From e10a57bf840a96ccab6b19de65531dc5374bb701 Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Tue, 21 Mar 2023 00:53:04 +0000 Subject: [PATCH 1/2] Fix license and dependencies installation Signed-off-by: Walter Hugo Lopez Pinaya --- .../2d_autoencoderkl_tutorial.ipynb | 33 +- .../2d_autoencoderkl_tutorial.py | 27 +- .../2d_ddpm/2d_ddpm_compare_schedulers.ipynb | 39 +- .../2d_ddpm/2d_ddpm_compare_schedulers.py | 26 +- .../2d_ddpm/2d_ddpm_inpainting.ipynb | 31 +- .../generative/2d_ddpm/2d_ddpm_inpainting.py | 24 +- .../generative/2d_ddpm/2d_ddpm_tutorial.ipynb | 31 +- .../generative/2d_ddpm/2d_ddpm_tutorial.py | 24 +- .../2d_ddpm/2d_ddpm_tutorial_ignite.ipynb | 1906 +---------------- .../2d_ddpm/2d_ddpm_tutorial_ignite.py | 29 +- .../2d_ddpm_tutorial_v_prediction.ipynb | 393 ++-- .../2d_ddpm/2d_ddpm_tutorial_v_prediction.py | 78 +- .../generative/2d_ldm/2d_ldm_tutorial.ipynb | 32 +- .../generative/2d_ldm/2d_ldm_tutorial.py | 25 +- ...stable_diffusion_v2_super_resolution.ipynb | 37 +- ...2d_stable_diffusion_v2_super_resolution.py | 31 +- .../2d_vqgan/2d_vqgan_tutorial.ipynb | 44 +- .../generative/2d_vqgan/2d_vqgan_tutorial.py | 38 +- .../2d_vqvae/2d_vqvae_tutorial.ipynb | 36 +- .../generative/2d_vqvae/2d_vqvae_tutorial.py | 30 +- .../2d_vqvae_transformer_tutorial.ipynb | 112 +- .../2d_vqvae_transformer_tutorial.py | 34 +- .../3d_autoencoderkl_tutorial.ipynb | 23 +- .../3d_autoencoderkl_tutorial.py | 19 +- .../generative/3d_ddpm/3d_ddpm_tutorial.ipynb | 30 +- .../generative/3d_ddpm/3d_ddpm_tutorial.py | 23 +- .../generative/3d_ldm/3d_ldm_tutorial.ipynb | 33 +- .../generative/3d_ldm/3d_ldm_tutorial.py | 27 +- .../3d_vqvae/3d_vqvae_tutorial.ipynb | 49 +- .../generative/3d_vqvae/3d_vqvae_tutorial.py | 32 +- ...e_guidance_anomalydetection_tutorial.ipynb | 172 +- ...free_guidance_anomalydetection_tutorial.py | 150 +- .../anomaly_detection_with_transformers.ipynb | 59 +- .../anomaly_detection_with_transformers.py | 28 +- ...pm_classifier_free_guidance_tutorial.ipynb | 33 +- ..._ddpm_classifier_free_guidance_tutorial.py | 26 +- 36 files changed, 1119 insertions(+), 2645 deletions(-) diff --git a/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb b/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb index c000b207..11d059c3 100644 --- a/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb +++ b/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "99d4a6b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "d6ae75bf", @@ -7,10 +26,10 @@ "source": [ "# AutoencoderKL\n", "\n", - "This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses\n", + "This demo is a toy example of how to use MONAI's `AutoencoderKL` class. In particular, it uses\n", "the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1].\n", "\n", - "[1] Rombach et. al - [\"High-Resolution Image Synthesis with Latent Diffusion Models\"](https://arxiv.org/pdf/2112.10752.pdf)\n", + "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf\n", "\n", "\n", "\n", @@ -38,9 +57,8 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -q \"monai-weekly[tqdm]==1.1.dev2239\"\n", - "!pip install -q matplotlib\n", - "!pip install -q einops\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] }, @@ -236,7 +254,7 @@ " ]\n", ")\n", "train_ds = Dataset(data=train_datalist, transform=train_transforms)\n", - "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4)" + "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)" ] }, { @@ -315,7 +333,7 @@ " ]\n", ")\n", "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n", - "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4)" + "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)" ] }, { @@ -794,7 +812,6 @@ "metadata": {}, "outputs": [], "source": [ - "# %%\n", "l1_loss = L1Loss()\n", "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n", "adv_weight = 0.01\n", diff --git a/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.py b/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.py index 6c2046c1..53ccf898 100644 --- a/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.py +++ b/tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.py @@ -1,9 +1,22 @@ +# + +# Copyright (c) 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. +# - + # # AutoencoderKL # -# This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses +# This demo is a toy example of how to use MONAI's `AutoencoderKL` class. In particular, it uses # the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1]. # -# [1] Rombach et. al - ["High-Resolution Image Synthesis with Latent Diffusion Models"](https://arxiv.org/pdf/2112.10752.pdf) +# [1] Rombach et. al "High-Resolution Image Synthesis with Latent Diffusion Models" https://arxiv.org/pdf/2112.10752.pdf # # # @@ -18,9 +31,8 @@ # ## Set up environment using Colab -# !pip install -q "monai-weekly[tqdm]==1.1.dev2239" -# !pip install -q matplotlib -# !pip install -q einops +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" +# !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline # ## Setup imports @@ -83,7 +95,7 @@ ] ) train_ds = Dataset(data=train_datalist, transform=train_transforms) -train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4) +train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True) # ### Visualise examples from the training set @@ -106,7 +118,7 @@ ] ) val_ds = Dataset(data=val_datalist, transform=val_transforms) -val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4) +val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True) # ## Define the network @@ -144,7 +156,6 @@ optimizer_g = torch.optim.Adam(params=model.parameters(), lr=1e-4) optimizer_d = torch.optim.Adam(params=discriminator.parameters(), lr=5e-4) -# %% l1_loss = L1Loss() adv_loss = PatchAdversarialLoss(criterion="least_squares") adv_weight = 0.01 diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb b/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb index 85d984da..086e2e10 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb +++ b/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6bbe8286", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "d462dbf4", @@ -27,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -81,16 +100,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -1064,14 +1073,6 @@ "if directory is None:\n", " shutil.rmtree(root_dir)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e9e8df5-4127-44f3-bdeb-d8bf381d01a2", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.py b/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.py index 13523f9f..b42383e1 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.py +++ b/tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Denoising Diffusion Probabilistic Models with MedNIST Dataset # @@ -29,7 +41,7 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -37,16 +49,6 @@ # ## Setup imports # %% -# 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 @@ -368,5 +370,3 @@ # %% if directory is None: shutil.rmtree(root_dir) - -# %% diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.ipynb b/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.ipynb index 69aa07fa..4d0ae043 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.ipynb +++ b/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "4c8ec6e8", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "9d71306f", @@ -24,7 +43,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -80,16 +99,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.py b/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.py index 6dcaee81..ae51f48f 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.py +++ b/tutorials/generative/2d_ddpm/2d_ddpm_inpainting.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Inpainting with Denoising Diffusion Probabilistic Models # @@ -26,7 +38,7 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -34,16 +46,6 @@ # ## Setup imports # %% tags=[] -# 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 diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.ipynb b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.ipynb index 67e1307a..2ad47e3d 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.ipynb +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "375b97e1", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "9d71306f", @@ -23,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -81,16 +100,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.py b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.py index 43394caf..744530bf 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.py +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Denoising Diffusion Probabilistic Models with MedNIST Dataset # @@ -25,7 +37,7 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -33,16 +45,6 @@ # ## Setup imports # %% jupyter={"outputs_hidden": false} -# 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 diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.ipynb b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.ipynb index 1ba60203..b11dbe20 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.ipynb +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.ipynb @@ -1,8 +1,27 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "f5cbf8da", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", - "id": "9d71306f", + "id": "53c8dfc0", "metadata": {}, "source": [ "# Denoising Diffusion Probabilistic Models with MedNIST Dataset\n", @@ -18,21 +37,20 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "6aa3774e", + "execution_count": null, + "id": "04629260", "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", - "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import ignite\" || pip install -q pytorch-ignite\n", - "\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] }, { "cell_type": "markdown", - "id": "f3154fee", + "id": "2342bc75", "metadata": {}, "source": [ "## Setup imports" @@ -40,59 +58,15 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "dd62a552", + "execution_count": null, + "id": "3382be3f", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONAI version: 1.1.dev2246\n", - "Numpy version: 1.23.3\n", - "Pytorch version: 1.8.0+cu111\n", - "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: c81b9467b43bb14e77956729d10f2aef4d69deec\n", - "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/__init__.py\n", - "\n", - "Optional dependencies:\n", - "Pytorch Ignite version: 0.4.10\n", - "Nibabel version: 4.0.2\n", - "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n", - "Pillow version: 9.2.0\n", - "Tensorboard version: 2.11.0\n", - "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n", - "TorchVision version: 0.9.0+cu111\n", - "tqdm version: 4.64.1\n", - "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", - "psutil version: 5.9.3\n", - "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", - "einops version: 0.6.0\n", - "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", - "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", - "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n", - "\n", - "For details about installing the optional dependencies, please visit:\n", - " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", - "\n" - ] - } - ], + "outputs": [], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -105,7 +79,7 @@ "from monai.apps import MedNISTDataset\n", "from monai.config import print_config\n", "from monai.data import CacheDataset, DataLoader\n", - "from monai.engines import PrepareBatch, SupervisedEvaluator, SupervisedTrainer, default_prepare_batch\n", + "from monai.engines import SupervisedEvaluator, SupervisedTrainer\n", "from monai.handlers import MeanAbsoluteError, MeanSquaredError, StatsHandler, ValidationHandler, from_engine\n", "from monai.utils import first, set_determinism\n", "\n", @@ -119,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "be99fa93", + "id": "a8a5e41b", "metadata": {}, "source": [ "## Setup data directory\n", @@ -133,22 +107,14 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "8fc58c80", + "execution_count": null, + "id": "10e3e959", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/tmp/tmp4s7ozrr7\n" - ] - } - ], + "outputs": [], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "root_dir = tempfile.mkdtemp() if directory is None else directory\n", @@ -157,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "a36b12f0", + "id": "0732a4a1", "metadata": {}, "source": [ "## Set deterministic training for reproducibility" @@ -165,8 +131,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "ad5a1948", + "execution_count": null, + "id": "b430e0f4", "metadata": { "jupyter": { "outputs_hidden": false @@ -179,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "b41e37b3", + "id": "1750d513", "metadata": {}, "source": [ "## Setup MedNIST Dataset and training and validation dataloaders\n", @@ -190,24 +156,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "65e1c200", + "execution_count": null, + "id": "b1355f26", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-11-26 00:25:55,677 - INFO - Downloaded: /tmp/tmp4s7ozrr7/MedNIST.tar.gz\n", - "2022-11-26 00:25:55,746 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2022-11-26 00:25:55,746 - INFO - Writing into directory: /tmp/tmp4s7ozrr7.\n" - ] - } - ], + "outputs": [], "source": [ "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, progress=False, seed=0)\n", "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]" @@ -215,7 +171,7 @@ }, { "cell_type": "markdown", - "id": "5d503ec9", + "id": "658a9a07", "metadata": {}, "source": [ "Here we use transforms to augment the training dataset:\n", @@ -228,22 +184,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "e2f9bebd", + "execution_count": null, + "id": "c97cb55d", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7999/7999 [00:04<00:00, 1792.22it/s]\n" - ] - } - ], + "outputs": [], "source": [ "train_transforms = transforms.Compose(\n", " [\n", @@ -267,31 +215,14 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "938318c2", + "execution_count": null, + "id": "6afd0f79", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-11-26 00:26:19,327 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2022-11-26 00:26:19,327 - INFO - File exists: /tmp/tmp4s7ozrr7/MedNIST.tar.gz, skipped downloading.\n", - "2022-11-26 00:26:19,328 - INFO - Non-empty folder exists in /tmp/tmp4s7ozrr7/MedNIST, skipped extracting.