merge master

.github/workflows/cleanup.yml

@@ -0,0 +1,20 @@
+name: cleanup-workflow
+
+on:
+  workflow_run:
+    workflows:
+      - "build"
+    types: ["requested"]
+
+jobs:
+  cancel-duplicated-workflow:
+    name: "Cancel duplicated workflow"
+    runs-on: ubuntu-latest
+    steps:
+      - uses: potiuk/cancel-workflow-runs@953e057dc81d3458935a18d1184c386b0f6b5738 # tested
+        name: "Cancel duplicate workflows"
+        with:
+          cancelMode: allDuplicates
+          token: ${{ secrets.GITHUB_TOKEN }}
+          sourceRunId: ${{ github.event.workflow_run.id }}
+          skipEventTypes: '["schedule"]'

.github/workflows/weekly-preview.yml

@@ -6,6 +6,7 @@ on:
 
 jobs:
   packaging:
+    if: github.repository == 'Project-MONAI/MONAI'
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
@@ -24,12 +25,16 @@ jobs:
         sed -i 's/name\ =\ monai$/name\ =\ monai-weekly/g' setup.cfg
         echo "__commit_id__ = \"$HEAD_COMMIT_ID\"" >> monai/__init__.py
         git diff setup.cfg monai/__init__.py
-        # build tar.gz and wheel
         git config user.name "CI Builder"
-        git config user.email "monai.miccai2019@gmail.com"
+        git config user.email "monai.contact@gmail.com"
         git add setup.cfg monai/__init__.py
         git commit -m "Weekly build at $HEAD_COMMIT_ID"
-        git tag 0.5.dev$(date +'%y%U')
+        export YEAR_WEEK=$(date +'%y%U')
+        echo "Year week for tag is ${YEAR_WEEK}"
+        if ! [[ $YEAR_WEEK =~ ^[0-9]{4}$ ]] ; then echo "Wrong 'year week' format.  Should be 4 digits."; exit 1 ; fi
+        git tag "0.5.dev${YEAR_WEEK}"
+        git log -1
+        git tag --list
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI

CODE_OF_CONDUCT.md

@@ -0,0 +1,76 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+ advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+ address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+ professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at monai.contact@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

Dockerfile

@@ -13,7 +13,7 @@ ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:20.10-py3
 
 FROM ${PYTORCH_IMAGE}
 
-LABEL maintainer="monai.miccai2019@gmail.com"
+LABEL maintainer="monai.contact@gmail.com"
 
 WORKDIR /opt/monai
 

README.md

@@ -30,23 +30,13 @@ Its ambitions are:
 
 ## Installation
 
-### Installing [the current release](https://pypi.org/project/monai/):
-```bash
-pip install monai
-```
+To install [the current release](https://pypi.org/project/monai/), you can simply run:
 
-### Installing the master branch from the source code repository:
 ```bash
-pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI
+pip install monai
 ```
 
-### Using the pre-built Docker image [DockerHub](https://hub.docker.com/r/projectmonai/monai):
-  ```bash
-  # with docker v19.03+
-  docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest
-  ```
-
-For more details, please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html).
+For other installation methods (using the master branch, using Docker, etc.), please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html).
 
 ## Getting Started
 

docs/source/handlers.rst

@@ -16,12 +16,25 @@ Model checkpoint saver
 .. autoclass:: CheckpointSaver
   :members:
 
+
+Metrics saver
+-------------
+.. autoclass:: MetricsSaver
+    :members:
+
+
 CSV saver
 ---------
 .. autoclass:: ClassificationSaver
     :members:
 
 
+Iteration Metric
+----------------
+.. autoclass:: IterationMetric
+    :members:
+
+
 Mean Dice metrics handler
 -------------------------
 .. autoclass:: MeanDice

docs/source/installation.md

@@ -1,17 +1,24 @@
 # Installation guide
 
+## Table of Contents
+1. [From PyPI](#from-pypi)
+	1. [Milestone release](#milestone-release)
+	2. [Weekly preview release](#weekly-preview-release)
+2. [From GitHub](#from-github)
+	1. [System-wide](#milestone-release)
+	2. [Editable](#weekly-preview-release)
+3. [Validating the install](#validating-the-install)
+4. [MONAI version string](#monai-version-string)
+5. [From DockerHub](#from-dockerhub)
+6. [Installing the recommended dependencies](#Installing-the-recommended-dependencies)
+
+---
+
 MONAI's core functionality is written in Python 3 (>= 3.6) and only requires [Numpy](https://numpy.org/) and [Pytorch](https://pytorch.org/).
 
