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78d2329 | 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 | from torch import nn as nn
from torchvision.models import resnet18
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None, num_groups=8):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.gn1 = nn.GroupNorm(num_groups, out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.gn2 = nn.GroupNorm(num_groups, out_channels)
self.downsample = downsample
self.gelu = nn.GELU()
def forward(self, x):
identity = x
out = self.gelu(self.gn1(self.conv1(x)))
out = self.gn2(self.conv2(out))
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.gelu(out)
return out
class ResNetFeatureWarpper(nn.Module):
def __init__(self, shallow_resnet_feature=False):
super(ResNetFeatureWarpper, self).__init__()
self.shallow_resnet_feature = shallow_resnet_feature
resnet = resnet18(pretrained=True)
self.conv1 = resnet.conv1
self.bn1 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
if not shallow_resnet_feature:
self.layer2 = resnet.layer2
def forward(self, x):
out = []
x = self.conv1(x)
out.append(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
out.append(x)
if not self.shallow_resnet_feature:
x = self.layer2(x)
out.append(x)
return out
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