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'''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.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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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 = 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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