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"cells": [
{
"cell_type": "code",
"metadata": {
"id": "-MzdfcwC5qDr",
"colab_type": "code",
"outputId": "3bf81e0e-4592-48a6-e5ce-f506a5fa6d75",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 207
}
},
"source": [
"!rm -r ../root/.kaggle\n",
"!mkdir ../root/.kaggle\n",
"!cp kaggle.json ../root/.kaggle\n",
"!chmod 600 ../root/.kaggle/kaggle.json\n",
"!rm -r sample_data\n",
"!pip install efficientnet-pytorch"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting efficientnet-pytorch\n",
" Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from efficientnet-pytorch) (1.4.0)\n",
"Building wheels for collected packages: efficientnet-pytorch\n",
" Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp36-none-any.whl size=12422 sha256=bf187f2821eb28440af51051fb3ad361f9750358f4be50775066a9b6244b5a99\n",
" Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n",
"Successfully built efficientnet-pytorch\n",
"Installing collected packages: efficientnet-pytorch\n",
"Successfully installed efficientnet-pytorch-0.6.3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "x_hAUAh_6Dk3",
"colab_type": "code",
"outputId": "cf11e1a4-5075-40da-9ee0-548a71e9b4b2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"!kaggle datasets download umangjpatel/pap-smear-datasets --unzip\n",
"!rm -r herlev_pap_smear\n",
"!rm -r sipakmed_fci_pap_smear"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading pap-smear-datasets.zip to /content\n",
"100% 6.44G/6.44G [02:18<00:00, 38.9MB/s]\n",
"100% 6.44G/6.44G [02:18<00:00, 49.9MB/s]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xJbKpakU6S6r",
"colab_type": "code",
"colab": {}
},
"source": [
"from fastai import *\n",
"from fastai.vision import *\n",
"from fastai.vision.models import efficientnet\n",
"from fastai.utils.ipython import *\n",
"from fastai.callbacks.tracker import SaveModelCallback\n",
"from sklearn.model_selection import StratifiedKFold"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WtLde_oj6Uqv",
"colab_type": "code",
"outputId": "5e753f05-d3d0-4633-f71e-620e34bf6038",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"path = Path(\".\")\n",
"path.ls()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PosixPath('.config'),\n",
" PosixPath('kaggle.json'),\n",
" PosixPath('sipakmed_wsi_pap_smear')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "e0L5bOab7Li0",
"colab_type": "code",
"outputId": "885b5951-b556-406d-f1bb-02583090bef9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
}
},
"source": [
"data_init = (ImageList.from_folder(path/\"sipakmed_wsi_pap_smear\")\n",
" .split_none()\n",
" .label_from_folder())\n",
"data_init"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LabelLists;\n",
"\n",
"Train: LabelList (966 items)\n",
"x: ImageList\n",
"Image (3, 1536, 2048),Image (3, 1536, 2048),Image (3, 1536, 2048),Image (3, 1536, 2048),Image (3, 1536, 2048)\n",
"y: CategoryList\n",
"abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic\n",
"Path: sipakmed_wsi_pap_smear;\n",
"\n",
"Valid: LabelList (0 items)\n",
"x: ImageList\n",
"\n",
"y: CategoryList\n",
"\n",
"Path: sipakmed_wsi_pap_smear;\n",
"\n",
"Test: None"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qg07Zti59Sg5",
"colab_type": "code",
"outputId": "5c8e3a59-55c9-4cff-8346-fe291d70b640",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=0)\n",
"skf"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"StratifiedKFold(n_splits=3, random_state=0, shuffle=True)"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-r38w3l8ExI1",
"colab_type": "code",
"colab": {}
},
"source": [
"!mkdir sipakmed_wsi_pap_smear/models"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Btupbe9q9V2M",
"colab_type": "code",
"colab": {}
},
"source": [
"def model_callback(model, model_name):\n",
" return [SaveModelCallback(model, every=\"improvement\", monitor=\"accuracy\", name=model_name)]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rLkuRJBZWLix",
"colab_type": "code",
"colab": {}
},
"source": [
