Buckets:
| {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyOC5VFS8bXHGW7/4Z/0s8WD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E4I1xjuDS33O","executionInfo":{"status":"ok","timestamp":1755138347983,"user_tz":-345,"elapsed":1037,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"6c8058b9-f8dd-4346-f597-ef4370f706e7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'EMCAD'...\n","remote: Enumerating objects: 241, done.\u001b[K\n","remote: Counting objects: 100% (64/64), done.\u001b[K\n","remote: Compressing objects: 100% (64/64), done.\u001b[K\n","remote: Total 241 (delta 35), reused 2 (delta 0), pack-reused 177 (from 1)\u001b[K\n","Receiving objects: 100% (241/241), 2.04 MiB | 24.92 MiB/s, done.\n","Resolving deltas: 100% (105/105), done.\n","/content/EMCAD\n"]}],"source":["!git clone https://github.com/SLDGroup/EMCAD.git\n","%cd EMCAD"]},{"cell_type":"code","source":["!wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n","!chmod +x Miniconda3-latest-Linux-x86_64.sh\n","!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n","\n","import sys\n","sys.path.append('/usr/local/lib/python3.9/site-packages')\n","\n","!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main\n","!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vpRXmMIWTJy8","executionInfo":{"status":"ok","timestamp":1755138374536,"user_tz":-345,"elapsed":26526,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"28c1c83e-58ff-4bc7-8abd-e7e8a8c4cb86"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["--2025-08-14 02:25:53-- https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n","Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:20f1, ...\n","Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 160039710 (153M) [application/octet-stream]\n","Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’\n","\n","Miniconda3-latest-L 100%[===================>] 152.62M 329MB/s in 0.5s \n","\n","2025-08-14 02:25:53 (329 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [160039710/160039710]\n","\n","PREFIX=/usr/local\n","Unpacking payload ...\n","entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n","entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n","\n","Installing base environment...\n","\n","Preparing transaction: ...working... done\n","Executing transaction: ...working... done\n","entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n","installation finished.\n","WARNING:\n"," You currently have a PYTHONPATH environment variable set. This may cause\n"," unexpected behavior when running the Python interpreter in Miniconda3.\n"," For best results, please verify that your PYTHONPATH only points to\n"," directories of packages that are compatible with the Python interpreter\n"," in Miniconda3: /usr/local\n","accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/main\u001b[0m\n","accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/r\u001b[0m\n"]}]},{"cell_type":"code","source":["!conda create -n newenv python=3.8 -y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yCuSe4AiTPLb","executionInfo":{"status":"ok","timestamp":1755138383302,"user_tz":-345,"elapsed":8754,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"75ff8708-1d78-4d85-acb0-cf3011b0f4d1","collapsed":true},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;32m2\u001b[0m\u001b[1;32m channel Terms of Service accepted\u001b[0m\n","Channels:\n"," - defaults\n","Platform: linux-64\n","Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n","Solving environment: | \b\bdone\n","\n","\n","==> WARNING: A newer version of conda exists. <==\n"," current version: 25.5.1\n"," latest version: 25.7.0\n","\n","Please update conda by running\n","\n"," $ conda update -n base -c defaults conda\n","\n","\n","\n","## Package Plan ##\n","\n"," environment location: /usr/local/envs/newenv\n","\n"," added / updated specs:\n"," - python=3.8\n","\n","\n","The following packages will be downloaded:\n","\n"," package | build\n"," ---------------------------|-----------------\n"," ca-certificates-2025.7.15 | h06a4308_0 126 KB\n"," ncurses-6.5 | h7934f7d_0 1.1 MB\n"," openssl-3.0.17 | h5eee18b_0 5.2 MB\n"," pip-24.2 | py38h06a4308_0 2.2 MB\n"," python-3.8.20 | he870216_0 23.8 MB\n"," readline-8.3 | hc2a1206_0 471 KB\n"," setuptools-75.1.0 | py38h06a4308_0 1.7 MB\n"," wheel-0.44.0 | py38h06a4308_0 108 KB\n"," ------------------------------------------------------------\n"," Total: 34.7 MB\n","\n","The following NEW packages will be INSTALLED:\n","\n"," _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main \n"," _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu \n"," ca-certificates pkgs/main/linux-64::ca-certificates-2025.7.15-h06a4308_0 \n"," ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 \n"," libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 \n"," libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 \n"," libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 \n"," libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 \n"," libxcb pkgs/main/linux-64::libxcb-1.17.0-h9b100fa_0 \n"," ncurses pkgs/main/linux-64::ncurses-6.5-h7934f7d_0 \n"," openssl pkgs/main/linux-64::openssl-3.0.17-h5eee18b_0 \n"," pip pkgs/main/linux-64::pip-24.2-py38h06a4308_0 \n"," pthread-stubs pkgs/main/linux-64::pthread-stubs-0.3-h0ce48e5_1 \n"," python pkgs/main/linux-64::python-3.8.20-he870216_0 \n"," readline pkgs/main/linux-64::readline-8.3-hc2a1206_0 \n"," setuptools pkgs/main/linux-64::setuptools-75.1.0-py38h06a4308_0 \n"," sqlite pkgs/main/linux-64::sqlite-3.50.2-hb25bd0a_1 \n"," tk pkgs/main/linux-64::tk-8.6.14-h993c535_1 \n"," wheel pkgs/main/linux-64::wheel-0.44.0-py38h06a4308_0 \n"," xorg-libx11 pkgs/main/linux-64::xorg-libx11-1.8.12-h9b100fa_1 \n"," xorg-libxau pkgs/main/linux-64::xorg-libxau-1.0.12-h9b100fa_0 \n"," xorg-libxdmcp pkgs/main/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0 \n"," xorg-xorgproto pkgs/main/linux-64::xorg-xorgproto-2024.1-h5eee18b_1 \n"," xz pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 \n"," zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 \n","\n","\n","\n","Downloading and Extracting Packages:\n","python-3.8.20 | 23.8 MB | : 0% 0/1 [00:00<?, ?it/s]\n","openssl-3.0.17 | 5.2 MB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\n","\n","pip-24.2 | 2.2 MB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n","\n","\n","setuptools-75.1.0 | 1.7 MB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","ncurses-6.5 | 1.1 MB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","readline-8.3 | 471 KB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","ca-certificates-2025 | 126 KB | : 0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","\n","python-3.8.20 | 23.8 MB | : 4% 0.04074645080871521/1 [00:00<00:02, 2.46s/it]\n","openssl-3.0.17 | 5.2 MB | : 36% 0.35673573598464076/1 [00:00<00:00, 3.55it/s]\u001b[A\n","\n","pip-24.2 | 2.2 MB | : 45% 0.44561102975713535/1 [00:00<00:00, 4.41it/s]\u001b[A\u001b[A\n","\n","\n","setuptools-75.1.0 | 1.7 MB | : 73% 0.7306911085443828/1 [00:00<00:00, 7.29it/s]\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","ncurses-6.5 | 1.1 MB | : 73% 0.7344844226724107/1 [00:00<00:00, 7.21it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","ncurses-6.5 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 7.21it/s] \u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","setuptools-75.1.0 | 1.7 MB | : 100% 1.0/1 [00:00<00:00, 7.29it/s] \u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","readline-8.3 | 471 KB | : 3% 0.03400397653924861/1 [00:00<00:04, 4.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","pip-24.2 | 2.2 MB | : 100% 1.0/1 [00:00<00:00, 4.41it/s] \u001b[A\u001b[A\n","\n","\n","\n","\n","readline-8.3 | 471 KB | : 100% 1.0/1 [00:00<00:00, 4.70s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","ca-certificates-2025 | 126 KB | : 13% 0.12723855676187815/1 [00:00<00:01, 1.45s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","python-3.8.20 | 23.8 MB | : 19% 0.19387424175114495/1 [00:00<00:00, 1.07it/s]\n","openssl-3.0.17 | 5.2 MB | : 93% 0.9323093604304478/1 [00:00<00:00, 4.85it/s] \u001b[A\n","\n","\n","\n","\n","\n","\n","wheel-0.44.0 | 108 KB | : 15% 0.14783134378186213/1 [00:00<00:01, 1.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","\n","python-3.8.20 | 23.8 MB | : 38% 0.37920487284884963/1 [00:00<00:00, 1.37it/s]\n","python-3.8.20 | 23.8 MB | : 100% 1.0/1 [00:00<00:00, 2.34it/s] \n","\n","\n","setuptools-75.1.0 | 1.7 MB | : 100% 1.0/1 [00:01<00:00, 7.29it/s]\u001b[A\u001b[A\u001b[A\n","\n","pip-24.2 | 2.2 MB | : 100% 1.0/1 [00:01<00:00, 2.06s/it]\u001b[A\u001b[A\n","\n","pip-24.2 | 2.2 MB | : 100% 1.0/1 [00:01<00:00, 2.06s/it]\u001b[A\u001b[A\n","\n","\n","\n","\n","readline-8.3 | 471 KB | : 100% 1.0/1 [00:01<00:00, 1.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","readline-8.3 | 471 KB | : 100% 1.0/1 [00:01<00:00, 1.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","ca-certificates-2025 | 126 KB | : 100% 1.0/1 [00:01<00:00, 1.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","ca-certificates-2025 | 126 KB | : 100% 1.0/1 [00:01<00:00, 1.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","\n","wheel-0.44.0 | 108 KB | : 100% 1.0/1 [00:01<00:00, 1.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","\n","wheel-0.44.0 | 108 KB | : 100% 1.0/1 [00:01<00:00, 1.