Merge branch 'main' into swin_unetr_btcv_v1

Author: ahatamiz

.github/workflows/pep8.yml

@@ -4,7 +4,7 @@ on:
   # quick tests for every pull request
   push:
     branches:
-      - master
+      - main
   pull_request:
 
 jobs:

.gitignore

@@ -138,3 +138,15 @@ tests/testing_data/*Hippocampus*
 # Ignore torch saves
 */torch/runs
 logs
+*/runs
+lightning_logs
+
+# ignore automatically created files
+*.ts
+nohup.out
+deepgrow/ignite/_image.nii.gz
+*.zip
+deployment/bentoml/mednist_classifier_bentoml.py
+deployment/ray/mednist_classifier_start.py
+*.nii.gz
+3d_segmentation/out

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.1.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace

2d_classification/mednist_tutorial.ipynb

@@ -15,7 +15,7 @@
     "* Train the model with a PyTorch program\n",
     "* Evaluate on test dataset\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/2d_classification/mednist_tutorial.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb)"
    ]
   },
   {
@@ -180,7 +180,7 @@
    },
    "outputs": [],
    "source": [
-    "resource = \"https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE\"\n",
+    "resource = \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz\"\n",
     "md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
     "\n",
     "compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
@@ -1328,7 +1328,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1342,7 +1342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,

2d_registration/registration_mednist.ipynb

@@ -13,7 +13,7 @@
     "- a UNet-like registration network with an affine head to predict the affine transformation parameters;\n",
     "- a warp function, implemented as a MONAI C++/CUDA module, to transform the moving image.\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/2d_registration/registration_mednist.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_registration/registration_mednist.ipynb)"
    ]
   },
   {
@@ -119,12 +119,36 @@
     "import torch\n",
     "from torch.nn import MSELoss\n",
     "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import tempfile\n",
     "\n",
     "\n",
     "print_config()\n",
     "set_determinism(42)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup data directory\n",
+    "\n",
+    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
+    "This allows you to save results and reuse downloads.  \n",
+    "If not specified a temporary directory will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+    "print(root_dir)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -188,7 +212,7 @@
     }
    ],
    "source": [
-    "train_data = MedNISTDataset(root_dir=\"./\", section=\"training\", download=True, transform=None)\n",
+    "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, transform=None)\n",
     "training_datadict = [\n",
     "    {\"fixed_hand\": item[\"image\"], \"moving_hand\": item[\"image\"]}\n",
     "    for item in train_data.data if item[\"label\"] == 4  # label 4 is for xray hands\n",