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#from aix.constants import *
import tensorflow as tf
def initialize_model(img_width, img_height, img_channels):
#,
# optimizer = 'adam', model_loss = 'binary_crossentropy', data_augm = False):
inputs = tf.keras.layers.Input((img_width, img_height, img_channels), name='input')
#Contraction path
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block1_conv2d_1')(inputs)
c1 = tf.keras.layers.Dropout(0.1, name='block1_dropout')(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block1_conv2d_2')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2), name='block1_max_pooling')(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block2_conv2d_1')(p1)
c2 = tf.keras.layers.Dropout(0.1, name='block2_dropout')(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block2_conv2d_2')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2), name='block2_max_pooling')(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block3_conv2d_1')(p2)
c3 = tf.keras.layers.Dropout(0.2, name='block3_dropout')(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block3_conv2d_2')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2), name='block3_max_pooling')(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block4_conv2d_1')(p3)
c4 = tf.keras.layers.Dropout(0.2, name='block4_dropout')(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block4_conv2d_2')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), name='block4_max_pooling')(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block5_conv2d_1')(p4)
c5 = tf.keras.layers.Dropout(0.3, name='block5_dropout')(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block5_conv2d_2')(c5)
#Expansive path
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='block6_conv2d_transpose')(c5)
u6 = tf.keras.layers.concatenate([u6, c4], name='block6_concatenate')
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block6_conv2d_1')(u6)
c6 = tf.keras.layers.Dropout(0.2, name='block6_dropout')(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block6_conv2d_2')(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='block7_conv2d_transpose')(c6)
u7 = tf.keras.layers.concatenate([u7, c3], name='block7_concatenate')
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block7_conv2d_1')(u7)
c7 = tf.keras.layers.Dropout(0.2, name='block7_dropout')(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block7_conv2d_2')(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same', name='block8_conv2d_transpose')(c7)
u8 = tf.keras.layers.concatenate([u8, c2], name='block8_concatenate')
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block8_conv2d_1')(u8)
c8 = tf.keras.layers.Dropout(0.1, name='block8_dropout')(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block8_conv2d_2')(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same', name='block9_conv2d_transpose')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3, name='block9_concatenate')
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block9_conv2d_1')(u9)
c9 = tf.keras.layers.Dropout(0.1, name='block9_dropout')(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='block9_conv2d_2')(c9)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid', name='output')(c9)
model = tf.keras.Model(inputs = [inputs], outputs = [outputs])
#model.compile(optimizer = optimizer, loss = model_loss, metrics = [dice_coef])
#model.summary()
return model
def initialize_model_v2(img_width, img_height, img_channels,
optimizer = 'adam', model_loss = 'binary_crossentropy', data_augm = False):
n = 1
inputs = tf.keras.layers.Input((img_width, img_height, img_channels))
#Contraction path
c1 = tf.keras.layers.Conv2D(16 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(inputs)
#c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
#c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
#c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
#c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
#c5 = tf.keras.layers.Dropout(0.5)(c5)
c5 = tf.keras.layers.Conv2D(256 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)
c5 = tf.keras.layers.Dropout(0.5)(c5)
#Expansive path
u6 = tf.keras.layers.Conv2DTranspose(128 * n, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
#c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
u7 = tf.keras.layers.Conv2DTranspose(64 * n, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
#c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
u8 = tf.keras.layers.Conv2DTranspose(32 * n, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
#c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
u9 = tf.keras.layers.Conv2DTranspose(16 * n, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
#c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16 * n, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)
model = tf.keras.Model(inputs = [inputs], outputs = [outputs])
#model.compile(optimizer = optimizer, loss = model_loss, metrics = [dice_coef])
#model.summary()
return model
# From pix2pix (https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/pix2pix/pix2pix.py)
#
def pix2pix_upsample(filters, size, norm_type='batchnorm', apply_dropout=False):
"""Upsamples an input.
Conv2DTranspose => Batchnorm => Dropout => Relu
Args:
filters: number of filters
size: filter size
norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.
apply_dropout: If True, adds the dropout layer
Returns:
Upsample Sequential Model
"""
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
# Adapted From: https://www.tensorflow.org/tutorials/images/segmentation
def unet_model(output_channels:int, input_shape=[128, 128, 3],
optimizer = 'adam', model_loss = 'binary_crossentropy'):
base_model = tf.keras.applications.MobileNetV2(input_shape=input_shape, include_top=False)
# Use the activations of these layers
layer_names = [
'block_1_expand_relu', # 64x64
'block_3_expand_relu', # 32x32
'block_6_expand_relu', # 16x16
'block_13_expand_relu', # 8x8
'block_16_project', # 4x4
]
base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=base_model_outputs)
down_stack.trainable = False
up_stack = [
pix2pix_upsample(512, 3), # 4x4 -> 8x8
pix2pix_upsample(256, 3), # 8x8 -> 16x16
pix2pix_upsample(128, 3), # 16x16 -> 32x32
pix2pix_upsample(64, 3), # 32x32 -> 64x64
]
inputs = tf.keras.layers.Input(shape=input_shape)
# Downsampling through the model
skips = down_stack(inputs)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(
filters=output_channels, kernel_size=3, strides=2,
padding='same') #64x64 -> 128x128
x = last(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
model.compile(optimizer=optimizer,
#loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
loss=model_loss,
metrics=['acc', dice_coef]
)
return model
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