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seg_fc_hrnet.py
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import keras.backend as K
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Activation
from keras.layers import UpSampling2D, add, concatenate, Dropout
def conv3x3(x, out_filters, strides=(1, 1)):
x = Conv2D(out_filters, 3, padding='same', strides=strides, use_bias=False, kernel_initializer='he_normal')(x)
return x
def basic_Block(input, out_filters, strides=(1, 1), with_conv_shortcut=False):
x = conv3x3(input, out_filters, strides)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = conv3x3(x, out_filters)
x = BatchNormalization(axis=3)(x)
if with_conv_shortcut:
residual = Conv2D(out_filters, 1, strides=strides, use_bias=False, kernel_initializer='he_normal')(input)
residual = BatchNormalization(axis=3)(residual)
x = add([x, residual])
else:
x = add([x, input])
x = Activation('relu')(x)
return x
def bottleneck_Block(input, out_filters, strides=(1, 1), with_conv_shortcut=False):
expansion = 4
de_filters = int(out_filters / expansion)
x = Conv2D(de_filters, 1, use_bias=False, kernel_initializer='he_normal')(input)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = Conv2D(de_filters, 3, strides=strides, padding='same', use_bias=False, kernel_initializer='he_normal')(x)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = Conv2D(out_filters, 1, use_bias=False, kernel_initializer='he_normal')(x)
x = BatchNormalization(axis=3)(x)
if with_conv_shortcut:
residual = Conv2D(out_filters, 1, strides=strides, use_bias=False, kernel_initializer='he_normal')(input)
residual = BatchNormalization(axis=3)(residual)
x = add([x, residual])
else:
x = add([x, input])
x = Activation('relu')(x)
return x
def _make_transition_layer(x, out_filters_list=[32, 64, 96, 128, 160, 192, 224, 256]):
transition_layers = []
xi = Conv2D(out_filters_list[0], 3, strides=(1, 1),
padding='same', use_bias=False, kernel_initializer='he_normal')(x)
xi = BatchNormalization(axis=3)(xi)
xi = Activation('relu')(xi)
transition_layers.append(xi)
for i in range(1, len(out_filters_list)):
xi = Conv2D(out_filters_list[i], 3, strides=(2, 2),
padding='same', use_bias=False, kernel_initializer='he_normal')(xi)
xi = BatchNormalization(axis=3)(xi)
xi = Activation('relu')(xi)
transition_layers.append(xi)
return transition_layers
def make_branch(x, out_filters=32):
x = basic_Block(x, out_filters, with_conv_shortcut=False)
# x = basic_Block(x, out_filters, with_conv_shortcut=False)
# x = basic_Block(x, out_filters, with_conv_shortcut=False)
# x = basic_Block(x, out_filters, with_conv_shortcut=False)
return x
def _make_fuse_layers(x, num_branches, out_filters_list=[32, 64, 96, 128, 160, 192, 224, 256], multi_scale_output=False):
fuse_layers = []
for i in range(num_branches if multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j == i:
fuse_layer.append(x[i])
elif j > i:
xi = Conv2D(out_filters_list[i], 1, strides=(1, 1),
padding='same', use_bias=False, kernel_initializer='he_normal')(x[j])
xi = BatchNormalization(axis=3)(xi)
xi = UpSampling2D(size=(2**(j-i), 2**(j-i)))(xi)
fuse_layer.append(xi)
else:
if j+1 < i:
xi = x[j]
for k in range(j+1, i):
xi = Conv2D(out_filters_list[k], 3, strides=(2, 2),
padding='same', use_bias=False, kernel_initializer='he_normal')(xi)
xi = BatchNormalization(axis=3)(xi)
xi = Activation('relu')(xi)
else:
xi = x[j]
xi = Conv2D(out_filters_list[i], 3, strides=(2, 2),
padding='same', use_bias=False, kernel_initializer='he_normal')(xi)
xi = BatchNormalization(axis=3)(xi)
fuse_layer.append(xi)
xi = add(fuse_layer)
fuse_layers.append(xi)
return fuse_layers
def seg_fc_hrnet(height=512, width=512, channel=3, classes=6):
inputs = Input(shape=(height, width, channel))
out_filters_list = [32, 64, 96, 128, 160, 192, 224, 256]
x = _make_transition_layer(inputs, out_filters_list=out_filters_list)
x1 = []
for i in range(len(out_filters_list)):
x1.append(make_branch(x[i], out_filters=out_filters_list[i]))
x1 = _make_fuse_layers(x1, len(out_filters_list),
out_filters_list=out_filters_list,
multi_scale_output=True)
x2 = []
for i in range(len(out_filters_list)):
x2.append(make_branch(x1[i], out_filters=out_filters_list[i]))
x2 = _make_fuse_layers(x2, len(out_filters_list),
out_filters_list=out_filters_list,
multi_scale_output=True)
x3 = []
for i in range(len(out_filters_list)):
x3.append(make_branch(x2[i], out_filters=out_filters_list[i]))
x3 = _make_fuse_layers(x3, len(out_filters_list),
out_filters_list=out_filters_list,
multi_scale_output=False)
out = Conv2D(classes, 1, use_bias=False, kernel_initializer='he_normal')(x3[0])
out = BatchNormalization(axis=3)(out)
out = Activation('softmax', name='Classification')(out)
model = Model(inputs=inputs, outputs=out)
return model