'device': None}, + {'img': torch.ones((2, 3, 3, 3)), 'spatial_size': (2, 2, 2)}, + np.ones((2, 2, 2, 2))], + [ + {'magnitude_range': (.3, .3), 'sigma_range': (1., 2.), 'prob': 0.9, 'as_tensor_output': False, 'device': None}, + {'img': torch.arange(27).reshape((1, 3, 3, 3)), 'spatial_size': (2, 2, 2)}, + np.array([[[[3.2385552, 4.753422], [7.779232, 9.286472]], [[16.769115, 18.287868], [21.300673, 22.808704]]]]), + ], + [ + { + 'magnitude_range': (.3, .3), 'sigma_range': (1., 2.), 'prob': 0.9, 'rotate_range': [1, 1, 1], + 'as_tensor_output': False, 'device': None, 'spatial_size': (2, 2, 2) + }, + {'img': torch.arange(27).reshape((1, 3, 3, 3)), 'mode': 'bilinear'}, + np.array([[[[1.6566806, 7.695548], [7.4342523, 13.580086]], [[11.776854, 18.669481], [18.396517, 21.551771]]]])], +] + + +class TestRand3DElastic(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_3d_elastic(self, input_param, input_data, expected_val): + g = Rand3DElastic(**input_param) + g.set_random_state(123) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_elasticd_2d.py b/tests/test_rand_elasticd_2d.py new file mode 100644 index 0000000000..1f560651ea --- /dev/null +++ b/tests/test_rand_elasticd_2d.py @@ -0,0 +1,88 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.composables import Rand2DElasticd + +TEST_CASES = [ + [ + { + 'keys': ('img', 'seg'), 'spacing': (.3, .3), 'magnitude_range': (1., 2.), 'prob': 0.0, 'as_tensor_output': + False, 'device': None, 'spatial_size': (2, 2) + }, + {'img': torch.ones((3, 3, 3)), 'seg': torch.ones((3, 3, 3))}, + np.ones((3, 2, 2)), + ], + [ + { + 'keys': ('img', 'seg'), 'spacing': (.3, .3), 'magnitude_range': (1., 2.), 'prob': 0.9, 'as_tensor_output': + False, 'device': None, 'spatial_size': (2, 2), 'mode': 'bilinear' + }, + {'img': torch.ones((3, 3, 3)), 'seg': torch.ones((3, 3, 3))}, + np.array([[[0., 0.], [0., 0.04970419]], [[0., 0.], [0., 0.04970419]], [[0., 0.], [0., 0.04970419]]]), + ], + [ + { + 'keys': ('img', 'seg'), 'spacing': (1., 1.), 'magnitude_range': (1., 1.), 'scale_range': [1.2, 2.2], 'prob': + 0.9, 'padding_mode': 'border', 'as_tensor_output': True, 'device': None, 'spatial_size': (2, 2) + }, + {'img': torch.arange(27).reshape((3, 3, 3)), 'seg': torch.arange(27).reshape((3, 3, 3))}, + torch.tensor([[[1.6605, 1.0083], [6.0000, 6.2224]], [[10.6605, 10.0084], [15.0000, 15.2224]], + [[19.6605, 19.0083], [24.0000, 24.2224]]]), + ], + [ + { + 'keys': ('img', 'seg'), 'spacing': (.3, .3), 'magnitude_range': (.1, .2), 'translate_range': [-.01, .01], + 'scale_range': [0.01, 0.02], 'prob': 0.9, 'as_tensor_output': False, 'device': None, 'spatial_size': (2, 2), + }, + {'img': torch.arange(27).reshape((3, 3, 3)), 'seg': torch.arange(27).reshape((3, 3, 3))}, + np.array([[[0.2001334, 1.2563337], [5.2274017, 7.90148]], [[8.675412, 6.9098353], [13.019891, 16.850012]], + [[17.15069, 12.563337], [20.81238, 25.798544]]]) + ], + [ + { + 'keys': ('img', 'seg'), 'mode': ('bilinear', 'nearest'), 'spacing': (.3, .3), 'magnitude_range': (.1, .2), + 'translate_range': [-.01, .01], + 'scale_range': [0.01, 0.02], 'prob': 0.9, 'as_tensor_output': True, 'device': None, 'spatial_size': (2, 2), + }, + {'img': torch.arange(27).reshape((3, 3, 3)), 'seg': torch.arange(27).reshape((3, 3, 3))}, + {'img': torch.tensor([[[0.2001334, 1.2563337], [5.2274017, 7.90148]], + [[8.675412, 6.9098353], [13.019891, 16.850012]], + [[17.15069, 12.563337], [20.81238, 25.798544]]]), + 'seg': torch.tensor([[[0., 2.], [6., 8.]], [[9., 11.], [15., 17.]], [[18., 20.], [24., 26.]]])} + ], +] + + +class TestRand2DElasticd(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_2d_elasticd(self, input_param, input_data, expected_val): + g = Rand2DElasticd(**input_param) + g.set_random_state(123) + res = g(input_data) + for key in res: + result = res[key] + expected = expected_val[key] if isinstance(expected_val, dict) else expected_val + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_elasticd_3d.py b/tests/test_rand_elasticd_3d.py new file mode 100644 index 0000000000..a72aa3bbb9 --- /dev/null +++ b/tests/test_rand_elasticd_3d.py @@ -0,0 +1,72 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.composables import Rand3DElasticd + +TEST_CASES = [ + [{'keys': ('img', 'seg'), 'magnitude_range': (.3, 2.3), 'sigma_range': (1., 20.), + 'prob': 0.0, 'as_tensor_output': False, 'device': None, 'spatial_size': (2, 2, 2)}, + {'img': torch.ones((2, 3, 3, 3)), 'seg': torch.ones((2, 3, 3, 3))}, + np.ones((2, 2, 2, 2))], + [ + {'keys': ('img', 'seg'), 'magnitude_range': (.3, .3), 'sigma_range': (1., 2.), + 'prob': 0.9, 'as_tensor_output': False, 'device': None, 'spatial_size': (2, 2, 2)}, + {'img': torch.arange(27).reshape((1, 3, 3, 3)), 'seg': torch.arange(27).reshape((1, 3, 3, 3))}, + np.array([[[[3.2385552, 4.753422], [7.779232, 9.286472]], [[16.769115, 18.287868], [21.300673, 22.808704]]]]), + ], + [ + { + 'keys': ('img', 'seg'), 'magnitude_range': (.3, .3), 'sigma_range': (1., 2.), 'prob': 0.9, + 'rotate_range': [1, 1, 1], 'as_tensor_output': False, 'device': None, + 'spatial_size': (2, 2, 2), 'mode': 'bilinear' + }, + {'img': torch.arange(27).reshape((1, 3, 3, 3)), 'seg': torch.arange(27).reshape((1, 3, 3, 3))}, + np.array([[[[1.6566806, 7.695548], [7.4342523, 13.580086]], [[11.776854, 18.669481], [18.396517, 21.551771]]]]), + ], + [ + { + 'keys': ('img', 'seg'), 'mode': ('bilinear', 'nearest'), 'magnitude_range': (.3, .3), + 'sigma_range': (1., 2.), 'prob': 0.9, 'rotate_range': [1, 1, 1], + 'as_tensor_output': True, 'device': torch.device('cpu:0'), 'spatial_size': (2, 2, 2) + }, + {'img': torch.arange(27).reshape((1, 3, 3, 3)), 'seg': torch.arange(27).reshape((1, 3, 3, 3))}, + {'img': torch.tensor([[[[1.6566806, 7.695548], [7.4342523, 13.580086]], + [[11.776854, 18.669481], [18.396517, 21.551771]]]]), + 'seg': torch.tensor([[[[1., 11.], [7., 17.]], [[9., 19.], [15., 25.]]]])