resnet50_vggface.py

import torch.nn as nn
import math
import torch
import torch.nn.functional as F

__all__ = ['ResNet', 'resnet50']


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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=8631, include_top=True):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.include_top = include_top

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # self.avgpool = nn.AvgPool2d(7, stride=1)
        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):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        end_points = {}

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

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        # f3 = x
        x = self.layer4(x)
        end_points['Feature'] = x #torch.Size([128, 512, 7, 7])


        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        end_points['Embedding'] = x #torch.Size([128, 512])

        ## f4 = x
        # x = self.fc(x)
        # end_points['Predictions'] = F.softmax(input=x, dim=-1) #torch.Size([128, 10])

        return x


def resnet50(weights_path=None ,**kwargs):
    """Constructs a ResNet-50 model.
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)

    if weights_path:
        import pickle
        with open(weights_path, 'rb') as f:
            obj = f.read()
        weights = {key: torch.from_numpy(arr) for key, arr in pickle.loads(obj, encoding='latin1').items()}
        model.load_state_dict(weights, strict = False)


    return model


def get_resnet(depth, num_classes, freeze=False):
    model = None


    if depth == 50:
        model = resnet50('./resnet50_ft_weight.pkl')
        num_ftrs = model.fc.in_features
        model.fc = torch.nn.Linear(num_ftrs, num_classes)
        torch.nn.init.kaiming_normal_(model.fc.weight)

    else:
        print('not implemented')



        # count = 0
        # for param in model.parameters():
        #     count = count + 1
        #     if count < 73:
        #         param.requires_grad = False
        #     else:
        #         param.requires_grad = True



        # for name, param in model.named_parameters():
        #     if param.requires_grad == True:
        #         print("\t", name)
        #     else:
        #         print("\t no requires_grad", name)

    if freeze:
        for param in model.parameters():
            param.requires_grad = False

        for param in model.fc.parameters():
            param.requires_grad = True

    return model