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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
@ATTENTIONS.register_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 |