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A10G
Running
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A10G
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# coding: utf-8
"""
Spade decoder(G) defined in the paper, which input the warped feature to generate the animated image.
"""
import torch
from torch import nn
import torch.nn.functional as F
from .util import SPADEResnetBlock
class SPADEDecoder(nn.Module):
def __init__(self, upscale=1, max_features=256, block_expansion=64, out_channels=64, num_down_blocks=2):
for i in range(num_down_blocks):
input_channels = min(max_features, block_expansion * (2 ** (i + 1)))
self.upscale = upscale
super().__init__()
norm_G = 'spadespectralinstance'
label_num_channels = input_channels # 256
self.fc = nn.Conv2d(input_channels, 2 * input_channels, 3, padding=1)
self.G_middle_0 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.G_middle_1 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.G_middle_2 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.G_middle_3 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.G_middle_4 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.G_middle_5 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
self.up_0 = SPADEResnetBlock(2 * input_channels, input_channels, norm_G, label_num_channels)
self.up_1 = SPADEResnetBlock(input_channels, out_channels, norm_G, label_num_channels)
self.up = nn.Upsample(scale_factor=2)
if self.upscale is None or self.upscale <= 1:
self.conv_img = nn.Conv2d(out_channels, 3, 3, padding=1)
else:
self.conv_img = nn.Sequential(
nn.Conv2d(out_channels, 3 * (2 * 2), kernel_size=3, padding=1),
nn.PixelShuffle(upscale_factor=2)
)
def forward(self, feature):
seg = feature # Bx256x64x64
x = self.fc(feature) # Bx512x64x64
x = self.G_middle_0(x, seg)
x = self.G_middle_1(x, seg)
x = self.G_middle_2(x, seg)
x = self.G_middle_3(x, seg)
x = self.G_middle_4(x, seg)
x = self.G_middle_5(x, seg)
x = self.up(x) # Bx512x64x64 -> Bx512x128x128
x = self.up_0(x, seg) # Bx512x128x128 -> Bx256x128x128
x = self.up(x) # Bx256x128x128 -> Bx256x256x256
x = self.up_1(x, seg) # Bx256x256x256 -> Bx64x256x256
x = self.conv_img(F.leaky_relu(x, 2e-1)) # Bx64x256x256 -> Bx3xHxW
x = torch.sigmoid(x) # Bx3xHxW
return x |