def check_backward(self, x_data, W_data, b_data, y_grad):
args = (x_data, W_data)
if b_data is not None:
args = args + (b_data, )
gradient_check.check_backward(
depthwise_convolution_2d.DepthwiseConvolution2D(
self.stride, self.pad), args, y_grad,
**self.check_backward_options)
def check_forward(self, x_data, W_data, b_data):
args1 = (x_data, W_data)
args2 = (x_data, W_data)
if b_data is not None:
args1 = args1 + (b_data, )
b_data = sum(numpy.split(b_data, W_data.shape[1]))
args2 = args2 + (b_data, )
f1 = depthwise_convolution_2d.DepthwiseConvolution2D(
self.stride, self.pad)
y1 = f1(*args1)
arys = numpy.split(y1.data, self.W.shape[1], axis=1)
y1 = sum(arys)
f2 = convolution_2d.Convolution2DFunction(self.stride, self.pad)
y2 = f2.apply(args2)[0].data
testing.assert_allclose(y1, y2, **self.check_forward_options)