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1005/1005 [00:00<00:00, 1773.89it/s]\n" - ] - } - ], + "outputs": [], "source": [ "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, progress=False, seed=0)\n", "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n", @@ -308,7 +239,7 @@ }, { "cell_type": "markdown", - "id": "a56a4e42", + "id": "021956eb", "metadata": {}, "source": [ "### Visualisation of the training images" @@ -316,32 +247,14 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "b698f4f8", + "execution_count": null, + "id": "469e4f76", "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "batch shape: (8, 1, 64, 64)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "check_data = first(train_loader)\n", "print(f\"batch shape: {check_data['image'].shape}\")\n", @@ -357,7 +270,7 @@ }, { "cell_type": "markdown", - "id": "d3090350", + "id": "31f564f8", "metadata": {}, "source": [ "### Define network, scheduler, optimizer, and inferer\n", @@ -368,8 +281,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "2c52e4f4", + "execution_count": null, + "id": "4b6a775c", "metadata": { "jupyter": { "outputs_hidden": false @@ -386,7 +299,7 @@ " num_channels=(64, 128, 128),\n", " attention_levels=(False, True, True),\n", " num_res_blocks=1,\n", - " num_head_channels=128,\n", + " num_head_channels=(0, 128, 128),\n", ")\n", "model.to(device)\n", "\n", @@ -400,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "5a316067", + "id": "62021a53", "metadata": {}, "source": [ "### Model training\n", @@ -409,1684 +322,15 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "0f697a13", + "execution_count": null, + "id": "25498b74", "metadata": { "jupyter": { "outputs_hidden": false }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-11-26 00:26:23,940 - Engine run resuming from iteration 0, epoch 0 until 75 epochs\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "48e16011b2cc4b0fa78c7c0d92db431d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "[1/1000] 0%| [00:00\n", - " warnings.warn(f\"Logger output_handler can not log metrics value type {type(value)}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-11-26 00:27:34,129 - Got new best metric of train_acc: 0.1597186028957367\n", - "2022-11-26 00:27:34,130 - Epoch[1] Complete. Time taken: 00:01:10.146\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f22412df96b5457cbcc067bdb39af189", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "[1/1000] 0%| [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "model.eval()\n", "noise = torch.randn((1, 1, 64, 64))\n", @@ -2185,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "1c45cead", + "id": "aeb3ccd4", "metadata": {}, "source": [ "### Cleanup data directory\n", @@ -2195,8 +421,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "bab2d719", + "execution_count": null, + "id": "babfbfd1", "metadata": { "tags": [] }, diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.py b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.py index 929715ad..6d556c92 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.py +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_ignite.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Denoising Diffusion Probabilistic Models with MedNIST Dataset # @@ -25,26 +37,15 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" -# !python -c "import matplotlib" || pip install -q matplotlib +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import ignite" || pip install -q pytorch-ignite - +# !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline # %% [markdown] # ## Setup imports # %% jupyter={"outputs_hidden": false} -# 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 @@ -170,7 +171,7 @@ num_channels=(64, 128, 128), attention_levels=(False, True, True), num_res_blocks=1, - num_head_channels=128, + num_head_channels=(0, 128, 128), ) model.to(device) diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.ipynb b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.ipynb index c0e809c1..99451d56 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.ipynb +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.ipynb @@ -1,8 +1,27 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a33dc913", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", - "id": "2278a8b4", + "id": "a1851245", "metadata": {}, "source": [ "# Denoising Diffusion Probabilistic Models using v-prediction parameterization\n", @@ -10,6 +29,7 @@ "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 2D images using v-prediction parameterization (Section 2.4 from [2]).\n", "\n", "[1] - Ho et al. \"Denoising Diffusion Probabilistic Models\" https://arxiv.org/abs/2006.11239\n", + "\n", "[2] - Ho et al. \"Imagen Video: High Definition Video Generation with Diffusion Models\" https://arxiv.org/abs/2210.02303\n", "\n", "\n", @@ -18,19 +38,19 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "87d63656", + "execution_count": 2, + "id": "70ca9dc1", "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] }, { "cell_type": "markdown", - "id": "de9a09ad", + "id": "1207a5cf", "metadata": {}, "source": [ "## Setup imports" @@ -38,41 +58,52 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "e991ba58", + "execution_count": 3, + "id": "a952676b", "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.1.dev2239\n", - "Numpy version: 1.23.3\n", - "Pytorch version: 1.8.0+cu111\n", + "2023-03-21 00:04:23,400 - A matching Triton is not available, some optimizations will not be enabled.\n", + "Error caught was: No module named 'triton'\n", + "MONAI version: 1.2.dev2304\n", + "Numpy version: 1.23.5\n", + "Pytorch version: 1.13.1+cu117\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 13b24fa92b9d98bd0dc6d5cdcb52504fd09e297b\n", - "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/__init__.py\n", + "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n", + "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.4.10\n", + "ITK version: 5.3.0\n", "Nibabel version: 4.0.2\n", - "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n", - "Pillow version: 9.2.0\n", + "scikit-image version: 0.19.3\n", + "Pillow version: 9.3.0\n", "Tensorboard version: 2.11.0\n", - "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n", - "TorchVision version: 0.9.0+cu111\n", + "gdown version: 4.6.0\n", + "TorchVision version: 0.14.1+cu117\n", "tqdm version: 4.64.1\n", - "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", - "psutil version: 5.9.3\n", - "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n", + "lmdb version: 1.4.0\n", + "psutil version: 5.9.4\n", + "pandas version: 1.5.3\n", "einops version: 0.6.0\n", - "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", - "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", - "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n", + "transformers version: 4.21.3\n", + "mlflow version: 2.1.1\n", + "pynrrd version: 1.0.0\n", "\n", "For details about installing the optional dependencies, please visit:\n", " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", @@ -81,16 +112,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -117,7 +138,7 @@ }, { "cell_type": "markdown", - "id": "a414cba7", + "id": "7b54fce0", "metadata": {}, "source": [ "## Setup data directory\n", @@ -131,8 +152,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "22061fd8", + "execution_count": 4, + "id": "f237aa7a", "metadata": { "jupyter": { "outputs_hidden": false @@ -143,7 +164,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmplcl8fv6u\n" + "/media/walter/Storage/Projects/GTC_2023_presentation/data\n" ] } ], @@ -155,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "8962d9b4", + "id": "c673f098", "metadata": {}, "source": [ "## Set deterministic training for reproducibility" @@ -163,8 +184,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "57bb62f0", + "execution_count": 5, + "id": "b9947a6e", "metadata": { "jupyter": { "outputs_hidden": false @@ -177,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "21210b86", + "id": "551b9f64", "metadata": {}, "source": [ "## Setup MedNIST Dataset and training and validation dataloaders\n", @@ -188,8 +209,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "46fc4bfb", + "execution_count": 6, + "id": "73c52318", "metadata": { "jupyter": { "outputs_hidden": false @@ -200,9 +221,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "2022-12-11 12:03:19,625 - INFO - Downloaded: /tmp/tmplcl8fv6u/MedNIST.tar.gz\n", - "2022-12-11 12:03:19,696 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2022-12-11 12:03:19,696 - INFO - Writing into directory: /tmp/tmplcl8fv6u.\n" + "2023-03-21 00:04:27,788 - INFO - Downloaded: /media/walter/Storage/Projects/GTC_2023_presentation/data/MedNIST.tar.gz\n", + "2023-03-21 00:04:27,859 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2023-03-21 00:04:27,859 - INFO - Writing into directory: /media/walter/Storage/Projects/GTC_2023_presentation/data.\n" ] } ], @@ -213,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "50c48aef", + "id": "2a4bd51e", "metadata": {}, "source": [ "Here we use transforms to augment the training dataset:\n", @@ -226,8 +247,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "a03c3f45", + "execution_count": 7, + "id": "fddafade", "metadata": { "jupyter": { "outputs_hidden": false @@ -238,7 +259,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7999/7999 [00:04<00:00, 1775.57it/s]\n" + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7999/7999 [00:04<00:00, 1825.83it/s]\n" ] } ], @@ -265,8 +286,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "7855726e", + "execution_count": 8, + "id": "9a79f1a4", "metadata": { "jupyter": { "outputs_hidden": false @@ -277,16 +298,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2022-12-11 12:03:42,998 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", - "2022-12-11 12:03:42,999 - INFO - File exists: /tmp/tmplcl8fv6u/MedNIST.tar.gz, skipped downloading.\n", - "2022-12-11 12:03:42,999 - INFO - Non-empty folder exists in /tmp/tmplcl8fv6u/MedNIST, skipped extracting.\n" + "2023-03-21 00:04:50,768 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2023-03-21 00:04:50,768 - INFO - File exists: /media/walter/Storage/Projects/GTC_2023_presentation/data/MedNIST.tar.gz, skipped downloading.\n", + "2023-03-21 00:04:50,768 - INFO - Non-empty folder exists in /media/walter/Storage/Projects/GTC_2023_presentation/data/MedNIST, skipped extracting.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1005/1005 [00:00<00:00, 1831.70it/s]\n" + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1005/1005 [00:00<00:00, 1821.12it/s]\n" ] } ], @@ -306,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "01452490", + "id": "a9f97389", "metadata": {}, "source": [ "### Visualisation of the training images" @@ -314,8 +335,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "3f68cdfe", + "execution_count": 9, + "id": "35e40075", "metadata": { "jupyter": { "outputs_hidden": false @@ -326,12 +347,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "batch shape: (96, 1, 64, 64)\n" + "batch shape: torch.Size([96, 1, 64, 64])\n" ] }, { "data": { - "image/png": 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" ] @@ -355,7 +376,7 @@ }, { "cell_type": "markdown", - "id": "7d026c35", + "id": "b4e7e745", "metadata": {}, "source": [ "### Define network, scheduler, optimizer, and inferer\n", @@ -366,8 +387,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "f7ba0c0f", + "execution_count": 10, + "id": "1fb9fd26", "metadata": { "jupyter": { "outputs_hidden": false @@ -398,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "315f8b47", + "id": "b1833335", "metadata": {}, "source": [ "### Model training\n", @@ -407,8 +428,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "f5f58b7e", + "execution_count": 11, + "id": "890ef804", "metadata": { "jupyter": { "outputs_hidden": false @@ -420,17 +441,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|█████████████| 84/84 [00:38<00:00, 2.17it/s, loss=0.13]\n", - "Epoch 1: 100%|███████████| 84/84 [00:38<00:00, 2.18it/s, loss=0.0485]\n", - "Epoch 2: 100%|███████████| 84/84 [00:38<00:00, 2.17it/s, loss=0.0433]\n", - "Epoch 3: 100%|███████████| 84/84 [00:38<00:00, 2.17it/s, loss=0.0406]\n", - "Epoch 4: 100%|███████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0385]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 71.19it/s]\n" + "Epoch 0: 100%|████████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.134]\n", + "Epoch 1: 100%|███████████| 84/84 [00:33<00:00, 2.50it/s, loss=0.0506]\n", + "Epoch 2: 100%|███████████| 84/84 [00:33<00:00, 2.49it/s, loss=0.0441]\n", + "Epoch 3: 100%|███████████| 84/84 [00:33<00:00, 2.48it/s, loss=0.0402]\n", + "Epoch 4: 100%|███████████| 84/84 [00:33<00:00, 2.47it/s, loss=0.0402]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 98.85it/s]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -442,17 +463,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 5: 100%|███████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0366]\n", - "Epoch 6: 100%|███████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0357]\n", - "Epoch 7: 100%|████████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.035]\n", - "Epoch 8: 100%|███████████| 84/84 [00:38<00:00, 2.15it/s, loss=0.0338]\n", - "Epoch 9: 100%|████████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.034]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 71.24it/s]\n" + "Epoch 5: 100%|███████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0373]\n", + "Epoch 6: 100%|███████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0365]\n", + "Epoch 7: 100%|███████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0361]\n", + "Epoch 8: 100%|███████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0343]\n", + "Epoch 9: 100%|███████████| 84/84 [00:34<00:00, 2.45it/s, loss=0.0343]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 99.59it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -464,17 +485,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 10: 100%|██████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0332]\n", - "Epoch 11: 100%|██████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0325]\n", - "Epoch 12: 100%|███████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.032]\n", - "Epoch 13: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0326]\n", - "Epoch 14: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0315]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:13<00:00, 72.64it/s]\n" + "Epoch 10: 100%|██████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0337]\n", + "Epoch 11: 100%|██████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0327]\n", + "Epoch 12: 100%|██████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0325]\n", + "Epoch 13: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0324]\n", + "Epoch 14: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0323]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 96.78it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -486,17 +507,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 15: 100%|██████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0315]\n", - "Epoch 16: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0313]\n", - "Epoch 17: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0307]\n", - "Epoch 18: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0309]\n", - "Epoch 19: 100%|████████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.03]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:13<00:00, 73.03it/s]\n" + "Epoch 15: 100%|██████████| 84/84 [00:34<00:00, 2.45it/s, loss=0.0314]\n", + "Epoch 16: 100%|███████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.032]\n", + "Epoch 17: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0316]\n", + "Epoch 18: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0312]\n", + "Epoch 19: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0309]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 96.73it/s]\n" ] }, { "data": { - "image/png": 