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--- a +++ b/nbs/RSNA_EfficientNet_B4.ipynb @@ -0,0 +1,609 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EfficientNet B4 model\n", + "\n", + "\n", + "**Due to GPU quota is only 30 hours/per week on Kaggle, each training need 15+ hours, so the notebook cann't commiting(otherwise will exceeding the quota), only download the csv files to submit**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install EfficentNet \n", + "!pip install efficientnet\n", + "!pip install iterative-stratification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import efficientnet.keras as efn \n", + "from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pydicom\n", + "import os\n", + "import collections\n", + "import sys\n", + "import glob\n", + "import random\n", + "import cv2\n", + "import tensorflow as tf\n", + "import multiprocessing\n", + "\n", + "from math import ceil, floor\n", + "from copy import deepcopy\n", + "from tqdm import tqdm\n", + "from imgaug import augmenters as iaa\n", + "\n", + "import keras\n", + "import keras.backend as K\n", + "from keras.callbacks import Callback, ModelCheckpoint\n", + "from keras.layers import Dense, Flatten, Dropout\n", + "from keras.models import Model, load_model\n", + "from keras.utils import Sequence\n", + "from keras.losses import binary_crossentropy\n", + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Parameters Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [], + "source": [ + "# Setting the parameters:\n", + "seed = 42\n", + "np.random.seed(seed)\n", + "tf.random.set_seed(seed)\n", + "\n", + "\n", + "input_image_width = 256\n", + "input_image_height = 256\n", + "\n", + "input_image_shape = (input_image_height,input_image_width,3)\n", + "\n", + "test_size = 0.01\n", + "batch_size = 16\n", + "train_batch_size = 16\n", + "valid_batch_size = 32\n", + "\n", + "# Setting the Path \n", + "path = '../input/rsna-intracranial-hemorrhage-detection/rsna-intracranial-hemorrhage-detection/'\n", + "train_img_path = path + 'stage_2_train/'\n", + "test_img_path = path + 'stage_2_test/'\n", + "\n", + "# Dataset Filenames\n", + "train_dataset_fns = path + 'stage_2_train.csv'\n", + "test_dataset_fns = path + 'stage_2_sample_submission.csv'\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dup_image_list = [56346, 56347, 56348, 56349,\n", + " 56350, 56351, 1171830, 1171831,\n", + " 1171832, 1171833, 1171834, 1171835,\n", + " 3705312, 3705313, 3705314, 3705315,\n", + " 3705316, 3705317, 3842478, 3842479,\n", + " 3842480, 3842481, 3842482, 3842483 ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_dataset_loader(filename):\n", + " df = pd.read_csv(filename)\n", + " df[\"Image\"] = df[\"ID\"].str.slice(stop=12)\n", + " df[\"Diagnosis\"] = df[\"ID\"].str.slice(start=13)\n", + " df = df.drop(index = dup_image_list)\n", + " df = df.reset_index(drop = True) \n", + " df = df.loc[:, [\"Label\", \"Diagnosis\", \"Image\"]]\n", + " df = df.set_index(['Image', 'Diagnosis']).unstack(level=-1)\n", + " return df\n", + "\n", + "\n", + "def test_dataset_loader(filename):\n", + " df = pd.read_csv(filename)\n", + " df[\"Image\"] = df[\"ID\"].str.slice(stop=12)\n", + " df[\"Diagnosis\"] = df[\"ID\"].str.slice(start=13)\n", + " df = df.loc[:, [\"Label\", \"Diagnosis\", \"Image\"]]\n", + " df = df.set_index(['Image', 'Diagnosis']).unstack(level=-1)\n", + " return df\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = train_dataset_loader(train_dataset_fns)\n", + "test_df = test_dataset_loader(test_dataset_fns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data EDA and Cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def correct_dcm(dcm):\n", + " x = dcm.pixel_array + 1000\n", + " px_mode = 4096\n", + " x[x>=px_mode] = x[x>=px_mode] - px_mode\n", + " dcm.PixelData = x.tobytes()\n", + " dcm.RescaleIntercept = -1000\n", + "\n", + "def window_image(dcm, window_center, window_width): \n", + " if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n", + " correct_dcm(dcm)\n", + " img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n", + " \n", + " # Resize\n", + " img = cv2.resize(img, SHAPE[:2], interpolation = cv2.INTER_LINEAR)\n", + " \n", + " img_min = window_center - window_width // 2\n", + " img_max = window_center + window_width // 2\n", + " img = np.clip(img, img_min, img_max)\n", + " return img\n", + "\n", + "def bsb_window(dcm):\n", + " brain_img = window_image(dcm, 40, 80)\n", + " subdural_img = window_image(dcm, 80, 200)\n", + " soft_img = window_image(dcm, 40, 380)\n", + " \n", + " brain_img = (brain_img - 0) / 80\n", + " subdural_img = (subdural_img - (-20)) / 200\n", + " soft_img = (soft_img - (-150)) / 380\n", + " bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n", + " return bsb_img\n", + "\n", + "def _read(path, SHAPE):\n", + " dcm = pydicom.dcmread(path)\n", + " try:\n", + " img = bsb_window(dcm)\n", + " except:\n", + " img = np.zeros(SHAPE)\n", + " return img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def window_with_correction(dcm, window_center, window_width):\n", + " if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n", + " correct_dcm(dcm)\n", + " img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n", + " img_min = window_center - window_width // 2\n", + " img_max = window_center + window_width // 2\n", + " img = np.clip(img, img_min, img_max)\n", + " return img\n", + "\n", + "def window_without_correction(dcm, window_center, window_width):\n", + " img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n", + " img_min = window_center - window_width // 2\n", + " img_max = window_center + window_width // 2\n", + " img = np.clip(img, img_min, img_max)\n", + " return img\n", + "\n", + "def window_testing(img, window):\n", + " brain_img = window(img, 40, 80)\n", + " subdural_img = window(img, 80, 200)\n", + " soft_img = window(img, 40, 380)\n", + " \n", + " brain_img = (brain_img - 0) / 80\n", + " subdural_img = (subdural_img - (-20)) / 200\n", + " soft_img = (soft_img - (-150)) / 380\n", + " bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n", + "\n", + " return bsb_img\n", + "\n", + "\n", + "# example of a \"bad data point\" (i.e. (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100) == True)\n", + "import matplotlib.pyplot as plt\n", + "dicom = pydicom.dcmread(train_img_path + train_df.index[101] + \".dcm\")\n", + "\n", + "fig, ax = plt.subplots(1, 2)\n", + "\n", + "ax[0].imshow(window_testing(dicom, window_without_correction), cmap=plt.cm.bone);\n", + "ax[0].set_title(\"original\")\n", + "ax[1].imshow(window_testing(dicom, window_with_correction), cmap=plt.cm.bone);\n", + "ax[1].set_title(\"corrected\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random image augmentation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Image Augmentation\n", + "sometimes = lambda aug: iaa.Sometimes(0.25, aug)\n", + " \n", + "augmentation = iaa.Sequential([ iaa.Fliplr(0.25),\n", + " iaa.Flipud(0.10),\n", + " iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),\n", + " iaa.Sometimes(0.5,iaa.GaussianBlur(sigma=(0, 0.5))),# Strengthen or weaken the contrast in each image.\n", + " iaa.ContrastNormalization((0.75, 1.5)),\n", + " sometimes(iaa.Crop(px=(0, 25), keep_size = True, sample_independently = False)) \n", + " ], random_order = True) \n", + " \n", + "\n", + "\n", + "# Generators\n", + "class DataGenerator_Train(keras.utils.Sequence):\n", + " def __init__(self, dataset, labels, batch_size = batch_size, image_shape = input_image_shape, image_path = train_img_path, augment = False, *args, **kwargs):\n", + " self.dataset = dataset\n", + " self.ids = dataset.index\n", + " self.labels = labels\n", + " self.batch_size = batch_size\n", + " self.image_shape = image_shape\n", + " self.image_path = train_img_path\n", + " self.augment = augment\n", + " self.on_epoch_end()\n", + "\n", + " def __len__(self):\n", + " return int(ceil(len(self.ids) / self.batch_size))\n", + "\n", + " def __getitem__(self, index):\n", + " indices = self.indices[index*self.batch_size:(index+1)*self.batch_size]\n", + " X, Y = self.__data_generation(indices)\n", + " return X, Y\n", + "\n", + " def augmentor(self, image):\n", + " augment_img = augmentation \n", + " image_aug = augment_img.augment_image(image)\n", + " return image_aug\n", + "\n", + " def on_epoch_end(self):\n", + " self.indices = np.arange(len(self.ids))\n", + " np.random.shuffle(self.indices)\n", + "\n", + " def __data_generation(self, indices):\n", + " X = np.empty((self.batch_size, *self.image_shape))\n", + " Y = np.empty((self.batch_size, 6), dtype=np.float32)\n", + " \n", + " for i, index in enumerate(indices):\n", + " ID = self.ids[index]\n", + " image = _read(self.image_path+ID+\".dcm\", self.image_shape)\n", + " if self.augment:\n", + " X[i,] = self.augmentor(image)\n", + " else:\n", + " X[i,] = image\n", + " Y[i,] = self.labels.iloc[index].values \n", + " return X, Y\n", + " \n", + "class DataGenerator_Test(keras.utils.Sequence):\n", + " def __init__(self, dataset, labels, batch_size = batch_size, image_shape = input_image_shape, image_path = test_img_path, *args, **kwargs):\n", + " self.dataset = dataset\n", + " self.ids = dataset.index\n", + " self.labels = labels\n", + " self.batch_size = batch_size\n", + " self.image_shape = image_shape\n", + " self.image_path = image_path\n", + " self.on_epoch_end()\n", + "\n", + " def __len__(self):\n", + " return int(ceil(len(self.ids) / self.batch_size))\n", + "\n", + " def __getitem__(self, index):\n", + " indices = self.indices[index*self.batch_size:(index+1)*self.batch_size]\n", + " X = self.