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Simplify the notebook (WIP)
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‎playing_with_pytorch.ipynb

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@@ -37,11 +37,23 @@
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"id": "oWi4McPs_NuY"
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},
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"source": [
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"!pip install -qq SimpleITK"
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"!pip install -qq SimpleITK nnunetv2"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%cd /content\n",
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"!git clone https://github.com/woctezuma/playing-with-simpleitk.git"
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],
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"metadata": {
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"id": "vL5WJI3NVxIe"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
@@ -163,18 +175,6 @@
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%cd /content\n",
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"!git clone https://github.com/woctezuma/playing-with-simpleitk.git\n"
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],
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"metadata": {
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"id": "h2Jbh5kO7Z9v"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
@@ -204,8 +204,8 @@
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"id": "HbPo9F8kB31W"
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},
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"source": [
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"%mkdir -p /content/data\n",
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"%mv /content/playing-with-simpleitk/data/output/* /content/data/"
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"%mkdir -p /content/nnUNet_base/Dataset000_MY_DATASET\n",
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"%mv /content/playing-with-simpleitk/data/output/* /content/nnUNet_base/Dataset000_MY_DATASET/"
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],
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"execution_count": null,
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"outputs": []
@@ -545,8 +545,7 @@
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"\n",
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" output_image_name = input_image_name\n",
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" sitk.WriteImage(output_image, output_image_name)\n",
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" print('Image size: {}'.format(output_image.GetSize()))\n",
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""
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" print('Image size: {}'.format(output_image.GetSize()))\n"
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],
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"execution_count": null,
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"outputs": []
@@ -572,65 +571,27 @@
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{
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"cell_type": "code",
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"source": [
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"%cd /content\n",
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"!git clone https://github.com/MIC-DKFZ/nnUNet.git\n",
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"%cd nnUNet\n",
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"%pip install -e ."
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"%env nnUNet_raw=/content/nnUNet_base/\n",
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"%env nnUNet_preprocessed=/content/nnUNet_preprocessed/\n",
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"%env nnUNet_results=/content/RESULTS_FOLDER/"
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],
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"metadata": {
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"id": "l3OXi7a40T5b"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "lOP7FXOdLP4G"
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},
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"source": [
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"### Copy data to the right folder"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "8EHGKR_uKTNA"
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},
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"source": [
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"%mkdir -p /content/nnUNet_base/nnUNet_raw_splitted/\n",
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"%mv /content/data /content/nnUNet_base/nnUNet_raw_splitted/Dataset000_MY_DATASET"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "9jdfq0ZASGDX"
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},
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"source": [
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"## Edit dataset.json at the root of Task00_MY_DATASET/\n",
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"## Edit dataset.json at the root of Dataset000_MY_DATASET/\n",
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"\n",
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"Reference: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/LiverTumorSegmentationChallenge.py"
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"Reference: https://github.com/MIC-DKFZ/nnUNet/tree/master/nnunetv2/dataset_conversion"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "yrd4WM8CsFLB"
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},
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"source": [
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"output_folder = \"/content/nnUNet_base/nnUNet_raw_splitted/Dataset000_MY_DATASET/\"\n",
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"\n",
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"train_ids = [\n",
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" 'patientID{}'.format(patient_no)\n",
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" for patient_no in get_patient_indices()\n",
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"]"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
@@ -641,6 +602,8 @@
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"import json\n",
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"from collections import OrderedDict\n",
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"\n",
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"output_folder = \"/content/nnUNet_base/Dataset000_MY_DATASET/\"\n",
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"\n",
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"json_dict = OrderedDict()\n",
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"\n",
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"json_dict['channel_names'] = {\n",
@@ -652,40 +615,16 @@
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" \"liver\": 1,\n",
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"}\n",
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"\n",
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"json_dict['numTraining'] = len(train_ids)\n",
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"num_patients = len(get_patient_indices())\n",
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"json_dict['numTraining'] = num_patients\n",
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"json_dict['file_ending'] = \".nii.gz\"\n",
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"\n",
