Fix dependencies and license

tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb

@@ -1,16 +1,35 @@
 {
  "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",
    "metadata": {},
    "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",

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

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": {

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
 #
@@ -29,24 +41,14 @@
 # ## 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
 
 # %%
-# 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)
-
-# %%

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",

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
 #
@@ -26,24 +38,14 @@
 # ## 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
 
 # %% 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