resnext.py

"""
MIT License

Copyright (c) 2017 liukuang

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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(num_classes: int = 10):
    return ResNeXt(num_blocks=[3, 3, 3], cardinality=2, bottleneck_width=64, num_classes=num_classes)


def ResNeXt29_4x64d(num_classes: int = 10):
    return ResNeXt(num_blocks=[3, 3, 3], cardinality=4, bottleneck_width=64, num_classes=num_classes)


def ResNeXt29_8x64d(num_classes: int = 10):
    return ResNeXt(num_blocks=[3, 3, 3], cardinality=8, bottleneck_width=64, num_classes=num_classes)


def ResNeXt29_32x4d(num_classes: int = 10):
    return ResNeXt(num_blocks=[3, 3, 3], cardinality=32, bottleneck_width=4, num_classes=num_classes)


def test_resnext():
    net = ResNeXt29_2x64d()
    x = torch.randn(1, 3, 32, 32)
    y = net(x)
    print(y.size())

# test_resnext()