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xzsfjBJXleHoN8UGXmX2rQYxqWce/p2shhU6dALRuuQh8uVyG1y8URREsZl3R1+v1gtuHPknmJzYajaNEiDLvgB2859avbdkpItjjnPrfK89KymcRx7qwSB9WJo5+Dzrl2YfDpx0C5vN5MFa4EHyz2YT1KwRjq9XC5eUlLi4uMB6PcXV1haIocH19jaIocHt7i/V6HbbSKHt9QkykWQmj3955uyakTG8rA19MVPO3PtfuR0iw2fS+3W5X6uS3dDY/4f8iMWGBO0h1Op0Q7mMnclsMfjOjmwYK/YlcWAU8bkZeZwE4UB7nTbkvlctVlZuqPlTV6ewg1MI9Z+3/ik6oRJCQG1LU8tUKvV4PX3/9ddgEnFyRb09qt9vYbrchRezNmzdHO10xZ1ETIlRxt2S5pLeUkueV09r+j3FQPVamP3r/bbn6X+tHzkidO4VOdtH8v0RUoNl53Llqu92GTSY7nU5QzJkIATx2Onc3Xa1WwfnNkKBNmlWwpfSnBxaSNQ7LDJJYOVW6YhUIq85V0ckRE50xukXY/yLp/i0AQlhvtVqFXU8Ze6bYbjQ+vU8OAEajEfb7PUajETabTdAZsyzDfD4PZXtZ3LQ6WQddsM9vT6x7hqInblOc51V+QHvcK0/vYeLx2SMm3gxktnWqrvElE7OrOeh8AQ3dL4yLciEVF1WR4wE44p6bzQZZlmE4HB4tM2UI0boydActJury2pjoZr1SgAVUi+nYtXZcy3RQgtAuICujWnvREHjqE/oSLORzEhfoLJfLYCnv9/uQNKtLTnW9LYCQCNHtdoNzm8AsisJ9oaHmLmpaGgeS2ePUV3UZAl1GZXomcLyFiyfSy7ihktUB7Qt86EFhBtLZDRP6zXQXT9t41R2+ZFB6HFsn1nq9xnw+D5YwAcXO5uo9HiPRtcOy9TVbq9XqSB9UsaxOXRW/BKEaOXQjcZIsFotKH6XduMi2XUmNotg11IkJOqbF0Rh5NhDqeykoBoC4E7suEK0PskocPIUDV818AqjRaARxnGUZgE/9MBwOw2J67ReCki4dAJ+98JBkdUKCT7+Z49jpdLDZbNDv90MqGkOJ/CZg+SFHZf/ZcbLtt+NpPwQYM9W5XELDnd1uF91uF2/evMF33313/nXHum2YVlyBcaphogkEtuFKOki6KY8q8amgrLqOovH+/h7tdhubzQbD4TC8JVM3AKJIJjj1RdV8FrmD3c9H26O+RZUyvEeXqVK80+BZLBbBPcQcSsa2vUlg+58TiABj+JKZ6P1+P7y8m5lGg8EAnU4Hw+EwGHD9fj+8Q+XsLpoYx+O5U4mDqKEyy01ICjxrWaoH/5x6Kv1x5HpFUWC5XAJA4AQ6gOTo+pZ07R+7pZ1OLHJNtsG+4sv64g6Hw5HriNY7d6XodrtHoPbUJtaFdafhxbK4AxqXzvIYOWG73Q5La/nGAL7GInUDrVqc0DqpSTrgdUUwGzAajYJDWMGoZergUAfSBUrKTWhpWmOgqj6qG/KZu90uhPioh43H45BRc3l5eZSxTZcOAarlE7AUrWwfn8027XY7ZFkW1laTm1Iftbol26t67XK5PNr4iWBUo4pcXJkBv6kTE2wcF7UPuFaHRhnH8s2bN+cHYRknPJWo1PJDAA6HwyMdUZVl9afpS6Y5gExIbTabofMJTJblqQ1VESAOsu6Ds1wuAwfg4DYajfBc6z4hd+dAWp1J17sQPLont04kusesisJ+4zYn+s47glS3SVHHunJ1RofYPsvxNROdzINeA77r+Owg1FfaW45orUzvuEcW1ASRzjSSimkVWRZo5IwEjS7ntFzScssyMc5BXK1WoQ5MgCUHUGlBg0LXRXc6ncD5qXORlGtyEhBEyu1s+C9GKXFrBZJuMKWWL0HKNlLcq97oAZrx9RQ62TomeWK4DhDt9dpwa6hwBlrOoGJYB4x+NYKNgKUzmEq9vlKrqr4sk5wGQFjVZ7/Zbxwo4HGnMA4c267ijtdydaDWm8D0HMa6XxDw+FIcTl5eq+4XnmN9yMEsF9d683s4HB61Q5/jvZAnRrV2YOBgAuWhnVSjgMBgyj2t0PV6fcRVLCjZcBVD9vl08CqXpFii/shrmBeo+qNGT1RnIxfTpAbgMfSneiUtW3W3cP8bii8FqDVarDjjBLA7RHAc2H41QNT4IMjZRuWEqt+pM15BpcYLy7P+RxvISKFkEKpzVRtuH0RRlEpcx6E+LnY0xbEqxMAj11BSi5r+Pd2nxkYZlBMyFUufTaDGQKicAXjMUbT9QguVYKQxo2KcFqlyJ09UAo/hRa4C5If187ZrYd+MRqOQ+cM62QlO7kb3iwUZxa6dJMqUOElSqbY49kStJ55TSfU2G/5RjqeA1B0XdCbbWU+jRcHEZ6qrh/l/usESv63/UbkEdSKKMFXcCRztIwU362GjJu12+ygdSo0G1ZN1V1oS204JYkW1fQ8xJ7NKG3VA81nat+o6U+5rRW8dHNSKHR8OhyP082Ha0R5nLKsQOQ/FBAfGWmrtdjskn9KytHqUKscqKrUu/FbQqAimzmXBqByG9dNYqeVkrAsBQeBzIb1uYcwlBUwDI6g1PEbQDwaDYECwz7Vttv+Ax7xEr48YauRkZn/atih5toGlsrCfpdoumiplM0W5VyJw7V4yqnNQNJMDMZbL1XDsJFqkwOOksWVai4+clmKanKjX6wWRrckFSuQQNjKg2djWka3cTUUsdWO2RSeJAl91cuVKamAoCC0XVzHPPvR0OKvj2THzxpCUYpkr1XLReAaAVtiraAog9/t9SI+3naRWMV0D6hoZjUbhm5xHrWrVYTgInO1UwjmAGm/lTLZ6JevmdbR6EFgP9alpX+nAE+R0/2g96RKhL5L9oc5mSgQCXDkbn2l1OwChH5kkAeCoTDWwLEC1PJUSVDvUgq+iWu87rqK6XNCS6mksi5yA/+mv5MA1m83ARRW05KLqxwIet/wgV1GFWzmL9Yuyk3mvOo55HcvW/8oBFQAWxGw3cGyYaN9om73oFcGhHI/l6Xn7m+Vby1ZBqM+IuejUW/EsOqEq6UoxHfApiiobRAuLHZbn+ZHBohkcw+EQrVYrWHfj8ThwS+qPdqsPXqvefnIUOlutvuepJNYPx0HXyIS+VUBJuZzmEaqTWp9N14waGJokS4tdjTXlzryXk5DqhrWuVcck8K3aoSlo1K3rumeAM71M57lJZxmAEM5qNj+Fp6jHsWMZqbDOVVrgvJY6JI0EAo7PVA7mcRB+a8YMB13FGl0eFswAwq6xCkIuvNKwmwKDfUF3GMFj66g6MO8DHjmtik3ea8tTDh0bF+0Ta6imUC1xXMYFnyKG65KKbQ4S/Y1ZlgVOpu82oTVIjthsPqZeqV7FYxcXF0dckh3NMq3uSm6nrizqsfpfAaKWJ+sMfAImLWnNNSR39DirOr412Vb1O6oYZUChimFfrUG1p4wU1FXJIkq1QFg2I/T79yLlkOwgimwVxargqw9suVwe+c4IQi5yUjFPADGrhIOroo6cVPVM1c+Us+pxikiqALTYGbZjv+r7W+gFIJdUXZdiU7my1os+UWts2P7UJAzqoUqW+6mezOMpVNtPWOai0YbYSv6eRHeLBv3VLaLWtupPTF3qdrshW4Yv59F1xhx0cjkOFoAjLqkhSBooypV0sqjznQAjVyEQWAdOJADB+W05q3JeFd3UtTXurm4mTgDrDWGZ3lh63PVZrGNl696DPaoC7XOS59cjkTOQI1pXR7fbRZZl6HQ6yLIM3W435DsS3JrxwjLplrERDhXLBCnBr2n7LIcc0VrkaujQmOEqPrXIrYuGYKJRQy7H8WP79X51VbEe1htgLWi9zjNiY/Skdcf6OwbI35sLphDrpPqWTfykhaqLmjTBwvojyckUhCp2qegr5202myFior4+1SMBHCWNsv7M4LGGQcytAjxm2WjEQy1tZTDkiHpdrC/LuGMK1Qah55v6EoFG8lwqwKPI5m92vIYCrS6pTmqGzdSXR0tdQ2Ne3NUaKZq7pyDk86+uroLLaTweH/lQqZ9qeRqbVi5n/YbqgOc15Hasp3JZm+6m3zZz59lBaE1ypd8LkNYdYd0m1hK199g662BQJGvSJsHF3woedeeo1eyBUOvF/6q2qPuFv9VC1bpbMMQmm+0322csl9dabun1uZbvMaY6algt61idlp575hQAplRW3Q2xweV/m8mi+Xr2XuVibCOVcrpoCEQu9lELmlyJ3EOTKRS0POZNFvabGi8EhTrZ1d0E4LPwojqvFRjeZCV3tGJbM47URaP6JvvR2gRWItKQTaGTtgF5qgi2g1EFRKtfqbizrg/qVbpgSl0nBKjHQdn5jEUriNXxTQuavjqbpeKBUJ/J52nfKgiBx2WwapB4qW1en5LsJIv1P+ug5HFQ+1/DjDbUquHLKkoGIR3CNomhDmnH2hV1LM+Ck5YldSU6Y62VaZMT1FmtIFSxyWfR2UyXTqfTCZuqh44y6VTkUJYT8hqbqs9nqyOZ5SpQ2Q/N5mOChRo61l/HPtRrPC4IPHIya9EqaX1ZlxhAbQBDQ408n0JPiph4FSsjFVWqUNsyLFiUC5HDWe5gF2/bRUgWhHwO8LhJOqMvjJgwSqCzW1ef0VdHEHKy8JUUqvBzgmmyKP9bwPJaRm94TJ3BamTop0yyWO7rAcyWUQZC1Yd5vbqVUvXC2gkMNqCeagURIEy94kJqzS7RilP8tlqtoJ9xUPQNTGqFKofTpASra9lAvOYprlarsGyRIkdntPoAbfsJEE2CpR6tiRGqHyoXszFogtX2ufXX8hpVS2zf8169R4/Zc2Xi2N5vM9V5/OwgVAenUqpIJgjtan7r2FWOxfN8MSINAhWxHChvDS8H1sZ61aoFHjtYfX3MbrZ7BKouynsJRBVD6ihXXZb1t1aygtCKVZYJHBsHGvUgCKy/zzPA9JyOD59jDQ79xBzVmnOp5aVQMgjVgWq5X5Whwo6ws966O3QWW3eIdoiKHnaGKsbsTNtp/PZWDAKPE01nt+1gHqM/UcGnwX9rgfMZulOCtknVD+tLtC4kK/60j7XtFjgkb/xsH/M6j5vZSIy2UaVaKiWDkGsbVDG2+gCPeZVWfZD6nHIEVdD5UTGqjVROZEUhScFs3UnqpLZl8Byzcjhg6/U6pJBRB+VzFNQ2jmvFXZ7noXwCXq9R1cK6XbTPODmV89k+8Xx99r+CVe+x4NO2WUak2TnPygltBT0/YYy0guQSVmSpL5DERvM6volJn2m5mfq0ms1mWFNsgev50lgnRhq0s3WZJHVKK9ZZNssh11XjhtfS06D1UnGsFr+Kbo1sxAwRC87Y+Gg/VHFA+xw+QzmxFypModqvkCjTDWKk3ISbPGpmSrP5KZDPztfXexGEzeanHbHIhei0Zfn6rXXWgbODou4de83Dw8NnOhMNnm63e5Royo9dzMTFS+r8Jbgp3tXKt64kdWfR+UuuYwHMdimXrhoTNZw4QcoMC8vdVDJ6TOHshoktWPWxKlLFnmJOdzjgAHO1m+U89sNF8vYZ3nM9V4GCkLqnKvwKXOVS7HCCR0UYgaWxWt1ihPXlfZ6FauugfaPt13vssarx8CSHlQw8XoebqbSrI4qBmtsFA+Ve+DKuSKVfX2iteqAOhFXQra5o9S2vXmUJuBTv+mx9Pp9JMDYajeBaYsRE3Thst+dcZt30WWpha9t4LfuAftHhcHi0ISXbp64fWw7HxLadz9B6e2Omlq6ntijpxlKa+Z1KJ22IRKozU4BjsczOtiC0YKOYAx69+daKjtXF09WAY6tO26TPtHoZ07rY2VzzbAdIXU4q6sv0Uc84sYacqi/K4W340qMqdSnWX/rbckk9rwC0mTYp9OQdGKos4xjxxYQ6K4HPHdb6HCv+rOWs13miSd039ln2XgKdoULuD53n+dEOpSrO1RJXjm1FKXC8HJTPJui4ClD9otTZdJMAJU4IbZM1FDzA2xi0VRPIEVk2JYkGL4qiCDrybrcLESBKhyqqlVntBc35raI4ZSZY39qpFAOfx7lJnMkx7qEchlyv3W4HA4M6IUGmQCUoSZ4eBxz3pwJXDRGGE9VXqpERr01W1+NzYwykTGTzvxpg6jelvsuNC9T/SXdWCtXagcEuK/SIFf29yBMb7PQqgKs4JfE/rXUCyu6ET06mICRoFERc46xJGK1WK3A5lkkdUzmsAt2Kc4KKbdR2WwCWjRWNLXXOaz+oo17TvLiDmb7iQq9hG1KoFggpCmJ+JHbSS1DMTVNFVhyzDLZR3R2WY2m+nwdCXUJKMDL5gTu2qt7LlX0MDFh3iYIFeNxqhHWKqSFlIOTHRoUsCBlXJwdUTsg3B6gxQ+9HCp28xsSSVVZ/b0oxkmJuHCvG+a1gGQwG4cNFT7SYuXE6vxWMlhPaFDTldDqAnrNZ+5j19iIUqcEDDSDolso8zv+6O62e0xcF6a4RuttrCp0UMSEp+/eO/x4UM2K831ZNsP+Vu5GDXV9fo9vt4urqKgDw4uICg8EgnHv79i16vR5ubm6CK0VXz9HosIaPWsDqNaCoJUBpCKghRhDqbhQaPlOubSUXORZBo1vikaMRVHmehw1EueZZVRRyRJ4viuJowqVQLRDamLFHvzdHtAaRrZ+e97gecOy0trt9XVxchFR+vlCGv5VLcsdVXZZp6wn4vjuPMxGomhZGUJKUe2r7Yv2vzyAAuVciQcjXThB43PGWYFUVhfXS7Zf1BZJnF8c2xSfWwJcgK6L0mP0NPFqmugG4pphRtGoamb5vhSC8vLxEt9vFxcXFZz5PGkaaHgYcv3qMupjWk5wFwJH+x3rb0KAaSZ4PUstleXSn8MVARVFgNpthvV6H7+VyifV6HT7AccaO1pmckJzx6uoqeApSqNYak5fW+1LI4zjWv6g7MNAg4CuxxuMxut1u0O/ICfXVWTHOp+qKijwLCk2OoLjUiaT5gVbqqOGhHgD159nJqE569fPxDQQEI2P7CkJyQO1brcvh8LhJPcu2m7pXUa01JspmY1znSyI1MKwTmNnd3DKX3xcXF+j3+/j6668DCC3QWJ5ajRq14DfFHfC5/mlj0nqfzUXk9dT/rB/Ty7u0koAcUK3Z2WyG+/t7LJdL3N3dhbeb6gbymjSrTmvWbb/fI8uykKIGAG/fvg31SKHaW8N9qYCzpAYAM1RoNHC5wNXVVYgF86NGB6+l+NbB5W8CUaMiHCzd1cGbwDpJ9Fv3rSHo1B8IHGcAAY9GguWqwCOX1DcT5HmOLMswm82wXC4xnU6xXq+RZdlRFr2m7Gsqm7Y/y7Kw02uz2TzaVzKFau/KpSD0LNNzg9RalLEIBz+6co3gY8LB1dVV4ID87vV6rk7I5Qe09OzzdIDUcm00GkGMcRctqgDaBpap28lZkW45kaoVBAR9i2yvdaswUZdg2Ww2uL+/R5ZluL+/x+3tLZbLJSaTCbbbT28L1Xbpnj62j1mvxWKB1WoVpMnhcAi/U+gkEHp6ynNwSG20Nt67hgCwK94Isl6vF/S80Wh0pOeNRqOjYwydqT9P200xuFqtAHyuhy6Xy6BT5Xke/I0sV+PNyhVt6r5yI9UteZ9mNlEkE4B8tlq6s9kMq9UK9/f3mM/nuL+/x8ePH1EUBR4eHrDZbJBl2RH4udRX1Q19gdDhcAgWNCXM4XAIftIUqm2YsLOta+HcRgu5mTdwJOtvazYfE2IJMIKQxka73Q4ilrohRbFardpmilPlSnyhDYAjTtVoNIKiTouRyjvrrgufdHJTjGlM2eqSmuRAFaPb7QZ3ir6CjG8lnc1mgQOuViu8f/8e9/f3eHh4wN3dHYqiwHQ6DffZ6IkmvqoKoH3BbPR2u31kWacAsbaLxjpMLQDPAUQ2RmOpXp6exm8JPt6jnFB1QRW1dNGwXBU9qk8BCP40iljqTwSoRwQROaem5quTmm4c3aMQ+Dw5Q32ZytWZn0lfH90vNBg+fPiAoihwd3eHPM/x448/4sOHD1gsFri/vw+cUCeZHUdOGnU32QlCzj2fz8P+juPxuHK8n6QTamefkzgQNjZrk1tt/JY6Ev+TE+peLppOz7pTqVd3hOWGTFfiu/Do4GXfaD9oyE0NGjXu1HdnVQp9PgedANTdJwhcOorJfff7PfI8x2w2Q57neHh4QJ7nuLu7Q5ZluLu7C1YxuRZdLJ4bRr/Z3pjRoWle7MsqSgahVlQrdU4OyHIJLopLOoUJPrsuQxMDKFaVy2nmiup7qsRTgdfXu+52u2AtcpD5YZuVcxFgmkXDTGi1nPksimNuKWffFEBdmNEZ1f14jtyYxgFVgMVigQ8fPiDPc7x//x7L5RI//vgjZrMZ3r9/j+l0+pkP0BpOnq9V62f1c42akBOmUK2wnfU9cQY/Fyfk7LfLAMgR+K36ok0OUB+cRiRardYRB7QxVIaiaFEqp7EvD7TpXgShvq+O/aRrTCiqlVOybz1ur64i3e2Bk0nfWLpYLLBYLLBcLoMbZjabBVHJ9nnLTj3w2d8kDR2S1FGfQrV35fKsY1bwHGBUTkj3ik3u1A2HdA8a1VXVv0WdzPq+8jw/eh+yGhMM5PN1DhqaosvDhrHIPQmYwWCA8XiMwWCAd+/eBQNC60rDAnhcxsn7dWNMdXfQ2NAISZZlwcCYTqd4eHjAb7/9hizL8NNPPyHLMnz8+BF5nh+931ktck8f9chyQOWa7CMN91XRSQkMMTpXBEUVcZsnp9EEPa6kfjvN8FVRSFFLcKkeYwP4elxjpJroqroluZgG/kejEXa7XVgcpQ5hulw4OVQnVtVDdUpdckrrm24ZcsH5fI75fB50QgKVY6QAVGClOJm9a1gfjbCk0FnyCVU08/9TSLM8Go3Gkd7CgeMzuVbF+thI6o9jx1DkEmD60ZR1xkF1PYUNY5W1tdVq4f7+PqxNGQ6H2O12uLy8DKFC+hrVsqc1z2uo9xJkdPswBLfZbDCZTLBcLvHx40d8/PgR0+kUP//8M/I8x8ePHwNnUlEfm8RW79ffeswaR+o+o46bQk8GoRdBeWp5qlN4ywqsDgPEt9JVvVWXnRKMygUphvUaXcCjddNnWKvYRji4aL8oihCTZrnKTVUH1A8nmIbeWBfm+81ms+BymUwmmE6nmEwmKIoC8/n8s7CfB74YeQBUUt2SbbdpZ2X05F25SDFntQeMKiIoCASrnHORvPcMLUPBomKY4FMHtIpqjfXqgNsJZxNWVX0AHv2d3W4X19fXITNbt/ylFc0MHo1tU50gUOmA1myXyWSCPM/x66+/Yjqd4vb2Fre3t1gsFsFJrZLK437W4GT7dJLFxC+v5fUabTm7ONYBi1HMxxT7793DAaelSp2Og6kZyrE6WN+filrldlasWj2mbGLRUmXqvkZ4NBGBRhSNDDVENptNCCkya5vX0nmuoTO6PoqiwP39/REIf/vtN9zd3WEymYQoyGw2C1yJ9bb6nx0HC7xY+63IVr372axjz6F6Sqgu5Xo2gIo9B10jJp5rQOtkOZyKVn5bi9/jCNp+AEdikn5HXUmn1i0nDiMb/X4f19fXGA6HR2lk9A8yZKZpWqpG0OhgwgGjIdQF7+/vg7/QOoqtzu7p7wSXBZ8Fne0nz2NSBxu1VtupUuux23Pph7T+qGcsl8sAPBvftXWgGCV5xoMVI7bu2oEqWhuNT1v4jkajkO6lhoRuI6wiWjN23r17FwwO5ZSHw6dwF61qzVJhfefzOabTKebzOX7++Wcsl0v88MMPyLIshOE0Xm2d6V77ysZKgeVxRSs1dHyezU9olf0y3+A5nNkaHmLDFIQe97Kgi/k1LcXOs2MJLC50Yu6hxqD5rbuNkTNyzYruqAA87olI7q3ciEROvlgs8PDwcJSMOpvNkGVZ4H7a/lTjQ7nfKczDSpQqr4GlWjqhikZSGTdMOV5FHBxSqqHjuRfsf2tR8xw7stF43Abk5uYGo9EI19fXuLm5OUp4tW8M4LdGPcgl+VYAun8YuWB7tL0sg071u7s7/PLLL5hOp/j3v/99pBtSMnnx56r+sgC0XDIGTGUEvEcXxccSOyydvHt/jE0/hfOlUGrZqbPau0YtX3Iuxm/5UfHLtwMweYJRHpIaLQSl9TuyztRZARypI8x0mU6nwR3DOC1XwdVpt9dXKVIjphvynKdfV9FZM6utiPRmUQykMR0ltTF2Flf5tspmOIHV6XTw9u1bDAYDvHnzBhcXF0EnpD7H/ESNYzM/kc5lqysdDo8RDhI5oxpONFRodNze3uKXX34JLhrVu2JWr9eHHock5/fOxcaLxzXvkJOKLrUUOkkntBQDY4y881Us/xQ6Rb/hYGpC7Hg8xsXFRdAF9Q3yGumg+FROaFPDONj0hfIcXUhMsNjtdiEpdTKZ4OPHj8H9ovda90sKxdwvVQaLneB6jL9/F2c1K+FZlmWVTPEZPoWqyq/yUTYajaMs6zdv3qDf7+Pt27fBCNEFUeontM/hYGjGje0vilOmyNF/udlsMJ/Pw3dRFLi9vQ3rgQniGJepYhT2Ok//07ra37Ys5e4s79kSGLwFN7aBZbMopnOcC4wpoIvNfh5n5stwOMRXX30V/Hp0o+jbP1meNQSU42nmDon/qefleR44XlEUwQHNjJj1eo3pdIrFYhH8k2ocVuloeq5MzbEAjI2RnUwxbvoshgmQlkUTU2yfwglPvdYOjMclCCSK4H6/HxbC6/vx1NrlPZYTci+ZPM9DbJq6m+56sN1u8fDwEFLyNYeRCanb7TaAc7/fH+VTlrVF25riIYj99/rNk3yx/q0zZmexjrUSnpVWVqFYp1aJ9Do6pXe9EqMbjN+ORqOwkIhZLLokU/VGcgbOfuYgMrxGEcsM7YeHB6zX67Ds0luaaePWtK7rtF8nYF2mYMdRjRZ9rk3k0N9lhpKlJ2XRlBkNZbPSnrdlxQBVNfvr1lVFj/r0bN3Vf0e9rNlsfmZ0EEwE4Wq1CkDjN/U97oKgFqWmqdlJ7InJU0jVj5Syyq7zDFH+fhadUIP8MSoDZRXAvHNVVnFKJ1Y9T0WrJ2I1u4Z1sh9yMOb6cftcLjaya4BVv7Z1sS6umPO5qu1lbrC6YlMjV1pX1Uu1L5kwnDphzrZJ5qlUpeOkWntPJbVodQGQrsMlN6S4pO+U3JH6HkUwl11q5o4XWkuhuu0t67tTyrNU5T98llQujzxF1TvvnasqyysjlaxOU3Yd8LiPC2dyr9cLuX7T6TTkMNKBTLCpc1nzFa2IjbUrpjPXkRbeectR7b1eGbEx1OvVOW3/W/BVpf0pnYUTVs261Gt4HVA/1uxZwqk6DztL/XqM1zKfUbfGVY6nli/BbLOvre5p9bu6fXMOSn1mGcXcM2WA9ugkEKYYCE8xImLXpgLd051i4qPROH6xNjO5GW7j/crZVBx7ZdPI8dpjjY2q9iqIq9rOe5QDpfajVz97vz3mgVAXbz3LuuOYm+apM+o5KBXwWncVyzFDhID1LFYPYLaP6gDK1i+1PWUTMYVSJoxa2XpMAxpnN0xSrOIUN4t3b8ozUsv0Oit2bxmX8Lgc2xiLktThIil6X8q1ZW06FXxVqoxlROpL1DzJZ0tqfYqbxl6n317DY66EOsaLB8SqQbWi1pani+xjMz72jDogTK1r6rNTnmfLLgOyp+pwgqpbq4pqrzHRTvdmhFdJrxGWc+o1dfTButzRO3eqOhEDSxUoU0FWpYNV3V9Wrm13GdeOkefP5D11sqtrvdsOeFS6Pd3DVo4VjM2oWCWrKl+mX6X4xsjl7JYXVXWzz0oRv/q/rj7olZti9cfAZeviXevpf1XP4T2eIZdCta3jU2ZeSjmporeqDjGukdKxsYGx19iyYuCsW+fnoCo15ynler+rkiw8OslF44mwmG8oVX+re1zLrzvoZc+p6sAyQ6Tu81PrWDYpUgxBry3euKiKFJt8nk2gEaXYvuJl9CxhO2sMfGkunDLAV4m+5wBgCgdOKaesvFPLSrnHTkzdhi+FaiUwlJncZTPpVCDGDJ8q3escFNPfPE7oXVNVXory/9S2pJbtAamsjwku3VCTwCOtVqvnWWOi3yn01E58is/RHq/SLeuqDVU6pnKjp4hwW3fvv1f/WLmeWyk2sWJ9EDMMtW66DqaKaqX3x/yEVYZEagfZe8oGLuW3HqsCaNXst8dTOJlXt1Pvi53TNqSQp3JomfacNwHUHcNNm9RZzbS2VKq9XXCVw1pnZoo7gfecYs3quZSB0nbwWJnYfeoz+axUTmjLSdERY+CJlePdXzZJvXGka0u3OmbMnf+51CGFahsm3NYixQ9U1kFKqbM4Nmvr6ohlYImd13tPEalPUU3K2pTKwZViqpX3HHtMDQ7uyGHzI7n2+uxvdCIx81i5oYo06zPSyuu1eryKY8aiLSzP+06llIHw/qeoAFU+upQJ45Wr3zZSwd9lz7FLNGPl62+Ok26CEAttctXi2UHY7Xax3+/DDlLkhh6g+Ik5Lr0OiCm4lmLizSsnRmWcuQ4nLQNkFbeqqwLEyvb28Lb7T1vA2vK8JQSxZQV21wcSx6XZbOKbb77B1dXV+UF4eXkZZoEu0GEF9KMmvDXd+dtuz2Y7L0baWLs1WSoxBJlyf5V+VjaAep26K8o2LUohzVfU3WLtYi0+hyv1+F9fa6ZvQdAxsDs76DnWmeOrmdTb7RZ/+MMf8P333x/tx1NGtRIY2Eg+mGKZlpHGZJVj6UCSy9kGacfa3aV4L8/pJun6/FSyYE+5356Pbb+h7dBrvOtTFXd9vgW69dVZEOoiLgWhviGKv739H+2ztA7cg5HMiVnoumNtUtsOX1o445X+v6P0qfhKr/RM9ArCV3pxegXhK704vYLwlV6cXkH4Si9OryB8pRenVxC+0ovTKwhf6cXpFYSv9OL0fzMV1wbX2kMVAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -508,17 +529,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 20: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0305]\n", - "Epoch 21: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0301]\n", - "Epoch 22: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0307]\n", - "Epoch 23: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0303]\n", - "Epoch 24: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0299]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.70it/s]\n" + "Epoch 20: 100%|███████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.031]\n", + "Epoch 21: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0306]\n", + "Epoch 22: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0304]\n", + "Epoch 23: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0303]\n", + "Epoch 24: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0304]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 99.18it/s]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -530,17 +551,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 25: 100%|████████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.03]\n", - "Epoch 26: 100%|████████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.03]\n", - "Epoch 27: 100%|████████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.03]\n", - "Epoch 28: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0294]\n", - "Epoch 29: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0294]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.75it/s]\n" + "Epoch 25: 100%|██████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0301]\n", + "Epoch 26: 100%|████████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.03]\n", + "Epoch 27: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0302]\n", + "Epoch 28: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0297]\n", + "Epoch 29: 100%|██████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0301]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 95.19it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -552,17 +573,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 30: 100%|██████████| 84/84 [00:38<00:00, 2.15it/s, loss=0.0291]\n", - "Epoch 31: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0297]\n", - "Epoch 32: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0294]\n", - "Epoch 33: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0294]\n", - "Epoch 