criteria/deeplab.py

# Taken from the https://github.com/chenxi116/DeepLabv3.pytorch repository.

import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
import os


__all__ = ["ResNet", "resnet50", "resnet101", "resnet152"]


model_urls = {
    "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",
}


class Conv2d(nn.Conv2d):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=True,
    ):
        super(Conv2d, self).__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
        )

    def forward(self, x):
        # return super(Conv2d, self).forward(x)
        weight = self.weight
        weight_mean = (
            weight.mean(dim=1, keepdim=True)
            .mean(dim=2, keepdim=True)
            .mean(dim=3, keepdim=True)
        )
        weight = weight - weight_mean
        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
        weight = weight / std.expand_as(weight)
        return F.conv2d(
            x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups
        )


class ASPP(nn.Module):
    def __init__(
        self,
        C,
        depth,
        num_classes,
        conv=nn.Conv2d,
        norm=nn.BatchNorm2d,
        momentum=0.0003,
        mult=1,
    ):
        super(ASPP, self).__init__()
        self._C = C
        self._depth = depth
        self._num_classes = num_classes

        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.relu = nn.ReLU(inplace=True)
        self.aspp1 = conv(C, depth, kernel_size=1, stride=1, bias=False)
        self.aspp2 = conv(
            C,
            depth,
            kernel_size=3,
            stride=1,
            dilation=int(6 * mult),
            padding=int(6 * mult),
            bias=False,
        )
        self.aspp3 = conv(
            C,
            depth,
            kernel_size=3,
            stride=1,
            dilation=int(12 * mult),
            padding=int(12 * mult),
            bias=False,
        )
        self.aspp4 = conv(
            C,
            depth,
            kernel_size=3,
            stride=1,
            dilation=int(18 * mult),
            padding=int(18 * mult),
            bias=False,
        )
        self.aspp5 = conv(C, depth, kernel_size=1, stride=1, bias=False)
        self.aspp1_bn = norm(depth, momentum)
        self.aspp2_bn = norm(depth, momentum)
        self.aspp3_bn = norm(depth, momentum)
        self.aspp4_bn = norm(depth, momentum)
        self.aspp5_bn = norm(depth, momentum)
        self.conv2 = conv(depth * 5, depth, kernel_size=1, stride=1, bias=False)
        self.bn2 = norm(depth, momentum)
        self.conv3 = nn.Conv2d(depth, num_classes, kernel_size=1, stride=1)

    def forward(self, x):
        x1 = self.aspp1(x)
        x1 = self.aspp1_bn(x1)
        x1 = self.relu(x1)
        x2 = self.aspp2(x)
        x2 = self.aspp2_bn(x2)
        x2 = self.relu(x2)
        x3 = self.aspp3(x)
        x3 = self.aspp3_bn(x3)
        x3 = self.relu(x3)
        x4 = self.aspp4(x)
        x4 = self.aspp4_bn(x4)
        x4 = self.relu(x4)
        x5 = self.global_pooling(x)
        x5 = self.aspp5(x5)
        x5 = self.aspp5_bn(x5)
        x5 = self.relu(x5)
        x5 = nn.Upsample((x.shape[2], x.shape[3]), mode="bilinear", align_corners=True)(
            x5
        )
        x = torch.cat((x1, x2, x3, x4, x5), 1)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)

        return x


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        dilation=1,
        conv=None,
        norm=None,
    ):
        super(Bottleneck, self).__init__()
        self.conv1 = conv(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = norm(planes)
        self.conv2 = conv(
            planes,
            planes,
            kernel_size=3,
            stride=stride,
            dilation=dilation,
            padding=dilation,
            bias=False,
        )
        self.bn2 = norm(planes)
        self.conv3 = conv(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = norm(planes * self.expansion)
        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, num_groups=None, weight_std=False, beta=False
    ):
        self.inplanes = 64
        self.norm = (
            lambda planes, momentum=0.05: nn.BatchNorm2d(planes, momentum=momentum)
            if num_groups is None
            else nn.GroupNorm(num_groups, planes)
        )
        self.conv = Conv2d if weight_std else nn.Conv2d

        super(ResNet, self).__init__()
        if not beta:
            self.conv1 = self.conv(
                3, 64, kernel_size=7, stride=2, padding=3, bias=False
            )
        else:
            self.conv1 = nn.Sequential(
                self.conv(3, 64, 3, stride=2, padding=1, bias=False),
                self.conv(64, 64, 3, stride=1, padding=1, bias=False),
                self.conv(64, 64, 3, stride=1, padding=1, bias=False),
            )
        self.bn1 = self.norm(64)
        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)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=2)
        self.aspp = ASPP(
            512 * block.expansion, 256, num_classes, conv=self.conv, norm=self.norm
        )

        for m in self.modules():
            if isinstance(m, self.conv):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.GroupNorm):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or dilation != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                self.conv(
                    self.inplanes,
                    planes * block.expansion,
                    kernel_size=1,
                    stride=stride,
                    dilation=max(1, dilation / 2),
                    bias=False,
                ),
                self.norm(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
                dilation=max(1, dilation / 2),
                conv=self.conv,
                norm=self.norm,
            )
        )
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    dilation=dilation,
                    conv=self.conv,
                    norm=self.norm,
                )
            )

        return nn.Sequential(*layers)

    def forward(self, x):
        size = (x.shape[2], x.shape[3])
        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)
        x = self.layer4(x)

        x = self.aspp(x)
        x = nn.Upsample(size, mode="bilinear", align_corners=True)(x)
        return x


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet50"]))
    return model


def resnet101(path=None, pretrained=False, num_groups=None, weight_std=False, device="cpu", **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(
        Bottleneck,
        [3, 4, 23, 3],
        num_groups=num_groups,
        weight_std=weight_std,
        **kwargs
    )
    if pretrained:
        model_dict = model.state_dict()
        if num_groups and weight_std:
            path = os.path.join(os.path.dirname(path), "R-101-GN-WS.pth.tar")
            pretrained_dict = torch.load(path, map_location=device)
            overlap_dict = {
                k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict
            }
            assert len(overlap_dict) == 312
        elif not num_groups and not weight_std:
            pretrained_dict = model_zoo.load_url(model_urls["resnet101"])
            overlap_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        else:
            raise ValueError("Currently only support BN or GN+WS")
        model_dict.update(overlap_dict)
        model.load_state_dict(model_dict)
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet152"]))
    return model