def __init__(self,
pool_size,
strides,
padding='same',
data_format='channels_last',
name=None,
**kwargs):
MaxPool2D_.__init__(self, pool_size, strides, padding, data_format,
name, **kwargs)
def DarknetTiny(name=None):
x = inputs = Input([None, None, 3])
x = DarknetConv(x, 16, 3)
x = MaxPool2D(2, 2, 'same')(x)
x = DarknetConv(x, 32, 3)
x = MaxPool2D(2, 2, 'same')(x)
x = DarknetConv(x, 64, 3)
x = MaxPool2D(2, 2, 'same')(x)
x = DarknetConv(x, 128, 3)
x = MaxPool2D(2, 2, 'same')(x)
x = x_8 = DarknetConv(x, 256, 3) # skip connection
x = MaxPool2D(2, 2, 'same')(x)
x = DarknetConv(x, 512, 3)
x = MaxPool2D(2, 1, 'same')(x)
x = DarknetConv(x, 1024, 3)
return tf.keras.Model(inputs, (x_8, x), name=name)
def _link(self, input_=None, **kwargs):
assert isinstance(input_, tf.Tensor)
output = MaxPool2D_.__call__(self, input_, scope=self.full_name)
# self.neuron_scale = get_scale(output)
return output