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https://github.com/AntoineHX/BU_Stoch_pool.git
synced 2025-05-03 17:20:45 +02:00
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
'''ResNeXt in PyTorch.
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See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
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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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class Block(nn.Module):
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'''Grouped convolution block.'''
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expansion = 2
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def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
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super(Block, self).__init__()
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group_width = cardinality * bottleneck_width
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self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(group_width)
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self.conv2 = nn.Conv2d(group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
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self.bn2 = nn.BatchNorm2d(group_width)
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self.conv3 = nn.Conv2d(group_width, self.expansion*group_width, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(self.expansion*group_width)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion*group_width:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion*group_width, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion*group_width)
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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 ResNeXt(nn.Module):
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def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
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super(ResNeXt, self).__init__()
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self.cardinality = cardinality
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self.bottleneck_width = bottleneck_width
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(num_blocks[0], 1)
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self.layer2 = self._make_layer(num_blocks[1], 2)
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self.layer3 = self._make_layer(num_blocks[2], 2)
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# self.layer4 = self._make_layer(num_blocks[3], 2)
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self.linear = nn.Linear(cardinality*bottleneck_width*8, num_classes)
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def _make_layer(self, 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, self.cardinality, self.bottleneck_width, stride))
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self.in_planes = Block.expansion * self.cardinality * self.bottleneck_width
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# Increase bottleneck_width by 2 after each stage.
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self.bottleneck_width *= 2
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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, 8)
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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 ResNeXt29_2x64d():
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return ResNeXt(num_blocks=[3,3,3], cardinality=2, bottleneck_width=64)
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def ResNeXt29_4x64d():
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return ResNeXt(num_blocks=[3,3,3], cardinality=4, bottleneck_width=64)
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def ResNeXt29_8x64d():
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return ResNeXt(num_blocks=[3,3,3], cardinality=8, bottleneck_width=64)
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def ResNeXt29_32x4d():
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return ResNeXt(num_blocks=[3,3,3], cardinality=32, bottleneck_width=4)
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def test_resnext():
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net = ResNeXt29_2x64d()
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x = torch.randn(1,3,32,32)
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y = net(x)
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print(y.size())
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# test_resnext()
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