x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + "_2_pad")(x)
x = layers.DepthwiseConv2D(kernel_size, strides=stride, depth_multiplier=c,
use_bias=False, name=name + "_2_conv")(x)
x_shape = backend.int_shape(x)[1:-1]
x = layers.Reshape(x_shape + (groups, c, c))(x)
output_shape = x_shape + (groups, c) if backend.backend() == "theano" else None
x = layers.Lambda(lambda x: sum([x[:, :, :, :, i] for i in range(c)]),
output_shape=output_shape, name=name + "_2_reduce")(x)
After Change
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + "_2_pad")(x)
x = layers.DepthwiseConv2D(kernel_size, strides=stride, depth_multiplier=c,
use_bias=False, name=name + "_2_conv")(x)
kernel = np.zeros((1, 1, filters * c, filters), dtype=np.float32)
for i in range(filters):
start = (i // c) * c * c + i % c
end = start + c * c