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","ncurses-6.5 | 1.1 MB | : 100% 1.0/1 [00:01<00:00, 7.21it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n"," \n"," \u001b[A\n","\n"," \u001b[A\u001b[A\n","\n","\n"," \u001b[A\u001b[A\u001b[A\n","\n","\n","\n"," \u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n"," \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n"," \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","\n","\n","\n","\n","\n","\n"," \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n","Preparing transaction: - \b\b\\ \b\b| \b\bdone\n","Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n","Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n","#\n","# To activate this environment, use\n","#\n","# $ conda activate newenv\n","#\n","# To deactivate an active environment, use\n","#\n","# $ conda deactivate\n","\n"]}]},{"cell_type":"code","source":["!source /usr/local/bin/activate newenv && pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n15A626mTSFV","executionInfo":{"status":"ok","timestamp":1755138483906,"user_tz":-345,"elapsed":100595,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"60b9cf06-a4d7-4580-db6c-507a9d4e299c"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu113\n","Collecting torch==1.11.0+cu113\n"," Downloading https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp38-cp38-linux_x86_64.whl (1637.0 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 GB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting torchvision==0.12.0+cu113\n"," Downloading https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp38-cp38-linux_x86_64.whl (22.3 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.3/22.3 MB\u001b[0m \u001b[31m160.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting torchaudio==0.11.0\n"," Downloading 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https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tjOoHiM-TW8j","executionInfo":{"status":"ok","timestamp":1755138495471,"user_tz":-345,"elapsed":11562,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"60d9a463-f13f-4bd6-c559-32c4eca20e3b"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html\n","Collecting mmcv-full\n"," Downloading https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/mmcv_full-1.7.2-cp38-cp38-manylinux1_x86_64.whl (45.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.6/45.6 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting addict (from mmcv-full)\n"," Downloading addict-2.4.0-py3-none-any.whl.metadata (1.0 kB)\n","Requirement already 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opencv-python-4.12.0.88 packaging-25.0 platformdirs-4.3.6 pyyaml-6.0.2 tomli-2.2.1 yapf-0.43.0\n"]}]},{"cell_type":"code","source":["!source /usr/local/bin/activate newenv && pip install --upgrade pip setuptools wheel"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"cVDNj5tRTcM2","executionInfo":{"status":"ok","timestamp":1755138499398,"user_tz":-345,"elapsed":3924,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"844df550-8ed9-4999-ef68-527498bae46e"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pip in /usr/local/envs/newenv/lib/python3.8/site-packages (24.2)\n","Collecting pip\n"," Downloading pip-25.0.1-py3-none-any.whl.metadata (3.7 kB)\n","Requirement already satisfied: setuptools in /usr/local/envs/newenv/lib/python3.8/site-packages (75.1.0)\n","Collecting setuptools\n"," Downloading setuptools-75.3.2-py3-none-any.whl.metadata (6.9 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threadpoolctl-3.5.0 tifffile-2023.7.10\n"]}]},{"cell_type":"code","source":["!source /usr/local/bin/activate newenv && pip install --only-binary :all: simpleitk nibabel h5py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LD446V9PXxi-","executionInfo":{"status":"ok","timestamp":1755138528296,"user_tz":-345,"elapsed":11478,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"90e417e3-8bcd-4d49-ffe1-b9b80a0cf5b9"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting simpleitk\n"," Downloading SimpleITK-2.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)\n","Collecting nibabel\n"," Downloading nibabel-5.2.1-py3-none-any.whl.metadata (8.8 kB)\n","Collecting h5py\n"," Downloading h5py-3.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\n","Collecting importlib-resources>=1.3 (from nibabel)\n"," Downloading 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medpy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6qbg8dchX8Fx","executionInfo":{"status":"ok","timestamp":1755138555845,"user_tz":-345,"elapsed":27546,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"ef1f3c12-7794-4485-f2c7-20eb41bd572b"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting loguru\n"," Downloading loguru-0.7.3-py3-none-any.whl.metadata (22 kB)\n","Collecting tqdm\n"," Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n","Requirement already satisfied: pyyaml in /usr/local/envs/newenv/lib/python3.8/site-packages (6.0.2)\n","Collecting pandas\n"," Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n","Collecting matplotlib\n"," Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB)\n","Collecting seaborn\n"," Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 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tensorboardX-2.6.2.2-py2.py3-none-any.whl (101 kB)\n","Building wheels for collected packages: warmup-scheduler, ml_collections, medpy\n"," Building wheel for warmup-scheduler (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for warmup-scheduler: