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- import numpy as np
- import os
- import skimage.io as io
- import skimage.transform as trans
- import numpy as np
- from tensorflow.keras.models import *
- from tensorflow.keras.layers import *
- from tensorflow.keras.optimizers import *
- from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
- from tensorflow.keras import backend as keras
- from metricFunction import my_IoU
- def unet(pretrained_weights = None,input_size = (256,256,3)):
- inputs = Input(input_size)
- conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
- conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
- pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
- conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
- conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
- pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
- conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
- conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
- pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
- conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
- conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
- drop4 = Dropout(0.5)(conv4)
- pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
- conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
- conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
- drop5 = Dropout(0.5)(conv5)
- up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
- merge6 = concatenate([drop4,up6], axis = 3)
- conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
- conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
- up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
- merge7 = concatenate([conv3,up7], axis = 3)
- conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
- conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
- up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
- merge8 = concatenate([conv2,up8], axis = 3)
- conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
- conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
- up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
- merge9 = concatenate([conv1,up9], axis = 3)
- conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
- conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
- conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
- conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
- model = Model(inputs = inputs, outputs = conv10)
- model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = [my_IoU])
- #model.summary()
- if(pretrained_weights):
- model.load_weights(pretrained_weights)
- return model
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