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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ..utils.stylization_block import StylizationBlock | |
| from ..builder import ATTENTIONS | |
| class EfficientSelfAttention(nn.Module): | |
| def __init__(self, latent_dim, num_heads, dropout, time_embed_dim=None): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| self.norm = nn.LayerNorm(latent_dim) | |
| self.query = nn.Linear(latent_dim, latent_dim) | |
| self.key = nn.Linear(latent_dim, latent_dim) | |
| self.value = nn.Linear(latent_dim, latent_dim) | |
| self.dropout = nn.Dropout(dropout) | |
| self.time_embed_dim = time_embed_dim | |
| if time_embed_dim is not None: | |
| self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout) | |
| def forward(self, x, src_mask, emb=None, **kwargs): | |
| """ | |
| x: B, T, D | |
| """ | |
| B, T, D = x.shape | |
| H = self.num_heads | |
| # B, T, D | |
| query = self.query(self.norm(x)) | |
| # B, T, D | |
| key = (self.key(self.norm(x)) + (1 - src_mask) * -1000000) | |
| query = F.softmax(query.view(B, T, H, -1), dim=-1) | |
| key = F.softmax(key.view(B, T, H, -1), dim=1) | |
| # B, T, H, HD | |
| value = (self.value(self.norm(x)) * src_mask).view(B, T, H, -1) | |
| # B, H, HD, HD | |
| attention = torch.einsum('bnhd,bnhl->bhdl', key, value) | |
| y = torch.einsum('bnhd,bhdl->bnhl', query, attention).reshape(B, T, D) | |
| if self.time_embed_dim is None: | |
| y = x + y | |
| else: | |
| y = x + self.proj_out(y, emb) | |
| return y | |
| class EfficientCrossAttention(nn.Module): | |
| def __init__(self, latent_dim, text_latent_dim, num_heads, dropout, time_embed_dim): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| self.norm = nn.LayerNorm(latent_dim) | |
| self.text_norm = nn.LayerNorm(text_latent_dim) | |
| self.query = nn.Linear(latent_dim, latent_dim) | |
| self.key = nn.Linear(text_latent_dim, latent_dim) | |
| self.value = nn.Linear(text_latent_dim, latent_dim) | |
| self.dropout = nn.Dropout(dropout) | |
| self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout) | |
| def forward(self, x, xf, emb, cond_type=None, **kwargs): | |
| """ | |
| x: B, T, D | |
| xf: B, N, L | |
| """ | |
| B, T, D = x.shape | |
| N = xf.shape[1] | |
| H = self.num_heads | |
| # B, T, D | |
| query = self.query(self.norm(x)) | |
| # B, N, D | |
| key = self.key(self.text_norm(xf)) | |
| query = F.softmax(query.view(B, T, H, -1), dim=-1) | |
| if cond_type is None: | |
| key = F.softmax(key.view(B, N, H, -1), dim=1) | |
| # B, N, H, HD | |
| value = self.value(self.text_norm(xf)).view(B, N, H, -1) | |
| else: | |
| text_cond_type = ((cond_type % 10) > 0).float().view(B, 1, 1).repeat(1, xf.shape[1], 1) | |
| key = key + (1 - text_cond_type) * -1000000 | |
| key = F.softmax(key.view(B, N, H, -1), dim=1) | |
| value = self.value(self.text_norm(xf) * text_cond_type).view(B, N, H, -1) | |
| # B, H, HD, HD | |
| attention = torch.einsum('bnhd,bnhl->bhdl', key, value) | |
| y = torch.einsum('bnhd,bhdl->bnhl', query, attention).reshape(B, T, D) | |
| y = x + self.proj_out(y, emb) | |
| return y | |