filename=warmup_scheduler-0.3-py3-none-any.whl size=2969 sha256=5d1c63bade93419599a32069961239925cb1f643805f0e61889fb6ef3ccd2d0e\n"," Stored in directory: /root/.cache/pip/wheels/f2/ce/4a/215c4f0add432420ff90fe04656bf2664ddfac7302e2b6fe51\n"," Building wheel for ml_collections (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for ml_collections: filename=ml_collections-0.1.1-py3-none-any.whl size=94507 sha256=932f69a11122e1dc74713222fcf42c4c228463efc1def5ca48503b594e8ccd43\n"," Stored in directory: /root/.cache/pip/wheels/6d/9f/a9/9e8309035a5bf09ed9086bbca8c9b74cb6413d3eb203e2bc8c\n"," Building wheel for medpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for medpy: filename=medpy-0.5.2-py3-none-any.whl size=224705 sha256=15904860b02b89fb69e580d79048ebc708e24b79c30d536ee9ea60a574a13c45\n"," Stored in directory: /root/.cache/pip/wheels/78/6d/75/a6ff00011078066b7e52b4dc48f6a76e66773be86b0f58aff2\n","Successfully built warmup-scheduler ml_collections medpy\n","Installing collected packages: warmup-scheduler, torchsummaryx, torchsummary, torchprofile, thop, tensorboardx, segmentation-mask-overlay, transformers, tqdm, torchmetrics, timm, tabulate, seaborn, ptflops, pandas, ml_collections, medpy, matplotlib, loguru, huggingface-hub, einops, albumentations\n","Successfully installed albumentations-1.1.0 einops-0.8.1 huggingface-hub-0.11.0 loguru-0.7.3 matplotlib-3.7.5 medpy-0.5.2 ml_collections-0.1.1 pandas-2.0.3 ptflops-0.7.3 seaborn-0.13.2 segmentation-mask-overlay-0.3.4 tabulate-0.9.0 tensorboardx-2.6.2.2 thop-0.1.1.post2209072238 timm-0.6.12 torchmetrics-1.5.2 torchprofile-0.0.4 torchsummary-1.5.1 torchsummaryx-1.3.0 tqdm-4.67.1 transformers-4.21.3 warmup-scheduler-0.3\n"]}]},{"cell_type":"code","source":["import os\n","import zipfile\n","import random\n","import urllib.request\n","import numpy as np\n","from PIL import Image\n","from tqdm import tqdm\n","\n","# ==== CONFIG ====\n","DATA_ROOT = './data/Kvasir-SEG'\n","PROC_ROOT = './data/Kvasir-SEG_proc'\n","LIST_DIR = './lists/kvasir'\n","IMG_SIZE = (224, 224)\n","VAL_RATIO = 0.2\n","\n","# ImageNet normalization\n","MEAN = np.array([0.485, 0.456, 0.406])\n","STD = np.array([0.229, 0.224, 0.225])\n","\n","# ==== STEP 1: Download & unzip ====\n","os.makedirs(DATA_ROOT, exist_ok=True)\n","url = \"https://datasets.simula.no/downloads/kvasir-seg.zip\"\n","zip_path = os.path.join(DATA_ROOT, \"kvasir-seg.zip\")\n","extracted_dir = os.path.join(DATA_ROOT, 'Kvasir-SEG') # Directory where zip contents should be\n","\n","if not os.path.exists(extracted_dir):\n"," print(\"Downloading Kvasir-SEG...\")\n"," try:\n"," urllib.request.urlretrieve(url, zip_path)\n"," with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n"," zip_ref.extractall(DATA_ROOT)\n"," os.remove(zip_path)\n"," print(\"Download and extraction complete.\")\n"," except Exception as e:\n"," print(f\"Error during download or extraction: {e}\")\n"," # Exit or handle the error appropriately\n","else:\n"," print(\"Kvasir-SEG already exists, skipping download.\")\n","\n","# ==== STEP 2: Gather file names ====\n","# Corrected image and mask directories based on extraction path\n","img_dir = os.path.join(extracted_dir, 'images')\n","mask_dir = os.path.join(extracted_dir, 'masks')\n","\n","# Ensure the directories exist before listing\n","if not os.path.exists(img_dir) or not os.path.exists(mask_dir):\n"," print(\"Error: Image or mask directory not found after extraction.\")\n"," # Exit or handle the error appropriately\n","else:\n"," filenames = sorted(os.listdir(img_dir))\n"," random.shuffle(filenames)\n","\n"," # ==== STEP 3: Split train/val ====\n"," split_idx = int(len(filenames) * (1 - VAL_RATIO))\n"," train_files = filenames[:split_idx]\n"," val_files = filenames[split_idx:]\n","\n"," # ==== STEP 4: Preprocess & save as .npy ====\n"," def preprocess_and_save(file_list, split):\n"," out_img_dir = os.path.join(PROC_ROOT, split, 'images')\n"," out_mask_dir = os.path.join(PROC_ROOT, split, 'masks')\n"," os.makedirs(out_img_dir, exist_ok=True)\n"," os.makedirs(out_mask_dir, exist_ok=True)\n","\n"," for fname in tqdm(file_list, desc=f\"Processing {split}\"):\n"," # Load\n"," img = Image.open(os.path.join(img_dir, fname)).convert('RGB')\n"," mask = Image.open(os.path.join(mask_dir, fname)).convert('L')\n","\n"," # Resize\n"," img = img.resize(IMG_SIZE, Image.BILINEAR)\n"," mask = mask.resize(IMG_SIZE, Image.NEAREST)\n","\n"," # To numpy & normalize\n"," img_np = np.array(img, dtype=np.float32) / 255.0\n"," img_np = (img_np - MEAN) / STD\n"," img_np = img_np.transpose(2, 0, 1) # CHW\n","\n"," mask_np = (np.array(mask) > 0).astype(np.uint8)\n","\n"," # Save as .npy\n"," np.save(os.path.join(out_img_dir, fname.replace('.jpg', '.npy').replace('.png', '.npy')), img_np)\n"," np.save(os.path.join(out_mask_dir, fname.replace('.jpg', '.npy').replace('.png', '.npy')), mask_np)\n","\n"," # Run preprocess\n"," preprocess_and_save(train_files, 'train')\n"," preprocess_and_save(val_files, 'val')\n","\n"," # ==== STEP 5: Create list files ====\n"," os.makedirs(LIST_DIR, exist_ok=True)\n"," with open(os.path.join(LIST_DIR, 'train.txt'), 'w') as f:\n"," for fname in train_files:\n"," f.write(fname.replace('.jpg', '.npy').replace('.png', '.npy') + '\\n')\n","\n"," # EMCAD's test loader uses \"test_vol.txt\"\n"," with open(os.path.join(LIST_DIR, 'test_vol.txt'), 'w') as f:\n"," for fname in val_files:\n"," f.write(fname.replace('.jpg', '.npy').replace('.png', '.npy') + '\\n')\n","\n"," print(\"\\n✅ All done!