Merge branch 'main' into fix-typo-fast-training

Author: Nic-Ma

.github/workflows/pep8.yml

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

.gitignore

@@ -148,3 +148,5 @@ deepgrow/ignite/_image.nii.gz
 *.zip
 deployment/bentoml/mednist_classifier_bentoml.py
 deployment/ray/mednist_classifier_start.py
+*.nii.gz
+3d_segmentation/out

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)"
    ]
   },
   {

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)"
    ]
   },
   {

3d_classification/densenet_training_array.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "Here, the task is given to classify MR images into male/female.\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/3d_classification/torch/densenet_training_array.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_classification/torch/densenet_training_array.ipynb)"
    ]
   },
   {

3d_registration/paired_lung_ct.ipynb

@@ -18,7 +18,7 @@
     "1. Compute DiceLoss and BendingEnergyLoss\n",
     "1. Compute MeanDice metric\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/3d_registration/paired_lung_ct.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_registration/paired_lung_ct.ipynb)"
    ]
   },
   {

3d_segmentation/brats_segmentation_3d.ipynb

@@ -35,7 +35,7 @@
     "1. the enhancing tumor structures (light blue) visible in T1Gd, surrounding the cystic/necrotic components of the core (green) (Fig. C).\n",
     "1. The segmentations are combined to generate the final labels of the tumor sub-regions (Fig.D): edema (yellow), non-enhancing solid core (red), necrotic/cystic core (green), enhancing core (blue).\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/3d_segmentation/brats_segmentation_3d.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb)"
    ]
   },
   {

3d_segmentation/challenge_baseline/README.md

@@ -109,9 +109,9 @@ This baseline method achieves 0.6904 ± 0.1801 Dice score on the challenge valid
 
 - For MONAI technical documentation, please visit [docs.monai.io](https://docs.monai.io/).
 - Please visit [`Project-MONAI/tutorials`](https://github.com/Project-MONAI/tutorials) for more examples, including:
-  - [`3D segmentation pipelines`](https://github.com/Project-MONAI/tutorials/tree/master/3d_segmentation),
-  - [`Dynamic UNet`](https://github.com/Project-MONAI/tutorials/blob/master/modules/dynunet_tutorial.ipynb),
-  - [`Training acceleration`](https://github.com/Project-MONAI/tutorials/tree/master/acceleration).
+  - [`3D segmentation pipelines`](https://github.com/Project-MONAI/tutorials/tree/main/3d_segmentation),
+  - [`Dynamic UNet`](https://github.com/Project-MONAI/tutorials/blob/main/modules/dynunet_tutorial.ipynb),
+  - [`Training acceleration`](https://github.com/Project-MONAI/tutorials/tree/main/acceleration).
 
 ## Submitting to the leaderboard
 
@@ -121,4 +121,4 @@ For any queries, please contact [the challenge organizers](https://covid-segment
 
 ## License
 
-The code is licensed under [the Apache License 2.0](https://github.com/Project-MONAI/tutorials/blob/master/LICENSE).
+The code is licensed under [the Apache License 2.0](https://github.com/Project-MONAI/tutorials/blob/main/LICENSE).

3d_segmentation/spleen_segmentation_3d.ipynb

@@ -29,7 +29,7 @@
     "Source: Memorial Sloan Kettering Cancer Center  \n",
     "Challenge: Large ranging foreground size\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/3d_segmentation/spleen_segmentation_3d.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d.ipynb)"
    ]
   },
   {

3d_segmentation/spleen_segmentation_3d_lightning.ipynb

@@ -34,7 +34,7 @@
     "Source: Memorial Sloan Kettering Cancer Center  \n",
     "Challenge: Large ranging foreground size\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/3d_segmentation/spleen_segmentation_3d_lightning.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d_lightning.ipynb)"
    ]
   },
   {

3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb

@@ -31,7 +31,7 @@
     "Source: Memorial Sloan Kettering Cancer Center  \n",
     "Challenge: Large ranging foreground size\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/3d_segmentation/spleen_segmentation_3d.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d.ipynb)"
    ]
   },
   {

3d_segmentation/unet_segmentation_3d_catalyst.ipynb

@@ -22,9 +22,9 @@
     "* Sliding window inference method.\n",
     "* Deterministic training for reproducibility.\n",
     "\n",
-    "This tutorial is based on [unet_training_dict.py](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_training_dict.py) and [spleen_segmentation_3d.ipynb](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb).\n",
+    "This tutorial is based on [unet_training_dict.py](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/torch/unet_training_dict.py) and [spleen_segmentation_3d.ipynb](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d.ipynb).\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/3d_segmentation/unet_segmentation_3d_catalyst.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_catalyst.ipynb)"
    ]
   },
   {

3d_segmentation/unet_segmentation_3d_ignite.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# 3D Segmentation with UNet\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/3d_segmentation/unet_segmentation_3d_ignite.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_ignite.ipynb)"
    ]
   },
   {

README.md

@@ -130,6 +130,12 @@ This tutorial demonstrates the use of MONAI for training of registration and seg
 #### [Deepgrow](./deepgrow)
 The example show how to train/validate a 2D/3D deepgrow model.  It also demonstrates running an inference for trained deepgrow models.
 
+**DeepEdit**
+#### [DeepEdit](./deepedit/ignite)
+This example shows how to train/test a DeepEdit model. In this tutorial there is a Notebook that shows how to run
+inference on a pretrained DeepEdit model.
+
+
 **deployment**
 #### [BentoML](./deployment/bentoml)
 This is a simple example of training and deploying a MONAI network with [BentoML](https://www.bentoml.ai/) as a web server, either locally using the BentoML respository or as a containerized service.

acceleration/automatic_mixed_precision.ipynb

@@ -16,7 +16,7 @@
     "\n",
     "The Spleen dataset can be downloaded from http://medicaldecathlon.com/.\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/acceleration/automatic_mixed_precision.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/acceleration/automatic_mixed_precision.ipynb)"
    ]
   },
   {

acceleration/dataset_type_performance.ipynb

@@ -15,9 +15,9 @@
     "\n",
     "`PersistentDataset` processes original data sources through the non-random transforms on first use, and stores these intermediate tensor values to an on-disk persistence representation.  The intermediate processed tensors are loaded from disk on each use for processing by the random-transforms for each analysis request.  The `PersistentDataset` has a similar memory footprint to the simple `Dataset`, with performance characteristics close to the `CacheDataset` at the expense of disk storage.  Additionally, the cost of first time processing of data is distributed across each first use.\n",
     "\n",
-    "It's modified from the [Spleen 3D segmentation tutorial notebook](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb).\n",
+    "It's modified from the [Spleen 3D segmentation tutorial notebook](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d.ipynb).\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/acceleration/dataset_type_performance.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/acceleration/dataset_type_performance.ipynb)"
    ]
   },
   {