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model.py
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model.py
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from tensorflow import keras
from tensorflow.keras import Model
from tensorflow.keras import layers
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dropout, Lambda, Add
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, BatchNormalization
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import concatenate
# Source:
# https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
# from .config import IMAGE_ORDERING
alpha = 1e-5
IMAGE_ORDERING = 'channels_last'
if IMAGE_ORDERING == 'channels_first':
pretrained_url = "https://github.com/fchollet/deep-learning-models/" \
"releases/download/v0.2/" \
"resnet50_weights_th_dim_ordering_th_kernels_notop.h5"
elif IMAGE_ORDERING == 'channels_last':
pretrained_url = "https://github.com/fchollet/deep-learning-models/" \
"releases/download/v0.2/" \
"resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
if IMAGE_ORDERING == 'channels_first':
MERGE_AXIS = 1
elif IMAGE_ORDERING == 'channels_last':
MERGE_AXIS = -1
def one_side_pad(x):
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
if IMAGE_ORDERING == 'channels_first':
x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
elif IMAGE_ORDERING == 'channels_last':
x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
return x
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at
main path
filters: list of integers, the filterss of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING,
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING,
name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block,
strides=(2, 2)):
"""conv_block is the block that has a conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at
main path
filters: list of integers, the filterss of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
Note that from stage 3, the first conv layer at main path is with
strides=(2,2) and the shortcut should have strides=(2,2) as well
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
pretrained_url = "https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING,
name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING,
strides=strides, name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(
axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def get_resnet50_encoder(input_height=224, input_width=224,
pretrained='imagenet',
include_top=True, weights='imagenet',
input_tensor=None, input_shape=None,
pooling=None,
classes=1000, channels=3):
assert input_height % 32 == 0
assert input_width % 32 == 0
if IMAGE_ORDERING == 'channels_first':
img_input = Input(shape=(channels, input_height, input_width))
elif IMAGE_ORDERING == 'channels_last':
img_input = Input(shape=(input_height, input_width, channels))
print("img_input shape", img_input.shape)
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING,
strides=(2, 2), name='conv1')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x
if pretrained == 'imagenet':
weights_path = keras.utils.get_file(
pretrained_url.split("/")[-1], pretrained_url)
Model(img_input, x).load_weights(weights_path, by_name=True, skip_mismatch=True)
return img_input, [f1, f2, f3, f4]
def get_resnet50_encoder2(input_height=224, input_width=224,
pretrained='imagenet',
include_top=True, weights='imagenet',
input_tensor=None, input_shape=None,
pooling=None,
classes=1000, channels=3):
assert input_height % 32 == 0
assert input_width % 32 == 0
if IMAGE_ORDERING == 'channels_first':
img_input = Input(shape=(channels, input_height, input_width))
elif IMAGE_ORDERING == 'channels_last':
img_input = Input(shape=(input_height, input_width, channels))
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING,
strides=(2, 2), name='conv21')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv21')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=22, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=22, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=22, block='c')
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=23, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=23, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=23, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=23, block='d')
f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=24, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=24, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=24, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=24, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=24, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=24, block='f')
f4 = x
if pretrained == 'imagenet':
weights_path = keras.utils.get_file(
pretrained_url.split("/")[-1], pretrained_url)
Model(img_input, x).load_weights(weights_path, by_name=True, skip_mismatch=True)
# Model(img_input, x).load_weights(weights_path)
return img_input, [f1, f2, f3, f4]
def Resnet50_UNet(n_classes, in_img, in_inf, l1_skip_conn=True):
_, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS = in_img.shape
img_input1, levels1 = get_resnet50_encoder(input_height = IMG_HEIGHT, input_width = IMG_WIDTH, channels = IMG_CHANNELS)
[f11, f12, f13, f14] = levels1
img_input2, levels2 = get_resnet50_encoder2(input_height = IMG_HEIGHT, input_width = IMG_WIDTH, channels = IMG_CHANNELS)
[f21, f22, f23, f24] = levels2
f1 = Add()([f11, f21])
f2 = Add()([f12, f22])
f3 = Add()([f13, f23])
f4 = Add()([f14, f24])
o = f4
o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
o = Conv2D(512, (3, 3), padding='valid' , activation='relu' , name='DEC_conv1', data_format=IMAGE_ORDERING)(o)
o = BatchNormalization( name='DEC_bn1')(o)
o = UpSampling2D((2, 2), name='DEC_up1', data_format=IMAGE_ORDERING)(o)
o = concatenate([o, f3], axis=MERGE_AXIS)
o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
o = Conv2D(256, (3, 3), padding='valid', activation='relu' , name='DEC_conv2', data_format=IMAGE_ORDERING)(o)
o = BatchNormalization( name='DEC_bn2')(o)
o = UpSampling2D((2, 2), name='DEC_up2', data_format=IMAGE_ORDERING)(o)
o = concatenate([o, f2], axis=MERGE_AXIS)
o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
o = Conv2D(128, (3, 3), padding='valid' , activation='relu' , name='DEC_conv3', data_format=IMAGE_ORDERING)(o)
o = BatchNormalization( name='DEC_bn3')(o)
o = UpSampling2D((2, 2), name='DEC_up3', data_format=IMAGE_ORDERING)(o)
if l1_skip_conn:
o = concatenate([o, f1], axis=MERGE_AXIS)
o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
o = Conv2D(64, (3, 3), padding='valid', activation='relu', data_format=IMAGE_ORDERING, name="DEC_seg_feats")(o)
o = BatchNormalization( name='DEC_bn4')(o)
o = UpSampling2D((2, 2), name='DEC_up4', data_format=IMAGE_ORDERING)(o)
outputs = Conv2D(n_classes, (1, 1), activation='sigmoid', name='DEC_conv5') (o)
print("outputs last shape", outputs.shape)
model = Model([img_input1,img_input2], outputs)
return model