\")\n"," print(f\"Processed data saved to: {PROC_ROOT}\")\n"," print(f\"List files saved to: {LIST_DIR}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pMCStATdTiLf","executionInfo":{"status":"ok","timestamp":1755138572618,"user_tz":-345,"elapsed":16776,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"46682185-d2d3-4934-d5fc-cc3861148968"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading Kvasir-SEG...\n","Download and extraction complete.\n"]},{"output_type":"stream","name":"stderr","text":["Processing train: 100%|██████████| 800/800 [00:12<00:00, 63.83it/s]\n","Processing val: 100%|██████████| 200/200 [00:02<00:00, 67.03it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","✅ All done!\n","Processed data saved to: ./data/Kvasir-SEG_proc\n","List files saved to: ./lists/kvasir\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":["#create a file under utils/dataset_kvasir.py and paste code below\n","\n","# import os\n","# import numpy as np\n","# import torch\n","# from torch.utils.data import Dataset\n","# from PIL import Image\n","# import random\n","\n","# class KvasirSEGDataset(Dataset):\n","# def __init__(self, base_dir, list_dir=None, split=\"train\",\n","# output_size=(224, 224), nclass=2, transform=None):\n","# \"\"\"\n","# base_dir: root folder containing 'images' and 'masks' subdirs\n","# list_dir: folder containing text files listing samples per split\n","# split: 'train' or 'test_vol'\n","# output_size: (H, W) for resize\n","# nclass: number of classes (2 for binary segmentation)\n","# transform: optional callable for augmentation\n","# \"\"\"\n","# self.base_dir = base_dir\n","# self.split = split\n","# self.transform = transform\n","# self.nclass = nclass\n","# self.output_size = output_size\n","\n","# if list_dir:\n","# list_path = os.path.join(list_dir, split + \".txt\")\n","# with open(list_path, \"r\") as f:\n","# self.sample_list = [line.strip() for line in f]\n","# else:\n","# # default: take all files in images/\n","# img_dir = os.path.join(base_dir, \"images\")\n","# self.sample_list = sorted(os.listdir(img_dir))\n","\n","# # Mean/std from ImageNet for 3‑channel normalization\n","# self.mean = np.array([0.485, 0.456, 0.406])\n","# self.std = np.array([0.229, 0.224, 0.225])\n","\n","# def __len__(self):\n","# return len(self.sample_list)\n","\n","# def __getitem__(self, idx):\n","# img_name = self.sample_list[idx]\n","# img_path = os.path.join(self.base_dir, \"images\", img_name)\n","# mask_path = os.path.join(self.base_dir, \"masks\", img_name)\n","\n","# image = Image.open(img_path).convert(\"RGB\")\n","# mask = Image.open(mask_path).convert(\"L\") # grayscale\n","\n","# # Resize\n","# image = image.resize(self.output_size, resample=Image.BILINEAR)\n","# mask = mask.resize(self.output_size, resample=Image.NEAREST)\n","\n","# image = np.array(image).astype(np.float32) / 255.0\n","# mask = np.array(mask).astype(np.uint8)\n","# mask = (mask > 0).astype(np.uint8) # binarize\n","\n","# # Normalize image\n","# image = (image - self.mean) / self.std\n","\n","# # HWC → CHW\n","# image = torch.from_numpy(image.transpose(2, 0, 1)).float()\n","# mask = torch.from_numpy(mask).long()\n","\n","# sample = {\"image\": image, \"label\": mask, \"case_name\": img_name}\n","\n","# if self.transform:\n","# sample = self.transform(sample)\n","\n","# return sample\n","\n","# # Match Synapse's RandomGenerator for augmentations if needed\n","# class RandomGenerator(object):\n","# def __init__(self, output_size):\n","# self.output_size = output_size\n","\n","# def __call__(self, sample):\n","# image, label, name = sample[\"image\"], sample[\"label\"], sample[\"case_name\"]\n","# # Add any Kvasir‑specific aug here if you want\n","# return {\"image\": image, \"label\": label, \"case_name\": name}"],"metadata":{"id":"vIB958pYYfoJ","executionInfo":{"status":"ok","timestamp":1755138572671,"user_tz":-345,"elapsed":33,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["!gdown --folder 1Eu8v9vMRvt-dyCH0XSV2i77lAd62nPXV #pretrained weights"],"metadata":{"id":"fZK6V-TkY8RQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1755138584784,"user_tz":-345,"elapsed":12112,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"b5649061-be9f-4e47-d914-836734297784"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Retrieving folder contents\n","Processing file 1BtzvKNjROmcqc2tKRH5ls7wWl7WDdju4 pvt_v2_b2.pth\n","Retrieving folder contents completed\n","Building directory structure\n","Building directory structure completed\n","Downloading...