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Update NNUnet to dynUNet in the tutorial #26

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Sep 19, 2020
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Update NNUnet to dynUNet in the tutorial #26

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cbcfa49
[DLMED] update NNUnet to dynUNet
Nic-Ma Sep 19, 2020
4a5253c
[DLMED] fix flake8 issue
Nic-Ma Sep 19, 2020
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -51,8 +51,8 @@ This tutorial shows how to leverage `EnsembleEvaluator`, `MeanEnsemble` and `Vot
This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
#### [nifti_read_example](./nifti_read_example.ipynb)
Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.
#### [nnunet_tutorial](./nnunet_tutorial.ipynb)
This tutorial shows how to train 3D segmentation tasks on all the 10 decathlon datasets with the reimplementation of NNUnet in MONAI.
#### [dynunet_tutorial](./dynunet_tutorial.ipynb)
This tutorial shows how to train 3D segmentation tasks on all the 10 decathlon datasets with the reimplementation of dynUNet in MONAI.
#### [post_transforms](./post_transforms.ipynb)
This notebook shows the usage of several post transforms based on the model output of spleen segmentation task.
#### [public_datasets](./public_datasets.ipynb)
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18 changes: 9 additions & 9 deletions nnunet_tutorial.ipynb → dynunet_tutorial.ipynb
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Expand Up @@ -4,8 +4,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train NNUNet on Decathlon datasets\n",
"This tutorial shows how to train 3D segmentation tasks on all the 10 decathlon datasets.\n",
"# Train DynUNet on Decathlon datasets\n",
"This tutorial shows how to train 3D segmentation tasks on all the 10 decathlon datasets with `DynUNet`.\n",
"\n",
"Refer to papers:\n",
"\n",
Expand Down Expand Up @@ -90,7 +90,7 @@
"from monai.handlers import MeanDice, StatsHandler\n",
"from monai.inferers import SimpleInferer\n",
"from monai.losses import DiceLoss\n",
"from monai.networks.nets import NNUnet\n",
"from monai.networks.nets import DynUNet\n",
"from monai.transforms import (\n",
" Compose,\n",
" LoadNiftid,\n",
Expand Down Expand Up @@ -118,7 +118,7 @@
"metadata": {},
"source": [
"## Select Decathlon task\n",
"The Decathlon dataset contains 10 tasks, this NNUNet tutorial can support all of them.\n",
"The Decathlon dataset contains 10 tasks, this dynUNet tutorial can support all of them.\n",
"\n",
"Just need to select task ID and other parameters will be automatically selected.\n",
"\n",
Expand Down Expand Up @@ -448,7 +448,7 @@
" strides.append(stride)\n",
"strides.insert(0, len(spacings) * [1])\n",
"kernels.append(len(spacings) * [3])\n",
"net = NNUnet(\n",
"net = DynUNet(\n",
" spatial_dims=3,\n",
" in_channels=in_channels,\n",
" out_channels=n_class,\n",
Expand Down Expand Up @@ -491,7 +491,7 @@
"]\n",
"\n",
"# Define customized evaluator\n",
"class nnUNetEvaluator(SupervisedEvaluator):\n",
"class DynUNetEvaluator(SupervisedEvaluator):\n",
" def _iteration(self, engine, batchdata):\n",
" inputs, targets = self.prepare_batch(batchdata)\n",
" inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device)\n",
Expand All @@ -513,7 +513,7 @@
" return {\"image\": inputs, \"label\": targets, \"pred\": predictions}\n",
"\n",
"\n",
"evaluator = nnUNetEvaluator(\n",
"evaluator = DynUNetEvaluator(\n",
" device=device,\n",
" val_data_loader=val_loader,\n",
" network=net,\n",
Expand Down Expand Up @@ -559,7 +559,7 @@
"]\n",
"\n",
"# define customized trainer\n",
"class nnUNetTrainer(SupervisedTrainer):\n",
"class DynUNetTrainer(SupervisedTrainer):\n",
" def _iteration(self, engine, batchdata):\n",
" inputs, targets = self.prepare_batch(batchdata)\n",
" inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device)\n",
Expand All @@ -585,7 +585,7 @@
" return {\"image\": inputs, \"label\": targets, \"pred\": predictions, \"loss\": loss.item()}\n",
"\n",
"\n",
"trainer = nnUNetTrainer(\n",
"trainer = DynUNetTrainer(\n",
" device=device,\n",
" max_epochs=max_epochs,\n",
" train_data_loader=train_loader,\n",
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