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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 | |
| def zero_module(module): | |
| """ | |
| Zero out the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().zero_() | |
| return module | |
| class SemanticsModulatedAttention(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_text = nn.Linear(text_latent_dim, latent_dim) | |
| self.value_text = nn.Linear(text_latent_dim, latent_dim) | |
| self.key_motion = nn.Linear(latent_dim, latent_dim) | |
| self.value_motion = nn.Linear(latent_dim, latent_dim) | |
| self.retr_norm1 = nn.LayerNorm(2 * latent_dim) | |
| self.retr_norm2 = nn.LayerNorm(latent_dim) | |
| self.key_retr = nn.Linear(2 * latent_dim, latent_dim) | |
| self.value_retr = zero_module(nn.Linear(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, src_mask, cond_type, re_dict=None): | |
| """ | |
| x: B, T, D | |
| xf: B, N, L | |
| """ | |
| B, T, D = x.shape | |
| re_motion = re_dict['re_motion'] | |
| re_text = re_dict['re_text'] | |
| re_mask = re_dict['re_mask'] | |
| re_mask = re_mask.reshape(B, -1, 1) | |
| N = xf.shape[1] + x.shape[1] + re_motion.shape[1] * re_motion.shape[2] | |
| H = self.num_heads | |
| # B, T, D | |
| query = self.query(self.norm(x)) | |
| # B, N, D | |
| text_cond_type = (cond_type % 10 > 0).float() | |
| retr_cond_type = (cond_type // 10 > 0).float() | |
| re_text = re_text.repeat(1, 1, re_motion.shape[2], 1) | |
| re_feat_key = torch.cat((re_motion, re_text), dim=-1).reshape(B, -1, 2 * D) | |
| key = torch.cat(( | |
| self.key_text(self.text_norm(xf)) + (1 - text_cond_type) * -1000000, | |
| self.key_retr(self.retr_norm1(re_feat_key)) + (1 - retr_cond_type) * -1000000 + (1 - re_mask) * -1000000, | |
| self.key_motion(self.norm(x)) + (1 - src_mask) * -1000000 | |
| ), dim=1) | |
| query = F.softmax(query.view(B, T, H, -1), dim=-1) | |
| key = F.softmax(key.view(B, N, H, -1), dim=1) | |
| # B, N, H, HD | |
| re_feat_value = re_motion.reshape(B, -1, D) | |
| value = torch.cat(( | |
| self.value_text(self.text_norm(xf)) * text_cond_type, | |
| self.value_retr(self.retr_norm2(re_feat_value)) * retr_cond_type * re_mask, | |
| self.value_motion(self.norm(x)) * src_mask, | |
| ), dim=1).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 |