122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
'''ResNet in PyTorch.
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For Pre-activation ResNet, see 'preact_resnet.py'.
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Reference:
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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Deep Residual Learning for Image Recognition. arXiv:1512.03385
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'''
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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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import triton
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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 = triton.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = triton.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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triton.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
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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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, in_planes, planes, stride=1):
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super(Bottleneck, self).__init__()
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self.conv1 = triton.Conv2d(in_planes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = triton.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = triton.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(self.expansion*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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triton.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
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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 = F.relu(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(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(ResNet, self).__init__()
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self.in_planes = 64
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self.conv1 = triton.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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def ResNet18():
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return ResNet(BasicBlock, [2,2,2,2])
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def ResNet34():
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return ResNet(BasicBlock, [3,4,6,3])
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def ResNet50():
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return ResNet(Bottleneck, [3,4,6,3])
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def ResNet101():
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return ResNet(Bottleneck, [3,4,23,3])
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def ResNet152():
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return ResNet(Bottleneck, [3,8,36,3])
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def test():
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net = ResNet18()
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y = net(torch.randn(1,3,32,32))
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print(y.size())
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# test()
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