File size: 2,862 Bytes
8f5bb6b | 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 | """RRDBNet (ESRGAN generator). Weight keys match official Real-ESRGAN checkpoints."""
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualDenseBlock(nn.Module):
def __init__(self, num_features: int, growth_channels: int):
super().__init__()
self.conv1 = nn.Conv2d(num_features, growth_channels, 3, 1, 1)
self.conv2 = nn.Conv2d(num_features + growth_channels, growth_channels, 3, 1, 1)
self.conv3 = nn.Conv2d(num_features + 2 * growth_channels, growth_channels, 3, 1, 1)
self.conv4 = nn.Conv2d(num_features + 3 * growth_channels, growth_channels, 3, 1, 1)
self.conv5 = nn.Conv2d(num_features + 4 * growth_channels, num_features, 3, 1, 1)
self.lrelu = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x):
f1 = self.lrelu(self.conv1(x))
f2 = self.lrelu(self.conv2(torch.cat((x, f1), 1)))
f3 = self.lrelu(self.conv3(torch.cat((x, f1, f2), 1)))
f4 = self.lrelu(self.conv4(torch.cat((x, f1, f2, f3), 1)))
f5 = self.conv5(torch.cat((x, f1, f2, f3, f4), 1))
return x + 0.2 * f5
class RRDB(nn.Module):
def __init__(self, num_features: int, growth_channels: int):
super().__init__()
# attribute names match the official Real-ESRGAN state_dict (rdb1/2/3)
self.rdb1 = ResidualDenseBlock(num_features, growth_channels)
self.rdb2 = ResidualDenseBlock(num_features, growth_channels)
self.rdb3 = ResidualDenseBlock(num_features, growth_channels)
def forward(self, x):
out = self.rdb3(self.rdb2(self.rdb1(x)))
return x + 0.2 * out
class RRDBNet(nn.Module):
def __init__(self, num_features=64, num_blocks=16, growth_channels=32, scale=4):
super().__init__()
assert scale == 4, "This head is built for 4x"
self.conv_first = nn.Conv2d(3, num_features, 3, 1, 1)
self.body = nn.Sequential(*[RRDB(num_features, growth_channels) for _ in range(num_blocks)])
self.conv_body = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_up1 = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_up2 = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_hr = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_last = nn.Conv2d(num_features, 3, 3, 1, 1)
self.lrelu = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x):
shallow = self.conv_first(x)
deep = self.conv_body(self.body(shallow))
features = shallow + deep
features = self.lrelu(self.conv_up1(F.interpolate(features, scale_factor=2, mode="nearest")))
features = self.lrelu(self.conv_up2(F.interpolate(features, scale_factor=2, mode="nearest")))
return self.conv_last(self.lrelu(self.conv_hr(features)))
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