def check_backward(self, x_data, W_data, b_data, y_grad):
xp = cuda.get_array_module(x_data)
if not self.c_contiguous:
x_data = xp.asfortranarray(x_data)
W_data = xp.asfortranarray(W_data)
y_grad = xp.asfortranarray(y_grad)
self.assertFalse(x_data.flags.c_contiguous)
self.assertFalse(W_data.flags.c_contiguous)
self.assertFalse(y_grad.flags.c_contiguous)
if b_data is not None:
b = xp.empty((len(b_data) * 2, ), dtype=self.b.dtype)
b[::2] = b_data
b_data = b[::2]
self.assertFalse(b_data.flags.c_contiguous)
args = (x_data, W_data)
if b_data is not None:
args = args + (b_data, )
with chainer.using_config('use_cudnn', self.use_cudnn):
with chainer.using_config('cudnn_deterministic',
self.cudnn_deterministic):
gradient_check.check_backward(
deconvolution_2d.Deconvolution2DFunction(
self.stride, self.pad, self.outsize), args, y_grad,
**self.check_backward_options)
def check_backward(self, x_data, W_data, b_data, y_grad):
xp = cuda.get_array_module(x_data)
if not self.c_contiguous:
x_data = xp.asfortranarray(x_data)
W_data = xp.asfortranarray(W_data)
y_grad = xp.asfortranarray(y_grad)
self.assertFalse(x_data.flags.c_contiguous)
self.assertFalse(W_data.flags.c_contiguous)
self.assertFalse(y_grad.flags.c_contiguous)
if b_data is not None:
b = xp.empty((len(b_data) * 2, ), dtype=self.b.dtype)
b[::2] = b_data
b_data = b[::2]
self.assertFalse(b_data.flags.c_contiguous)
args = (x_data, W_data)
if b_data is not None:
args = args + (b_data, )
gradient_check.check_backward(deconvolution_2d.Deconvolution2DFunction(
self.stride, self.pad, self.outsize, self.use_cudnn),
args,
y_grad,
eps=1e-2)