Source code for distil.utils.models.resnext

'''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


[docs]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) )
[docs] 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
[docs]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.embDim = cardinality*bottleneck_width*8 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)
[docs] def forward(self, x,last=False): 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) e = out.view(out.size(0), -1) out = self.linear(e) if last: return out, e else: return out
[docs] def get_embedding_dim(self): return self.embDim
[docs]def ResNeXt29_2x64d(num_classes=10): return ResNeXt([3,3,3], 2, 64, num_classes)
[docs]def ResNeXt29_4x64d(num_classes=10): return ResNeXt([3,3,3], 4, 64, num_classes)
[docs]def ResNeXt29_8x64d(num_classes=10): return ResNeXt([3,3,3], 8, 64, num_classes)
[docs]def ResNeXt29_32x4d(num_classes=10): return ResNeXt([3,3,3], 32, 4, num_classes)
[docs]def test_resnext(): net = ResNeXt29_2x64d() x = torch.randn(1,3,32,32) y = net(x) print(y.size())
# test_resnext()