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8672bad | 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 | import torch
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=bn_momentum)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# class Bottleneck_Tranpose(nn.Module):
# expansion = 4
# def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
# super(Bottleneck, self).__init__()
# nn.ConvTranspose2d(c, 64, (3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)),
# self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
# self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
# self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
# self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
# self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
# self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=bn_momentum)
# self.relu = nn.ReLU(inplace=True)
# self.downsample = downsample
# self.stride = stride
# def forward(self, x):
# residual = x
# out = self.conv1(x)
# out = self.bn1(out)
# out = self.relu(out)
# out = self.conv2(out)
# out = self.bn2(out)
# out = self.relu(out)
# out = self.conv3(out)
# out = self.bn3(out)
# if self.downsample is not None:
# residual = self.downsample(x)
# out += residual
# out = self.relu(out)
# return out
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out |