unet added
This commit is contained in:
patrycjalazna
parent
85d806f231
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**/data/train_features*
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**/data/train_features*
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**/data/train_labels*
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**/data/train_labels*
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**/__pycache__*
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**/__pycache__*
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**/benchmark/__pycache__*
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**/models*
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unet.ipynb
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unet.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"import shutil\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras import backend as K\n",
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"from tensorflow.keras.layers import Activation, Lambda, GlobalAveragePooling2D, concatenate\n",
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"from tensorflow.keras.layers import UpSampling2D, Conv2D, Dropout, MaxPooling2D, Conv2DTranspose\n",
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"from tensorflow.keras.layers import Dense, Flatten, Input\n",
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"from tensorflow.keras.models import Model, Sequential, load_model\n",
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"from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import cv2\n",
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"import pickle\n",
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"import random\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.metrics import IOU\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMG_HEIGHT = 512\n",
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"IMG_WIDTH = 512\n",
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"img_dir = '/images'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"EPOCHS = 30\n",
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"batch_size = 16"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Unet model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Unet():\n",
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" def __init__(self, num_classes=1):\n",
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" self.num_classes=num_classes\n",
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"\n",
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" def build_model(self):\n",
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" in1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3 ))\n",
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"\n",
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" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(in1)\n",
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" conv1 = Dropout(0.2)(conv1)\n",
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" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1)\n",
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" pool1 = MaxPooling2D((2, 2))(conv1)\n",
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"\n",
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" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)\n",
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" conv2 = Dropout(0.2)(conv2)\n",
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" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2)\n",
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" pool2 = MaxPooling2D((2, 2))(conv2)\n",
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"\n",
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" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool2)\n",
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" conv3 = Dropout(0.2)(conv3)\n",
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" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3)\n",
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" pool3 = MaxPooling2D((2, 2))(conv3)\n",
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"\n",
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" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)\n",
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" conv4 = Dropout(0.2)(conv4)\n",
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" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4)\n",
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"\n",
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" up1 = concatenate([UpSampling2D((2, 2))(conv4), conv3], axis=-1)\n",
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" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up1)\n",
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" conv5 = Dropout(0.2)(conv5)\n",
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" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5)\n",
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" \n",
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" up2 = concatenate([UpSampling2D((2, 2))(conv5), conv2], axis=-1)\n",
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" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
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" conv6 = Dropout(0.2)(conv6)\n",
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" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6)\n",
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"\n",
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" up2 = concatenate([UpSampling2D((2, 2))(conv6), conv1], axis=-1)\n",
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" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
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" conv7 = Dropout(0.2)(conv7)\n",
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" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv7)\n",
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" segmentation = Conv2D(self.num_classes, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
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" #segmentation = Conv2D(3, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
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" model = Model(inputs=[in1], outputs=[segmentation])\n",
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"\n",
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" return model\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.loss import jaccard_loss"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'jaccard_loss' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb Cell 7'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m Unet(num_classes\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mbuild_model()\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=2'>3</a>\u001b[0m compile_params \u001b[39m=\u001b[39m{\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=3'>4</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mloss\u001b[39m\u001b[39m'\u001b[39m:jaccard_loss(smooth\u001b[39m=\u001b[39m\u001b[39m90\u001b[39m), \n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=4'>5</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39moptimizer\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39mrmsprop\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=5'>6</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mmetrics\u001b[39m\u001b[39m'\u001b[39m:[IOU]\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=6'>7</a>\u001b[0m }\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=9'>10</a>\u001b[0m model\u001b[39m.\u001b[39mcompile(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcompile_params)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=11'>12</a>\u001b[0m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mplot_model(model, show_shapes\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'jaccard_loss' is not defined"
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]
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}
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],
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"source": [
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"model = Unet(num_classes=1).build_model()\n",
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"\n",
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"compile_params ={\n",
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" 'loss':jaccard_loss(smooth=90), \n",
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" 'optimizer':'rmsprop',\n",
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" 'metrics':[IOU]\n",
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" }\n",
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" \n",
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" \n",
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"model.compile(**compile_params)\n",
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"\n",
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"tf.keras.utils.plot_model(model, show_shapes=True)\n",
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"\n",
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"model_name = \"models/unet.h5\"\n",
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"\n",
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"modelcheckpoint = ModelCheckpoint(model_name,\n",
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" monitor='val_loss',\n",
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" mode='auto',\n",
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" verbose=1,\n",
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" save_best_only=True)\n",
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"\n",
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"\n",
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"history = model.fit_generator(train_gen,\n",
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" validation_data=val_gen,\n",
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" epochs=EPOCHS,\n",
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" steps_per_epoch=100,\n",
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" validation_steps = 100,\n",
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" shuffle=True,\n",
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" validation_freq=1\n",
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")"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "1be0d4179f0eed29061aece4de36a3b887fc5990f515aa1f08bfa33b9ed547bc"
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},
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"kernelspec": {
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"display_name": "Python 3.9.7 64-bit ('widzenie-komputerowe-env': conda)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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