34: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0287]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.80it/s]\n" + "Epoch 30: 100%|██████████| 84/84 [00:34<00:00, 2.45it/s, loss=0.0293]\n", + "Epoch 31: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0298]\n", + "Epoch 32: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0295]\n", + "Epoch 33: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0293]\n", + "Epoch 34: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0293]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 96.87it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -574,17 +595,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 35: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0292]\n", - "Epoch 36: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0289]\n", - "Epoch 37: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0284]\n", - "Epoch 38: 100%|███████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.029]\n", - "Epoch 39: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0295]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.70it/s]\n" + "Epoch 35: 100%|██████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0294]\n", + "Epoch 36: 100%|██████████| 84/84 [00:34<00:00, 2.40it/s, loss=0.0292]\n", + "Epoch 37: 100%|██████████| 84/84 [00:35<00:00, 2.36it/s, loss=0.0292]\n", + "Epoch 38: 100%|██████████| 84/84 [00:35<00:00, 2.38it/s, loss=0.0287]\n", + "Epoch 39: 100%|██████████| 84/84 [00:35<00:00, 2.36it/s, loss=0.0295]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 94.99it/s]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -596,17 +617,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 40: 100%|██████████| 84/84 [00:38<00:00, 2.16it/s, loss=0.0289]\n", - "Epoch 41: 100%|███████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.029]\n", - "Epoch 42: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0288]\n", - "Epoch 43: 100%|███████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.029]\n", - "Epoch 44: 100%|███████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.029]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 69.12it/s]\n" + "Epoch 40: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0291]\n", + "Epoch 41: 100%|██████████| 84/84 [00:35<00:00, 2.38it/s, loss=0.0291]\n", + "Epoch 42: 100%|███████████| 84/84 [00:36<00:00, 2.30it/s, loss=0.029]\n", + "Epoch 43: 100%|██████████| 84/84 [00:38<00:00, 2.19it/s, loss=0.0286]\n", + "Epoch 44: 100%|██████████| 84/84 [00:38<00:00, 2.20it/s, loss=0.0289]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:11<00:00, 86.19it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -618,17 +639,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 45: 100%|██████████| 84/84 [00:39<00:00, 2.13it/s, loss=0.0282]\n", - "Epoch 46: 100%|██████████| 84/84 [00:40<00:00, 2.06it/s, loss=0.0286]\n", - "Epoch 47: 100%|██████████| 84/84 [00:40<00:00, 2.09it/s, loss=0.0282]\n", - "Epoch 48: 100%|███████████| 84/84 [00:40<00:00, 2.09it/s, loss=0.028]\n", - "Epoch 49: 100%|██████████| 84/84 [00:40<00:00, 2.07it/s, loss=0.0289]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.00it/s]\n" + "Epoch 45: 100%|██████████| 84/84 [00:37<00:00, 2.22it/s, loss=0.0284]\n", + "Epoch 46: 100%|██████████| 84/84 [00:37<00:00, 2.24it/s, loss=0.0291]\n", + "Epoch 47: 100%|██████████| 84/84 [00:35<00:00, 2.34it/s, loss=0.0286]\n", + "Epoch 48: 100%|██████████| 84/84 [00:35<00:00, 2.34it/s, loss=0.0282]\n", + "Epoch 49: 100%|██████████| 84/84 [00:36<00:00, 2.32it/s, loss=0.0289]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:11<00:00, 87.15it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -640,17 +661,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 50: 100%|██████████| 84/84 [00:41<00:00, 2.02it/s, loss=0.0282]\n", - "Epoch 51: 100%|██████████| 84/84 [00:39<00:00, 2.10it/s, loss=0.0285]\n", - "Epoch 52: 100%|██████████| 84/84 [00:40<00:00, 2.08it/s, loss=0.0283]\n", - "Epoch 53: 100%|██████████| 84/84 [00:42<00:00, 2.00it/s, loss=0.0281]\n", - "Epoch 54: 100%|██████████| 84/84 [00:42<00:00, 1.99it/s, loss=0.0285]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 67.98it/s]\n" + "Epoch 50: 100%|██████████| 84/84 [00:36<00:00, 2.32it/s, loss=0.0284]\n", + "Epoch 51: 100%|██████████| 84/84 [00:35<00:00, 2.34it/s, loss=0.0288]\n", + "Epoch 52: 100%|██████████| 84/84 [00:35<00:00, 2.37it/s, loss=0.0288]\n", + "Epoch 53: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0282]\n", + "Epoch 54: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0284]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 98.34it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -662,17 +683,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 55: 100%|██████████| 84/84 [00:39<00:00, 2.13it/s, loss=0.0287]\n", - "Epoch 56: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0281]\n", - "Epoch 57: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0288]\n", - "Epoch 58: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0285]\n", - "Epoch 59: 100%|███████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.028]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.07it/s]\n" + "Epoch 55: 100%|██████████| 84/84 [00:34<00:00, 2.45it/s, loss=0.0285]\n", + "Epoch 56: 100%|███████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.028]\n", + "Epoch 57: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0285]\n", + "Epoch 58: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0286]\n", + "Epoch 59: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0283]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 98.34it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -684,17 +705,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 60: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0281]\n", - "Epoch 61: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0282]\n", - "Epoch 62: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0279]\n", - "Epoch 63: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0283]\n", - "Epoch 64: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0278]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:13<00:00, 73.18it/s]\n" + "Epoch 60: 100%|██████████| 84/84 [00:34<00:00, 2.45it/s, loss=0.0282]\n", + "Epoch 61: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0282]\n", + "Epoch 62: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0279]\n", + "Epoch 63: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0284]\n", + "Epoch 64: 100%|██████████| 84/84 [00:34<00:00, 2.44it/s, loss=0.0282]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 99.70it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -706,17 +727,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 65: 100%|██████████| 84/84 [00:39<00:00, 2.15it/s, loss=0.0282]\n", - "Epoch 66: 100%|██████████| 84/84 [00:39<00:00, 2.14it/s, loss=0.0278]\n", - "Epoch 67: 100%|██████████| 84/84 [00:39<00:00, 2.11it/s, loss=0.0277]\n", - "Epoch 68: 100%|██████████| 84/84 [00:41<00:00, 2.04it/s, loss=0.0281]\n", - "Epoch 69: 100%|██████████| 84/84 [00:39<00:00, 2.11it/s, loss=0.0277]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 69.83it/s]\n" + "Epoch 65: 100%|██████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0282]\n", + "Epoch 66: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0278]\n", + "Epoch 67: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0279]\n", + "Epoch 68: 100%|███████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.028]\n", + "Epoch 69: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0281]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 96.78it/s]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -728,17 +749,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 70: 100%|███████████| 84/84 [00:40<00:00, 2.07it/s, loss=0.028]\n", - "Epoch 71: 100%|██████████| 84/84 [00:40<00:00, 2.09it/s, loss=0.0275]\n", - "Epoch 72: 100%|██████████| 84/84 [00:41<00:00, 2.02it/s, loss=0.0274]\n", - "Epoch 73: 100%|██████████| 84/84 [00:40<00:00, 2.07it/s, loss=0.0276]\n", - "Epoch 74: 100%|██████████| 84/84 [00:39<00:00, 2.10it/s, loss=0.0278]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 70.73it/s]\n" + "Epoch 70: 100%|██████████| 84/84 [00:34<00:00, 2.46it/s, loss=0.0279]\n", + "Epoch 71: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0274]\n", + "Epoch 72: 100%|██████████| 84/84 [00:34<00:00, 2.42it/s, loss=0.0278]\n", + "Epoch 73: 100%|███████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.028]\n", + "Epoch 74: 100%|██████████| 84/84 [00:34<00:00, 2.43it/s, loss=0.0278]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 99.31it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -750,7 +771,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 3196.6940882205963.\n" + "train completed, total time: 2794.554279088974.\n" ] } ], @@ -833,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "a70fd533", + "id": "693e48c5", "metadata": {}, "source": [ "### Learning curves" @@ -841,8 +862,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "3db336e6", + "execution_count": 12, + "id": "b767dbf8", "metadata": { "jupyter": { "outputs_hidden": false @@ -851,7 +872,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -881,7 +902,7 @@ }, { "cell_type": "markdown", - "id": "a4ce9887", + "id": "3768aac2", "metadata": {}, "source": [ "### Plotting sampling process along DDPM's Markov chain" @@ -889,8 +910,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "7d3fee5b", + "execution_count": 13, + "id": "0a414e21", "metadata": { "jupyter": { "outputs_hidden": false @@ -901,12 +922,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:14<00:00, 69.29it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 98.97it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -936,7 +957,7 @@ }, { "cell_type": "markdown", - "id": "c07a83c4", + "id": "dfb247a0", "metadata": {}, "source": [ "### Cleanup data directory\n", @@ -946,8 +967,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "f685296b", + "execution_count": 14, + "id": "cc0ab338", "metadata": {}, "outputs": [], "source": [ @@ -958,7 +979,7 @@ ], "metadata": { "jupytext": { - "formats": "py:percent,ipynb" + "formats": "ipynb,py:light" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", @@ -980,4 +1001,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.py b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.py index 815ef0d9..7993caf3 100644 --- a/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.py +++ b/tutorials/generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.py @@ -1,11 +1,11 @@ # --- # jupyter: # jupytext: -# formats: py:percent,ipynb +# formats: ipynb,py:light # text_representation: # extension: .py -# format_name: percent -# format_version: '1.3' +# format_name: light +# format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 (ipykernel) @@ -13,36 +13,37 @@ # name: python3 # --- -# %% [markdown] +# + +# Copyright (c) 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. +# - + # # Denoising Diffusion Probabilistic Models using v-prediction parameterization # # This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 2D images using v-prediction parameterization (Section 2.4 from [2]). # # [1] - Ho et al. "Denoising Diffusion Probabilistic Models" https://arxiv.org/abs/2006.11239 +# # [2] - Ho et al. "Imagen Video: High Definition Video Generation with Diffusion Models" https://arxiv.org/abs/2210.02303 # # # ## Setup environment -# %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline -# %% [markdown] # ## Setup imports -# %% jupyter={"outputs_hidden": false} -# 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. +# + jupyter={"outputs_hidden": false} import os import shutil import tempfile @@ -65,8 +66,8 @@ from generative.networks.schedulers import DDPMScheduler print_config() +# - -# %% [markdown] # ## Setup data directory # # You can specify a directory with the MONAI_DATA_DIRECTORY environment variable. @@ -75,28 +76,28 @@ # # If not specified a temporary directory will be used. -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} directory = os.environ.get("MONAI_DATA_DIRECTORY") root_dir = tempfile.mkdtemp() if directory is None else directory print(root_dir) +# - -# %% [markdown] # ## Set deterministic training for reproducibility -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} set_determinism(42) +# - -# %% [markdown] # ## Setup MedNIST Dataset and training and validation dataloaders # In this tutorial, we will train our models on the MedNIST dataset available on MONAI # (https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just # one of the available classes ("Hand"), resulting in a training set with 7999 2D images. -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} train_data = MedNISTDataset(root_dir=root_dir, section="training", download=True, progress=False, seed=0) train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "Hand"] +# - -# %% [markdown] # Here we use transforms to augment the training dataset: # # 1. `LoadImaged` loads the hands images from files. @@ -104,7 +105,7 @@ # 1. `ScaleIntensityRanged` extracts intensity range [0, 255] and scales to [0, 1]. # 1. `RandAffined` efficiently performs rotate, scale, shear, translate, etc. together based on PyTorch affine transform. -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} train_transforms = transforms.Compose( [ transforms.LoadImaged(keys=["image"]), @@ -124,7 +125,7 @@ train_ds = CacheDataset(data=train_datalist, transform=train_transforms) train_loader = DataLoader(train_ds, batch_size=96, shuffle=True, num_workers=4, persistent_workers=True) -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, progress=False, seed=0) val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "Hand"] val_transforms = transforms.Compose( @@ -136,11 +137,11 @@ ) val_ds = CacheDataset(data=val_datalist, transform=val_transforms) val_loader = DataLoader(val_ds, batch_size=96, shuffle=False, num_workers=4, persistent_workers=True) +# - -# %% [markdown] # ### Visualisation of the training images -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} check_data = first(train_loader) print(f"batch shape: {check_data['image'].shape}") image_visualisation = torch.cat( @@ -151,14 +152,14 @@ plt.axis("off") plt.tight_layout() plt.show() +# - -# %% [markdown] # ### Define network, scheduler, optimizer, and inferer # At this step, we instantiate the MONAI components to create a DDPM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using # the original DDPM scheduler containing 1000 timesteps in its Markov chain, and a 2D UNET with attention mechanisms # in the 2nd and 3rd levels, each with 1 attention head. -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} device = torch.device("cuda") model = DiffusionModelUNet( @@ -177,11 +178,11 @@ optimizer = torch.optim.Adam(params=model.parameters(), lr=1.0e-4) inferer = DiffusionInferer(scheduler) -# %% [markdown] +# - # ### Model training # Here, we are training our model for 75 epochs (training time: ~50 minutes). -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} n_epochs = 75 val_interval = 5 epoch_loss_list = [] @@ -256,10 +257,10 @@ total_time = time.time() - total_start print(f"train completed, total time: {total_time}.") -# %% [markdown] +# - # ### Learning