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a762108 a141401 a762108 a141401 a762108 a141401 a762108 a141401 a762108 a141401 | 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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import math
import torch
from torch import nn
def compute_gamma(scale: int, base_gamma: float = 0.5) -> float:
"""
Compute balanced gamma for given upscale factor.
"""
if scale <= 1:
return base_gamma
gamma = base_gamma / math.sqrt(scale)
return float(max(0.05, min(gamma, 1.0)))
def conv3x3(in_channels, out_channels, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias
)
class LWGRB(nn.Module):
def __init__(self, channels: int, bias: bool = True, identity=True):
super().__init__()
self.identity = identity
self.conv1 = conv3x3(channels, channels, bias)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.conv2 = conv3x3(channels, channels, bias)
nn.init.zeros_(self.conv2.weight)
nn.init.zeros_(self.conv2.bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
r = self.conv2(self.act(self.conv1(x)))
a = torch.sigmoid(r)
return x + r * a
class LWDRB(nn.Module):
def __init__(self, c, bias=True, dil=4):
super().__init__()
self.conv1 = conv3x3(c, c, bias)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.conv2 = nn.Conv2d(c, c, 3, padding=dil, dilation=dil, bias=bias)
nn.init.zeros_(self.conv2.weight)
nn.init.zeros_(self.conv2.bias)
def forward(self, x):
r = self.conv2(self.act(self.conv1(x)))
return x + r
class LWGRBShuffle(nn.Module):
def __init__(self, in_ch, out_ch, scale, bias: bool = True):
super().__init__()
self.expand = nn.Sequential(
conv3x3(in_ch, out_ch * scale * scale, bias),
nn.LeakyReLU(0.1, inplace=True),
conv3x3(out_ch * scale * scale, out_ch * scale * scale, bias),
)
self.up = nn.PixelShuffle(scale)
self.refine = LWGRB(out_ch, bias=bias)
def forward(self, x):
x = self.expand(x)
x = self.up(x)
return self.refine(x)
class GRDFNet(nn.Module):
"""
GRDFNet (Gated + Residual Dilated Fast Network)
Configurable block stacking via integer `num_sets`.
Structure:
Stem: 3xLWGRB + 2xLWDRB
Repeated: num_sets x [LWGRB + LWDRB]
"""
def __init__(
self,
num_in_ch: int = 3,
num_out_ch: int = 3,
feature_channels: int = 32,
upscale: int = 1,
bias: bool = True,
norm: bool = False,
img_range: float = 1.0,
rgb_mean=(0.5, 0.5, 0.5),
num_sets: int = 3,
):
super().__init__()
self.in_ch = num_in_ch
self.out_ch = num_out_ch
self.c = feature_channels
self.scale = upscale
self.img_range = img_range
self.gamma = nn.Parameter(torch.tensor(compute_gamma(scale=upscale)))
self.num_sets = num_sets
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
if not norm:
self.register_buffer("no_norm", torch.zeros(1))
else:
self.no_norm = None
self.head = conv3x3(self.in_ch, self.c, bias)
self.body = self._make_body(bias)
self.tail = conv3x3(self.c, self.out_ch, bias)
if self.scale == 1:
self.upsample0 = nn.Identity()
else:
self.upsample0 = LWGRBShuffle(
self.out_ch, self.out_ch, self.scale, bias=bias
)
def _make_body(self, bias: bool):
blocks = [
LWGRB(self.c, bias),
LWGRB(self.c, bias),
LWGRB(self.c, bias),
LWDRB(self.c, bias),
LWDRB(self.c, bias),
]
for _ in range(self.num_sets):
blocks += [LWGRB(self.c, bias), LWDRB(self.c, bias)]
return nn.Sequential(*blocks)
@property
def is_norm(self) -> bool:
return getattr(self, "no_norm", None) is None
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.is_norm:
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
feat = self.head(x)
feat = self.body(feat)
out_feat = self.tail(feat)
out_feat = (1.0 - self.gamma) * x + self.gamma * out_feat
out = self.upsample0(out_feat)
if self.is_norm:
out = out / self.img_range + self.mean
return out
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