isbi2019cancer-master/.ipynb_checkpoints/run-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd8aaf96",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26bd5e25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b753e6b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:41,  1.43it/s]                                                         \n",
      "Positive: 1234\n",
      "Negative: 633\n",
      "AUC: 0.8797024225483345\n"
     ]
    }
   ],
   "source": [
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/20220216T154306Z.AZHL\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3246460b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 32\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a953a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 128\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c15b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 224\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ba15b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 256\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cf25ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73b9d9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_Grayscale\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_grayscale\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce16353c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_no_red\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_red\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "959ab837",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_no_green\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_green\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "879beb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_no_blue\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_blue\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d545dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_red_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_red_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25480226",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_green_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_green_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a064d169",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC_blue_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_blue_only\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d53828a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(batch_size=32, dataroot='/home/feoktistovar67431/data/isbi2019/CNMC', device='cuda', epochs=100, out='results/20220221T191207Z.OEHG', res=450, seed=30042022)\n",
      "Model parameters: 25512945\n",
      "Trainset length: 10625\n",
      "Validset length: 1867\n",
      "class_weights = tensor([0.3156, 0.6844], device='cuda:0')\n",
      "/home/feoktistovar67431/.local/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "epoch=0 f1=0.1789\n",
      "WARNING:root:NaN or Inf found in input tensor.\n",
      "Epoch:   0%|                                            | 0/100 [00:00<?, ?it/s]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
      "/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
      "\n",
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      "                                                                                \u001b[Aepoch=1 f1=0.7620\n",
      "New best model at 0.761968\n",
      "Epoch:   1%|▎                                | 1/100 [05:02<8:19:36, 302.79s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
      "\n",
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      "                                                                                \u001b[Aepoch=2 f1=0.8557\n",
      "New best model at 0.855660\n",
      "Epoch:   2%|▋                                | 2/100 [11:02<9:09:12, 336.25s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
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      "                                                                                \u001b[Aepoch=3 f1=0.8555\n",
      "Epoch:   3%|▉                                | 3/100 [17:04<9:22:35, 347.99s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
      "\n",
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      "                                                                                \u001b[Aepoch=4 f1=0.8694\n",
      "New best model at 0.869430\n",
      "Epoch:   4%|█▎                               | 4/100 [23:05<9:24:55, 353.08s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
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      "                                                                                \u001b[Aepoch=5 f1=0.8656\n",
      "Epoch:   5%|█▋                               | 5/100 [29:08<9:24:32, 356.55s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
      "\n"
     ]
    },
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     "name": "stdout",
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     "text": [
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      "                                                                                \u001b[Aepoch=6 f1=0.8702\n",
      "New best model at 0.870210\n",
      "Epoch:   6%|█▉                               | 6/100 [35:11<9:22:22, 358.96s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
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     "name": "stdout",
     "output_type": "stream",
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      "                                                                                \u001b[Aepoch=7 f1=0.8638\n",
      "Epoch:   7%|██▎                              | 7/100 [41:08<9:15:30, 358.39s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
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      "                                                                                \u001b[Aepoch=8 f1=0.8710\n",
      "New best model at 0.871018\n",
      "Epoch:   8%|██▋                              | 8/100 [47:14<9:13:18, 360.86s/it]/home/feoktistovar67431/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:154: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n",
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     ]
    }
   ],
   "source": [
    "# TRAIN\n",
    "# dataset : CNMC\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 main_manual.py --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC\" --batch-size 32 --epochs 100 --seed 30042022 --device cuda --out results --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ea9c2f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROPOSAL\n",
      "68.51\t83.57 ± 5.16\t89.61\n",
      "84.33\t89.06 ± 2.09\t92.95\n",
      "38.73\t73.26 ± 11.77\t84.72\n",
      "66.76\t83.35 ± 5.61\t89.57\n",
      "66.81\t83.36 ± 5.60\t89.55\n",
      "68.51\t83.57 ± 5.16\t89.61\n",
      "\n",
      "Figure(900x500)\n",
      "Figure(900x200)\n",
      "Figure(900x200)\n"
     ]
    }
   ],
   "source": [
    "# PLOT\n",
    "# dataset : CNMC\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 plot.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c92073d",
   "metadata": {},
   "source": [
    "# EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "b25a4267",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:05, 11.69it/s]                                                         \n",
      "Positive: 1425\n",
      "Negative: 442\n",
      "AUC: 0.6153299354864846\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION \n",
    "# dataset : CNMC\n",
    "# res     : 32\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_32\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b14e3e67",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:09,  6.24it/s]                                                         \n",
      "Positive: 1315\n",
      "Negative: 552\n",
      "AUC: 0.7711131113339208\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC\n",
    "# res     : 128\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_128\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "dfb25744",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:14,  4.19it/s]                                                         \n",
      "Positive: 1262\n",
      "Negative: 605\n",
      "AUC: 0.8143717274835677\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC\n",
    "# res     : 224\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_224\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "68600db4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:41,  1.44it/s]                                                         \n",
      "Positive: 1195\n",
      "Negative: 672\n",
      "AUC: 0.8400701597139936\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC\n",
    "# res     : 256\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_256\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "71a5547e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:41,  1.42it/s]                                                         \n",
      "Positive: 1241\n",
      "Negative: 626\n",
      "AUC: 0.8813918512441892\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "58450362",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:41,  1.42it/s]                                                         \n",
      "Positive: 1261\n",
      "Negative: 606\n",
      "AUC: 0.8045073375262055\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_Grayscale\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_grayscale\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_grayscale/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "48c40f18",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:44,  1.33it/s]                                                         \n",
      "Positive: 1178\n",
      "Negative: 689\n",
      "AUC: 0.8661869929814967\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_no_red\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_red\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_red/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b6ad9232",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:52,  1.12it/s]                                                         \n",
      "Positive: 1266\n",
      "Negative: 601\n",
      "AUC: 0.8018310900454735\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_no_green\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_green\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_green/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1ba76d51",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:48,  1.23it/s]                                                         \n",
      "Positive: 1248\n",
      "Negative: 619\n",
      "AUC: 0.8570821813062721\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_no_blue\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_no_blue\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_no_blue/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "05cfaf9c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:52,  1.12it/s]                                                         \n",
      "Positive: 1239\n",
      "Negative: 628\n",
      "AUC: 0.8013924335875389\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_red_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_red_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_red_only/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "1ad09456",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:52,  1.13it/s]                                                         \n",
      "Positive: 1221\n",
      "Negative: 646\n",
      "AUC: 0.8590070792695896\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_green_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_green_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_green_only/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "41e8d3a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [00:52,  1.12it/s]                                                         \n",
      "Positive: 1255\n",
      "Negative: 612\n",
      "AUC: 0.8268636253152251\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC_blue_only\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_blue_only\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC_blue_only/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "88bc18db",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model\n",
      "Classifying\n",
      "59it [01:24,  1.43s/it]                                                         \n",
      "Positive: 1235\n",
      "Negative: 632\n",
      "AUC: 0.8588406050294211\n"
     ]
    }
   ],
   "source": [
    "# EVALUATION\n",
    "# dataset : CNMC-blackborder\n",
    "# res     : 450\n",
    "# epochs  : 100\n",
    "!python3 submission.py --modelroot \"/home/feoktistovar67431/isbi2019cancer-master/results/model_cnmc_res_450_w_blackborder\" --dataroot \"/home/feoktistovar67431/data/isbi2019/CNMC/phase2\" --batch-size 32 --res 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec31125a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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