curves -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} plt.style.use("seaborn-v0_8") plt.title("Learning Curves", fontsize=20) plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color="C0", linewidth=2.0, label="Train") @@ -276,11 +277,11 @@ plt.ylabel("Loss", fontsize=16) plt.legend(prop={"size": 14}) plt.show() +# - -# %% [markdown] # ### Plotting sampling process along DDPM's Markov chain -# %% jupyter={"outputs_hidden": false} +# + jupyter={"outputs_hidden": false} model.eval() noise = torch.randn((1, 1, 64, 64)) noise = noise.to(device) @@ -297,12 +298,11 @@ plt.tight_layout() plt.axis("off") plt.show() +# - -# %% [markdown] # ### Cleanup data directory # # Remove directory if a temporary was used. -# %% if directory is None: shutil.rmtree(root_dir) diff --git a/tutorials/generative/2d_ldm/2d_ldm_tutorial.ipynb b/tutorials/generative/2d_ldm/2d_ldm_tutorial.ipynb index 42052339..cb4bd4b4 100644 --- a/tutorials/generative/2d_ldm/2d_ldm_tutorial.ipynb +++ b/tutorials/generative/2d_ldm/2d_ldm_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8f4a5032", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "c862ce1e", @@ -94,16 +113,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -116,7 +125,6 @@ "from monai.apps import MedNISTDataset\n", "from monai.config import print_config\n", "from monai.data import DataLoader, Dataset\n", - "from monai.networks.layers import Act\n", "from monai.utils import first, set_determinism\n", "from torch.cuda.amp import GradScaler, autocast\n", "from tqdm import tqdm\n", @@ -362,7 +370,7 @@ " spatial_dims=2,\n", " in_channels=1,\n", " out_channels=1,\n", - " num_channels=[128, 128, 256],\n", + " num_channels=(128, 128, 256),\n", " latent_channels=3,\n", " num_res_blocks=2,\n", " attention_levels=(False, False, False),\n", diff --git a/tutorials/generative/2d_ldm/2d_ldm_tutorial.py b/tutorials/generative/2d_ldm/2d_ldm_tutorial.py index 7c193141..9face129 100644 --- a/tutorials/generative/2d_ldm/2d_ldm_tutorial.py +++ b/tutorials/generative/2d_ldm/2d_ldm_tutorial.py @@ -14,6 +14,19 @@ # name: python3 # --- +# + +# Copyright (c) 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. +# - + # # 2D Latent Diffusion Model # # In this tutorial, we will walk through the process of using the MONAI Generative Models package to generate synthetic data using Latent Diffusion Models (LDM) [1, 2]. Specifically, we will focus on training an LDM to create synthetic X-ray images of hands from the MEDNIST dataset. @@ -32,16 +45,6 @@ # ### Setup imports # + -# 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 @@ -141,7 +144,7 @@ spatial_dims=2, in_channels=1, out_channels=1, - num_channels=[128, 128, 256], + num_channels=(128, 128, 256), latent_channels=3, num_res_blocks=2, attention_levels=(False, False, False), diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb index 52ef0894..f5705d3a 100644 --- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb +++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "bc11fdc9", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "95c08725", @@ -412,27 +431,15 @@ " spatial_dims=2,\n", " in_channels=1,\n", " out_channels=1,\n", - " num_channels=256,\n", + " num_channels=(256, 512, 512),\n", " latent_channels=3,\n", - " ch_mult=(1, 2, 2),\n", " num_res_blocks=2,\n", " norm_num_groups=32,\n", " attention_levels=(False, False, True),\n", ")\n", "autoencoderkl = autoencoderkl.to(device)\n", "\n", - "discriminator = PatchDiscriminator(\n", - " spatial_dims=2,\n", - " num_layers_d=3,\n", - " num_channels=64,\n", - " in_channels=1,\n", - " out_channels=1,\n", - " kernel_size=4,\n", - " activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n", - " norm=\"BATCH\",\n", - " bias=False,\n", - " padding=1,\n", - ")\n", + "discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, num_channels=64)\n", "discriminator = discriminator.to(device)" ] }, @@ -871,7 +878,7 @@ " num_res_blocks=2,\n", " num_channels=(256, 256, 512, 1024),\n", " attention_levels=(False, False, True, True),\n", - " num_head_channels=64,\n", + " num_head_channels=(0, 0, 64, 64),\n", ")\n", "unet = unet.to(device)\n", "\n", diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.py index fa0c9dc0..1234935d 100644 --- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.py +++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.py @@ -14,6 +14,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Super-resolution using Stable Diffusion v2 Upscalers # @@ -53,7 +65,6 @@ from monai.apps import MedNISTDataset from monai.config import print_config from monai.data import CacheDataset, DataLoader -from monai.networks.layers import Act from monai.utils import first, set_determinism from torch import nn from torch.cuda.amp import GradScaler, autocast @@ -161,27 +172,15 @@ spatial_dims=2, in_channels=1, out_channels=1, - num_channels=256, + num_channels=(256, 512, 512), latent_channels=3, - ch_mult=(1, 2, 2), num_res_blocks=2, norm_num_groups=32, attention_levels=(False, False, True), ) autoencoderkl = autoencoderkl.to(device) -discriminator = PatchDiscriminator( - spatial_dims=2, - num_layers_d=3, - num_channels=64, - in_channels=1, - out_channels=1, - kernel_size=4, - activation=(Act.LEAKYRELU, {"negative_slope": 0.2}), - norm="BATCH", - bias=False, - padding=1, -) +discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, num_channels=64) discriminator = discriminator.to(device) @@ -320,7 +319,7 @@ num_res_blocks=2, num_channels=(256, 256, 512, 1024), attention_levels=(False, False, True, True), - num_head_channels=64, + num_head_channels=(0, 0, 64, 64), ) unet = unet.to(device) diff --git a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb index 17420da7..9d195f74 100644 --- a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb +++ b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a6bd7bd8", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "1fa35264", @@ -20,7 +39,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -74,16 +93,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -356,18 +365,7 @@ ")\n", "model.to(device)\n", "\n", - "discriminator = PatchDiscriminator(\n", - " spatial_dims=2,\n", - " num_layers_d=3,\n", - " num_channels=64,\n", - " in_channels=1,\n", - " out_channels=1,\n", - " kernel_size=4,\n", - " activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n", - " norm=\"BATCH\",\n", - " bias=False,\n", - " padding=1,\n", - ")\n", + "discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, num_channels=64)\n", "discriminator.to(device)\n", "\n", "perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n", diff --git a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.py b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.py index 7c9d4104..fc7cf76e 100644 --- a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.py +++ b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Vector Quantized Generative Adversarial Networks with MedNIST Dataset # @@ -22,7 +34,7 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -30,16 +42,6 @@ # ## Setup imports # %% -# 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 @@ -52,7 +54,6 @@ from monai.apps import MedNISTDataset from monai.config import print_config from monai.data import CacheDataset, DataLoader -from monai.networks.layers import Act from monai.utils import first, set_determinism from torch.nn import L1Loss from tqdm import tqdm @@ -172,18 +173,7 @@ ) model.to(device) -discriminator = PatchDiscriminator( - spatial_dims=2, - num_layers_d=3, - num_channels=64, - in_channels=1, - out_channels=1, - kernel_size=4, - activation=(Act.LEAKYRELU, {"negative_slope": 0.2}), - norm="BATCH", - bias=False, - padding=1, -) +discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, num_channels=64) discriminator.to(device) perceptual_loss = PerceptualLoss(spatial_dims=2, network_type="alex") diff --git a/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.ipynb b/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.ipynb index 9f5db16d..3b76e744 100644 --- a/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.ipynb +++ b/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "6f9b7ad6", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "aa69c25f", @@ -14,7 +33,7 @@ "\n", "The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial.\n", "\n", - "[1] - [Oord et al. \"Neural Discrete Representation Learning\"](https://arxiv.org/abs/1711.00937)\n", + "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n", "\n", "\n", "### Setup environment" @@ -29,6 +48,7 @@ }, "outputs": [], "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -93,16 +113,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -244,7 +254,7 @@ " ]\n", ")\n", "train_ds = Dataset(data=train_datalist, transform=train_transforms)\n", - "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4)" + "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)" ] }, { @@ -323,7 +333,7 @@ " ]\n", ")\n", "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n", - "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4)" + "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True)" ] }, { diff --git a/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.py b/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.py index 42ba7b14..70af2635 100644 --- a/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.py +++ b/tutorials/generative/2d_vqvae/2d_vqvae_tutorial.py @@ -1,3 +1,16 @@ +# + +# Copyright (c) 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. +# - + # # Vector Quantized Variational Autoencoders with MedNIST Dataset # # This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] on 2D images. @@ -7,11 +20,12 @@ # # The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial. # -# [1] - [Oord et al. "Neural Discrete Representation Learning"](https://arxiv.org/abs/1711.00937) +# [1] - Oord et al. "Neural Discrete Representation Learning" https://arxiv.org/abs/1711.00937 # # # ### Setup environment +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -19,16 +33,6 @@ # ### Setup imports # + -# 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 @@ -84,7 +88,7 @@ ] ) train_ds = Dataset(data=train_datalist, transform=train_transforms) -train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4) +train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True) # ### Visualise examples from the training set @@ -107,7 +111,7 @@ ] ) val_ds = Dataset(data=val_datalist, transform=val_transforms) -val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4) +val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4, persistent_workers=True) # ### Define network, optimizer and losses diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb index cda42589..9a8d609e 100644 --- a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb +++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "98f3e028", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "7f44f602", @@ -15,11 +34,31 @@ "\n", "We will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI. In order to train faster, we will select just one of the available classes (\"HeadCT\"), resulting in a training set with 7999 2D images.\n", "\n", - "[1] - [Oord et al. \"Neural Discrete Representation Learning\"](https://arxiv.org/abs/1711.00937)\n", + "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n", "\n", - "[2] - [Tudosiu et al. \"Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain\"](https://arxiv.org/abs/2209.03177)\n", + "[2] - Tudosiu et al. \"Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain\" https://arxiv.org/abs/2209.03177\n", "\n", "\n", + "### Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e01f159", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "e3440cd3", + "metadata": {}, + "source": [ "### Setup imports" ] }, @@ -30,16 +69,6 @@ "metadata": {}, "outputs": [], "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", "import os\n", "import tempfile\n", "import shutil\n", @@ -557,11 +586,7 @@ "cell_type": "code", "execution_count": null, "id": "b4e89ee2", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [], "source": [ "@torch.no_grad()\n", @@ -599,11 +624,7 @@ { "cell_type": "markdown", "id": "32db0efc", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, + "metadata": {}, "source": [ "### Transformer Model Training\n", "We will train the model for 50 epochs" @@ -613,11 +634,7 @@ "cell_type": "code", "execution_count": null, "id": "af539d65", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [], "source": [ "n_epochs = 50\n", @@ -709,11 +726,7 @@ { "cell_type": "markdown", "id": "acea5335", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, + "metadata": {}, "source": [ "### Transformer Loss Curve" ] @@ -722,11 +735,7 @@ "cell_type": "code", "execution_count": null, "id": "c2b6bb2d", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [], "source": [ "plt.style.use(\"ggplot\")\n", @@ -750,11 +759,7 @@ { "cell_type": "markdown", "id": "90b5b4b7", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, + "metadata": {}, "source": [ "### Plot evoluation of Generated Samples" ] @@ -764,10 +769,7 @@ "execution_count": null, "id": "732d7c76", "metadata": { - "lines_to_next_cell": 2, - "pycharm": { - "name": "#%%\n" - } + "lines_to_next_cell": 2 }, "outputs": [], "source": [ @@ -787,11 +789,7 @@ { "cell_type": "markdown", "id": "ddf951ac", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, + "metadata": {}, "source": [ "### Generating samples from the trained model" ] @@ -800,19 +798,13 @@ "cell_type": "code", "execution_count": null, "id": "29463149", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "metadata": {}, "outputs": [], "source": [ "samples = []\n", "for i in range(5):\n", " starting_token = vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n", - " generated_latent = generate(\n", - " transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token\n", - " )\n", + " generated_latent = generate(transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token)\n", " generated_latent = generated_latent[0]\n", " vqvae_latent = generated_latent[revert_sequence_ordering]\n", " vqvae_latent = vqvae_latent.reshape((1,) + spatial_shape)\n", @@ -892,4 +884,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py index 7485e2e6..92fbf87a 100644 --- a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py +++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Vector Quantized Variational Autoencoders and Transformers with MedNIST Dataset # @@ -24,24 +36,22 @@ # # We will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI. In order to train faster, we will select just one of the available classes ("HeadCT"), resulting in a training set with 7999 2D images. # -# [1] - [Oord et al. "Neural Discrete Representation Learning"](https://arxiv.org/abs/1711.00937) +# [1] - Oord et al. "Neural Discrete Representation Learning" https://arxiv.org/abs/1711.00937 # -# [2] - [Tudosiu et al. "Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain"](https://arxiv.org/abs/2209.03177) +# [2] - Tudosiu et al. "Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain" https://arxiv.org/abs/2209.03177 # # +# ### Setup environment + +# %% +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" +# !python -c "import matplotlib" || pip install -q matplotlib +# %matplotlib inline + +# %% [markdown] # ### Setup imports # %% -# 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 tempfile import shutil diff --git a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb index 0eb2f79c..e8455bd3 100644 --- a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb +++ b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb @@ -1,11 +1,22 @@ { "cells": [ { - "cell_type": "markdown", - "id": "dd4a1d6c", + "cell_type": "code", + "execution_count": null, + "id": "8a7e6369", "metadata": {}, + "outputs": [], "source": [ - "# 3D AutoencoderKL" + "# Copyright (c) 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." ] }, { @@ -15,9 +26,11 @@ "lines_to_next_cell": 2 }, "source": [ + "# 3D AutoencoderKL\n", + "\n", "This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1].