def make_last_layers(x, num_filters, out_filters):
'''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(x)
y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
DarknetConv2D(out_filters, (1, 1)))(x)
return x, y
def yolo_body(inputs, num_anchors, num_classes):
"""Create YOLO_V3 model CNN body in Keras."""
darknet = Model(inputs, darknet_body(inputs))
x, y1 = make_last_layers(darknet.output, 512,
num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[152].output])
x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))
x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[92].output])
x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))
return Model(inputs, [y1, y2, y3])
def DarknetConv2D_BN_Mish(*args, **kwargs):
"""Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(
DarknetConv2D(*args, **no_bias_kwargs),
BatchNormalization(),
Mish())
def resblock_body(x, num_filters, num_blocks):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
x = ZeroPadding2D(((1, 0), (1, 0)))(x)
x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x)
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x)
x = Add()([x, y])
return x
def resblock_body(x, num_filters, num_blocks, all_narrow=True):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
preconv1 = ZeroPadding2D(((1,0),(1,0)))(x)
preconv1 = DarknetConv2D_BN_Mish(num_filters, (3,3), strides=(2,2))(preconv1)
shortconv = DarknetConv2D_BN_Mish(num_filters//2 if all_narrow else num_filters, (1,1))(preconv1)
mainconv = DarknetConv2D_BN_Mish(num_filters//2 if all_narrow else num_filters, (1,1))(preconv1)
for i in range(num_blocks):
y = compose(
DarknetConv2D_BN_Mish(num_filters//2, (1,1)),
DarknetConv2D_BN_Mish(num_filters//2 if all_narrow else num_filters, (3,3)))(mainconv)
mainconv = Add()([mainconv,y])
postconv = DarknetConv2D_BN_Mish(num_filters//2 if all_narrow else num_filters, (1,1))(mainconv)
route = Concatenate()([postconv, shortconv])
return DarknetConv2D_BN_Mish(num_filters, (1,1))(route)
def yolo4_body(inputs, num_anchors, num_classes):
"""Create YOLO_V4 model CNN body in Keras."""
darknet = Model(inputs, darknet_body(inputs))
#19x19 head
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(darknet.output)
y19 = DarknetConv2D_BN_Leaky(1024, (3,3))(y19)
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
maxpool1 = MaxPooling2D(pool_size=(13,13), strides=(1,1), padding='same')(y19)
maxpool2 = MaxPooling2D(pool_size=(9,9), strides=(1,1), padding='same')(y19)
maxpool3 = MaxPooling2D(pool_size=(5,5), strides=(1,1), padding='same')(y19)
y19 = Concatenate()([maxpool1, maxpool2, maxpool3, y19])
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
y19 = DarknetConv2D_BN_Leaky(1024, (3,3))(y19)
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
y19_upsample = compose(DarknetConv2D_BN_Leaky(256, (1,1)), UpSampling2D(2))(y19)
#38x38 head
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(darknet.layers[204].output)
y38 = Concatenate()([y38, y19_upsample])
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38 = DarknetConv2D_BN_Leaky(512, (3,3))(y38)
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38 = DarknetConv2D_BN_Leaky(512, (3,3))(y38)
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38_upsample = compose(DarknetConv2D_BN_Leaky(128, (1,1)), UpSampling2D(2))(y38)
#76x76 head
y76 = DarknetConv2D_BN_Leaky(128, (1,1))(darknet.layers[131].output)
y76 = Concatenate()([y76, y38_upsample])
y76 = DarknetConv2D_BN_Leaky(128, (1,1))(y76)
y76 = DarknetConv2D_BN_Leaky(256, (3,3))(y76)
y76 = DarknetConv2D_BN_Leaky(128, (1,1))(y76)
y76 = DarknetConv2D_BN_Leaky(256, (3,3))(y76)
y76 = DarknetConv2D_BN_Leaky(128, (1,1))(y76)
#76x76 output
y76_output = DarknetConv2D_BN_Leaky(256, (3,3))(y76)
y76_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(y76_output)
#38x38 output
y76_downsample = ZeroPadding2D(((1,0),(1,0)))(y76)
y76_downsample = DarknetConv2D_BN_Leaky(256, (3,3), strides=(2,2))(y76_downsample)
y38 = Concatenate()([y76_downsample, y38])
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38 = DarknetConv2D_BN_Leaky(512, (3,3))(y38)
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38 = DarknetConv2D_BN_Leaky(512, (3,3))(y38)
y38 = DarknetConv2D_BN_Leaky(256, (1,1))(y38)
y38_output = DarknetConv2D_BN_Leaky(512, (3,3))(y38)
y38_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(y38_output)
#19x19 output
y38_downsample = ZeroPadding2D(((1,0),(1,0)))(y38)
y38_downsample = DarknetConv2D_BN_Leaky(512, (3,3), strides=(2,2))(y38_downsample)
y19 = Concatenate()([y38_downsample, y19])
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
y19 = DarknetConv2D_BN_Leaky(1024, (3,3))(y19)
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
y19 = DarknetConv2D_BN_Leaky(1024, (3,3))(y19)
y19 = DarknetConv2D_BN_Leaky(512, (1,1))(y19)
y19_output = DarknetConv2D_BN_Leaky(1024, (3,3))(y19)
y19_output = DarknetConv2D(num_anchors*(num_classes+5), (1,1))(y19_output)
yolo4_model = Model(inputs, [y19_output, y38_output, y76_output])
return yolo4_model
def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""Darknet Convolution2D 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))
