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import functools
import torch
import torch.nn as nn
from models.transformer import RETURNX, Transformer
from models.base_blocks import Conv2d, LayerNorm2d, FirstBlock2d, DownBlock2d, UpBlock2d, \
FFCADAINResBlocks, Jump, FinalBlock2d
class Visual_Encoder(nn.Module):
def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(Visual_Encoder, self).__init__()
self.layers = layers
self.first_inp = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
self.first_ref = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
for i in range(layers):
in_channels = min(ngf*(2**i), img_f)
out_channels = min(ngf*(2**(i+1)), img_f)
model_ref = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
model_inp = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
if i < 2:
ca_layer = RETURNX()
else:
ca_layer = Transformer(2**(i+1) * ngf,2,4,ngf,ngf*4)
setattr(self, 'ca' + str(i), ca_layer)
setattr(self, 'ref_down' + str(i), model_ref)
setattr(self, 'inp_down' + str(i), model_inp)
self.output_nc = out_channels * 2
def forward(self, maskGT, ref):
x_maskGT, x_ref = self.first_inp(maskGT), self.first_ref(ref)
out=[x_maskGT]
for i in range(self.layers):
model_ref = getattr(self, 'ref_down'+str(i))
model_inp = getattr(self, 'inp_down'+str(i))
ca_layer = getattr(self, 'ca'+str(i))
x_maskGT, x_ref = model_inp(x_maskGT), model_ref(x_ref)
x_maskGT = ca_layer(x_maskGT, x_ref)
if i < self.layers - 1:
out.append(x_maskGT)
else:
out.append(torch.cat([x_maskGT, x_ref], dim=1)) # concat ref features !
return out
class Decoder(nn.Module):
def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(Decoder, self).__init__()
self.layers = layers
for i in range(layers)[::-1]:
if i == layers-1:
in_channels = ngf*(2**(i+1)) * 2
else:
in_channels = min(ngf*(2**(i+1)), img_f)
out_channels = min(ngf*(2**i), img_f)
up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
res = FFCADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
setattr(self, 'up' + str(i), up)
setattr(self, 'res' + str(i), res)
setattr(self, 'jump' + str(i), jump)
self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'sigmoid')
self.output_nc = out_channels
def forward(self, x, z):
out = x.pop()
for i in range(self.layers)[::-1]:
res_model = getattr(self, 'res' + str(i))
up_model = getattr(self, 'up' + str(i))
jump_model = getattr(self, 'jump' + str(i))
out = res_model(out, z)
out = up_model(out)
out = jump_model(x.pop()) + out
out_image = self.final(out)
return out_image
class LNet(nn.Module):
def __init__(
self,
image_nc=3,
descriptor_nc=512,
layer=3,
base_nc=64,
max_nc=512,
num_res_blocks=9,
use_spect=True,
encoder=Visual_Encoder,
decoder=Decoder
):
super(LNet, self).__init__()
nonlinearity = nn.LeakyReLU(0.1)
norm_layer = functools.partial(LayerNorm2d, affine=True)
kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
self.descriptor_nc = descriptor_nc
self.encoder = encoder(image_nc, base_nc, max_nc, layer, **kwargs)
self.decoder = decoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
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, 512, kernel_size=3, stride=1, padding=0),
Conv2d(512, descriptor_nc, kernel_size=1, stride=1, padding=0),
)
def forward(self, audio_sequences, face_sequences):
B = audio_sequences.size(0)
input_dim_size = len(face_sequences.size())
if input_dim_size > 4:
audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
cropped, ref = torch.split(face_sequences, 3, dim=1)
vis_feat = self.encoder(cropped, ref)
audio_feat = self.audio_encoder(audio_sequences)
_outputs = self.decoder(vis_feat, audio_feat)
if input_dim_size > 4:
_outputs = torch.split(_outputs, B, dim=0)
outputs = torch.stack(_outputs, dim=2)
else:
outputs = _outputs
return outputs |