} + ], +] + + +class TestRand3DElasticd(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_rand_3d_elasticd(self, input_param, input_data, expected_val): + g = Rand3DElasticd(**input_param) + g.set_random_state(123) + res = g(input_data) + for key in res: + result = res[key] + expected = expected_val[key] if isinstance(expected_val, dict) else expected_val + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_flip.py b/tests/test_rand_flip.py new file mode 100644 index 0000000000..1206c85571 --- /dev/null +++ b/tests/test_rand_flip.py @@ -0,0 +1,46 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms import RandFlip +from tests.utils import NumpyImageTestCase2D + +INVALID_CASES = [("wrong_axis", ['s', 1], TypeError), + ("not_numbers", 's', TypeError)] + +VALID_CASES = [("no_axis", None), + ("one_axis", 1), + ("many_axis", [0, 1])] + +class TestRandFlip(NumpyImageTestCase2D): + + @parameterized.expand(INVALID_CASES) + def test_invalid_inputs(self, _, spatial_axis, raises): + with self.assertRaises(raises): + flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) + flip(self.imt[0]) + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, _, spatial_axis): + flip = RandFlip(prob=1.0, spatial_axis=spatial_axis) + expected = list() + for channel in self.imt[0]: + expected.append(np.flip(channel, spatial_axis)) + expected = np.stack(expected) + self.assertTrue(np.allclose(expected, flip(self.imt[0]))) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_flipd.py b/tests/test_rand_flipd.py new file mode 100644 index 0000000000..bcda54eecd --- /dev/null +++ b/tests/test_rand_flipd.py @@ -0,0 +1,38 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms import RandFlipd +from tests.utils import NumpyImageTestCase2D + +VALID_CASES = [("no_axis", None), + ("one_axis", 1), + ("many_axis", [0, 1])] + +class TestRandFlipd(NumpyImageTestCase2D): + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, _, spatial_axis): + flip = RandFlipd(keys='img', prob=1.0, spatial_axis=spatial_axis) + res = flip({'img': self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.flip(channel, spatial_axis)) + expected = np.stack(expected) + self.assertTrue(np.allclose(expected, res['img'])) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_rotate.py b/tests/test_rand_rotate.py new file mode 100644 index 0000000000..1e5a18bfc8 --- /dev/null +++ b/tests/test_rand_rotate.py @@ -0,0 +1,46 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np + +import scipy.ndimage +from parameterized import parameterized + +from monai.transforms import RandRotate +from tests.utils import NumpyImageTestCase2D + + +class TestRandRotate(NumpyImageTestCase2D): + + @parameterized.expand([ + (90, (0, 1), True, 1, 'reflect', 0, True), + ((-45, 45), (1, 0), True, 3, 'constant', 0, True), + (180, (1, 0), False, 2, 'constant', 4, False), + ]) + def test_correct_results(self, degrees, spatial_axes, reshape, + order, mode, cval, prefilter): + rotate_fn = RandRotate(degrees, prob=1.0, spatial_axes=spatial_axes, reshape=reshape, + order=order, mode=mode, cval=cval, prefilter=prefilter) + rotate_fn.set_random_state(243) + rotated = rotate_fn(self.imt[0]) + + angle = rotate_fn.angle + expected = list() + for channel in self.imt[0]: + expected.append(scipy.ndimage.rotate(channel, angle, spatial_axes, reshape, order=order, + mode=mode, cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, rotated)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_rotate90.py b/tests/test_rand_rotate90.py index 4b291d8cf0..e50c3e0c67 100644 --- a/tests/test_rand_rotate90.py +++ b/tests/test_rand_rotate90.py @@ -17,34 +17,46 @@ from tests.utils import NumpyImageTestCase2D -class Rotate90Test(NumpyImageTestCase2D): +class TestRandRotate90(NumpyImageTestCase2D): def test_default(self): rotate = RandRotate90() rotate.set_random_state(123) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 0, (1, 2)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) def test_k(self): rotate = RandRotate90(max_k=2) rotate.set_random_state(234) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 0, (1, 2)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) - def test_axes(self): - rotate = RandRotate90(axes=(1, 2)) + def test_spatial_axes(self): + rotate = RandRotate90(spatial_axes=(0, 1)) rotate.set_random_state(234) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 0, (1, 2)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) - def test_prob_k_axes(self): - rotate = RandRotate90(prob=1.0, max_k=2, axes=(2, 3)) + def test_prob_k_spatial_axes(self): + rotate = RandRotate90(prob=1.0, max_k=2, spatial_axes=(0, 1)) rotate.set_random_state(234) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 1, (2, 3)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) diff --git a/tests/test_rand_rotate90d.py b/tests/test_rand_rotate90d.py index c52a82389f..193627fef1 100644 --- a/tests/test_rand_rotate90d.py +++ b/tests/test_rand_rotate90d.py @@ -17,45 +17,57 @@ from tests.utils import NumpyImageTestCase2D -class Rotate90Test(NumpyImageTestCase2D): +class TestRandRotate90d(NumpyImageTestCase2D): def test_default(self): key = None rotate = RandRotate90d(keys=key) rotate.set_random_state(123) - rotated = rotate({key: self.imt}) - expected = np.rot90(self.imt, 0, (1, 2)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated[key], expected)) def test_k(self): key = 'test' rotate = RandRotate90d(keys=key, max_k=2) rotate.set_random_state(234) - rotated = rotate({key: self.imt}) - expected = np.rot90(self.imt, 0, (1, 2)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated[key], expected)) - def test_axes(self): - key = ['test'] - rotate = RandRotate90d(keys=key, axes=(1, 2)) + def test_spatial_axes(self): + key = 'test' + rotate = RandRotate90d(keys=key, spatial_axes=(0, 1)) rotate.set_random_state(234) - rotated = rotate({key[0]: self.imt}) - expected = np.rot90(self.imt, 0, (1, 2)) - self.assertTrue(np.allclose(rotated[key[0]], expected)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 0, (0, 1))) + expected = np.stack(expected) + self.assertTrue(np.allclose(rotated[key], expected)) - def test_prob_k_axes(self): - key = ('test',) - rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, axes=(2, 3)) + def test_prob_k_spatial_axes(self): + key = 'test' + rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, spatial_axes=(0, 1)) rotate.set_random_state(234) - rotated = rotate({key[0]: self.imt}) - expected = np.rot90(self.imt, 1, (2, 3)) - self.assertTrue(np.allclose(rotated[key[0]], expected)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) + self.assertTrue(np.allclose(rotated[key], expected)) def test_no_key(self): key = 'unknown' - rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, axes=(2, 3)) + rotate = RandRotate90d(keys=key, prob=1.0, max_k=2, spatial_axes=(0, 1)) with self.assertRaisesRegex(KeyError, ''): - rotated = rotate({'test': self.imt}) + rotated = rotate({'test': self.imt[0]}) if __name__ == '__main__': diff --git a/tests/test_rand_rotated.py b/tests/test_rand_rotated.py new file mode 100644 index 0000000000..1c9d98e83e --- /dev/null +++ b/tests/test_rand_rotated.py @@ -0,0 +1,46 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np + +import scipy.ndimage +from parameterized import parameterized + +from monai.transforms import RandRotated +from tests.utils import NumpyImageTestCase2D + + +class TestRandRotated(NumpyImageTestCase2D): + + @parameterized.expand([ + (90, (0, 1), True, 1, 'reflect', 0, True), + ((-45, 45), (1, 0), True, 3, 'constant', 0, True), + (180, (1, 0), False, 2, 'constant', 4, False), + ]) + def test_correct_results(self, degrees, spatial_axes, reshape, + order, mode, cval, prefilter): + rotate_fn = RandRotated('img', degrees, prob=1.0, spatial_axes=spatial_axes, reshape=reshape, + order=order, mode=mode, cval=cval, prefilter=prefilter) + rotate_fn.set_random_state(243) + rotated = rotate_fn({'img': self.imt[0]}) + + angle = rotate_fn.angle + expected = list() + for channel in self.imt[0]: + expected.append(scipy.ndimage.rotate(channel, angle, spatial_axes, reshape, order=order, + mode=mode, cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, rotated['img'])) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_uniform_rand_patch.py b/tests/test_rand_uniform_patch.py similarity index 67% rename from tests/test_uniform_rand_patch.py rename to tests/test_rand_uniform_patch.py index f11c4b43f4..923f302c97 100644 --- a/tests/test_uniform_rand_patch.py +++ b/tests/test_rand_uniform_patch.py @@ -13,17 +13,17 @@ import numpy as np -from monai.transforms.transforms import UniformRandomPatch +from monai.transforms.transforms import RandUniformPatch from tests.utils import NumpyImageTestCase2D -class UniformRandomPatchTest(NumpyImageTestCase2D): +class TestRandUniformPatch(NumpyImageTestCase2D): def test_2d(self): - patch_size = (1, 10, 10) - patch_transform = UniformRandomPatch(patch_size=patch_size) - patch = patch_transform(self.imt) - self.assertTrue(np.allclose(patch.shape[:-2], patch_size[:-2])) + patch_spatial_size = (10, 10) + patch_transform = RandUniformPatch(patch_spatial_size=patch_spatial_size) + patch = patch_transform(self.imt[0]) + self.assertTrue(np.allclose(patch.shape[1:], patch_spatial_size)) if __name__ == '__main__': diff --git a/tests/test_uniform_rand_patchd.py b/tests/test_rand_uniform_patchd.py similarity index 65% rename from tests/test_uniform_rand_patchd.py rename to tests/test_rand_uniform_patchd.py index 1ab03b4b6f..b68d555178 100644 --- a/tests/test_uniform_rand_patchd.py +++ b/tests/test_rand_uniform_patchd.py @@ -13,24 +13,24 @@ import numpy as np -from monai.transforms.composables import UniformRandomPatchd +from monai.transforms.composables import RandUniformPatchd from tests.utils import NumpyImageTestCase2D -class UniformRandomPatchdTest(NumpyImageTestCase2D): +class TestRandUniformPatchd(NumpyImageTestCase2D): def test_2d(self): - patch_size = (1, 10, 10) + patch_spatial_size = (10, 10) key = 'test' - patch_transform = UniformRandomPatchd(keys='test', patch_size=patch_size) - patch = patch_transform({key: self.imt}) - self.assertTrue(np.allclose(patch[key].shape[:-2], patch_size[:-2])) + patch_transform = RandUniformPatchd(keys='test', patch_spatial_size=patch_spatial_size) + patch = patch_transform({key: self.imt[0]}) + self.assertTrue(np.allclose(patch[key].shape[1:], patch_spatial_size)) def test_sync(self): - patch_size = (1, 4, 4) + patch_spatial_size = (4, 4) key_1, key_2 = 'foo', 'bar' rand_image = np.random.rand(3, 10, 10) - patch_transform = UniformRandomPatchd(keys=(key_1, key_2), patch_size=patch_size) + patch_transform = RandUniformPatchd(keys=(key_1, key_2), patch_spatial_size=patch_spatial_size) patch = patch_transform({key_1: rand_image, key_2: rand_image}) self.assertTrue(np.allclose(patch[key_1], patch[key_2])) diff --git a/tests/test_rand_zoom.py b/tests/test_rand_zoom.py new file mode 100644 index 0000000000..7dfdb7a522 --- /dev/null +++ b/tests/test_rand_zoom.py @@ -0,0 +1,79 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import importlib + +from scipy.ndimage import zoom as zoom_scipy +from parameterized import parameterized + +from monai.transforms import RandZoom +from tests.utils import NumpyImageTestCase2D + +VALID_CASES = [(0.9, 1.1, 3, 'constant', 0, True, False, False)] + +class TestRandZoom(NumpyImageTestCase2D): + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, min_zoom, max_zoom, order, mode, + cval, prefilter, use_gpu, keep_size): + random_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order, + mode=mode, cval=cval, prefilter=prefilter, use_gpu=use_gpu, + keep_size=keep_size) + random_zoom.set_random_state(234) + zoomed = random_zoom(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed)) + + @parameterized.expand([ + (0.8, 1.2, 1, 'constant', 0, True) + ]) + def test_gpu_zoom(self, min_zoom, max_zoom, order, mode, cval, prefilter): + if importlib.util.find_spec('cupy'): + random_zoom = RandZoom( + prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order, + mode=mode, cval=cval, prefilter=prefilter, use_gpu=True, + keep_size=False) + random_zoom.set_random_state(234) + + zoomed = random_zoom(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + + self.assertTrue(np.allclose(expected, zoomed)) + + def test_keep_size(self): + random_zoom = RandZoom(prob=1.0, min_zoom=0.6, + max_zoom=0.7, keep_size=True) + zoomed = random_zoom(self.imt[0]) + self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:])) + + @parameterized.expand([ + ("no_min_zoom", None, 1.1, 1, TypeError), + ("invalid_order", 0.9, 1.1 , 's', AssertionError) + ]) + def test_invalid_inputs(self, _, min_zoom, max_zoom, order, raises): + with self.assertRaises(raises): + random_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order) + zoomed = random_zoom(self.imt[0]) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rand_zoomd.py b/tests/test_rand_zoomd.py new file mode 100644 index 0000000000..9a5838da4b --- /dev/null +++ b/tests/test_rand_zoomd.py @@ -0,0 +1,83 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import importlib + +from scipy.ndimage import zoom as zoom_scipy +from parameterized import parameterized + +from monai.transforms import RandZoomd +from tests.utils import NumpyImageTestCase2D + +VALID_CASES = [(0.9, 1.1, 3, 'constant', 0, True, False, False)] + +class TestRandZoomd(NumpyImageTestCase2D): + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, min_zoom, max_zoom, order, mode, + cval, prefilter, use_gpu, keep_size): + key = 'img' + random_zoom = RandZoomd(key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order, + mode=mode, cval=cval, prefilter=prefilter, use_gpu=use_gpu, + keep_size=keep_size) + random_zoom.set_random_state(234) + + zoomed = random_zoom({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed[key])) + + @parameterized.expand([ + (0.8, 1.2, 1, 'constant', 0, True) + ]) + def test_gpu_zoom(self, min_zoom, max_zoom, order, mode, cval, prefilter): + key = 'img' + if importlib.util.find_spec('cupy'): + random_zoom = RandZoomd( + key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order, + mode=mode, cval=cval, prefilter=prefilter, use_gpu=True, + keep_size=False) + random_zoom.set_random_state(234) + + zoomed = random_zoom({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=random_zoom._zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed)) + + def test_keep_size(self): + key = 'img' + random_zoom = RandZoomd(key, prob=1.0, min_zoom=0.6, + max_zoom=0.7, keep_size=True) + zoomed = random_zoom({key: self.imt[0]}) + self.assertTrue(np.array_equal(zoomed[key].shape, self.imt.shape[1:])) + + @parameterized.expand([ + ("no_min_zoom", None, 1.1, 1, TypeError), + ("invalid_order", 0.9, 1.1 , 's', AssertionError) + ]) + def test_invalid_inputs(self, _, min_zoom, max_zoom, order, raises): + key = 'img' + with self.assertRaises(raises): + random_zoom = RandZoomd(key, prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, order=order) + zoomed = random_zoom({key: self.imt[0]}) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_resampler.py b/tests/test_resampler.py new file mode 100644 index 0000000000..fa62e126c6 --- /dev/null +++ b/tests/test_resampler.py @@ -0,0 +1,75 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.transforms import Resample +from monai.transforms.utils import create_grid + +TEST_CASES = [ + [ + dict(padding_mode='zeros', as_tensor_output=False, device=None), + {'grid': create_grid((2, 2)), 'img': np.arange(4).reshape((1, 2, 2))}, + np.array([[[0., 0.25], [0.5, 0.75]]]) + ], + [ + dict(padding_mode='zeros', as_tensor_output=False, device=None), + {'grid': create_grid((4, 4)), 'img': np.arange(4).reshape((1, 2, 2))}, + np.array([[[0., 0., 0., 0.], [0., 0., 0.25, 0.], [0., 0.5, 0.75, 0.], [0., 0., 0., 0.]]]) + ], + [ + dict(padding_mode='border', as_tensor_output=False, device=None), + {'grid': create_grid((4, 4)), 'img': np.arange(4).reshape((1, 2, 2))}, + np.array([[[0., 0., 1., 1.], [0., 0., 1., 1.], [2., 2., 3, 3.], [2., 2., 3., 3.]]]) + ], + [ + dict(padding_mode='reflection', as_tensor_output=False, device=None), + {'grid': create_grid((4, 4)), 'img': np.arange(4).reshape((1, 2, 2)), 'mode': 'nearest'}, + np.array([[[3., 2., 3., 2.], [1., 0., 