def reverseSequence(inputs: Tensor,
lengths: list[int],
batch_first=True) -> Tensor:
if batch_first:
inputs = inputs.transpose(0, 1)
maxLen, batchSize = inputs.size(0), inputs.size(1)
if len(lengths) != batchSize:
raise RuntimeError(
"inputs error with batchSize[%d] Required length[%d]" %
(len(lengths), batchSize))
ind = [
list(reversed(range(0, l))) + list(range(l, maxLen)) for l in lengths
]
ind = torch.LongTensor(ind).transpose(0, 1)
for dim in range(2, inputs.dim()):
ind = ind.unsqueeze(dim)
ind = Variable(ind.expand_as(inputs))
if inputs.is_cuda:
ind = ind.cuda(inputs.get_device())
reversedInput = torch.gather(inputs, 0, ind)
if batch_first:
reversedInput = reversedInput.transpose(0, 1)
return reversedInput
def forward(self, input: torch.tensor):
device = input.get_device()
(batch_size, num_channels, height, width) = input.shape
means = self.means.repeat(
(batch_size, height, width, 1)).permute(0, 3, 1, 2).to(device)
sds = self.sds.repeat(
(batch_size, height, width, 1)).permute(0, 3, 1, 2).to(device)
return (input - means) / sds
