def check_forward(self, x, offset, W, b, stride, pad):
with chainer.using_config('use_cudnn', self.use_cudnn):
x = chainer.Variable(x)
offset = chainer.Variable(offset)
out = deformable_convolution_2d_sampler(x, offset, W, b, stride,
pad).data
expeceted = convolution_2d(x, W, b, stride, pad).data
testing.assert_allclose(out, expeceted)
def check_forward(self, x, offset, W, b, stride, pad):
with chainer.using_config('use_cudnn', self.use_cudnn):
x = chainer.Variable(x)
offset = chainer.Variable(offset)
out = deformable_convolution_2d_sampler(
x, offset, W, b, stride, pad).data
expeceted = convolution_2d(
x, W, b, stride, pad).data
testing.assert_allclose(out, expeceted)
def forward(self, x, offset):
if self.W.array is None:
self._initialize_params(x.shape[1])
pad_width = [(0, 0), (0, 0)] + list(map(lambda x: (x, x), self.pad))
x = F.pad(x, pad_width, self.pad_mode)
return F.deformable_convolution_2d_sampler(x, offset, self.W, self.b,
self.stride, 0)
def check_forward(self, x, offset, W, b, stride, pad):
with chainer.using_config('use_cudnn', self.use_cudnn):
_, _, h, w = x.shape
_, _, kh, kw = W.shape
offset[:, :kh * kw] = -1 * stride[1]
offset[:, kh * kw:] = 1 * stride[0]
x = chainer.Variable(x)
offset = chainer.Variable(offset)
out = deformable_convolution_2d_sampler(x, offset, W, b, stride,
pad).data
pad = (pad[0] + 1 * stride[0], pad[1] + 1 * stride[1])
expeceted = convolution_2d(x, W, b, stride, pad).data
expeceted = expeceted[:, :, 2:, :-2]
testing.assert_allclose(out, expeceted)
def check_forward(self, x, offset, W, b, stride, pad):
with chainer.using_config('use_cudnn', self.use_cudnn):
_, _, h, w = x.shape
_, _, kh, kw = W.shape
offset[:, :kh * kw] = -1 * stride[1]
offset[:, kh * kw:] = 1 * stride[0]
x = chainer.Variable(x)
offset = chainer.Variable(offset)
out = deformable_convolution_2d_sampler(
x, offset, W, b, stride, pad).data
pad = (pad[0] + 1 * stride[0], pad[1] + 1 * stride[1])
expeceted = convolution_2d(
x, W, b, stride, pad).data
expeceted = expeceted[:, :, 2:, :-2]
testing.assert_allclose(out, expeceted)
def forward(self, x, offset):
if self.W.array is None:
self._initialize_params(x.shape[1])
return deformable_convolution_2d_sampler(
x, offset, self.W, self.b, self.stride, self.pad)
def __call__(self, x, offset):
if self.W.data is None:
self._initialize_params(x.shape[1])
return deformable_convolution_2d_sampler(
x, offset, self.W, self.b, self.stride, self.pad)