\n", "\n", - "[1] Rombach et. al - [\"High-Resolution Image Synthesis with Latent Diffusion Models\"](https://arxiv.org/pdf/2112.10752.pdf)\n", + "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf\n", "\n", "This tutorial was based on:\n", "\n", @@ -31,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops, nibabel]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] diff --git a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py index f18f91d2..8d94dedf 100644 --- a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py +++ b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py @@ -13,18 +13,31 @@ # name: python3 # --- -# # 3D AutoencoderKL +# + +# Copyright (c) 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. +# - +# # 3D AutoencoderKL +# # This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1]. # -# [1] Rombach et. al - ["High-Resolution Image Synthesis with Latent Diffusion Models"](https://arxiv.org/pdf/2112.10752.pdf) +# [1] Rombach et. al "High-Resolution Image Synthesis with Latent Diffusion Models" https://arxiv.org/pdf/2112.10752.pdf # # This tutorial was based on: # # [Brain tumor 3D segmentation with MONAI](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb) -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops, nibabel]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm, nibabel]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb index 04038223..e5d0f2fb 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "fa57bdf5", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "6286986e", @@ -77,17 +96,6 @@ } ], "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 tempfile\n", "import time\n", diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py index bbb00eea..7ea85756 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Denoising Diffusion Probabilistic Model on 3D data # @@ -32,17 +44,6 @@ # ## Setup imports # %% -# 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 tempfile import time diff --git a/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb b/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb index c050db40..48e96ffe 100644 --- a/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb +++ b/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8efe4285", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "e0a3f076", @@ -216,7 +235,7 @@ " seed=0,\n", " transform=train_transforms,\n", ")\n", - "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8)\n", + "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8, persistent_workers=True)\n", "print(f'Image shape {train_ds[0][\"image\"].shape}')" ] }, @@ -324,13 +343,7 @@ "autoencoder.to(device)\n", "\n", "\n", - "discriminator = PatchDiscriminator(\n", - " spatial_dims=3,\n", - " num_layers_d=3,\n", - " num_channels=32,\n", - " in_channels=1,\n", - " out_channels=1,\n", - ")\n", + "discriminator = PatchDiscriminator(spatial_dims=3, num_layers_d=3, num_channels=32, in_channels=1, out_channels=1)\n", "discriminator.to(device)" ] }, @@ -721,9 +734,9 @@ " in_channels=3,\n", " out_channels=3,\n", " num_res_blocks=1,\n", - " num_channels=[32, 64, 64],\n", + " num_channels=(32, 64, 64),\n", " attention_levels=(False, True, True),\n", - " num_head_channels=1,\n", + " num_head_channels=(0, 64, 64),\n", ")\n", "unet.to(device)\n", "\n", diff --git a/tutorials/generative/3d_ldm/3d_ldm_tutorial.py b/tutorials/generative/3d_ldm/3d_ldm_tutorial.py index 6806072e..0cf6a302 100644 --- a/tutorials/generative/3d_ldm/3d_ldm_tutorial.py +++ b/tutorials/generative/3d_ldm/3d_ldm_tutorial.py @@ -14,6 +14,19 @@ # name: python3 # --- +# + +# Copyright (c) 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. +# - + # # 3D Latent Diffusion Model # In this tutorial, we will walk through the process of using the MONAI Generative Models package to generate synthetic data using Latent Diffusion Models (LDM) [1, 2]. Specifically, we will focus on training an LDM to create synthetic brain images from the Brats dataset. # @@ -89,7 +102,7 @@ seed=0, transform=train_transforms, ) -train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8) +train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8, persistent_workers=True) print(f'Image shape {train_ds[0]["image"].shape}') # - @@ -137,13 +150,7 @@ autoencoder.to(device) -discriminator = PatchDiscriminator( - spatial_dims=3, - num_layers_d=3, - num_channels=32, - in_channels=1, - out_channels=1, -) +discriminator = PatchDiscriminator(spatial_dims=3, num_layers_d=3, num_channels=32, in_channels=1, out_channels=1) discriminator.to(device) # - @@ -294,9 +301,9 @@ def KL_loss(z_mu, z_sigma): in_channels=3, out_channels=3, num_res_blocks=1, - num_channels=[32, 64, 64], + num_channels=(32, 64, 64), attention_levels=(False, True, True), - num_head_channels=1, + num_head_channels=(0, 64, 64), ) unet.to(device) diff --git a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb index 587b82fb..7f66d076 100644 --- a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb +++ b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "8bd4c6b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "0b4285e3", @@ -13,8 +32,22 @@ "\n", "The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial.\n", "\n", - "[1] - [Oord et al. \"Neural Discrete Representation Learning\"](https://arxiv.org/abs/1711.00937)\n", - "\n" + "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n", + "\n", + "\n", + "### Set up environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2859b87c", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" ] }, { @@ -66,16 +99,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -256,7 +279,7 @@ " root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n", ")\n", "\n", - "val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8)" + "val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8, persistent_workers=True)" ] }, { diff --git a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py index 1a64a9df..d5a59d7d 100644 --- a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py +++ b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py @@ -1,3 +1,15 @@ +# %% +# Copyright (c) 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. + # %% [markdown] # # Vector Quantized Variational Autoencoders for 3D reconstruction of images # @@ -7,24 +19,20 @@ # # The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial. # -# [1] - [Oord et al. "Neural Discrete Representation Learning"](https://arxiv.org/abs/1711.00937) +# [1] - Oord et al. "Neural Discrete Representation Learning" https://arxiv.org/abs/1711.00937 # # +# ### Set up environment + +# %% +# !python -c "import monai" || pip install -q "monai-weekly[tqdm, nibabel]" +# !python -c "import matplotlib" || pip install -q matplotlib +# %matplotlib inline # %% [markdown] # ### Setup imports # %% -# 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 @@ -97,7 +105,7 @@ root_dir=root_dir, task="Task01_BrainTumour", transform=val_transform, section="validation", download=True ) -val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8) +val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8, persistent_workers=True) # %% [markdown] # ### Visualize the training images diff --git a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb index c092372b..62556862 100644 --- a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb +++ b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb @@ -1,7 +1,25 @@ { "cells": [ { - "attachments": {}, + "cell_type": "code", + "execution_count": null, + "id": "70eef519", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, + { "cell_type": "markdown", "id": "63d95da6", "metadata": {}, @@ -35,7 +53,6 @@ "execution_count": 9, "id": "972ed3f3", "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false }, @@ -77,16 +94,6 @@ } ], "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", - "# WITHOUtotal_timestepsWARRANTIES 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", "import os\n", "import tempfile\n", "import time\n", @@ -104,12 +111,15 @@ "from monai.utils import first, set_determinism\n", "from torch.cuda.amp import GradScaler, autocast\n", "from tqdm import tqdm\n", - "torch.multiprocessing.set_sharing_strategy('file_system')\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", - "from generative.inferers import DiffusionInferer\n", "\n", + "\n", + "from generative.inferers import DiffusionInferer\n", "from generative.networks.nets.diffusion_model_unet import DiffusionModelUNet\n", "from generative.networks.schedulers.ddim import DDIMScheduler\n", + "\n", + "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "\n", "print_config()" ] }, @@ -126,7 +136,6 @@ "execution_count": 3, "id": "8b4323e7", "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } @@ -150,7 +159,6 @@ "execution_count": 4, "id": "34ea510f", "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } @@ -161,12 +169,9 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "c3f70dd1-236a-47ff-a244-575729ad92ba", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ "## Setup BRATS Dataset - Transforms for extracting 2D slices from 3D volumes\n", "\n", @@ -174,7 +179,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "6986f55c", "metadata": {}, @@ -228,7 +232,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "9d378ac6", "metadata": {}, @@ -241,7 +244,6 @@ "execution_count": 6, "id": "da1927b0", "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false } @@ -286,36 +288,34 @@ } ], "source": [ - "\n", "train_ds = DecathlonDataset(\n", " root_dir=root_dir,\n", " task=\"Task01_BrainTumour\",\n", - " section=\"training\", # validation\n", + " section=\"training\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", " download=False, # Set download to True if the dataset hasnt been downloaded yet\n", " seed=0,\n", " transform=train_transforms,\n", ")\n", - "print(f\"Lenght of training data: {len(train_ds)}\")\n", + "print(f\"Length of training data: {len(train_ds)}\")\n", "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n", "\n", "val_ds = DecathlonDataset(\n", " root_dir=root_dir,\n", " task=\"Task01_BrainTumour\",\n", - " section=\"validation\", # validation\n", + " section=\"validation\",\n", " cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise\n", " num_workers=4,\n", " download=False, # Set download to True if the dataset hasnt been downloaded yet\n", " seed=0,\n", " transform=train_transforms,\n", ")\n", - "print(f\"Lenght of training data: {len(val_ds)}\")\n", + "print(f\"Length of training data: {len(val_ds)}\")\n", "print(f'Validation Image shape {val_ds[0][\"image\"].shape}')" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "08428bc6", "metadata": {}, @@ -325,7 +325,7 @@ "At this step, we instantiate the MONAI components to create a DDIM, the UNET with conditioning, the noise scheduler, and the inferer used for training and sampling. We are using\n", "the deterministic DDIM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms.\n", "\n", - "The `attention` mechanism is essential for ensuring good conditioning and images manipulation here. \n", + "The `attention` mechanism is essential for ensuring good conditioning and images manipulation here.\n", "\n", "An `embedding layer`, which is also optimised during training, is used in the original work because it was empirically shown to improve conditioning compared to a single scalar information.\n" ] @@ -335,7 +335,6 @@ "execution_count": 7, "id": "bee5913e", "metadata": { - "collapsed": false, "jupyter": { "outputs_hidden": false }, @@ -355,19 +354,16 @@ " num_head_channels=16,\n", " with_conditioning=True,\n", " cross_attention_dim=embedding_dimension,\n", - " ).to(device)\n", + ").to(device)\n", "embed = torch.nn.Embedding(num_embeddings=3, embedding_dim=embedding_dimension, padding_idx=0).to(device)\n", "\n", - "scheduler = DDIMScheduler(\n", - " num_train_timesteps=1000,\n", - ")\n", + "scheduler = DDIMScheduler(num_train_timesteps=1000)\n", "optimizer = torch.optim.Adam(params=list(model.parameters()) + list(embed.parameters()), lr=1e-5)\n", "\n", "inferer = DiffusionInferer(scheduler)" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "f815ff34", "metadata": {}, @@ -617,8 +613,12 @@ "iteration = 0\n", "iter_loss = 0\n", "\n", - "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True)\n", - "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True)\n", + "train_loader = DataLoader(\n", + " train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True\n", + ")\n", + "val_loader = DataLoader(\n", + " val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True\n", + ")\n", "\n", "scaler = GradScaler()\n", "total_start = time.time()\n", @@ -627,49 +627,59 @@ " for batch in train_loader:\n", " iteration += 1\n", " model.train()\n", - " images, classes = batch['image'].to(device), batch['slice_label'].to(device)\n", + " images, classes = batch[\"image\"].to(device), batch[\"slice_label\"].to(device)\n", " # 15% of the time, class conditioning dropout\n", " classes = classes * (torch.rand_like(classes) > condition_dropout)\n", " # cross attention expects shape [batch size, sequence length, channels]\n", - " class_embedding = embed(classes.long().to(device)).unsqueeze(1) \n", + " class_embedding = embed(classes.long().to(device)).unsqueeze(1)\n", " optimizer.zero_grad(set_to_none=True)\n", " # pick a random time step t\n", - " timesteps = torch.randint(0, 1000, (len(images),)).to(device) \n", + " timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n", "\n", " with autocast(enabled=True):\n", " # Generate random noise\n", " noise = torch.randn_like(images).to(device)\n", " # Get model prediction\n", - " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding)\n", + " noise_pred = inferer(\n", + " inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding\n", + " )\n", " loss = F.mse_loss(noise_pred.float(), noise.float())\n", "\n", " scaler.scale(loss).backward()\n", " scaler.step(optimizer)\n", " scaler.update()\n", " iter_loss += loss.item()\n", - " sys.stdout.write(f\"Iteration {iteration}/{n_iterations} - train Loss {loss.item():.4f}\" + '\\r')\n", + " sys.stdout.write(f\"Iteration {iteration}/{n_iterations} - train Loss {loss.item():.4f}\" + \"\\r\")\n", " sys.stdout.flush()\n", - " \n", + "\n", " if (iteration) % val_interval == 0:\n", - " model.eval() \n", + " model.eval()\n", " val_iter_loss = 0\n", " for val_step, val_batch in enumerate(val_loader):\n", - " images, classes = val_batch['image'].to(device), val_batch['slice_label'].to(device)\n", + " images, classes = val_batch[\"image\"].to(device), val_batch[\"slice_label\"].to(device)\n", " # cross attention expects shape [batch size, sequence length, channels]\n", - " class_embedding = embed(classes.long().to(device)).unsqueeze(1) \n", + " class_embedding = embed(classes.long().to(device)).unsqueeze(1)\n", " timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n", " with torch.no_grad():\n", " with autocast(enabled=True):\n", " noise = torch.randn_like(images).to(device)\n", - " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding)\n", + " noise_pred = inferer(\n", + " inputs=images,\n", + " diffusion_model=model,\n", + " noise=noise,\n", + " timesteps=timesteps,\n", + " condition=class_embedding,\n", + " )\n", " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", " val_iter_loss += val_loss.item()\n", " iter_loss_list.append(iter_loss / val_interval)\n", - " val_iter_loss_list.append(val_iter_loss / (val_step+1))\n", + " val_iter_loss_list.append(val_iter_loss / (val_step + 1))\n", " iterations.append(iteration)\n", " iter_loss = 0\n", - " print(f\"Train Loss {loss.item():.4f}, Interval Loss {iter_loss_list[-1]:.4f}, Interval Loss Val {val_iter_loss_list[-1]:.4f}\")\n", - " \n", + " print(\n", + " f\"Train Loss {loss.item():.4f}, Interval Loss {iter_loss_list[-1]:.4f}, Interval Loss Val {val_iter_loss_list[-1]:.4f}\"\n", + " )\n", + "\n", "\n", "total_time = time.time() - total_start\n", "\n", @@ -678,7 +688,9 @@ "plt.style.use(\"seaborn-bright\")\n", "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n", "plt.plot(iterations, iter_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n", - "plt.plot(iterations, val_iter_loss_list, color=\"C1\", linewidth=2.0, label=\"Validation\") # np.linspace(1, n_iterations, len(val_iter_loss_list))\n", + "plt.plot(\n", + " iterations, val_iter_loss_list, color=\"C1\", linewidth=2.0, label=\"Validation\"\n", + ") # np.linspace(1, n_iterations, len(val_iter_loss_list))\n", "plt.yticks(fontsize=12), plt.xticks(fontsize=12)\n", "plt.xlabel(\"Iterations\", fontsize=16), plt.ylabel(\"Loss\", fontsize=16)\n", "plt.legend(prop={\"size\": 14})\n", @@ -686,7 +698,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "fd2b79a4", "metadata": {}, @@ -723,8 +734,12 @@ "model.eval()\n", "scheduler.clip_sample = True\n", "guidance_scale = 3\n", - "conditioning = torch.cat([torch.zeros(1).long(), 2*torch.ones(1).long()], dim=0).to(device) # 2*torch.ones(1).long() is the class label for the UNHEALTHY (tumor) class\n", - "class_embedding = embed(conditioning).unsqueeze(1) # cross attention expects shape [batch size, sequence length, channels]\n", + "conditioning = torch.cat([torch.zeros(1).long(), 2 * torch.ones(1).long()], dim=0).to(\n", + " device\n", + ") # 2*torch.ones(1).long() is the class label for the UNHEALTHY (tumor) class\n", + "class_embedding = embed(conditioning).unsqueeze(\n", + " 1\n", + ") # cross attention expects shape [batch size, sequence length, channels]\n", "noise = torch.randn((1, 1, 64, 64))\n", "noise = noise.to(device)\n", "scheduler.set_timesteps(num_inference_steps=100)\n", @@ -781,24 +796,23 @@ ], "source": [ "\n", - "idx_unhealthy = np.argwhere(val_batch['slice_label'].numpy() == 2).squeeze()\n", + "idx_unhealthy = np.argwhere(val_batch[\"slice_label\"].numpy() == 2).squeeze()\n", "\n", - "idx = idx_unhealthy[4] # Pick a random slice of the validation set to be transformed\n", - "inputting = val_batch['image'][idx] # Pick an input slice of the validation set to be transformed \n", - "inputlabel= val_batch['slice_label'][idx] # Check whether it is healthy or diseased\n", + "idx = idx_unhealthy[4] # Pick a random slice of the validation set to be transformed\n", + "inputting = val_batch[\"image\"][idx] # Pick an input slice of the validation set to be transformed\n", + "inputlabel = val_batch[\"slice_label\"][idx] # Check whether it is healthy or diseased\n", "\n", - "plt.figure(\"input\"+str(inputlabel))\n", + "plt.figure(\"input\" + str(inputlabel))\n", "plt.imshow(inputting[0], vmin=0, vmax=1, cmap=\"gray\")\n", "plt.axis(\"off\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", - "model.eval();\n", + "model.eval()\n", "print(\"input label: \", inputlabel)" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "a7c8346a-6296-4800-b978-c10fcdf09779", "metadata": {}, @@ -832,10 +846,10 @@ "model.eval()\n", "\n", "guidance_scale = 3.0\n", - "total_timesteps= 500 \n", + "total_timesteps = 500\n", "latent_space_depth = int(total_timesteps * 0.25)\n", "\n", - "current_img = inputting[None,...].to(device)\n", + "current_img = inputting[None, ...].to(device)\n", "scheduler.set_timesteps(num_inference_steps=total_timesteps)\n", "\n", "## Encoding\n", @@ -843,7 +857,7 @@ "scheduler.clip_sample = False\n", "class_embedding = embed(torch.zeros(1).long().to(device)).unsqueeze(1)\n", "progress_bar = tqdm(range(latent_space_depth))\n", - "for i in progress_bar: #go through the noising process\n", + "for i in progress_bar: # go through the noising process\n", " t = i\n", " with torch.no_grad():\n", " model_output = model(current_img, timesteps=torch.Tensor((t,)).to(current_img.device), context=class_embedding)\n", @@ -856,12 +870,14 @@ "conditioning = torch.cat([torch.zeros(1).long(), torch.ones(1).long()], dim=0).to(device)\n", "class_embedding = embed(conditioning).unsqueeze(1)\n", "\n", - "progress_bar = tqdm(range(latent_space_depth)) \n", - "for i in progress_bar: #go through the denoising process\n", + "progress_bar = tqdm(range(latent_space_depth))\n", + "for i in progress_bar: # go through the denoising process\n", " t = latent_space_depth - i\n", " current_img_double = torch.cat([current_img] * 2)\n", " with torch.no_grad():\n", - " model_output = model(current_img_double, timesteps=torch.Tensor([t,t]).to(current_img.device), context=class_embedding)\n", + " model_output = model(\n", + " current_img_double, timesteps=torch.Tensor([t, t]).to(current_img.device), context=class_embedding\n", + " )\n", " noise_pred_uncond, noise_pred_text = model_output.chunk(2)\n", " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", " current_img, _ = scheduler.step(noise_pred, t, current_img)\n", @@ -870,7 +886,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "188fe33b", "metadata": {}, @@ -899,29 +914,30 @@ "def visualize(img):\n", " _min = img.min()\n", " _max = img.max()\n", - " normalized_img = (img - _min)/ (_max - _min)\n", + " normalized_img = (img - _min) / (_max - _min)\n", " return normalized_img\n", "\n", - "diff = abs(inputting.cpu()-current_img[0].cpu()).detach().numpy()\n", + "\n", + "diff = abs(inputting.cpu() - current_img[0].cpu()).detach().numpy()\n", "row = 4\n", "plt.style.use(\"default\")\n", "\n", "fig = plt.figure(figsize=(10, 30))\n", "\n", - "ax = plt.subplot(1,row,2)\n", + "ax = plt.subplot(1, row, 2)\n", "ax.imshow(latent_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n", "ax.set_title(\"Latent Image\"), plt.tight_layout(), plt.axis(\"off\")\n", "\n", - "ax = plt.subplot(1,row,3)\n", + "ax = plt.subplot(1, row, 3)\n", "ax.imshow(current_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n", "ax.set_title(\"Reconstructed \\n Image\"), plt.tight_layout(), plt.axis(\"off\")\n", "\n", "\n", - "ax = plt.subplot(1,row,4)\n", + "ax = plt.subplot(1, row, 4)\n", "ax.imshow(diff[0], cmap=\"inferno\")\n", "ax.set_title(\"Anomaly Map\"), plt.tight_layout(), plt.axis(\"off\")\n", "\n", - "ax = plt.subplot(1,row,1)\n", + "ax = plt.subplot(1, row, 1)\n", "ax.imshow(inputting[0], vmin=0, vmax=1, cmap=\"gray\")\n", "ax.set_title(\"Original Image\"), plt.tight_layout(), plt.axis(\"off\")\n", "plt.show()" @@ -933,7 +949,7 @@ "formats": "py:percent,ipynb" }, "kernelspec": { - "display_name": "pytorch_monai", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -947,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.6" }, "vscode": { "interpreter": { diff --git a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py index 04f78aa5..978826a7 100644 --- a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py +++ b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py @@ -6,13 +6,25 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.14.5 +# jupytext_version: 1.14.4 # kernelspec: -# display_name: pytorch_monai +# display_name: Python 3 (ipykernel) # language: python # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Weakly Supervised Anomaly Detection with Classifier Guidance # @@ -33,20 +45,9 @@ # ## Setup imports # %% jupyter={"outputs_hidden": false} -# 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, -# WITHOUtotal_timestepsWARRANTIES 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 tempfile import time -from typing import Dict import os import matplotlib.pyplot as plt import numpy as np @@ -57,15 +58,18 @@ from monai.apps import DecathlonDataset from monai.config import print_config from monai.data import DataLoader -from monai.utils import first, set_determinism +from monai.utils import set_determinism from torch.cuda.amp import GradScaler, autocast from tqdm import tqdm -torch.multiprocessing.set_sharing_strategy('file_system') -os.environ["CUDA_VISIBLE_DEVICES"]="0" -from generative.inferers import DiffusionInferer + +from generative.inferers import DiffusionInferer from generative.networks.nets.diffusion_model_unet import DiffusionModelUNet from generative.networks.schedulers.ddim import DDIMScheduler + +torch.multiprocessing.set_sharing_strategy("file_system") +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + print_config() @@ -124,31 +128,30 @@ # ### Load Training and Validation Datasets # %% jupyter={"outputs_hidden": false} - train_ds = DecathlonDataset( root_dir=root_dir, task="Task01_BrainTumour", - section="training", # validation + section="training", cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise num_workers=4, download=False, # Set download to True if the dataset hasnt been downloaded yet seed=0, transform=train_transforms, ) -print(f"Lenght of training data: {len(train_ds)}") +print(f"Length of training data: {len(train_ds)}") print(f'Train image shape {train_ds[0]["image"].shape}') val_ds = DecathlonDataset( root_dir=root_dir, task="Task01_BrainTumour", - section="validation", # validation + section="validation", cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise num_workers=4, download=False, # Set download to True if the dataset hasnt been downloaded yet seed=0, transform=train_transforms, ) -print(f"Lenght of training data: {len(val_ds)}") +print(f"Length of training data: {len(val_ds)}") print(f'Validation Image shape {val_ds[0]["image"].shape}') # %% [markdown] @@ -157,7 +160,7 @@ # At this step, we instantiate the MONAI components to create a DDIM, the UNET with conditioning, the noise scheduler, and the inferer used for training and sampling. We are using # the deterministic DDIM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms. # -# The `attention` mechanism is essential for ensuring good conditioning and images manipulation here. +# The `attention` mechanism is essential for ensuring good conditioning and images manipulation here. # # An `embedding layer`, which is also optimised during training, is used in the original work because it was empirically shown to improve conditioning compared to a single scalar information. # @@ -175,12 +178,10 @@ num_head_channels=16, with_conditioning=True, cross_attention_dim=embedding_dimension, - ).to(device) +).to(device) embed = torch.nn.Embedding(num_embeddings=3, embedding_dim=embedding_dimension, padding_idx=0).to(device) -scheduler = DDIMScheduler( - num_train_timesteps=1000, -) +scheduler = DDIMScheduler(num_train_timesteps=1000) optimizer = torch.optim.Adam(params=list(model.parameters()) + list(embed.parameters()), lr=1e-5) inferer = DiffusionInferer(scheduler) @@ -200,8 +201,12 @@ iteration = 0 iter_loss = 0 -train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True) -val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True) +train_loader = DataLoader( + train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True +) +val_loader = DataLoader( + val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True +) scaler = GradScaler() total_start = time.time() @@ -210,49 +215,59 @@ for batch in train_loader: iteration += 1 model.train() - images, classes = batch['image'].to(device), batch['slice_label'].to(device) + images, classes = batch["image"].to(device), batch["slice_label"].to(device) # 15% of the time, class conditioning dropout classes = classes * (torch.rand_like(classes) > condition_dropout) # cross attention expects shape [batch size, sequence length, channels] - class_embedding = embed(classes.long().to(device)).unsqueeze(1) + class_embedding = embed(classes.long().to(device)).unsqueeze(1) optimizer.zero_grad(set_to_none=True) # pick a random time step t - timesteps = torch.randint(0, 1000, (len(images),)).to(device) + timesteps = torch.randint(0, 1000, (len(images),)).to(device) with autocast(enabled=True): # Generate random noise noise = torch.randn_like(images).to(device) # Get model prediction - noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding) + noise_pred = inferer( + inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding + ) loss = F.mse_loss(noise_pred.float(), noise.float()) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() iter_loss += loss.item() - sys.stdout.write(f"Iteration {iteration}/{n_iterations} - train Loss {loss.item():.4f}" + '\r') + sys.stdout.write(f"Iteration {iteration}/{n_iterations} - train Loss {loss.item():.4f}" + "\r") sys.stdout.flush() - + if (iteration) % val_interval == 0: - model.eval() + model.eval() val_iter_loss = 0 for val_step, val_batch in enumerate(val_loader): - images, classes = val_batch['image'].to(device), val_batch['slice_label'].to(device) + images, classes = val_batch["image"].to(device), val_batch["slice_label"].to(device) # cross attention expects shape [batch size, sequence length, channels] - class_embedding = embed(classes.long().to(device)).unsqueeze(1) + class_embedding = embed(classes.long().to(device)).unsqueeze(1) timesteps = torch.randint(0, 1000, (len(images),)).to(device) with torch.no_grad(): with autocast(enabled=True): noise = torch.randn_like(images).to(device) - noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps, condition=class_embedding) + noise_pred = inferer( + inputs=images, + diffusion_model=model, + noise=noise, + timesteps=timesteps, + condition=class_embedding, + ) val_loss = F.mse_loss(noise_pred.float(), noise.float()) val_iter_loss += val_loss.item() iter_loss_list.append(iter_loss / val_interval) - val_iter_loss_list.append(val_iter_loss / (val_step+1)) + val_iter_loss_list.append(val_iter_loss / (val_step + 1)) iterations.append(iteration) iter_loss = 0 - print(f"Train Loss {loss.item():.4f}, Interval Loss {iter_loss_list[-1]:.4f}, Interval Loss Val {val_iter_loss_list[-1]:.4f}") - + print( + f"Train Loss {loss.item():.4f}, Interval Loss {iter_loss_list[-1]:.4f}, Interval Loss Val {val_iter_loss_list[-1]:.4f}" + ) + total_time = time.time() - total_start @@ -261,7 +276,9 @@ plt.style.use("seaborn-bright") plt.title("Learning Curves Diffusion