(self, x, feat_cache=None, feat_idx=[0])
| 567 | CausalConv3d(out_dim, z_dim, 3, padding=1)) |
| 568 | |
| 569 | def forward(self, x, feat_cache=None, feat_idx=[0]): |
| 570 | if feat_cache is not None: |
| 571 | idx = feat_idx[0] |
| 572 | cache_x = x[:, :, -CACHE_T:, :, :].clone() |
| 573 | if cache_x.shape[2] < 2 and feat_cache[idx] is not None: |
| 574 | # cache last frame of last two chunk |
| 575 | cache_x = torch.cat([ |
| 576 | feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( |
| 577 | cache_x.device), cache_x |
| 578 | ], |
| 579 | dim=2) |
| 580 | x = self.conv1(x, feat_cache[idx]) |
| 581 | feat_cache[idx] = cache_x |
| 582 | feat_idx[0] += 1 |
| 583 | else: |
| 584 | x = self.conv1(x) |
| 585 | |
| 586 | ## downsamples |
| 587 | for layer in self.downsamples: |
| 588 | if feat_cache is not None: |
| 589 | x = layer(x, feat_cache, feat_idx) |
| 590 | else: |
| 591 | x = layer(x) |
| 592 | |
| 593 | ## middle |
| 594 | for layer in self.middle: |
| 595 | if check_is_instance(layer, ResidualBlock) and feat_cache is not None: |
| 596 | x = layer(x, feat_cache, feat_idx) |
| 597 | else: |
| 598 | x = layer(x) |
| 599 | |
| 600 | ## head |
| 601 | for layer in self.head: |
| 602 | if check_is_instance(layer, CausalConv3d) and feat_cache is not None: |
| 603 | idx = feat_idx[0] |
| 604 | cache_x = x[:, :, -CACHE_T:, :, :].clone() |
| 605 | if cache_x.shape[2] < 2 and feat_cache[idx] is not None: |
| 606 | # cache last frame of last two chunk |
| 607 | cache_x = torch.cat([ |
| 608 | feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( |
| 609 | cache_x.device), cache_x |
| 610 | ], |
| 611 | dim=2) |
| 612 | x = layer(x, feat_cache[idx]) |
| 613 | feat_cache[idx] = cache_x |
| 614 | feat_idx[0] += 1 |
| 615 | else: |
| 616 | x = layer(x) |
| 617 | return x |
| 618 | |
| 619 | |
| 620 | class Encoder3d_38(nn.Module): |
nothing calls this directly
no test coverage detected