 The package is currently distributed via Github as the primary source code repository,
 and the Python package index (PyPI). The pre-built Docker images are made available on DockerHub.
 
-This page provides steps to:
-- [Install MONAI from PyPI](#from-pypi)
-- [Install MONAI from GitHub](#from-github)
-- [Validate the install](#validating-the-install)
-- [Understand MONAI version string](#monai-version-string)
-- [Run MONAI From DockerHub](#from-dockerhub)
-
 To install optional features such as handling the NIfTI files using
 [Nibabel](https://nipy.org/nibabel/), or building workflows using [Pytorch
 Ignite](https://pytorch.org/ignite/), please follow the instructions:

monai/config/__init__.py

@@ -18,4 +18,4 @@
     print_gpu_info,
     print_system_info,
 )
-from .type_definitions import IndexSelection, KeysCollection
+from .type_definitions import DtypeLike, IndexSelection, KeysCollection, NdarrayTensor

monai/config/deviceconfig.py

@@ -162,7 +162,7 @@ def get_system_info() -> OrderedDict:
             _dict_append(
                 output,
                 "Avg. sensor temp. (Celsius)",
-                lambda: round(
+                lambda: np.round(
                     np.mean([item.current for sublist in psutil.sensors_temperatures().values() for item in sublist], 1)
                 ),
             )
@@ -196,28 +196,34 @@ def get_gpu_info() -> OrderedDict:
     _dict_append(output, "Num GPUs", lambda: num_gpus)
 
     _dict_append(output, "Has CUDA", lambda: bool(torch.cuda.is_available()))
+
     if output["Has CUDA"]:
         _dict_append(output, "CUDA version", lambda: torch.version.cuda)
     cudnn_ver = torch.backends.cudnn.version()
     _dict_append(output, "cuDNN enabled", lambda: bool(cudnn_ver))
+
     if cudnn_ver:
         _dict_append(output, "cuDNN version", lambda: cudnn_ver)
 
     if num_gpus > 0:
         _dict_append(output, "Current device", torch.cuda.current_device)
         if hasattr(torch.cuda, "get_arch_list"):  # get_arch_list is new in torch 1.7.1
             _dict_append(output, "Library compiled for CUDA architectures", torch.cuda.get_arch_list)
+
     for gpu in range(num_gpus):
-        _dict_append(output, "Info for GPU", gpu)
         gpu_info = torch.cuda.get_device_properties(gpu)
-        _dict_append(output, "\tName", lambda: gpu_info.name)
-        _dict_append(output, "\tIs integrated", lambda: bool(gpu_info.is_integrated))
-        _dict_append(output, "\tIs multi GPU board", lambda: bool(gpu_info.is_multi_gpu_board))
-        _dict_append(output, "\tMulti processor count", lambda: gpu_info.multi_processor_count)
-        _dict_append(output, "\tTotal memory (GB)", lambda: round(gpu_info.total_memory / 1024 ** 3, 1))
-        _dict_append(output, "\tCached memory (GB)", lambda: round(torch.cuda.memory_reserved(gpu) / 1024 ** 3, 1))
-        _dict_append(output, "\tAllocated memory (GB)", lambda: round(torch.cuda.memory_allocated(gpu) / 1024 ** 3, 1))
-        _dict_append(output, "\tCUDA capability (maj.min)", lambda: f"{gpu_info.major}.{gpu_info.minor}")
+        _dict_append(output, f"GPU {gpu} Name", lambda: gpu_info.name)
+        _dict_append(output, f"GPU {gpu} Is integrated", lambda: bool(gpu_info.is_integrated))
+        _dict_append(output, f"GPU {gpu} Is multi GPU board", lambda: bool(gpu_info.is_multi_gpu_board))
+        _dict_append(output, f"GPU {gpu} Multi processor count", lambda: gpu_info.multi_processor_count)
+        _dict_append(output, f"GPU {gpu} Total memory (GB)", lambda: round(gpu_info.total_memory / 1024 ** 3, 1))
+        _dict_append(
+            output, f"GPU {gpu} Cached memory (GB)", lambda: round(torch.cuda.memory_reserved(gpu) / 1024 ** 3, 1)
+        )
+        _dict_append(
+            output, f"GPU {gpu} Allocated memory (GB)", lambda: round(torch.cuda.memory_allocated(gpu) / 1024 ** 3, 1)
+        )
+        _dict_append(output, f"GPU {gpu} CUDA capability (maj.min)", lambda: f"{gpu_info.major}.{gpu_info.minor}")
 
     return output
 

monai/config/type_definitions.py

@@ -9,9 +9,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Collection, Hashable, Iterable, Union
+from typing import Collection, Hashable, Iterable, TypeVar, Union
 