1., 0.], [3., 2., 3., 2.], [1., 0., 1., 0.]]]) + ], + [ + dict(padding_mode='zeros', as_tensor_output=False, device=None), + {'grid': create_grid((4, 4, 4)), 'img': np.arange(8).reshape((1, 2, 2, 2)), 'mode': 'bilinear'}, + np.array([[[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0., 0.125, 0.], [0., 0.25, 0.375, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0.5, 0.625, 0.], [0., 0.75, 0.875, 0.], [0., 0., 0., 0.]], + [[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]]]) + ], + [ + dict(padding_mode='border', as_tensor_output=False, device=None), + {'grid': create_grid((4, 4, 4)), 'img': np.arange(8).reshape((1, 2, 2, 2)), 'mode': 'bilinear'}, + np.array([[[[0., 0., 1., 1.], [0., 0., 1., 1.], [2., 2., 3., 3.], [2., 2., 3., 3.]], + [[0., 0., 1., 1.], [0., 0., 1., 1.], [2., 2., 3., 3.], [2., 2., 3., 3.]], + [[4., 4., 5., 5.], [4., 4., 5., 5.], [6., 6., 7., 7.], [6., 6., 7., 7.]], + [[4., 4., 5., 5.], [4., 4., 5., 5.], [6., 6., 7., 7.], [6., 6., 7., 7.]]]]) + ], +] + + +class TestResample(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_resample(self, input_param, input_data, expected_val): + g = Resample(**input_param) + result = g(**input_data) + self.assertEqual(torch.is_tensor(result), torch.is_tensor(expected_val)) + if torch.is_tensor(result): + np.testing.assert_allclose(result.cpu().numpy(), expected_val.cpu().numpy(), rtol=1e-4, atol=1e-4) + else: + np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_resize.py b/tests/test_resize.py new file mode 100644 index 0000000000..30f8101baa --- /dev/null +++ b/tests/test_resize.py @@ -0,0 +1,56 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import skimage +from parameterized import parameterized + +from monai.transforms import Resize +from tests.utils import NumpyImageTestCase2D + + +class TestResize(NumpyImageTestCase2D): + + @parameterized.expand([ + ("invalid_order", "order", AssertionError) + ]) + def test_invalid_inputs(self, _, order, raises): + with self.assertRaises(raises): + resize = Resize(output_spatial_shape=(128, 128, 3), order=order) + resize(self.imt[0]) + + @parameterized.expand([ + ((64, 64), 1, 'reflect', 0, True, True, True, None), + ((32, 32), 2, 'constant', 3, False, False, False, None), + ((256, 256), 3, 'constant', 3, False, False, False, None), + ]) + def test_correct_results(self, output_spatial_shape, order, mode, + cval, clip, preserve_range, + anti_aliasing, anti_aliasing_sigma): + resize = Resize(output_spatial_shape, order, mode, cval, clip, + preserve_range, anti_aliasing, + anti_aliasing_sigma) + expected = list() + for channel in self.imt[0]: + expected.append(skimage.transform.resize(channel, output_spatial_shape, + order=order, mode=mode, + cval=cval, clip=clip, + preserve_range=preserve_range, + anti_aliasing=anti_aliasing, + anti_aliasing_sigma=anti_aliasing_sigma)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(resize(self.imt[0]), expected)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_resized.py b/tests/test_resized.py new file mode 100644 index 0000000000..d7830d3e1d --- /dev/null +++ b/tests/test_resized.py @@ -0,0 +1,56 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import skimage +from parameterized import parameterized + +from monai.transforms import Resized +from tests.utils import NumpyImageTestCase2D + + +class TestResized(NumpyImageTestCase2D): + + @parameterized.expand([ + ("invalid_order", "order", AssertionError) + ]) + def test_invalid_inputs(self, _, order, raises): + with self.assertRaises(raises): + resize = Resized(keys='img', output_spatial_shape=(128, 128, 3), order=order) + resize({'img': self.imt[0]}) + + @parameterized.expand([ + ((64, 64), 1, 'reflect', 0, True, True, True, None), + ((32, 32), 2, 'constant', 3, False, False, False, None), + ((256, 256), 3, 'constant', 3, False, False, False, None), + ]) + def test_correct_results(self, output_spatial_shape, order, mode, + cval, clip, preserve_range, + anti_aliasing, anti_aliasing_sigma): + resize = Resized('img', output_spatial_shape, order, mode, cval, clip, + preserve_range, anti_aliasing, + anti_aliasing_sigma) + expected = list() + for channel in self.imt[0]: + expected.append(skimage.transform.resize(channel, output_spatial_shape, + order=order, mode=mode, + cval=cval, clip=clip, + preserve_range=preserve_range, + anti_aliasing=anti_aliasing, + anti_aliasing_sigma=anti_aliasing_sigma)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(resize({'img': self.imt[0]})['img'], expected)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rotate.py b/tests/test_rotate.py new file mode 100644 index 0000000000..7d6d1b531b --- /dev/null +++ b/tests/test_rotate.py @@ -0,0 +1,41 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np + +import scipy.ndimage +from parameterized import parameterized + +from monai.transforms import Rotate +from tests.utils import NumpyImageTestCase2D + +TEST_CASES = [(90, (0, 1), True, 1, 'reflect', 0, True), + (-90, (1, 0), True, 3, 'constant', 0, True), + (180, (1, 0), False, 2, 'constant', 4, False)] + +class TestRotate(NumpyImageTestCase2D): + + @parameterized.expand(TEST_CASES) + def test_correct_results(self, angle, spatial_axes, reshape, + order, mode, cval, prefilter): + rotate_fn = Rotate(angle, spatial_axes, reshape, + order, mode, cval, prefilter) + rotated = rotate_fn(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(scipy.ndimage.rotate(channel, angle, spatial_axes, reshape, order=order, + mode=mode, cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, rotated)) + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_rotate90.py