60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class ModifiedBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_channels, out_channels):
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super(ModifiedBasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(out_channels)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(out_channels)
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# 确保shortcut路径的维度匹配，如果不匹配则通过1x1卷积进行调整
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self.shortcut = nn.Sequential()
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if in_channels != out_channels:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(out_channels)
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x) # 添加shortcut连接
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out = F.relu(out)
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return out
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class ModifiedResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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super(ModifiedResNet, self).__init__()
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self.in_channels = 512 # 假设起始通道数为512
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self.conv1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(512)
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self.layer1 = self._make_layer(block, 512, layers[0])
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self.layer2 = self._make_layer(block, 512, layers[1])
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self.layer3 = self._make_layer(block, 512, layers[2])
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self.layer4 = self._make_layer(block, 512, layers[3])
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self.linear = nn.Linear(512*block.expansion, num_classes)
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def _make_layer(self, block, out_channels, blocks):
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layers = []
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for _ in range(blocks):
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layers.append(block(self.in_channels, out_channels))
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self.in_channels = out_channels * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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# 假设你有一个特定的方式来处理最终的特征图，以得到256x512维的输出
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# 例如，可以使用自适应池化层调整尺寸或者直接reshape（根据实际需求）
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out = F.adaptive_avg_pool2d(out, (x.size(0), x.size(1)))
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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