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Update decollate tutorial to highlight ToTensor #256

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Merged
yiheng-wang-nv merged 1 commit into from
Jul 9, 2021
Merged

Update decollate tutorial to highlight ToTensor #256

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20 changes: 12 additions & 8 deletions modules/decollate_batch.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -76,11 +76,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"MONAI version: 0.4.0+546.gfbd6e65\n",
"MONAI version: 0.4.0+540.g8803885\n",
"Numpy version: 1.21.0\n",
"Pytorch version: 1.9.0+cu102\n",
"MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
"MONAI rev id: fbd6e6597a66b64821f6ef0b4da2560b103c00b9\n",
"MONAI rev id: 88038854f6cd256989695c2368e3ee9fca213e8d\n",
"\n",
"Optional dependencies:\n",
"Pytorch Ignite version: 0.4.5\n",
Expand Down Expand Up @@ -260,19 +260,22 @@
"metadata": {},
"source": [
"## Setup postprocessing transforms, metrics\n",
"Here we try to invert the preprocessing predictions for `pred` and save into Nifti files."
"Here we try to invert the preprocessing predictions for `pred` and save into Nifti files.\n",
"\n",
"As all the post processing transforms expect `Tensor` input, apply `ToTensord` first to ensure the data type after `decollate_batch`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 9,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"postprocessing = Compose(\n",
" [\n",
" ToTensord(keys=[\"pred\", \"seg\"]), # ensure Tensor type after `decollate`\n",
" Activationsd(keys=\"pred\", sigmoid=True),\n",
" Invertd(\n",
" keys=\"pred\", # invert the `pred` data field, also support multiple fields\n",
Expand Down Expand Up @@ -329,9 +332,10 @@
"\n",
" # decollate the batch data into list of dictionaries, every dictionary maps to an input data\n",
" data = [postprocessing(i) for i in decollate_batch(data)]\n",
" # compute metric for current iteration\n",
" # extract a list of `prections` and a list of `labels` with the `from_engine` utility\n",
" dice_metric(y_pred=from_engine(\"pred\")(data), y=from_engine(\"seg\")(data))\n",
" pred, y = from_engine([\"pred\", \"seg\"])(data)\n",
" # compute mean dice for current iteration\n",
" dice_metric(y_pred=pred, y=y)\n",
" # aggregate the final mean dice result\n",
" print(f\"evaluation metric: {dice_metric.aggregate().item()}\")\n",
" # reset the metric status\n",
Expand All @@ -349,7 +353,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 12,
"metadata": {
"pycharm": {
"is_executing": true
Expand Down Expand Up @@ -378,7 +382,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
"version": "3.8.5"
}
},
"nbformat": 4,
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4 changes: 3 additions & 1 deletion modules/transfer_mmar.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -424,6 +424,7 @@
],
"source": [
"train_ds = LMDBDataset(data=train_files, transform=train_transforms, cache_dir=root_dir)\n",
"# initialize cache and print meta information\n",
"print(train_ds.info())\n",
"\n",
"# use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
Expand All @@ -433,6 +434,7 @@
"# the validation data loader will be created on the fly to ensure \n",
"# a deterministic validation set for demo purpose.\n",
"val_ds = LMDBDataset(data=val_files, transform=val_transforms, cache_dir=root_dir)\n",
"# initialize cache and print meta information\n",
"print(val_ds.info())"
]
},
Expand Down Expand Up @@ -825,7 +827,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
"version": "3.8.5"
}
},
"nbformat": 4,
Expand Down