def DarknetConv2D_BN_Leaky(*args, **kwargs):
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(DarknetConv2D(*args, **no_bias_kwargs),
BatchNormalization(), LeakyReLU(alpha=0.1))
def darknet_body():
"""
Generate first 10 conv layers of the Darknet
"""
return compose(DarknetConv2D_BN_Leaky(32, (3, 3)), MaxPooling2D(), DarknetConv2D_BN_Leaky(64, (3, 3)),
MaxPooling2D(), bottleneck_block(128, 64), MaxPooling2D(), bottleneck_block(256, 128),
MaxPooling2D(), bottleneck_x2_block(512, 256), MaxPooling2D(), bottleneck_x2_block(1024, 512))
def bottleneck_x2_block(outer_filters, bottleneck_filters):
"""
Bottleneck block of 3x3, 1x1, 3x3, 1x1, 3x3 convolutions
"""
return compose(bottleneck_block(outer_filters, bottleneck_filters),
DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))
def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""
Darknet Convolutional2D followed by BatchNormalization and LeakyReLu;
"""
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(DarknetConv2D(*args, **no_bias_kwargs), BatchNormalization(), LeakyReLU(alpha=0.1))
def darknet_body():
return compose(DarknetConv2D_BN_Leaky(32, (3, 3)), MaxPooling2D(),
DarknetConv2D_BN_Leaky(64, (3, 3)), MaxPooling2D(),
bottleneck_block(128, 64), MaxPooling2D(),
bottleneck_block(256, 128), MaxPooling2D(),
bottleneck_x2_block(512, 256), MaxPooling2D(),
bottleneck_x2_block(1024, 512))
def yolo_body(inputs, num_anchors, num_classes):
"""
Create YOLO_V2 model CNN body in Keras
"""
darknet = Model(inputs, darknet_body()(inputs))
conv20 = compose(DarknetConv2D_BN_Leaky(1024, (3, 3)),
DarknetConv2D_BN_Leaky(1024, (3, 3)))(darknet.output)
conv13 = darknet.layers[43].output
conv21 = DarknetConv2D_BN_Leaky(64, (1, 1))(conv13)
conv21_reshaped = Lambda(space_to_depth_x2,
output_shape=space_to_depth_x2_output_shape,
name='space_to_depth')(conv21)
x = concatenate([conv21_reshaped, conv20])
x = DarknetConv2D_BN_Leaky(1024, (3, 3))
return Model(inputs, x)
def bottleneck_x2_block(outer_filters, bottleneck_filters):
return compose(bottleneck_block(outer_filters, bottleneck_filters),
DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))