Spaces:
Sleeping
Sleeping
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import cv2 | |
| import tensorflow as tf | |
| from tensorflow.python.keras import Sequential | |
| from tensorflow.keras import layers, optimizers | |
| from tensorflow.keras.layers import * | |
| from tensorflow.keras.models import Model | |
| from tensorflow.keras.initializers import glorot_uniform | |
| from tensorflow.keras.utils import plot_model | |
| from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler | |
| import tensorflow.keras.backend as K | |
| # lets create model now | |
| def resblock(X, f): | |
| ''' | |
| function for creating res block | |
| ''' | |
| X_copy = X #copy of input | |
| # main path | |
| X = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X) | |
| X = BatchNormalization()(X) | |
| X = Activation('relu')(X) | |
| X = Conv2D(f, kernel_size=(3,3), padding='same', kernel_initializer='he_normal')(X) | |
| X = BatchNormalization()(X) | |
| # shortcut path | |
| X_copy = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X_copy) | |
| X_copy = BatchNormalization()(X_copy) | |
| # Adding the output from main path and short path together | |
| X = Add()([X, X_copy]) | |
| X = Activation('relu')(X) | |
| return X | |
| def upsample_concat(x, skip): | |
| ''' | |
| funtion for upsampling image | |
| ''' | |
| X = UpSampling2D((2,2))(x) | |
| merge = Concatenate()([X, skip]) | |
| return merge | |
| def load_model(): | |
| input_shape = (256,256,3) | |
| X_input = Input(input_shape) #iniating tensor of input shape | |
| # Stage 1 | |
| conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input) | |
| conv_1 = BatchNormalization()(conv_1) | |
| conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv_1) | |
| conv_1 = BatchNormalization()(conv_1) | |
| pool_1 = MaxPool2D((2,2))(conv_1) | |
| # stage 2 | |
| conv_2 = resblock(pool_1, 32) | |
| pool_2 = MaxPool2D((2,2))(conv_2) | |
| # Stage 3 | |
| conv_3 = resblock(pool_2, 64) | |
| pool_3 = MaxPool2D((2,2))(conv_3) | |
| # Stage 4 | |
| conv_4 = resblock(pool_3, 128) | |
| pool_4 = MaxPool2D((2,2))(conv_4) | |
| # Stage 5 (bottle neck) | |
| conv_5 = resblock(pool_4, 256) | |
| # Upsample Stage 1 | |
| up_1 = upsample_concat(conv_5, conv_4) | |
| up_1 = resblock(up_1, 128) | |
| # Upsample Stage 2 | |
| up_2 = upsample_concat(up_1, conv_3) | |
| up_2 = resblock(up_2, 64) | |
| # Upsample Stage 3 | |
| up_3 = upsample_concat(up_2, conv_2) | |
| up_3 = resblock(up_3, 32) | |
| # Upsample Stage 4 | |
| up_4 = upsample_concat(up_3, conv_1) | |
| up_4 = resblock(up_4, 16) | |
| # final output | |
| out = Conv2D(1, (1,1), kernel_initializer='he_normal', padding='same', activation='sigmoid')(up_4) | |
| seg_model = Model(X_input, out) | |
| return seg_model |