| 677 | |
| 678 | class SpatialImageLanguageAttention(nn.Module): |
| 679 | def __init__(self, v_in_channels, l_in_channels, key_channels, value_channels, out_channels=None, num_heads=1): |
| 680 | super(SpatialImageLanguageAttention, self).__init__() |
| 681 | # x shape: (B, H*W, v_in_channels) |
| 682 | # l input shape: (B, l_in_channels, N_l) |
| 683 | # l_mask shape: (B, N_l, 1) |
| 684 | self.v_in_channels = v_in_channels |
| 685 | self.l_in_channels = l_in_channels |
| 686 | self.out_channels = out_channels |
| 687 | self.key_channels = key_channels |
| 688 | self.value_channels = value_channels |
| 689 | self.num_heads = num_heads |
| 690 | if out_channels is None: |
| 691 | self.out_channels = self.value_channels |
| 692 | |
| 693 | # Keys: language features: (B, l_in_channels, #words) |
| 694 | # avoid any form of spatial normalization because a sentence contains many padding 0s |
| 695 | self.f_key = nn.Sequential( |
| 696 | nn.Conv1d(self.l_in_channels, self.key_channels, kernel_size=1, stride=1), |
| 697 | ) |
| 698 | |
| 699 | # Queries: visual features: (B, H*W, v_in_channels) |
| 700 | self.f_query = nn.Sequential( |
| 701 | nn.Conv1d(self.v_in_channels, self.key_channels, kernel_size=1, stride=1), |
| 702 | nn.InstanceNorm1d(self.key_channels), |
| 703 | ) |
| 704 | |
| 705 | # Values: language features: (B, l_in_channels, #words) |
| 706 | self.f_value = nn.Sequential( |
| 707 | nn.Conv1d(self.l_in_channels, self.value_channels, kernel_size=1, stride=1), |
| 708 | ) |
| 709 | |
| 710 | # Out projection |
| 711 | self.W = nn.Sequential( |
| 712 | nn.Conv1d(self.value_channels, self.out_channels, kernel_size=1, stride=1), |
| 713 | nn.InstanceNorm1d(self.out_channels), |
| 714 | ) |
| 715 | |
| 716 | def forward(self, x, l, l_mask): |
| 717 | B, HW = x.size(0), x.size(1) |