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31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 c21b884 31e19a7 0b3883b 31e19a7 | 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 | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(channels, channels, 3),
nn.InstanceNorm2d(channels),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(channels, channels, 3),
nn.InstanceNorm2d(channels),
)
def forward(self, x):
return x + self.block(x)
class SelfAttention(nn.Module):
def __init__(self, channels):
super().__init__()
self.query = nn.Conv2d(channels, channels // 8, 1)
self.key = nn.Conv2d(channels, channels // 8, 1)
self.value = nn.Conv2d(channels, channels, 1)
self.gamma = nn.Parameter(torch.zeros(1))
def forward(self, x):
B, C, H, W = x.shape
q = self.query(x).flatten(2)
k = self.key(x).flatten(2)
v = self.value(x).flatten(2)
attn = torch.softmax(torch.bmm(q.transpose(1,2), k), dim=-1)
out = torch.bmm(v, attn.transpose(1,2)).view(B, C, H, W)
return x + self.gamma * out
class ResNetGenerator(nn.Module):
def __init__(self, in_channels=3, out_channels=3, n_filters=64, n_res_blocks=9):
super().__init__()
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(in_channels, n_filters, 7),
nn.InstanceNorm2d(n_filters),
nn.ReLU(inplace=True),
nn.Conv2d(n_filters, n_filters*2, 3, stride=2, padding=1),
nn.InstanceNorm2d(n_filters*2),
nn.ReLU(inplace=True),
nn.Conv2d(n_filters*2, n_filters*4, 3, stride=2, padding=1),
nn.InstanceNorm2d(n_filters*4),
nn.ReLU(inplace=True),
]
for _ in range(n_res_blocks):
model.append(ResidualBlock(n_filters*4))
model.append(SelfAttention(n_filters*4))
model += [
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(n_filters*4, n_filters*2, 3, padding=1),
nn.InstanceNorm2d(n_filters*2),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(n_filters*2, n_filters, 3, padding=1),
nn.InstanceNorm2d(n_filters),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(3),
nn.Conv2d(n_filters, out_channels, 7),
nn.Tanh()
]
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
@torch.no_grad()
def load_generator(path, device="cpu"):
gen = ResNetGenerator()
state_dict = torch.load(path, map_location="cpu")
state_dict = {k: v.float() for k, v in state_dict.items()}
gen.load_state_dict(state_dict)
gen.to(device).eval()
return gen
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