arcface_torch/backbones/iresnet.py

import torch
from torch import nn
from torch.utils.checkpoint import checkpoint

__all__ = ["iresnet18", "iresnet34", "iresnet50", "iresnet100", "iresnet200"]
using_ckpt = False


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 IBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1):
        super(IBasicBlock, 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")
        self.bn1 = nn.BatchNorm2d(
            inplanes,
            eps=1e-05,
        )
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = nn.BatchNorm2d(
            planes,
            eps=1e-05,
        )
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = nn.BatchNorm2d(
            planes,
            eps=1e-05,
        )
        self.downsample = downsample
        self.stride = stride

    def forward_impl(self, x):
        identity = x
        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        return out

    def forward(self, x):
        if self.training and using_ckpt:
            return checkpoint(self.forward_impl, x)
        else:
            return self.forward_impl(x)


class IResNet(nn.Module):
    fc_scale = 7 * 7

    def __init__(
        self,
        block,
        layers,
        dropout=0,
        num_features=512,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        fp16=False,
    ):
        super(IResNet, self).__init__()
        self.extra_gflops = 0.0
        self.fp16 = fp16
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            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=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        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])
        self.bn2 = nn.BatchNorm2d(
            512 * block.expansion,
            eps=1e-05,
        )
        self.dropout = nn.Dropout(p=dropout, inplace=True)
        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
        nn.init.constant_(self.features.weight, 1.0)
        self.features.weight.requires_grad = False

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, 0, 0.1)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, IBasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        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),
                nn.BatchNorm2d(
                    planes * block.expansion,
                    eps=1e-05,
                ),
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation))
        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)
            )

        return nn.Sequential(*layers)

    def forward(self, x):
        with torch.cuda.amp.autocast(self.fp16):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            x = self.bn2(x)
            x = torch.flatten(x, 1)
            x = self.dropout(x)
        x = self.fc(x.float() if self.fp16 else x)
        x = self.features(x)
        return x


def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
    model = IResNet(block, layers, **kwargs)
    if pretrained:
        raise ValueError()
    return model


def iresnet18(pretrained=False, progress=True, **kwargs):
    return _iresnet("iresnet18", IBasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)


def iresnet34(pretrained=False, progress=True, **kwargs):
    return _iresnet("iresnet34", IBasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)


def iresnet50(pretrained=False, progress=True, **kwargs):
    return _iresnet("iresnet50", IBasicBlock, [3, 4, 14, 3], pretrained, progress, **kwargs)


def iresnet100(pretrained=False, progress=True, **kwargs):
    return _iresnet("iresnet100", IBasicBlock, [3, 13, 30, 3], pretrained, progress, **kwargs)


def iresnet200(pretrained=False, progress=True, **kwargs):
    return _iresnet("iresnet200", IBasicBlock, [6, 26, 60, 6], pretrained, progress, **kwargs)