\n","From: https://drive.google.com/uc?id=1BtzvKNjROmcqc2tKRH5ls7wWl7WDdju4\n","To: /content/EMCAD/pretrained_pth/pvt_v2_b2.pth\n","100% 102M/102M [00:01<00:00, 81.0MB/s] \n","Download completed\n"]}]},{"cell_type":"code","source":["%cd pretrained_pth"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kQukJPU3XIV7","executionInfo":{"status":"ok","timestamp":1755138584811,"user_tz":-345,"elapsed":22,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"5ad7e3bf-d593-4235-b9b2-cb06e17a01cd"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/EMCAD/pretrained_pth\n"]}]},{"cell_type":"code","source":["%mkdir pvt"],"metadata":{"id":"46Is_rNOXRe3","executionInfo":{"status":"ok","timestamp":1755138584889,"user_tz":-345,"elapsed":63,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["%cd ..\n","!mv /content/EMCAD/pretrained_pth/pvt_v2_b2.pth /content/EMCAD/pretrained_pth/pvt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LuXZRP5gXjq2","executionInfo":{"status":"ok","timestamp":1755138585010,"user_tz":-345,"elapsed":104,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"7c95902c-8eab-4881-d3d1-6190e959991f"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/EMCAD\n"]}]},{"cell_type":"code","source":["!source /usr/local/bin/activate newenv && pip install protobuf pyparsing cycler python-dateutil kiwisolver pytz\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3oYAo8cbXyE1","executionInfo":{"status":"ok","timestamp":1755138588737,"user_tz":-345,"elapsed":3725,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"be951d66-7782-4cf3-9ef0-d4e36b254d97"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting protobuf\n"," Downloading protobuf-5.29.5-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)\n","Collecting pyparsing\n"," Downloading pyparsing-3.1.4-py3-none-any.whl.metadata (5.1 kB)\n","Collecting cycler\n"," Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n","Collecting python-dateutil\n"," Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n","Collecting kiwisolver\n"," Downloading kiwisolver-1.4.7-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.3 kB)\n","Collecting pytz\n"," Downloading pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n","Collecting six>=1.5 (from python-dateutil)\n"," Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)\n","Downloading protobuf-5.29.5-cp38-abi3-manylinux2014_x86_64.whl (319 kB)\n","Downloading pyparsing-3.1.4-py3-none-any.whl (104 kB)\n","Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n","Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\n","Downloading kiwisolver-1.4.7-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m39.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading pytz-2025.2-py2.py3-none-any.whl (509 kB)\n","Downloading six-1.17.0-py2.py3-none-any.whl (11 kB)\n","Installing collected packages: pytz, six, pyparsing, protobuf, kiwisolver, cycler, python-dateutil\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","matplotlib 3.7.5 requires contourpy>=1.0.1, which is not installed.\n","matplotlib 3.7.5 requires fonttools>=4.22.0, which is not installed.\n","ml-collections 0.1.1 requires absl-py, which is not installed.\n","ml-collections 0.1.1 requires contextlib2, which is not installed.\n","pandas 2.0.3 requires tzdata>=2022.1, which is not installed.\n","segmentation-mask-overlay 0.3.4 requires opencv-python-headless>=4.5.3, which is not installed.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed cycler-0.12.1 kiwisolver-1.4.7 protobuf-5.29.5 pyparsing-3.1.4 python-dateutil-2.9.0.post0 pytz-2025.2 six-1.17.0\n"]}]},{"cell_type":"code","source":["!source /usr/local/bin/activate newenv && pip install --upgrade torch --index-url https://download.pytorch.org/whl/cu118\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"iaprvWUoYmUY","executionInfo":{"status":"ok","timestamp":1755138740013,"user_tz":-345,"elapsed":151273,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"f8841b91-5d63-4a20-ef50-79da36b8b0e1"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://download.pytorch.org/whl/cu118\n","Requirement already satisfied: torch in /usr/local/envs/newenv/lib/python3.8/site-packages (1.11.0+cu113)\n","Collecting torch\n"," Downloading https://download.pytorch.org/whl/cu118/torch-2.4.1%2Bcu118-cp38-cp38-linux_x86_64.whl (857.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m857.6/857.6 MB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting filelock (from torch)\n"," Downloading 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This behaviour is the source of the following dependency conflicts.\n","torchmetrics 1.5.2 requires lightning-utilities>=0.8.0, which is not installed.\n","transformers 4.21.3 requires regex!=2019.12.17, which is not installed.\n","transformers 4.21.3 requires tokenizers!=0.11.3,<0.13,>=0.11.1, which is not installed.