def build(self, input_shapes):
input_shape = input_shapes[0]
channel_axis = -1
input_dim = input_shape[channel_axis]
self.kernel_shape = self.kernel_size + (input_dim, self.filters)
self.depthwise_kernel_shape = self.kernel_size + (input_dim, 1)
self.pointwise_kernel_shape = (1, ) * 2 + (1 * input_dim, self.filters)
param_indices = [
np.product(self.depthwise_kernel_shape),
np.product(self.pointwise_kernel_shape)
]
if self.use_bias:
self.bias_shape = (self.filters, )
param_indices.append(self.filters)
else:
self.bias_shape = None
self.param_indices = np.cumsum(param_indices)
print("need", param_indices)
self.initializer = BK.placeholder(
shape=Conv2D.compute_output_shape(self, input_shape))
self.built = True
