data/lib/models/backbones/resnet/resnext_models.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
from collections import OrderedDict
import torch.nn as nn

from lib.models.tools.module_helper import ModuleHelper

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, bn_type=None):
        super(BasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes)
        self.relu = nn.ReLU(inplace=False)
        self.relu_in = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out = out + identity
        out = self.relu_in(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, bn_type=None):
        super(Bottleneck, self).__init__()
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * self.expansion)
        self.relu = nn.ReLU(inplace=False)
        self.relu_in = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out = out + identity
        out = self.relu_in(out)
        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 bn_type=None):
        super(ResNet, self).__init__()

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.resinit = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
            ('bn1', ModuleHelper.BatchNorm2d(bn_type=bn_type)(self.inplanes)),
            ('relu1', nn.ReLU(inplace=False))]
        ))

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], bn_type=bn_type)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0], bn_type=bn_type)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1], bn_type=bn_type)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2], bn_type=bn_type)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False, bn_type=None):
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, bn_type=bn_type))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                bn_type=bn_type))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.resinit(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)

        return x


def ResNext(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


class ResNextModels(object):

    def __init__(self, configer):
        self.configer = configer


    def resnext101_32x8d(self, **kwargs):
        """Constructs a ResNeXt-101 32x8d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs['groups'] = 32
        kwargs['width_per_group'] = 8
        model = ResNext('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                       pretrained, progress, bn_type=self.configer.get('network', 'bn_type'),
                       **kwargs)
        model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), 
            all_match=False, network="resnext")
        return model


    def resnext101_32x16d(self, **kwargs):
        """Constructs a ResNeXt-101 32x16d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs['groups'] = 32
        kwargs['width_per_group'] = 16
        model = ResNext('resnext101_32x16d', Bottleneck, [3, 4, 23, 3],
                       pretrained, progress, bn_type=self.configer.get('network', 'bn_type'),
                       **kwargs)
        model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), 
            all_match=False, network="resnext")
        return model


    def resnext101_32x32d(self, **kwargs):
        """Constructs a ResNeXt-101 32x32d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs['groups'] = 32
        kwargs['width_per_group'] = 32
        model = ResNext('resnext101_32x32d', Bottleneck, [3, 4, 23, 3],
                       pretrained, progress, bn_type=self.configer.get('network', 'bn_type'),
                       **kwargs)
        model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), 
            all_match=False, network="resnext")
        return model


    def resnext101_32x48d(self, **kwargs):
        """Constructs a ResNeXt-101 32x48d model.

        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
            progress (bool): If True, displays a progress bar of the download to stderr
        """
        pretrained = False
        progress = False
        kwargs['groups'] = 32
        kwargs['width_per_group'] = 48
        model = ResNext('resnext101_32x48d', Bottleneck, [3, 4, 23, 3],
                       pretrained, progress, bn_type=self.configer.get('network', 'bn_type'),
                       **kwargs)
        model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), 
            all_match=False, network="resnext")
        return model