| | |
|---|
| | | if mask is not None: |
|---|
| | | mask = self.create_new_mask(mask) |
|---|
| | | olens = max(mask.eq(0).sum(1)) |
|---|
| | | |
|---|
| | | b, t_input, f = x.size() |
|---|
| | | |
|---|
| | | b, t, f = x.size() |
|---|
| | | x = x.unsqueeze(1) # (b. 1. t. f) |
|---|
| | | |
|---|
| | | if chunk_size is not None: |
|---|
| | | max_input_length = int( |
|---|
| | | chunk_size * self.subsampling_factor * (math.ceil(float(t_input) / (chunk_size * self.subsampling_factor) )) |
|---|
| | | chunk_size * self.subsampling_factor * (math.ceil(float(t) / (chunk_size * self.subsampling_factor) )) |
|---|
| | | ) |
|---|
| | | x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x) |
|---|
| | | x = list(x) |
|---|
| | | x = torch.stack(x, dim=0) |
|---|
| | | N_chunks = max_input_length // ( chunk_size * self.subsampling_factor) |
|---|
| | | x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f) |
|---|
| | | |
|---|
| | | x = self.conv(x) |
|---|
| | | |
|---|
| | | _, c, t, f = x.size() |
|---|
| | | |
|---|
| | | _, c, _, f = x.size() |
|---|
| | | if chunk_size is not None: |
|---|
| | | x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:,:olens,:] |
|---|
| | | else: |
|---|
| | | x = x.transpose(1, 2).contiguous().view(b, t, c * f) |
|---|
| | | x = x.transpose(1, 2).contiguous().view(b, -1, c * f) |
|---|
| | | |
|---|
| | | if self.output is not None: |
|---|
| | | x = self.output(x) |
|---|
| | | |
|---|
| | | |
|---|
| | | return x, mask[:,:olens][:,:x.size(1)] |
|---|
| | | |
|---|
| | | def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor: |
|---|
