Spaces:
Sleeping
Sleeping
File size: 3,141 Bytes
bc3753a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import torch
from torch import nn
from torch.nn import functional as F
from .conv import Conv2d
class SyncNet_color(nn.Module):
def __init__(self):
super(SyncNet_color, self).__init__()
self.face_encoder = nn.Sequential(
Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3),
Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1),
Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
Conv2d(512, 512, kernel_size=3, stride=1, padding=0),
Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
self.audio_encoder = nn.Sequential(
Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T)
face_embedding = self.face_encoder(face_sequences)
audio_embedding = self.audio_encoder(audio_sequences)
audio_embedding = audio_embedding.view(audio_embedding.size(0), -1)
face_embedding = face_embedding.view(face_embedding.size(0), -1)
audio_embedding = F.normalize(audio_embedding, p=2, dim=1)
face_embedding = F.normalize(face_embedding, p=2, dim=1)
return audio_embedding, face_embedding
|