Update NNUnet to dynUNet in the tutorial (#26)

* [DLMED] update NNUnet to dynUNet

Signed-off-by: Nic Ma <[email protected]>

* [DLMED] fix flake8 issue

Signed-off-by: Nic Ma <[email protected]>

Author: Nic-Ma

README.md

@@ -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)

nnunet_tutorial.ipynb renamed to dynunet_tutorial.ipynb

@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",