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from collections import OrderedDict
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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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from ...core import register
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(
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in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(
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in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False
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),
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nn.BatchNorm2d(self.expansion * planes),
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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)
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out = F.relu(out)
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return out
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class _ResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10):
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super().__init__()
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear = nn.Linear(512 * block.expansion, num_classes)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * 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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out = F.avg_pool2d(out, 4)
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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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@register()
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class MResNet(nn.Module):
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def __init__(self, num_classes=10, num_blocks=[2, 2, 2, 2]) -> None:
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super().__init__()
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self.model = _ResNet(BasicBlock, num_blocks, num_classes)
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def forward(self, x):
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return self.model(x)
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