bc.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable
try:
    from torch.hub import load_state_dict_from_url
except ImportError:
    from torch.utils.model_zoo import load_url as load_state_dict_from_url

def global_std_pool2d(x):
    """2D global standard variation pooling"""
    return torch.std(x.view(x.size()[0], x.size()[1], -1, 1),
                     dim=2, keepdim=True)


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']


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',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.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
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        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 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(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 += identity
        out = self.relu(out)


        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        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 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = 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 += identity
        out = self.relu(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,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        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.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])

        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        scale = 0.25*block.expansion
        self.hyper1_1 = self.hyper_structure1(64, int(256*scale))
        self.hyper2_1 = self.hyper_structure2(int(256*scale),int(512*scale))
        self.hyper3_1 = self.hyper_structure2(int(512*scale),int(1024*scale))
        self.hyper4_1 = self.hyper_structure2(int(1024*scale),int(2048*scale))

        self.hyper2_2 = self.hyper_structure2(int(256*scale),int(512*scale))
        self.hyper3_2 = self.hyper_structure2(int(512*scale),int(1024*scale))
        self.hyper4_2 = self.hyper_structure2(int(1024*scale),int(2048*scale))

        self.hyper3_3 = self.hyper_structure2(int(512*scale),int(1024*scale))
        self.hyper4_3 = self.hyper_structure2(int(1024*scale),int(2048*scale))

        self.hyper4_4 = self.hyper_structure2(int(1024*scale),int(2048*scale))


        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))




        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):
        norm_layer = self._norm_layer
        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),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        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,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)


    def hyper_structure1(self,in_channels,out_channels):
 
        hyper_block = nn.Sequential(
            nn.Conv2d(in_channels,in_channels//4,kernel_size=1,stride=1, padding=0,bias=False),
            nn.Conv2d(in_channels//4,in_channels//4,kernel_size=3,stride=1, padding=1,bias=False),
            nn.Conv2d(in_channels//4,out_channels,kernel_size=1,stride=1, padding=0,bias=False),
        )

        return hyper_block

    def hyper_structure2(self,in_channels,out_channels):
        hyper_block = nn.Sequential(
            nn.Conv2d(in_channels,in_channels//4,kernel_size=1,stride=1, padding=0,bias=False),
            nn.Conv2d(in_channels//4,in_channels//4,kernel_size=3,stride=2, padding=1,bias=False),
            nn.Conv2d(in_channels//4,out_channels,kernel_size=1,stride=1, padding=0,bias=False),
        )

        return hyper_block


    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x_hyper1 = self.hyper1_1(x)
        x = self.layer1(x)

        x_hyper1 = self.hyper2_1(x_hyper1+x)
        x_hyper2 = self.hyper2_2(x)
        x = self.layer2(x)
        x_hyper1 = self.hyper3_1(x_hyper1+x)
        x_hyper2 = self.hyper3_2(x_hyper2+x)
        x_hyper3 = self.hyper3_3(x)
        x = self.layer3(x)
        x_hyper1 = self.hyper4_1(x_hyper1+x)
        x_hyper2 = self.hyper4_2(x_hyper2+x)
        x_hyper3 = self.hyper4_3(x_hyper3+x)       
        x_hyper4 = self.hyper4_4(x)

        x = self.layer4(x)

        x = x+x_hyper1+x_hyper2+x_hyper3+x_hyper4

        x_avg = self.avgpool(x)
        x_std = global_std_pool2d(x)
        x = torch.cat((x_avg, x_std), dim = 1)
        x = torch.flatten(x, 1)
            
        return x


def resnet34(pretrained=False, progress=True, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    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
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnet34'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


def resnet50(pretrained=False, progress=True, **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    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
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    # input = torch.randn(1, 3, 224, 224)
    # flops, params = profile(model, inputs=(input, ))
    # print('The flops is {:.4f}, and the params is {:.4f}'.format(flops/10e9, params/10e6))
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnet50'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


def resnet101(pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    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
    """
    # return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
    #                **kwargs)
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnet101'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


def resnet152(pretrained=False, progress=True, **kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    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
    """
    # return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
    #                **kwargs)
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnet152'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    #return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
       #            pretrained, progress, **kwargs)
    model = ResNet(Bottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)
    if pretrained:
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnext50_32x4d'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    # return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
    #                pretrained, progress, **kwargs)
    model = ResNet(Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)
    if pretrained:
        model_dict = model.state_dict()
        pre_train_model = model_zoo.load_url(model_urls['resnext101_32x8d'])
        pre_train_model = {k:v for k,v in pre_train_model.items() if k in model_dict}
        model_dict.update(pre_train_model)
        model.load_state_dict(model_dict)
    return model


# 

def test():

    net = resnet34(pretrained=True) 
    net.cuda()
 
    x1 = torch.randn(6,3,448,448)
    x1 = Variable(x1.cuda())
   
    y1= net.forward(x1)

    print(y1.shape)

   
if __name__== '__main__':
    test()