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"# json_dict['training'] = [{'image': \"./imagesTr/%s.nii.gz\" % i, \"label\": \"./labelsTr/%s.nii.gz\" % i} for i in train_ids]\n",
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"# json_dict['test'] = [\"./imagesTs/%s.nii.gz\" % i for i in test_ids]\n",
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"\n",
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"with open(os.path.join(output_folder, \"dataset.json\"), 'w') as f:\n",
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" json.dump(json_dict, f, indent=4, sort_keys=True)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "IhKCU_jl0gIU"
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},
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"source": [
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"## How many processes (pl, pf) on Google Colab?\n",
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"\n",
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"Apparently, 2.\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Tj-lOLy26lJt"
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},
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"source": [
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"!cat /proc/cpuinfo"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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{
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"cell_type": "code",
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"source": [
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"%cd /content/nnUNet/nnunetv2"
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],
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"metadata": {
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"id": "FO53PI9-23v1"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%env nnUNet_raw=/content/nnUNet_base/nnUNet_raw_splitted/\n",
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"%env nnUNet_preprocessed=/content/nnUNet_preprocessed/\n",
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"%env nnUNet_results=/content/RESULTS_FOLDER/"
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],
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"metadata": {
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"id": "0v4Ty7Vi9Uzh"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"!nnUNetv2_plan_and_preprocess -d 0 --verify_dataset_integrity"
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"!nnUNetv2_plan_and_preprocess -d 000 --verify_dataset_integrity"
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],
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"metadata": {
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"id": "JrqUAqQH3GQ8"
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"## Training"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Ns26aJUi2O1V"
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},
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"source": [
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"!python run/run_training.py -h"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "WC_sIpdZ2E8I"
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},
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"source": [
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"!OMP_NUM_THREADS=1 python run/run_training.py Dataset000_MY_DATASET 3d_fullres all"
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"!nnUNetv2_train Dataset000_MY_DATASET 3d_fullres all\n",
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"\n",
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"# To continue training, add --c as follows:\n",
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"# !nnUNetv2_train Dataset000_MY_DATASET 3d_fullres all --c"
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],
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"execution_count": null,
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"outputs": []
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"## Inference"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "cNDhwjmSsrCe"
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},
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"source": [
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"!OMP_NUM_THREADS=1 python inference/predict_from_raw_data.py -h"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "NujgNKHarzLw"
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},
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"source": [
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"INPUT_FOLDER='/content/nnUNet_base/nnUNet_raw_splitted/Dataset000_MY_DATASET/imagesTr/'"
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"INPUT_FOLDER='/content/nnUNet_base/Dataset000_MY_DATASET/imagesTr/'"
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],
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"execution_count": null,
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"outputs": []
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%pip install nnunetv2"
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],
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"metadata": {
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"id": "HcvYQtXfDssP"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zf9hZKqCrNph"
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},
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"source": [
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"!nnUNetv2_predict -i $INPUT_FOLDER -o $OUTPUT_FOLDER -d Dataset000_MY_DATASET -f all -c 3d_fullres"
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"for fname in [\"2d\", \"3d_fullres\"]\n",
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" %cd /content/RESULTS_FOLDER/Dataset000_MY_DATASET/nnUNetTrainer__nnUNetPlans__{fname}/fold_all\n",
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" %cp checkpoint_best.pth checkpoint_final.pth\n",
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"\n",
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"# !nnUNetv2_predict -i $INPUT_FOLDER -o $OUTPUT_FOLDER -d Dataset000_MY_DATASET -f all -c 3d_fullres\n",
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"!nnUNetv2_predict -i $INPUT_FOLDER -o $OUTPUT_FOLDER -d Dataset000_MY_DATASET -f all -c 2d"
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],
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"execution_count": null,
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"outputs": []
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"import SimpleITK as sitk\n",
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"\n",
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"dice_scores = []\n",
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"original_data_folder = '/content/nnUNet_base/nnUNet_raw_splitted/Dataset000_MY_DATASET/'\n",
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"original_data_folder = '/content/nnUNet_base/Dataset000_MY_DATASET/'\n",
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"downsampled_data_folder = '/content/data/'\n",
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"\n",
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"for patient_no in get_patient_indices():\n",
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mNWntxcMtOpx"
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},
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"source": [
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"[DONE] Download data and display segmentation overlayed on CT images with ITK-SNAP."
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]
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}
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]
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}

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