File size: 5,311 Bytes
0667c13 |
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 |
import torch
from torch import nn
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = self._conv3x3(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = self._conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def _conv3x3(self, in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
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
class ResNet(nn.Module):
def __init__(self, input_channel, output_channel, block, layers):
super(ResNet, self).__init__()
self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]
self.inplanes = int(output_channel / 8)
self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),
kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn0_2 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])
self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[
0], kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)
self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[
1], kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))
self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)
self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[
2], kernel_size=3, stride=1, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])
self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)
self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)
self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
3], kernel_size=2, stride=1, padding=0, bias=False)
self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv0_1(x)
x = self.bn0_1(x)
x = self.relu(x)
x = self.conv0_2(x)
x = self.bn0_2(x)
x = self.relu(x)
x = self.maxpool1(x)
x = self.layer1(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool2(x)
x = self.layer2(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.maxpool3(x)
x = self.layer3(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.layer4(x)
x = self.conv4_1(x)
x = self.bn4_1(x)
x = self.relu(x)
x = self.conv4_2(x)
x = self.bn4_2(x)
conv = self.relu(x)
conv = conv.transpose(-1, -2)
conv = conv.flatten(2)
conv = conv.permute(-1, 0, 1)
return conv
def Resnet50(ss, hidden):
return ResNet(3, hidden, BasicBlock, [1, 2, 5, 3])
|