b/tests/test_rotate90.py index 1b1aca78df..990e489cd9 100644 --- a/tests/test_rotate90.py +++ b/tests/test_rotate90.py @@ -17,30 +17,42 @@ from tests.utils import NumpyImageTestCase2D -class Rotate90Test(NumpyImageTestCase2D): +class TestRotate90(NumpyImageTestCase2D): def test_rotate90_default(self): rotate = Rotate90() - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 1, (1, 2)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) def test_k(self): rotate = Rotate90(k=2) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 2, (1, 2)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 2, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) - def test_axes(self): - rotate = Rotate90(axes=(1, 2)) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 1, (1, 2)) + def test_spatial_axes(self): + rotate = Rotate90(spatial_axes=(0, 1)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) - def test_k_axes(self): - rotate = Rotate90(k=2, axes=(2, 3)) - rotated = rotate(self.imt) - expected = np.rot90(self.imt, 2, (2, 3)) + def test_prob_k_spatial_axes(self): + rotate = Rotate90(k=2, spatial_axes=(0, 1)) + rotated = rotate(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 2, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated, expected)) diff --git a/tests/test_rotate90d.py b/tests/test_rotate90d.py index ccfb2380f0..4b54a9a296 100644 --- a/tests/test_rotate90d.py +++ b/tests/test_rotate90d.py @@ -17,41 +17,53 @@ from tests.utils import NumpyImageTestCase2D -class Rotate90Test(NumpyImageTestCase2D): +class TestRotate90d(NumpyImageTestCase2D): def test_rotate90_default(self): key = 'test' rotate = Rotate90d(keys=key) - rotated = rotate({key: self.imt}) - expected = np.rot90(self.imt, 1, (1, 2)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated[key], expected)) def test_k(self): key = None rotate = Rotate90d(keys=key, k=2) - rotated = rotate({key: self.imt}) - expected = np.rot90(self.imt, 2, (1, 2)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 2, (0, 1))) + expected = np.stack(expected) self.assertTrue(np.allclose(rotated[key], expected)) - def test_axes(self): - key = ['test'] - rotate = Rotate90d(keys=key, axes=(1, 2)) - rotated = rotate({key[0]: self.imt}) - expected = np.rot90(self.imt, 1, (1, 2)) - self.assertTrue(np.allclose(rotated[key[0]], expected)) + def test_spatial_axes(self): + key = 'test' + rotate = Rotate90d(keys=key, spatial_axes=(0, 1)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 1, (0, 1))) + expected = np.stack(expected) + self.assertTrue(np.allclose(rotated[key], expected)) - def test_k_axes(self): - key = ('test',) - rotate = Rotate90d(keys=key, k=2, axes=(2, 3)) - rotated = rotate({key[0]: self.imt}) - expected = np.rot90(self.imt, 2, (2, 3)) - self.assertTrue(np.allclose(rotated[key[0]], expected)) + def test_prob_k_spatial_axes(self): + key = 'test' + rotate = Rotate90d(keys=key, k=2, spatial_axes=(0, 1)) + rotated = rotate({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(np.rot90(channel, 2, (0, 1))) + expected = np.stack(expected) + self.assertTrue(np.allclose(rotated[key], expected)) def test_no_key(self): key = 'unknown' rotate = Rotate90d(keys=key) with self.assertRaisesRegex(KeyError, ''): - rotate({'test': self.imt}) + rotate({'test': self.imt[0]}) if __name__ == '__main__': diff --git a/tests/test_rotated.py b/tests/test_rotated.py new file mode 100644 index 0000000000..af7a758d8d --- /dev/null +++ b/tests/test_rotated.py @@ -0,0 +1,43 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np + +import scipy.ndimage +from parameterized import parameterized + +from monai.transforms import Rotated +from tests.utils import NumpyImageTestCase2D + +TEST_CASES = [(90, (0, 1), True, 1, 'reflect', 0, True), + (-90, (1, 0), True, 3, 'constant', 0, True), + (180, (1, 0), False, 2, 'constant', 4, False)] + +class TestRotated(NumpyImageTestCase2D): + + @parameterized.expand(TEST_CASES) + def test_correct_results(self, angle, spatial_axes, reshape, + order, mode, cval, prefilter): + key = 'img' + rotate_fn = Rotated(key, angle, spatial_axes, reshape, order, + mode, cval, prefilter) + rotated = rotate_fn({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(scipy.ndimage.rotate(channel, angle, spatial_axes, reshape, order=order, + mode=mode, cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, rotated[key])) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_scale_intensity_range.py b/tests/test_scale_intensity_range.py new file mode 100644 index 0000000000..05d9b1fa9e --- /dev/null +++ b/tests/test_scale_intensity_range.py @@ -0,0 +1,31 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms import ScaleIntensityRange +from tests.utils import NumpyImageTestCase2D + + +class IntensityScaleIntensityRange(NumpyImageTestCase2D): + + def test_image_scale_intensity_range(self): + scaler = ScaleIntensityRange(a_min=20, a_max=108, b_min=50, b_max=80) + scaled = scaler(self.imt) + expected = (self.imt - 20) / 88 + expected = expected * 30 + 50 + self.assertTrue(np.allclose(scaled, expected)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_scale_intensity_ranged.py b/tests/test_scale_intensity_ranged.py new file mode 100644 index 0000000000..57944f0d87 --- /dev/null +++ b/tests/test_scale_intensity_ranged.py @@ -0,0 +1,32 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms import ScaleIntensityRanged +from tests.utils import NumpyImageTestCase2D + + +class IntensityScaleIntensityRanged(NumpyImageTestCase2D): + + def