"def find_appropriate_lr(model, lr_diff = 15, loss_threshold = .05, adjust_value = 1, plot = True):\n",
" #Run the Learning Rate Finder\n",
" model.lr_find()\n",
" \n",
" #Get loss values and their corresponding gradients, and get lr values\n",
" losses = np.array(model.recorder.losses)\n",
" assert(lr_diff < len(losses))\n",
" loss_grad = np.gradient(losses)\n",
" lrs = model.recorder.lrs\n",
" \n",
" #Search for index in gradients where loss is lowest before the loss spike\n",
" #Initialize right and left idx using the lr_diff as a spacing unit\n",
" #Set the local min lr as -1 to signify if threshold is too low\n",
" r_idx = -1\n",
" l_idx = r_idx - lr_diff\n",
" while (l_idx >= -len(losses)) and (abs(loss_grad[r_idx] - loss_grad[l_idx]) > loss_threshold):\n",
" local_min_lr = lrs[l_idx]\n",
" r_idx -= 1\n",
" l_idx -= 1\n",
"\n",
" lr_to_use = local_min_lr * adjust_value\n",
" \n",
" if plot:\n",
" # plots the gradients of the losses in respect to the learning rate change\n",
" plt.plot(loss_grad)\n",
" plt.plot(len(losses)+l_idx, loss_grad[l_idx],markersize=10,marker='o',color='red')\n",
" plt.ylabel(\"Loss\")\n",
" plt.xlabel(\"Index of LRs\")\n",
" plt.show()\n",
"\n",
" plt.plot(np.log10(lrs), losses)\n",
" plt.ylabel(\"Loss\")\n",
" plt.xlabel(\"Log 10 Transform of Learning Rate\")\n",
" loss_coord = np.interp(np.log10(lr_to_use), np.log10(lrs), losses)\n",
" plt.plot(np.log10(lr_to_use), loss_coord, markersize=10,marker='o',color='red')\n",
" plt.show()\n",
" \n",
" return lr_to_use"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YJ_fRg52-CCa",
"colab_type": "code",
"outputId": "5c9e3e71-a9a3-4c04-9946-6543d3647c1c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"source": [
"i = 0\n",
"with gpu_mem_restore_ctx():\n",
" model_name = input(\"Enter the model name : \")\n",
" for train_idx, valid_idx in skf.split(data_init.x.items, data_init.y.items):\n",
" print(\"&*\" * 50)\n",
" print(f\"FOLD-{i}\")\n",
" fold_data = (ImageList.from_folder(path/\"sipakmed_wsi_pap_smear\")\n",
" .split_by_idxs(train_idx, valid_idx)\n",
" .label_from_folder()\n",
" .transform(get_transforms(flip_vert=True, max_warp=0.0, max_rotate=60.0, max_zoom=0.50), size=224)\n",
" .databunch()\n",
" .normalize(imagenet_stats))\n",
" if i == 0:\n",
" learner = cnn_learner(fold_data, models.resnet34, metrics=[accuracy, Precision(), Recall(), FBeta(beta=1), KappaScore(weights=\"quadratic\")]).to_fp16()\n",
" else:\n",
" load_name = model_name + f\"-stage2-fold{i-1}\"\n",
" learner.purge()\n",
" learner.load(load_name)\n",
" stage1_name = model_name + f\"-stage1-fold{i}\"\n",
" learner.fit_one_cycle(20, max_lr=slice(1e-03, 1e-02), callbacks=model_callback(learner, stage1_name))\n",
" learner.load(stage1_name)\n",
" learner.unfreeze()\n",
" stage2_name = model_name + f\"-stage2-fold{i}\"\n",
" learner.fit_one_cycle(10, max_lr=slice(1e-06, 1e-05), callbacks=model_callback(learner, stage2_name))\n",
" i += 1\n",
" print(\"&*\" * 50)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"Enter the model name : best-rn34-sipak\n",
"&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*\n",
"FOLD-0\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
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"application/vnd.jupyter.widget-view+json": {
"model_id": "b138df8514524d63bd8527cc50c2cd06",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"HBox(children=(IntProgress(value=0, max=87306240), HTML(value='')))"
]
},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
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"data": {
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"Better model found at epoch 3 with accuracy value: 0.8322981595993042.\n",
"Better model found at epoch 5 with accuracy value: 0.850931704044342.\n",
"Better model found at epoch 6 with accuracy value: 0.8695651888847351.\n",
"Better model found at epoch 7 with accuracy value: 0.8819875717163086.\n",
"Better model found at epoch 8 with accuracy value: 0.8850931525230408.\n",
"Better model found at epoch 9 with accuracy value: 0.8975155353546143.\n",