\n","torchaudio 0.11.0+cu113 requires torch==1.11.0, but you have torch 2.4.1+cu118 which is incompatible.\n","torchvision 0.12.0+cu113 requires torch==1.11.0, but you have torch 2.4.1+cu118 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed MarkupSafe-2.1.5 filelock-3.13.1 fsspec-2024.6.1 jinja2-3.1.4 mpmath-1.3.0 nvidia-cublas-cu11-11.11.3.6 nvidia-cuda-cupti-cu11-11.8.87 nvidia-cuda-nvrtc-cu11-11.8.89 nvidia-cuda-runtime-cu11-11.8.89 nvidia-cudnn-cu11-9.1.0.70 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.3.0.86 nvidia-cusolver-cu11-11.4.1.48 nvidia-cusparse-cu11-11.7.5.86 nvidia-nccl-cu11-2.20.5 nvidia-nvtx-cu11-11.8.86 sympy-1.13.3 torch-2.4.1+cu118 triton-3.0.0\n"]}]},{"cell_type":"code","source":["!gdown --folder 1ACJEoTp-uqfFJ73qS3eUObQh52nGuzCd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yUrapQ3GbgiB","executionInfo":{"status":"ok","timestamp":1755138915825,"user_tz":-345,"elapsed":16376,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"24be8f55-89e3-4f28-acdb-9b8d24f199dd"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Retrieving folder contents\n","Processing file 1BvpY0g9mKkkhdHpAX1HqDw8iTJNbFuwq project_TransUNet.zip\n","Retrieving folder contents completed\n","Building directory structure\n","Building directory structure completed\n","Downloading...\n","From (original): https://drive.google.com/uc?id=1BvpY0g9mKkkhdHpAX1HqDw8iTJNbFuwq\n","From (redirected): https://drive.google.com/uc?id=1BvpY0g9mKkkhdHpAX1HqDw8iTJNbFuwq&confirm=t&uuid=0431b06d-cf77-4e5b-a59f-41e90cd1c2b5\n","To: /content/EMCAD/project_transunet/project_TransUNet.zip\n","100% 983M/983M [00:12<00:00, 78.2MB/s]\n","Download completed\n"]}]},{"cell_type":"code","source":["!unzip /content/EMCAD/project_transunet/project_TransUNet.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hBJQjExubq9M","executionInfo":{"status":"ok","timestamp":1755139024084,"user_tz":-345,"elapsed":85506,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"1c3715a9-1193-436d-bc79-6b6d843f8f7d"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive: /content/EMCAD/project_transunet/project_TransUNet.zip\n"," creating: project_TransUNet/\n"," creating: project_TransUNet/data/\n"," creating: project_TransUNet/data/Synapse/\n"," creating: project_TransUNet/data/Synapse/test_vol_h5/\n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0001.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0002.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0003.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0004.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0008.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0022.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0025.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0029.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0032.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0035.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0036.npy.h5 \n"," inflating: project_TransUNet/data/Synapse/test_vol_h5/case0038.npy.h5 \n"," creating: project_TransUNet/data/Synapse/train_npz/\n"," inflating: project_TransUNet/data/Synapse/train_npz/case0005_slice000.npz \n"," inflating: project_TransUNet/data/Synapse/train_npz/case0005_slice001.npz \n"," inflating: 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project_TransUNet/TransUNet/networks/\n"," inflating: project_TransUNet/TransUNet/networks/vit_seg_configs.py \n"," inflating: project_TransUNet/TransUNet/networks/vit_seg_modeling.py \n"," inflating: project_TransUNet/TransUNet/networks/vit_seg_modeling_resnet_skip.py \n"," inflating: project_TransUNet/TransUNet/README.md \n"," inflating: project_TransUNet/TransUNet/requirements.txt \n"," inflating: project_TransUNet/TransUNet/test.py \n"," inflating: project_TransUNet/TransUNet/train.py \n"," inflating: project_TransUNet/TransUNet/trainer.py \n"," inflating: project_TransUNet/TransUNet/utils.py \n"]}]},{"cell_type":"code","source":["\n","!source /usr/local/bin/activate newenv && python -W ignore train_synapse.py --root_path /content/EMCAD/project_TransUNet/data/Synapse/train_npz --volume_path /content/EMCAD/project_TransUNet/data/Synapse/test_vol_h5 --encoder pvt_v2_b2 # replace --root_path and --volume_path with your actual path to data.\n"],"metadata":{"id":"e9HZcre_ZTPa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1755139691301,"user_tz":-345,"elapsed":418668,"user":{"displayName":"Santosh Upreti","userId":"01961227760879466523"}},"outputId":"ab60436c-76bf-4524-ad60-3a6e5a8c8c0f"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Model pvt_v2_b2 backbone: created, param count: 24849856\n","Model EMCAD decoder: created, param count: 1913515\n","Model successfully created.