test_image_scale_intensity_ranged(self): + key = 'img' + scaler = ScaleIntensityRanged(keys=key, a_min=20, a_max=108, b_min=50, b_max=80) + scaled = scaler({key: self.imt}) + expected = (self.imt - 20) / 88 + expected = expected * 30 + 50 + self.assertTrue(np.allclose(scaled[key], expected)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_sliding_window_inference.py b/tests/test_sliding_window_inference.py index e0d727c407..142d3e31e5 100644 --- a/tests/test_sliding_window_inference.py +++ b/tests/test_sliding_window_inference.py @@ -34,7 +34,7 @@ def test_sliding_window_default(self, image_shape, roi_shape, sw_batch_size): device = torch.device("cpu:0") def compute(data): - return data.to(device) + 1 + return data + 1 result = sliding_window_inference(inputs, roi_shape, sw_batch_size, compute, device) expected_val = np.ones(image_shape, dtype=np.float32) + 1 diff --git a/tests/test_spacing.py b/tests/test_spacing.py new file mode 100644 index 0000000000..ceaff9a9e6 --- /dev/null +++ b/tests/test_spacing.py @@ -0,0 +1,47 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms.transforms import Spacing + +TEST_CASES = [ + [{'pixdim': (1.0, 2.0, 1.5)}, + np.ones((2, 10, 15, 20)), {'original_pixdim': (0.5, 0.5, 1.0)}, (2, 5, 4, 13)], + [{'pixdim': (1.0, 2.0, 1.5), 'keep_shape': True}, + np.ones((1, 2, 1, 2)), {'original_pixdim': (0.5, 0.5, 1.0)}, (1, 2, 1, 2)], + [{'pixdim': (1.0, 0.2, 1.5), 'keep_shape': False}, + np.ones((1, 2, 1, 2)), {'original_affine': np.eye(4)}, (1, 2, 5, 1)], + [{'pixdim': (1.0, 2.0), 'keep_shape': True}, + np.ones((3, 2, 2)), {'original_pixdim': (1.5, 0.5)}, (3, 2, 2)], + [{'pixdim': (1.0, 0.2), 'keep_shape': False}, + np.ones((5, 2, 1)), {'original_pixdim': (1.5, 0.5)}, (5, 3, 2)], + [{'pixdim': (1.0,), 'keep_shape': False}, + np.ones((1, 2)), {'original_pixdim': (1.5,), 'interp_order': 0}, (1, 3)], +] + + +class TestSpacingCase(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_spacing(self, init_param, img, data_param, expected_shape): + res = Spacing(**init_param)(img, **data_param) + np.testing.assert_allclose(res[0].shape, expected_shape) + if 'original_pixdim' in data_param: + np.testing.assert_allclose(res[1], data_param['original_pixdim']) + np.testing.assert_allclose(res[2], init_param['pixdim']) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_spacingd.py b/tests/test_spacingd.py new file mode 100644 index 0000000000..3ee0b66ae1 --- /dev/null +++ b/tests/test_spacingd.py @@ -0,0 +1,63 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +from monai.transforms.composables import Spacingd + + +class TestSpacingDCase(unittest.TestCase): + + def test_spacingd_3d(self): + data = {'image': np.ones((2, 10, 15, 20)), 'affine': np.eye(4)} + spacing = Spacingd(keys='image', affine_key='affine', pixdim=(1, 2, 1.4)) + res = spacing(data) + np.testing.assert_allclose(res['image'].shape, (2, 10, 8, 14)) + np.testing.assert_allclose(res['spacing']['current_pixdim'], (1, 2, 1.4)) + np.testing.assert_allclose(res['spacing']['original_pixdim'], (1, 1, 1)) + + def test_spacingd_2d(self): + data = {'image': np.ones((2, 10, 20)), 'affine': np.eye(4)} + spacing = Spacingd(keys='image', affine_key='affine', pixdim=(1, 2, 1.4)) + res = spacing(data) + np.testing.assert_allclose(res['image'].shape, (2, 10, 10)) + np.testing.assert_allclose(res['spacing']['current_pixdim'], (1, 2)) + np.testing.assert_allclose(res['spacing']['original_pixdim'], (1, 1)) + + def test_spacingd_1d(self): + data = {'image': np.ones((2, 10)), 'affine': np.eye(4)} + spacing = Spacingd(keys='image', affine_key='affine', pixdim=(0.2,)) + res = spacing(data) + np.testing.assert_allclose(res['image'].shape, (2, 50)) + np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,)) + np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,)) + + def test_interp_all(self): + data = {'image': np.ones((2, 10)), 'seg': np.ones((2, 10)), 'affine': np.eye(4)} + spacing = Spacingd(keys=('image', 'seg'), affine_key='affine', interp_order=0, pixdim=(0.2,)) + res = spacing(data) + np.testing.assert_allclose(res['image'].shape, (2, 50)) + np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,)) + np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,)) + + def test_interp_sep(self): + data = {'image': np.ones((2, 10)), 'seg': np.ones((2, 10)), 'affine': np.eye(4)} + spacing = Spacingd(keys=('image', 'seg'), affine_key='affine', interp_order=(2, 0), pixdim=(0.2,)) + res = spacing(data) + np.testing.assert_allclose(res['image'].shape, (2, 50)) + np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,)) + np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_spatial_crop.py b/tests/test_spatial_crop.py index 2a3c2e7f9c..3d8862cad9 100644 --- a/tests/test_spatial_crop.py +++ b/tests/test_spatial_crop.py @@ -20,7 +20,7 @@ 'roi_size': [2, 2, 2] }, np.random.randint(0, 2, size=[3, 3, 3, 3]), - (3, 2, 2, 2), + (3, 2, 2, 2) ] TEST_CASE_2 = [ @@ -29,12 +29,31 @@ 'roi_end': [2, 2, 2] }, np.random.randint(0, 2, size=[3, 3, 3, 3]), + (3, 2, 2, 2) +] + +TEST_CASE_3 = [ + { + 'roi_start': [0, 0], + 'roi_end': [2, 2] + }, + np.random.randint(0, 2, size=[3, 3, 3, 3]), + (3, 2, 2, 3), +] + +TEST_CASE_4 = [ + { + 'roi_start': [0, 0, 0, 0, 0], + 'roi_end': [2, 2, 2, 2, 2] + }, + np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 2, 2, 2), ] + class TestSpatialCrop(unittest.TestCase): - @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_shape(self, input_param, input_data, expected_shape): result = SpatialCrop(**input_param)(input_data) self.assertTupleEqual(result.shape, expected_shape) diff --git a/tests/test_unet.py b/tests/test_unet.py index 98102375a6..5b8e85f915 100644 --- a/tests/test_unet.py +++ b/tests/test_unet.py @@ -20,39 +20,39 @@ { 'dimensions': 2, 'in_channels': 1, - 'num_classes': 3, + 'out_channels': 3, 'channels': (16, 32, 64), 'strides': (2, 2), 'num_res_units': 1, }, torch.randn(16, 1, 32, 32), - (16, 32, 32), + (16, 3, 32, 32), ] TEST_CASE_2 = [ # single channel 3D, batch 16 { 'dimensions': 3, 'in_channels': 1, - 'num_classes': 3, + 'out_channels': 3, 'channels': (16, 32, 64), 'strides': (2, 2), 'num_res_units': 1, }, torch.randn(16, 1, 32, 24, 48), - (16, 32, 24, 48), + (16, 3, 32, 24, 48), ] TEST_CASE_3 = [ # 4-channel 3D, batch 16 { 'dimensions': 3, 'in_channels': 4, - 'num_classes': 3, + 'out_channels': 3, 'channels': (16, 32, 64), 'strides': (2, 2), 'num_res_units': 1, }, torch.randn(16, 4, 32, 64, 48), - (16, 32, 64, 48), + (16, 3, 32, 64, 48), ] @@ -63,7 +63,7 @@ def test_shape(self, input_param, input_data, expected_shape): net = UNet(**input_param) net.eval() with torch.no_grad(): - result = net.forward(input_data)[1] + result = net.forward(input_data) self.assertEqual(result.shape, expected_shape) diff --git a/tests/test_zoom.py b/tests/test_zoom.py new file mode 100644 index 0000000000..cb0af47fef --- /dev/null +++ b/tests/test_zoom.py @@ -0,0 +1,77 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import importlib + +from scipy.ndimage import zoom as zoom_scipy +from parameterized import parameterized + +from monai.transforms import Zoom +from tests.utils import NumpyImageTestCase2D + +VALID_CASES = [(1.1, 3, 'constant', 0, True, False, False), + (0.9, 3, 'constant', 0, True, False, False), + (0.8, 1, 'reflect', 0, False, False, False)] + +GPU_CASES = [("gpu_zoom", 0.6, 1, 'constant', 0, True)] + +INVALID_CASES = [("no_zoom", None, 1, TypeError), + ("invalid_order", 0.9, 's', AssertionError)] + + +class TestZoom(NumpyImageTestCase2D): + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, zoom, order, mode, cval, prefilter, use_gpu, keep_size): + zoom_fn = Zoom(zoom=zoom, order=order, mode=mode, cval=cval, + prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size) + zoomed = zoom_fn(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed)) + + @parameterized.expand(GPU_CASES) + def test_gpu_zoom(self, _, zoom, order, mode, cval, prefilter): + if importlib.util.find_spec('cupy'): + zoom_fn = Zoom(zoom=zoom, order=order, mode=mode, cval=cval, + prefilter=prefilter, use_gpu=True, keep_size=False) + zoomed = zoom_fn(self.imt[0]) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed)) + + def test_keep_size(self): + zoom_fn = Zoom(zoom=0.6, keep_size=True) + zoomed = zoom_fn(self.imt[0]) + self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:])) + + zoom_fn = Zoom(zoom=1.3, keep_size=True) + zoomed = zoom_fn(self.imt[0]) + self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:])) + + @parameterized.expand(INVALID_CASES) + def test_invalid_inputs(self, _, zoom, order, raises): + with self.assertRaises(raises): + zoom_fn = Zoom(zoom=zoom, order=order) + zoomed = zoom_fn(self.imt[0]) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_zoomd.py b/tests/test_zoomd.py new file mode 100644 index 0000000000..6ef85cb1fe --- /dev/null +++ b/tests/test_zoomd.py @@ -0,0 +1,82 @@ +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import importlib + +from scipy.ndimage import zoom as zoom_scipy +from parameterized import parameterized + +from monai.transforms import Zoomd +from tests.utils import NumpyImageTestCase2D + +VALID_CASES = [(1.1, 3, 'constant', 0, True, False, False), + (0.9, 3, 'constant', 0, True, False, False), + (0.8, 1, 'reflect', 0, False, False, False)] + +GPU_CASES = [("gpu_zoom", 0.6, 1, 'constant', 0, True)] + +INVALID_CASES = [("no_zoom", None, 1, TypeError), + ("invalid_order", 0.9, 's', AssertionError)] + + +class TestZoomd(NumpyImageTestCase2D): + + @parameterized.expand(VALID_CASES) + def test_correct_results(self, zoom, order, mode, cval, prefilter, use_gpu, keep_size): + key = 'img' + zoom_fn = Zoomd(key, zoom=zoom, order=order, mode=mode, cval=cval, + prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size) + zoomed = zoom_fn({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed[key])) + + + @parameterized.expand(GPU_CASES) + def test_gpu_zoom(self, _, zoom, order, mode, cval, prefilter): + key = 'img' + if importlib.util.find_spec('cupy'): + zoom_fn = Zoomd(key, zoom=zoom, order=order, mode=mode, cval=cval, + prefilter=prefilter, use_gpu=True, keep_size=False) + zoomed = zoom_fn({key: self.imt[0]}) + expected = list() + for channel in self.imt[0]: + expected.append(zoom_scipy(channel, zoom=zoom, mode=mode, order=order, + cval=cval, prefilter=prefilter)) + expected = np.stack(expected).astype(np.float32) + self.assertTrue(np.allclose(expected, zoomed[key])) + + def test_keep_size(self): + key = 'img' + zoom_fn = Zoomd(key, zoom=0.6, keep_size=True) + zoomed = zoom_fn({key: self.imt[0]}) + self.assertTrue(np.array_equal(zoomed[key].shape, self.imt.shape[1:])) + + zoom_fn = Zoomd(key, zoom=1.3, keep_size=True) + zoomed = zoom_fn({key: self.imt[0]}) + self.assertTrue(np.array_equal(zoomed[key].shape, self.imt.shape[1:])) + + @parameterized.expand(INVALID_CASES) + def test_invalid_inputs(self, _, zoom, order, raises): + key = 'img' + with self.assertRaises(raises): + zoom_fn = Zoomd(key, zoom=zoom, order=order) + zoomed = zoom_fn({key: self.imt[0]}) + + +if __name__ == '__main__': + unittest.main()