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- '''ResNeXt in PyTorch.
-
- See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
- '''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class Block(nn.Module):
- '''Grouped convolution block.'''
- expansion = 2
-
- def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
- super(Block, self).__init__()
- group_width = cardinality * bottleneck_width
- self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(group_width)
- self.conv2 = nn.Conv2d(group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
- self.bn2 = nn.BatchNorm2d(group_width)
- self.conv3 = nn.Conv2d(group_width, self.expansion*group_width, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(self.expansion*group_width)
-
- self.shortcut = nn.Sequential()
- if stride != 1 or in_planes != self.expansion*group_width:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_planes, self.expansion*group_width, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(self.expansion*group_width)
- )
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = F.relu(self.bn2(self.conv2(out)))
- out = self.bn3(self.conv3(out))
- out += self.shortcut(x)
- out = F.relu(out)
- return out
-
-
- class ResNeXt(nn.Module):
- def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
- super(ResNeXt, self).__init__()
- self.cardinality = cardinality
- self.bottleneck_width = bottleneck_width
- self.in_planes = 64
-
- self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.layer1 = self._make_layer(num_blocks[0], 1)
- self.layer2 = self._make_layer(num_blocks[1], 2)
- self.layer3 = self._make_layer(num_blocks[2], 2)
- # self.layer4 = self._make_layer(num_blocks[3], 2)
- self.linear = nn.Linear(cardinality*bottleneck_width*8, num_classes)
-
- def _make_layer(self, num_blocks, stride):
- strides = [stride] + [1]*(num_blocks-1)
- layers = []
- for stride in strides:
- layers.append(Block(self.in_planes, self.cardinality, self.bottleneck_width, stride))
- self.in_planes = Block.expansion * self.cardinality * self.bottleneck_width
- # Increase bottleneck_width by 2 after each stage.
- self.bottleneck_width *= 2
- return nn.Sequential(*layers)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.layer1(out)
- out = self.layer2(out)
- out = self.layer3(out)
- # out = self.layer4(out)
- out = F.avg_pool2d(out, 8)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
-
- def ResNeXt29_2x64d():
- return ResNeXt(num_blocks=[3,3,3], cardinality=2, bottleneck_width=64)
-
- def ResNeXt29_4x64d():
- return ResNeXt(num_blocks=[3,3,3], cardinality=4, bottleneck_width=64)
-
- def ResNeXt29_8x64d():
- return ResNeXt(num_blocks=[3,3,3], cardinality=8, bottleneck_width=64)
-
- def ResNeXt29_32x4d():
- return ResNeXt(num_blocks=[3,3,3], cardinality=32, bottleneck_width=4)
-
- def test_resnext():
- net = ResNeXt29_2x64d()
- x = torch.randn(1,3,32,32)
- y = net(x)
- print(y.size())
-
- # test_resnext()
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