__data_generation(indices)\n", + " return X\n", + "\n", + " def on_epoch_end(self):\n", + " self.indices = np.arange(len(self.ids))\n", + " \n", + " def __data_generation(self, indices):\n", + " X = np.empty((self.batch_size, *self.image_shape))\n", + " \n", + " for i, index in enumerate(indices):\n", + " ID = self.ids[index]\n", + " image = _read(self.image_path+ID+\".dcm\", self.image_shape)\n", + " X[i,] = image \n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the training and test datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- oversample the minority class 'epidural' " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Oversampling\n", + "epidural_df = train_df[train_df.Label['epidural'] == 1]\n", + "train_oversample_df = pd.concat([train_df, epidural_df])\n", + "train_df = train_oversample_df\n", + "\n", + "# Summary\n", + "print('Train Shape: {}'.format(train_df.shape))\n", + "print('Test Shape: {}'.format(test_df.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EfficientNet model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def predictions(test_df, model): \n", + " test_preds = model.predict_generator(DataGenerator_Test(test_df, None, 5, input_image_shape, test_img_path), verbose = 1)\n", + " return test_preds[:test_df.iloc[range(test_df.shape[0])].shape[0]]\n", + "\n", + "def ModelCheckpointFull(model_name):\n", + " return ModelCheckpoint(model_name, \n", + " monitor = 'val_loss', \n", + " verbose = 1, \n", + " save_best_only = False, \n", + " save_weights_only = True, \n", + " mode = 'min', \n", + " period = 1)\n", + "\n", + "# Create Model\n", + "def create_model():\n", + " K.clear_session()\n", + " \n", + " base_model = efn.EfficientNetB4(weights = 'imagenet', include_top = False, pooling = 'avg', input_shape = input_image_shape)\n", + " x = base_model.output\n", + " x = Dropout(0.2)(x)\n", + " y_pred = Dense(6, activation = 'sigmoid')(x)\n", + "\n", + " return Model(inputs = base_model.input, outputs = y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Labels Train/Valid Dataset Split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Submission Placeholder\n", + "submission_predictions = []\n", + "\n", + "\n", + "Multi_Stratified_split = MultilabelStratifiedShuffleSplit(n_splits = 10, test_size = test_size, random_state = seed)\n", + "X = train_df.index\n", + "Y = train_df.Label.values\n", + "\n", + "# Get train and test index\n", + "Multi_Stratified_splits = next(Multi_Stratified_split.split(X, Y))\n", + "train_idx = Multi_Stratified_splits[0]\n", + "valid_idx = Multi_Stratified_splits[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Loop through Folds of Multi Label Stratified Split\n", + "\n", + "for epoch in range(0, 4):\n", + " print('=========== EPOCH {}'.format(epoch))\n", + "\n", + " # Shuffle Train data\n", + " np.random.shuffle(train_idx)\n", + " print(train_idx[:5]) \n", + " print(valid_idx[:5])\n", + "\n", + " # Create Data Generators for Train and Valid\n", + " data_generator_train = DataGenerator_Train(train_df.iloc[train_idx], \n", + " train_df.iloc[train_idx], \n", + " train_batch_size, \n", + " input_image_shape,\n", + " augment = True)\n", + " data_generator_val = DataGenerator_Train(train_df.iloc[valid_idx], \n", + " train_df.iloc[valid_idx], \n", + " valid_batch_size, \n", + " input_image_shape,\n", + " augment = False)\n", + "\n", + " # Create Model\n", + " model = create_model()\n", + " \n", + " # Full Training Model\n", + " for base_layer in model.layers[:-1]:\n", + " base_layer.trainable = True\n", + " steps = int(len(data_generator_train) / 6)\n", + " LR = 0.0001\n", + "\n", + " if epoch != 0:\n", + " # Load Model Weights\n", + " model.load_weights('model.h5') \n", + "\n", + " model.compile(optimizer = Adam(learning_rate = LR), \n", + " loss = 'binary_crossentropy',\n", + " metrics = ['acc', tf.keras.metrics.AUC()])\n", + " \n", + " # Train Model\n", + " model.fit_generator(generator = data_generator_train,\n", + " validation_data = data_generator_val,\n", + " steps_per_epoch = steps,\n", + " epochs = 1,\n", + " callbacks = [ModelCheckpointFull('model.h5')],\n", + " verbose = 1)\n", + " \n", + " # Starting with the 6th epoch we create predictions for the test set on each epoch\n", + " if epoch >= 1:\n", + " preds = predictions(test_df, model)\n", + " submission_predictions.append(preds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ensemble and average all submission_predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_df.iloc[:, :] = np.average(submission_predictions, axis = 0, weights = [2**i for i in range(len(submission_predictions))])\n", + "test_df = test_df.stack().reset_index()\n", + "test_df.insert(loc = 0, column = 'ID', value = test_df['Image'].astype(str) + \"_\" + test_df['Diagnosis'])\n", + "test_df = test_df.drop([\"Image\", \"Diagnosis\"], axis=1)\n", + "test_df.to_csv('submission.csv', index = False)\n", + "print(test_df.head(12))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import FileLink, FileLinks\n", + "FileLink('submission.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}