Model", fontsize=20) plt.plot(iterations, iter_loss_list, color="C0", linewidth=2.0, label="Train") -plt.plot(iterations, val_iter_loss_list, color="C1", linewidth=2.0, label="Validation") # np.linspace(1, n_iterations, len(val_iter_loss_list)) +plt.plot( + iterations, val_iter_loss_list, color="C1", linewidth=2.0, label="Validation" +) # np.linspace(1, n_iterations, len(val_iter_loss_list)) plt.yticks(fontsize=12), plt.xticks(fontsize=12) plt.xlabel("Iterations", fontsize=16), plt.ylabel("Loss", fontsize=16) plt.legend(prop={"size": 14}) @@ -275,8 +292,12 @@ model.eval() scheduler.clip_sample = True guidance_scale = 3 -conditioning = torch.cat([torch.zeros(1).long(), 2*torch.ones(1).long()], dim=0).to(device) # 2*torch.ones(1).long() is the class label for the UNHEALTHY (tumor) class -class_embedding = embed(conditioning).unsqueeze(1) # cross attention expects shape [batch size, sequence length, channels] +conditioning = torch.cat([torch.zeros(1).long(), 2 * torch.ones(1).long()], dim=0).to( + device +) # 2*torch.ones(1).long() is the class label for the UNHEALTHY (tumor) class +class_embedding = embed(conditioning).unsqueeze( + 1 +) # cross attention expects shape [batch size, sequence length, channels] noise = torch.randn((1, 1, 64, 64)) noise = noise.to(device) scheduler.set_timesteps(num_inference_steps=100) @@ -303,19 +324,19 @@ # %% -idx_unhealthy = np.argwhere(val_batch['slice_label'].numpy() == 2).squeeze() +idx_unhealthy = np.argwhere(val_batch["slice_label"].numpy() == 2).squeeze() -idx = idx_unhealthy[4] # Pick a random slice of the validation set to be transformed -inputting = val_batch['image'][idx] # Pick an input slice of the validation set to be transformed -inputlabel= val_batch['slice_label'][idx] # Check whether it is healthy or diseased +idx = idx_unhealthy[4] # Pick a random slice of the validation set to be transformed +inputting = val_batch["image"][idx] # Pick an input slice of the validation set to be transformed +inputlabel = val_batch["slice_label"][idx] # Check whether it is healthy or diseased -plt.figure("input"+str(inputlabel)) +plt.figure("input" + str(inputlabel)) plt.imshow(inputting[0], vmin=0, vmax=1, cmap="gray") plt.axis("off") plt.tight_layout() plt.show() -model.eval(); +model.eval() print("input label: ", inputlabel) # %% [markdown] @@ -332,10 +353,10 @@ model.eval() guidance_scale = 3.0 -total_timesteps= 500 +total_timesteps = 500 latent_space_depth = int(total_timesteps * 0.25) -current_img = inputting[None,...].to(device) +current_img = inputting[None, ...].to(device) scheduler.set_timesteps(num_inference_steps=total_timesteps) ## Encoding @@ -343,7 +364,7 @@ scheduler.clip_sample = False class_embedding = embed(torch.zeros(1).long().to(device)).unsqueeze(1) progress_bar = tqdm(range(latent_space_depth)) -for i in progress_bar: #go through the noising process +for i in progress_bar: # go through the noising process t = i with torch.no_grad(): model_output = model(current_img, timesteps=torch.Tensor((t,)).to(current_img.device), context=class_embedding) @@ -356,12 +377,14 @@ conditioning = torch.cat([torch.zeros(1).long(), torch.ones(1).long()], dim=0).to(device) class_embedding = embed(conditioning).unsqueeze(1) -progress_bar = tqdm(range(latent_space_depth)) -for i in progress_bar: #go through the denoising process +progress_bar = tqdm(range(latent_space_depth)) +for i in progress_bar: # go through the denoising process t = latent_space_depth - i current_img_double = torch.cat([current_img] * 2) with torch.no_grad(): - model_output = model(current_img_double, timesteps=torch.Tensor([t,t]).to(current_img.device), context=class_embedding) + model_output = model( + current_img_double, timesteps=torch.Tensor([t, t]).to(current_img.device), context=class_embedding + ) noise_pred_uncond, noise_pred_text = model_output.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) current_img, _ = scheduler.step(noise_pred, t, current_img) @@ -376,29 +399,30 @@ def visualize(img): _min = img.min() _max = img.max() - normalized_img = (img - _min)/ (_max - _min) + normalized_img = (img - _min) / (_max - _min) return normalized_img -diff = abs(inputting.cpu()-current_img[0].cpu()).detach().numpy() + +diff = abs(inputting.cpu() - current_img[0].cpu()).detach().numpy() row = 4 plt.style.use("default") fig = plt.figure(figsize=(10, 30)) -ax = plt.subplot(1,row,2) +ax = plt.subplot(1, row, 2) ax.imshow(latent_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap="gray") ax.set_title("Latent Image"), plt.tight_layout(), plt.axis("off") -ax = plt.subplot(1,row,3) +ax = plt.subplot(1, row, 3) ax.imshow(current_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap="gray") ax.set_title("Reconstructed \n Image"), plt.tight_layout(), plt.axis("off") -ax = plt.subplot(1,row,4) +ax = plt.subplot(1, row, 4) ax.imshow(diff[0], cmap="inferno") ax.set_title("Anomaly Map"), plt.tight_layout(), plt.axis("off") -ax = plt.subplot(1,row,1) +ax = plt.subplot(1, row, 1) ax.imshow(inputting[0], vmin=0, vmax=1, cmap="gray") ax.set_title("Original Image"), plt.tight_layout(), plt.axis("off") plt.show() diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb index e3d112fa..52abd169 100644 --- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb +++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "fdc5edce", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "f6090d00", @@ -13,7 +32,7 @@ "\n", "Finally, we will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution).\n", "\n", - "[1] - [Pinaya et al. \"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers\"](https://doi.org/10.1016/j.media.2022.102475)" + "[1] - Pinaya et al. \"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers\" https://doi.org/10.1016/j.media.2022.102475" ] }, { @@ -31,6 +50,8 @@ "metadata": {}, "outputs": [], "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "!python -c \"import seaborn\" || pip install -q seaborn\n", "%matplotlib inline" ] @@ -93,16 +114,6 @@ } ], "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", "import os\n", "import tempfile\n", "import time\n", @@ -532,7 +543,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "f1d81a89", "metadata": {}, @@ -949,16 +959,15 @@ " progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n", " progress_bar.set_description(f\"Epoch {epoch}\")\n", " for step, batch in progress_bar:\n", - "\n", " images = batch[\"image\"].to(device)\n", "\n", " optimizer.zero_grad(set_to_none=True)\n", "\n", " logits, quantizations_target, _ = inferer(images, vqvae_model, transformer_model, ordering, return_latent=True)\n", " logits = logits.transpose(1, 2)\n", - " \n", + "\n", " # train the transformer to predict token n+1 using tokens 0-n\n", - " loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n", + " loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n", "\n", " loss.backward()\n", " optimizer.step()\n", @@ -973,7 +982,6 @@ " val_loss = 0\n", " with torch.no_grad():\n", " for val_step, batch in enumerate(val_loader, start=1):\n", - "\n", " images = batch[\"image\"].to(device)\n", "\n", " logits, quantizations_target, _ = inferer(\n", @@ -981,14 +989,19 @@ " )\n", " logits = logits.transpose(1, 2)\n", "\n", - " loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n", + " loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n", "\n", " val_loss += loss.item()\n", " # get sample\n", - " sample = inferer.sample( vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering, latent_spatial_dim=(spatial_shape[0], spatial_shape[1]), starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n", - " )\n", - " plt.imshow(sample[0,0,...].cpu().detach())\n", - " plt.title(f'Sample epoch {epoch}')\n", + " sample = inferer.sample(\n", + " vqvae_model=vqvae_model,\n", + " transformer_model=transformer_model,\n", + " ordering=ordering,\n", + " latent_spatial_dim=(spatial_shape[0], spatial_shape[1]),\n", + " starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device),\n", + " )\n", + " plt.imshow(sample[0, 0, ...].cpu().detach())\n", + " plt.title(f\"Sample epoch {epoch}\")\n", " plt.show()\n", " val_loss /= val_step\n", " val_epoch_losses.append(val_loss)\n", @@ -1141,7 +1154,7 @@ ], "source": [ "sns.kdeplot(in_likelihoods, color=\"dodgerblue\", bw_adjust=1, label=\"In-distribution\")\n", - "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=40, label=\"OOD\")\n", + "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=10, label=\"OOD\")\n", "plt.legend()\n", "plt.xlabel(\"Log-likelihood\")" ] @@ -1174,7 +1187,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py index 613bdd6e..b28fda5a 100644 --- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py +++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py @@ -6,13 +6,25 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.14.1 +# jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Anomaly Detection with Transformers # @@ -22,12 +34,14 @@ # # Finally, we will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution). # -# [1] - [Pinaya et al. "Unsupervised brain imaging 3D anomaly detection and segmentation with transformers"](https://doi.org/10.1016/j.media.2022.102475) +# [1] - Pinaya et al. "Unsupervised brain imaging 3D anomaly detection and segmentation with transformers" https://doi.org/10.1016/j.media.2022.102475 # %% [markdown] # ### Setup environment # %% +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" +# !python -c "import matplotlib" || pip install -q matplotlib # !python -c "import seaborn" || pip install -q seaborn # %matplotlib inline @@ -35,16 +49,6 @@ # ### Setup imports # %% -# 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 tempfile import time diff --git a/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb b/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb index d99cf37d..d417ff1d 100644 --- a/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb +++ b/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb @@ -1,5 +1,24 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "470cb233", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 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." + ] + }, { "cell_type": "markdown", "id": "63d95da6", @@ -25,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm, einops]\"\n", + "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -83,16 +102,6 @@ } ], "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", "import os\n", "import shutil\n", "import tempfile\n", @@ -426,7 +435,7 @@ " num_channels=(64, 64, 64),\n", " attention_levels=(False, False, True),\n", " num_res_blocks=1,\n", - " num_head_channels=64,\n", + " num_head_channels=(0, 0, 64),\n", " with_conditioning=True,\n", " cross_attention_dim=1,\n", ")\n", diff --git a/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.py b/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.py index 3828229b..a46622a3 100644 --- a/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.py +++ b/tutorials/generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.py @@ -13,6 +13,18 @@ # name: python3 # --- +# %% +# Copyright (c) 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. + # %% [markdown] # # Classifier-free Guidance # @@ -27,7 +39,7 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[pillow, tqdm, einops]" +# !python -c "import monai" || pip install -q "monai-weekly[tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -35,16 +47,6 @@ # ## Setup imports # %% jupyter={"outputs_hidden": false} -# 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 @@ -195,7 +197,7 @@ num_channels=(64, 64, 64), attention_levels=(False, False, True), num_res_blocks=1, - num_head_channels=64, + num_head_channels=(0, 0, 64), with_conditioning=True, cross_attention_dim=1, ) From d27bb95da4de557f8fd112225d75e89c45856422 Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Tue, 21 Mar 2023 00:58:57 +0000 Subject: [PATCH 2/2] Fix license and dependencies installation Signed-off-by: Walter Hugo Lopez Pinaya --- .../2d_stable_diffusion_v2_super_resolution.ipynb | 1 - tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb | 1 - ...d_classifierfree_guidance_anomalydetection_tutorial.ipynb | 5 ++--- .../2d_classifierfree_guidance_anomalydetection_tutorial.py | 2 +- 4 files changed, 3 insertions(+), 6 deletions(-) diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb index f5705d3a..bad152f6 100644 --- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb +++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb @@ -127,7 +127,6 @@ "from monai.apps import MedNISTDataset\n", "from monai.config import print_config\n", "from monai.data import CacheDataset, DataLoader\n", - "from monai.networks.layers import Act\n", "from monai.utils import first, set_determinism\n", "from torch import nn\n", "from torch.cuda.amp import GradScaler, autocast\n", diff --git a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb index 9d195f74..5878990f 100644 --- a/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb +++ b/tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb @@ -105,7 +105,6 @@ "from monai.apps import MedNISTDataset\n", "from monai.config import print_config\n", "from monai.data import CacheDataset, DataLoader\n", - "from monai.networks.layers import Act\n", "from monai.utils import first, set_determinism\n", "from torch.nn import L1Loss\n", "from tqdm import tqdm\n", diff --git a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb index 62556862..fbcbc263 100644 --- a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb +++ b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb @@ -97,7 +97,6 @@ "import os\n", "import tempfile\n", "import time\n", - "from typing import Dict\n", "import os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -108,7 +107,7 @@ "from monai.apps import DecathlonDataset\n", "from monai.config import print_config\n", "from monai.data import DataLoader\n", - "from monai.utils import first, set_determinism\n", + "from monai.utils import set_determinism\n", "from torch.cuda.amp import GradScaler, autocast\n", "from tqdm import tqdm\n", "\n", @@ -185,7 +184,7 @@ "source": [ "Here we use transforms to augment the training dataset, as usual:\n", "\n", - "1. `LoadImaged` loads the hands images from files.\n", + "1. `LoadImaged` loads the brain images from files.\n", "2. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n", "3. The first `Lambdad` transform chooses the first channel of the image, which is the T1-weighted image.\n", "4. `Spacingd` resamples the image to the specified voxel spacing, we use 3,3,2 mm to match the original paper.\n", diff --git a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py index 978826a7..aa7a2b39 100644 --- a/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py +++ b/tutorials/generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.py @@ -94,7 +94,7 @@ # %% [markdown] # Here we use transforms to augment the training dataset, as usual: # -# 1. `LoadImaged` loads the hands images from files. +# 1. `LoadImaged` loads the brain images from files. # 2. `EnsureChannelFirstd` ensures the original data to construct "channel first" shape. # 3. The first `Lambdad` transform chooses the first channel of the image, which is the T1-weighted image. # 4. `Spacingd` resamples the image to the specified voxel spacing, we use 3,3,2 mm to match the original paper.