"Better model found at epoch 10 with accuracy value: 0.9006211161613464.\n",
"Better model found at epoch 13 with accuracy value: 0.909937858581543.\n",
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"Better model found at epoch 13 with accuracy value: 0.9223602414131165.\n",
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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/fastai/metrics.py:191: UserWarning: average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.\n",
" warn(\"average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.\")\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Better model found at epoch 0 with accuracy value: 0.9223602414131165.\n",
"Better model found at epoch 15 with accuracy value: 0.9285714030265808.\n",
"Better model found at epoch 16 with accuracy value: 0.9316770434379578.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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""
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Better model found at epoch 0 with accuracy value: 0.9347826242446899.\n",
"Better model found at epoch 5 with accuracy value: 0.9378882050514221.\n",
"&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*&*\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YSezyQT5B1oY",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
},
"outputId": "b7a8363a-3837-4a51-e6f6-6c1ff4a09caf"
},
"source": [
"full_data = (ImageList.from_folder(path/\"sipakmed_wsi_pap_smear\")\n",
" .split_none()\n",
" .label_from_folder()\n",
" .transform(get_transforms(flip_vert=True, max_warp=0.0, max_rotate=60.0, max_zoom=0.50), size=224)\n",
" .databunch()\n",
" .normalize(imagenet_stats))\n",
"full_data"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ImageDataBunch;\n",
"\n",
"Train: LabelList (966 items)\n",
"x: ImageList\n",
"Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
"y: CategoryList\n",
"abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic,abnormal_Dyskeratotic\n",
"Path: sipakmed_wsi_pap_smear;\n",
"\n",
"Valid: LabelList (0 items)\n",
"x: ImageList\n",
"\n",
"y: CategoryList\n",
"\n",
"Path: sipakmed_wsi_pap_smear;\n",
"\n",
"Test: None"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pEsj03BHY_ie",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 311
},
"outputId": "cd371553-c425-4d48-83ea-1572d132094d"
},
"source": [
"learner.load(\"best-rn34-sipak-stage2-fold2\")\n",
"interp = ClassificationInterpretation.from_learner(learner, ds_type=DatasetType.Train)\n",
"interp.plot_confusion_matrix(figsize=(7, 7))"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
},
{
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"data": {
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KPpNj9o811MwPq9BLLtGyoDCai5o5zY6aTuwMSUsA9wHjzGxAoWfNbNsqdqWoEQLaAwuM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\nwZs0WMUAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "w-SEiQMkZR_5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 527
},
"outputId": "3737129a-f8a7-4ba8-d08c-6d80852d07a4"
},
"source": [
"interp.plot_confusion_matrix(figsize=(7, 7))"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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GxDPzeX5dtwAHAT8CSsNwe5JL/8akdW4PzOsWtydIehYYBaxB8gECYDYwJP39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\nd0RExCSSwh0RETGJpHBHRERMIv8PdPOugtF5oHMAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MqqCmCRqZesK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "f8f55924-d587-4296-9fe6-03ef264af5d9"
},
"source": [
"learner_32 = to_fp32(learner)\n",
"learner_32.validate(dl=full_data.train_dl, metrics=[accuracy, Precision(average=\"macro\"), Recall(average=\"macro\"), FBeta(beta=1, average=\"macro\"), KappaScore(weights=\"quadratic\")])"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.106774084,\n",
" tensor(0.9656),\n",
" tensor(0.9719),\n",
" tensor(0.9727),\n",
" tensor(0.9722),\n",
" tensor(0.9819)]"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jZqKfmZGZjFw",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}