def stage_4(mult=1):
x = inputs = Input([None, None, 3])
x = darknet_conv(x, 32 * mult, 3)
x = blocking_convolution(x, 64 * mult, 1)
x = blocking_convolution(x, 128 * mult, 2)
x = x_36 = blocking_convolution(x, 256 * mult, 8)
return tf.keras.Model(inputs, x)
def backbone(name=None, size=(None, None)):
x = inputs = Input([size[0], size[0], 3])
x = darknet_conv(x, 32, 3)
x = blocking_convolution(x, 64, 1)
x = blocking_convolution(x, 128, 2) # skip connection
x = x_36 = blocking_convolution(x, 256, 8) # skip connection
x = x_61 = blocking_convolution(x, 512, 8)
x = blocking_convolution(x, 1024, 4)
return tf.keras.Model(inputs, (x_36, x_61, x), name=name)
def stage_2(mult=1):
x = inputs = Input([None, None, 3])
x = darknet_conv(x, 32 * mult, 3)
x = blocking_convolution(x, 64 * mult, 1)
return tf.keras.Model(inputs, x)
def stage_1(mult=1):
x = inputs = Input([None, None, 3])
x = darknet_conv(x, 32 * mult, 3)
return tf.keras.Model(inputs, x)