\n","Namespace(activation_mscb='relu6', base_lr=0.0001, batch_size=6, concatenation=False, dataset='Synapse', deterministic=1, encoder='pvt_v2_b2', exp='pvt_v2_b2_EMCAD_kernel_sizes_[1, 3, 5]_dw_parallel_add_lgag_ks_3_ef2_act_mscb_relu6_loss_mutation_output_final_layer_Run1_Synapse224', expansion_factor=2, img_size=224, kernel_sizes=[1, 3, 5], lgag_ks=3, list_dir='./lists/lists_Synapse', max_epochs=300, max_iterations=50000, n_gpu=1, no_dw_parallel=False, no_pretrain=False, num_classes=9, pretrained_dir='./pretrained_pth/pvt/', root_path='/content/EMCAD/project_TransUNet/data/Synapse/train_npz', seed=2222, supervision='mutation', volume_path='/content/EMCAD/project_TransUNet/data/Synapse/test_vol_h5', z_spacing=1)\n","The length of train set is: 2211\n","369 iterations per epoch. 110700 max iterations \n"," 0%| | 0/300 [00:00<?, ?it/s][[0, 1, 2, 3], [1, 2, 3], [0, 2, 3], [2, 3], [0, 1, 3], [1, 3], [0, 3], [3], [0, 1, 2], [1, 2], [0, 2], [2], [0, 1], [1], [0], []]\n","iteration 50, epoch 0 : loss : 11.522570, lr: 0.000100\n","iteration 100, epoch 0 : loss : 10.060051, lr: 0.000100\n","iteration 150, epoch 0 : loss : 6.748409, lr: 0.000100\n","iteration 200, epoch 0 : loss : 6.598964, lr: 0.000100\n","iteration 250, epoch 0 : loss : 6.065333, lr: 0.000100\n","iteration 300, epoch 0 : loss : 5.181314, lr: 0.000100\n","iteration 350, epoch 0 : loss : 6.413750, lr: 0.000100\n","iteration 369, epoch 0 : loss : 6.758160, lr: 0.000100\n","12 test iterations per epoch\n","\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:12, 12.53s/it]\u001b[A\n","2it [00:18, 8.87s/it]\u001b[A\n","3it [00:26, 8.30s/it]\u001b[A\n","4it [00:39, 10.18s/it]\u001b[A\n","5it [00:49, 10.08s/it]\u001b[A\n","6it [00:59, 9.92s/it]\u001b[A\n","7it [01:06, 9.18s/it]\u001b[A\n","8it [01:20, 10.67s/it]\u001b[A\n","9it [01:30, 10.48s/it]\u001b[A\n","10it [01:40, 10.34s/it]\u001b[A\n","11it [01:45, 8.77s/it]\u001b[A\n","12it [01:52, 9.36s/it]\n","Testing performance in val model: mean_dice : 0.401896, best_dice : 0.000000\n","save model to model_pth/pvt_v2_b2_EMCAD_kernel_sizes_1_3_5_dw_parallel_add_lgag_ks_3_ef2_act_mscb_relu6_loss_mutation_output_final_layer_Run1_Synapse224/pvt_v2_b2_EMCAD_kernel_sizes_1_3_5_dw_parallel_add_lgag_ks_3_ef2_act_mscb_relu6_loss_mutation_output_final_layer_Run1_pretrain_bs6_224_s2222/best.pth\n"," 0%| | 1/300 [04:06<20:28:33, 246.53s/it]iteration 400, epoch 1 : loss : 5.877350, lr: 0.000100\n","iteration 450, epoch 1 : loss : 6.018301, lr: 0.000100\n","iteration 500, epoch 1 : loss : 2.778723, lr: 0.000100\n","iteration 550, epoch 1 : loss : 5.490460, lr: 0.000100\n","iteration 600, epoch 1 : loss : 5.176212, lr: 0.000100\n","iteration 650, epoch 1 : loss : 5.685831, lr: 0.000100\n","iteration 700, epoch 1 : loss : 4.409635, lr: 0.000100\n","iteration 738, epoch 1 : loss : 1.574034, lr: 0.000100\n","12 test iterations per epoch\n","\n","0it [00:00, ?it/s]\u001b[A\n","1it [00:11, 11.04s/it]\u001b[A\n","2it [00:18, 8.65s/it]\u001b[A\n","3it [00:31, 10.46s/it]\n"," 0%| | 1/300 [06:49<33:59:53, 409.34s/it]\n","Traceback (most recent call last):\n"," File \"train_synapse.py\", line 127, in <module>\n"," trainer[dataset_name](args, model, snapshot_path)\n"," File \"/content/EMCAD/trainer.py\", line 136, in trainer_synapse\n"," performance = inference(args, model, best_performance)\n"," File \"/content/EMCAD/trainer.py\", line 32, in inference\n"," metric_i = val_single_volume(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size],\n"," File \"/content/EMCAD/utils/utils.py\", line 252, in val_single_volume\n"," P = net(input)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," File \"/content/EMCAD/lib/networks.py\", line 95, in forward\n"," x1, x2, x3, x4 = self.backbone(x)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," File \"/content/EMCAD/lib/pvtv2.py\", line 354, in forward\n"," x = self.forward_features(x)\n"," File \"/content/EMCAD/lib/pvtv2.py\", line 328, in forward_features\n"," x = blk(x, H, W)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," File \"/content/EMCAD/lib/pvtv2.py\", line 146, in forward\n"," x = x + self.drop_path(self.attn(self.norm1(x), H, W))\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," File \"/content/EMCAD/lib/pvtv2.py\", line 107, in forward\n"," x = self.proj_drop(x)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n"," return self._call_impl(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n"," return forward_call(*args, **kwargs)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/modules/dropout.py\", line 59, in forward\n"," return F.dropout(input, self.p, self.training, self.inplace)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/nn/functional.py\", line 1295, in dropout\n"," return _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)\n"," File \"/usr/local/envs/newenv/lib/python3.8/site-packages/torch/_VF.py\", line 27, in __getattr__\n"," return getattr(self.vf, attr)\n","KeyboardInterrupt\n"]}]}]} |
Xet Storage Details
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- 256 kB
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- 2d093b59b617f3815ac7ef09fd55e0156aabb40634f6b5bfaf996c0478dcaf7b
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