-__all__ = ["KeysCollection", "IndexSelection"]
+import numpy as np
+import torch
+
+__all__ = ["KeysCollection", "IndexSelection", "DtypeLike", "NdarrayTensor"]
 
 """Commonly used concepts
 This module provides naming and type specifications for commonly used concepts
@@ -51,3 +54,16 @@
 The indices must be integers, and if a container of indices is specified, the
 container must be iterable.
 """
+
+DtypeLike = Union[
+    np.dtype,
+    type,
+    None,
+]
+"""Type of datatypes
+adapted from https://github.com/numpy/numpy/blob/master/numpy/typing/_dtype_like.py
+"""
+
+# Generic type which can represent either a numpy.ndarray or a torch.Tensor
+# Unlike Union can create a dependence between parameter(s) / return(s)
+NdarrayTensor = TypeVar("NdarrayTensor", np.ndarray, torch.Tensor)

monai/data/csv_saver.py

@@ -75,7 +75,7 @@ def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict]
         """
         save_key = meta_data["filename_or_obj"] if meta_data else str(self._data_index)
         self._data_index += 1
-        if torch.is_tensor(data):
+        if isinstance(data, torch.Tensor):
             data = data.detach().cpu().numpy()
         if not isinstance(data, np.ndarray):
             raise AssertionError

monai/data/dataset.py

@@ -498,7 +498,13 @@ def _fill_cache(self) -> List:
             warnings.warn("tqdm is not installed, will not show the caching progress bar.")
         with ThreadPool(self.num_workers) as p:
             if has_tqdm:
-                return list(tqdm(p.imap(self._load_cache_item, range(self.cache_num)), total=self.cache_num))
+                return list(
+                    tqdm(
+                        p.imap(self._load_cache_item, range(self.cache_num)),
+                        total=self.cache_num,
+                        desc="Loading dataset",
+                    )
+                )
             return list(p.imap(self._load_cache_item, range(self.cache_num)))
 
     def _load_cache_item(self, idx: int):

monai/data/image_dataset.py

@@ -14,6 +14,7 @@
 import numpy as np
 from torch.utils.data import Dataset
 
+from monai.config import DtypeLike
 from monai.data.image_reader import ImageReader
 from monai.transforms import LoadImage, Randomizable, apply_transform
 from monai.utils import MAX_SEED, get_seed
@@ -36,7 +37,7 @@ def __init__(
         transform: Optional[Callable] = None,
         seg_transform: Optional[Callable] = None,
         image_only: bool = True,
-        dtype: Optional[np.dtype] = np.float32,
+        dtype: DtypeLike = np.float32,
         reader: Optional[Union[ImageReader, str]] = None,
         *args,
         **kwargs,

monai/data/image_reader.py

@@ -16,7 +16,7 @@
 import numpy as np
 from torch.utils.data._utils.collate import np_str_obj_array_pattern
 
-from monai.config import KeysCollection
+from monai.config import DtypeLike, KeysCollection
 from monai.data.utils import correct_nifti_header_if_necessary
 from monai.utils import ensure_tuple, optional_import
 
@@ -244,7 +244,7 @@ def _get_affine(self, img) -> np.ndarray:
         affine = np.eye(direction.shape[0] + 1)
         affine[(slice(-1), slice(-1))] = direction @ np.diag(spacing)
         affine[(slice(-1), -1)] = origin
-        return affine
+        return np.asarray(affine)
 
     def _get_spatial_shape(self, img) -> np.ndarray:
         """
@@ -258,7 +258,7 @@ def _get_spatial_shape(self, img) -> np.ndarray:
         shape.reverse()
         return np.asarray(shape)
 
-    def _get_array_data(self, img) -> np.ndarray:
+    def _get_array_data(self, img):
         """
         Get the raw array data of the image, converted to Numpy array.
 
@@ -295,7 +295,7 @@ class NibabelReader(ImageReader):
 
     """
 
-    def __init__(self, as_closest_canonical: bool = False, dtype: Optional[np.dtype] = np.float32, **kwargs):
+    def __init__(self, as_closest_canonical: bool = False, dtype: DtypeLike = np.float32, **kwargs):
         super().__init__()
         self.as_closest_canonical = as_closest_canonical
         self.dtype = dtype
@@ -385,7 +385,7 @@ def _get_affine(self, img) -> np.ndarray:
             img: a Nibabel image object loaded from a image file.
 
         """
-        return img.affine.copy()
+        return np.array(img.affine, copy=True)
 
     def _get_spatial_shape(self, img) -> np.ndarray:
         """