modification done, pending final checks

Author: Yuchen Xu

acceleration/automatic_mixed_precision.ipynb

@@ -71,7 +71,7 @@
     "import torch\n",
     "from monai.apps import download_and_extract\n",
     "from monai.config import print_config\n",
-    "from monai.data import CacheDataset, list_data_collate, decollate_batch\n",
+    "from monai.data import DataLoader, CacheDataset, list_data_collate, decollate_batch\n",
     "from monai.inferers import sliding_window_inference\n",
     "from monai.losses import DiceLoss\n",
     "from monai.metrics import DiceMetric\n",
@@ -90,7 +90,6 @@
     "    Spacingd,\n",
     ")\n",
     "from monai.utils import get_torch_version_tuple, set_determinism\n",
-    "from torch.utils.data import DataLoader\n",
     "\n",
     "print_config()\n",
     "\n",
@@ -286,7 +285,6 @@
     "        batch_size=2,\n",
     "        shuffle=True,\n",
     "        num_workers=1,\n",
-    "        collate_fn=list_data_collate,\n",
     "    )\n",
     "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=1)\n",
     "    device = torch.device(\"cuda:0\")\n",
@@ -373,8 +371,8 @@
     "                        val_outputs = sliding_window_inference(\n",
     "                            val_inputs, roi_size, sw_batch_size, model\n",
     "                        )\n",
-    "                    val_outputs = [post_pred(i) for i in decollate_batch(val_outputs.as_tensor())]\n",
-    "                    val_labels = [post_label(i) for i in decollate_batch(val_labels.as_tensor())]\n",
+    "                    val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
+    "                    val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
     "                    dice_metric(y_pred=val_outputs, y=val_labels)\n",
     "\n",
     "                metric = dice_metric.aggregate().item()\n",

acceleration/fast_training_tutorial.ipynb

@@ -636,8 +636,8 @@
     "\n",
     "                        # profiling: decollate batch\n",
     "                        with nvtx.annotate(\"decollate batch\", color=\"blue\") if profiling else no_profiling:\n",
-    "                            val_outputs = [post_pred(i) for i in decollate_batch(val_outputs.as_tensor())]\n",
-    "                            val_labels = [post_label(i) for i in decollate_batch(val_labels.as_tensor())]\n",
+    "                            val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
+    "                            val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
     "\n",
     "                        # profiling: compute metric\n",
     "                        with nvtx.annotate(\"compute metric\", color=\"yellow\") if profiling else no_profiling:\n",