{ "metadata": { "kernelspec": { "language": "python", "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python", "version": "3.10.12", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" }, "kaggle": { "accelerator": "nvidiaTeslaT4", "dataSources": [ { "sourceId": 1267593, "sourceType": "datasetVersion", "datasetId": 723383 }, { "sourceId": 1299795, "sourceType": "datasetVersion", "datasetId": 751906 } ], "dockerImageVersionId": 30823, "isInternetEnabled": true, "language": "python", "sourceType": "notebook", "isGpuEnabled": true } }, "nbformat_minor": 4, "nbformat": 4, "cells": [ { "metadata": { "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "trusted": true, "execution": { "iopub.status.busy": "2025-01-09T16:36:25.227303Z", "iopub.execute_input": "2025-01-09T16:36:25.227597Z", "iopub.status.idle": "2025-01-09T16:36:35.081281Z", "shell.execute_reply.started": "2025-01-09T16:36:25.227573Z", "shell.execute_reply": "2025-01-09T16:36:35.080659Z" }, "ExecuteTime": { "end_time": "2025-01-22T23:43:43.873814Z", "start_time": "2025-01-22T23:43:37.780946Z" } }, "cell_type": "code", "source": [ "import segmentation_models_pytorch as smp\n", "import os\n", "import matplotlib.pyplot as plt\n", "from PIL import Image\n", "import numpy as np\n", "import torch\n", "from torch.fx.experimental.meta_tracer import torch_abs_override\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision import transforms, utils\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim import lr_scheduler\n", "import time\n", "import albumentations as Album\n", "import torch.nn.functional as Functional\n", "import pandas as pd\n", "import nibabel as nib\n", "from tqdm import tqdm\n" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\sammi\\anaconda3\\envs\\tensorflow-env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2025-01-22T23:45:08.341728Z", "start_time": "2025-01-22T23:45:07.700331Z" } }, "cell_type": "code", "source": "! pip show albumentations", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name: albumentations\n", "Version: 1.1.0\n", "Summary: Fast image augmentation library and easy to use wrapper around other libraries\n", "Home-page: https://github.com/albumentations-team/albumentations\n", "Author: Buslaev Alexander, Alexander Parinov, Vladimir Iglovikov, Eugene Khvedchenya, Druzhinin Mikhail\n", "Author-email: \n", "License: MIT\n", "Location: c:\\users\\sammi\\anaconda3\\envs\\tensorflow-env\\lib\\site-packages\n", "Requires: numpy, opencv-python-headless, PyYAML, qudida, scikit-image, scipy\n", "Required-by: \n" ] } ], "execution_count": 2 }, { "cell_type": "code", "source": [ "training_df = pd.read_csv('data/archive/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/name_mapping.csv')\n", "root_df = 'data/archive/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData'" ], "metadata": { "trusted": true, "execution": { "iopub.status.busy": "2025-01-09T16:36:48.478879Z", "iopub.execute_input": "2025-01-09T16:36:48.479196Z", "iopub.status.idle": "2025-01-09T16:36:48.500028Z", "shell.execute_reply.started": "2025-01-09T16:36:48.479170Z", "shell.execute_reply": "2025-01-09T16:36:48.499404Z" }, "ExecuteTime": { "end_time": "2025-01-22T23:45:09.393585Z", "start_time": "2025-01-22T23:45:09.379074Z" } }, "outputs": [], "execution_count": 3 }, { "cell_type": "code", "source": "training_df.head(10)", "metadata": { "trusted": true, "execution": { "iopub.status.busy": "2025-01-09T16:36:51.383835Z", "iopub.execute_input": "2025-01-09T16:36:51.384165Z", "iopub.status.idle": "2025-01-09T16:36:51.401352Z", "shell.execute_reply.started": "2025-01-09T16:36:51.384140Z", "shell.execute_reply": "2025-01-09T16:36:51.400713Z" }, "ExecuteTime": { "end_time": "2025-01-22T23:45:11.768222Z", "start_time": "2025-01-22T23:45:11.753114Z" } }, "outputs": [ { "data": { "text/plain": [ " Grade BraTS_2017_subject_ID BraTS_2018_subject_ID TCGA_TCIA_subject_ID \\\n", "0 HGG Brats17_CBICA_AAB_1 Brats18_CBICA_AAB_1 NaN \n", "1 HGG Brats17_CBICA_AAG_1 Brats18_CBICA_AAG_1 NaN \n", "2 HGG Brats17_CBICA_AAL_1 Brats18_CBICA_AAL_1 NaN \n", "3 HGG Brats17_CBICA_AAP_1 Brats18_CBICA_AAP_1 NaN \n", "4 HGG Brats17_CBICA_ABB_1 Brats18_CBICA_ABB_1 NaN \n", "5 HGG Brats17_CBICA_ABE_1 Brats18_CBICA_ABE_1 NaN \n", "6 HGG Brats17_CBICA_ABM_1 Brats18_CBICA_ABM_1 NaN \n", "7 HGG Brats17_CBICA_ABN_1 Brats18_CBICA_ABN_1 NaN \n", "8 HGG Brats17_CBICA_ABO_1 Brats18_CBICA_ABO_1 NaN \n", "9 HGG Brats17_CBICA_ABY_1 Brats18_CBICA_ABY_1 NaN \n", "\n", " BraTS_2019_subject_ID BraTS_2020_subject_ID \n", "0 BraTS19_CBICA_AAB_1 BraTS20_Training_001 \n", "1 BraTS19_CBICA_AAG_1 BraTS20_Training_002 \n", "2 BraTS19_CBICA_AAL_1 BraTS20_Training_003 \n", "3 BraTS19_CBICA_AAP_1 BraTS20_Training_004 \n", "4 BraTS19_CBICA_ABB_1 BraTS20_Training_005 \n", "5 BraTS19_CBICA_ABE_1 BraTS20_Training_006 \n", "6 BraTS19_CBICA_ABM_1 BraTS20_Training_007 \n", "7 BraTS19_CBICA_ABN_1 BraTS20_Training_008 \n", "8 BraTS19_CBICA_ABO_1 BraTS20_Training_009 \n", "9 BraTS19_CBICA_ABY_1 BraTS20_Training_010 " ], "text/html": [ "
\n", " | Grade | \n", "BraTS_2017_subject_ID | \n", "BraTS_2018_subject_ID | \n", "TCGA_TCIA_subject_ID | \n", "BraTS_2019_subject_ID | \n", "BraTS_2020_subject_ID | \n", "
---|---|---|---|---|---|---|
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7 | \n", "HGG | \n", "Brats17_CBICA_ABN_1 | \n", "Brats18_CBICA_ABN_1 | \n", "NaN | \n", "BraTS19_CBICA_ABN_1 | \n", "BraTS20_Training_008 | \n", "
8 | \n", "HGG | \n", "Brats17_CBICA_ABO_1 | \n", "Brats18_CBICA_ABO_1 | \n", "NaN | \n", "BraTS19_CBICA_ABO_1 | \n", "BraTS20_Training_009 | \n", "
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