models/stacked_hg.py

"""
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) Nan Xue (HAWP)
(c) Yichao Zhou (LCNN)
(c) YANG, Wei
"""
import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = ["HourglassNet", "hg"]


class Bottleneck2D(nn.Module):
    expansion = 2

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck2D, self).__init__()

        self.bn1 = nn.BatchNorm2d(inplanes)
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

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

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

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

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

        out += residual

        return out


class Hourglass(nn.Module):
    def __init__(self, block, num_blocks, planes, depth):
        super(Hourglass, self).__init__()
        self.depth = depth
        self.block = block
        self.hg = self._make_hour_glass(block, num_blocks, planes, depth)

    def _make_residual(self, block, num_blocks, planes):
        layers = []
        for i in range(0, num_blocks):
            layers.append(block(planes * block.expansion, planes))
        return nn.Sequential(*layers)

    def _make_hour_glass(self, block, num_blocks, planes, depth):
        hg = []
        for i in range(depth):
            res = []
            for j in range(3):
                res.append(self._make_residual(block, num_blocks, planes))
            if i == 0:
                res.append(self._make_residual(block, num_blocks, planes))
            hg.append(nn.ModuleList(res))
        return nn.ModuleList(hg)

    def _hour_glass_forward(self, n, x):
        up1 = self.hg[n - 1][0](x)
        low1 = F.max_pool2d(x, 2, stride=2)
        low1 = self.hg[n - 1][1](low1)

        if n > 1:
            low2 = self._hour_glass_forward(n - 1, low1)
        else:
            low2 = self.hg[n - 1][3](low1)
        low3 = self.hg[n - 1][2](low2)
        up2 = F.interpolate(low3, scale_factor=2)
        out = up1 + up2
        return out

    def forward(self, x):
        return self._hour_glass_forward(self.depth, x)


class HourglassNet(nn.Module):
    """Hourglass model from Newell et al ECCV 2016"""

    def __init__(self, inplanes, num_feats, block, head, depth, num_stacks, num_blocks, num_classes):
        super(HourglassNet, self).__init__()

        self.inplanes = inplanes
        self.num_feats = num_feats
        self.num_stacks = num_stacks
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_residual(block, self.inplanes, 1)
        self.layer2 = self._make_residual(block, self.inplanes, 1)
        self.layer3 = self._make_residual(block, self.num_feats, 1)
        self.maxpool = nn.MaxPool2d(2, stride=2)

        # build hourglass modules
        ch = self.num_feats * block.expansion
        # vpts = []
        hg, res, fc, score, fc_, score_ = [], [], [], [], [], []
        for i in range(num_stacks):
            hg.append(Hourglass(block, num_blocks, self.num_feats, depth))
            res.append(self._make_residual(block, self.num_feats, num_blocks))
            fc.append(self._make_fc(ch, ch))
            score.append(head(ch, num_classes))
            # vpts.append(VptsHead(ch))
            # vpts.append(nn.Linear(ch, 9))
            # score.append(nn.Conv2d(ch, num_classes, kernel_size=1))
            # score[i].bias.data[0] += 4.6
            # score[i].bias.data[2] += 4.6
            if i < num_stacks - 1:
                fc_.append(nn.Conv2d(ch, ch, kernel_size=1))
                score_.append(nn.Conv2d(num_classes, ch, kernel_size=1))
        self.hg = nn.ModuleList(hg)
        self.res = nn.ModuleList(res)
        self.fc = nn.ModuleList(fc)
        self.score = nn.ModuleList(score)
        # self.vpts = nn.ModuleList(vpts)
        self.fc_ = nn.ModuleList(fc_)
        self.score_ = nn.ModuleList(score_)

    def _make_residual(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,
                )
            )

        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 _make_fc(self, inplanes, outplanes):
        bn = nn.BatchNorm2d(inplanes)
        conv = nn.Conv2d(inplanes, outplanes, kernel_size=1)
        return nn.Sequential(conv, bn, self.relu)

    def forward(self, x):
        out = []
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

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

        for i in range(self.num_stacks):
            y = self.hg[i](x)
            y = self.res[i](y)
            y = self.fc[i](y)
            score = self.score[i](y)
            out.append(score)

            if i < self.num_stacks - 1:
                fc_ = self.fc_[i](y)
                score_ = self.score_[i](score)
                x = x + fc_ + score_

        return out[::-1], y

    def train(self, mode=True):
        # Override train so that the training mode is set as we want
        nn.Module.train(self, mode)
        if mode:
            # fix all bn layers
            def set_bn_eval(m):
                classname = m.__class__.__name__
                if classname.find('BatchNorm') != -1:
                    m.eval()

            self.apply(set_bn_eval)


class MultitaskHead(nn.Module):
    def __init__(self, input_channels, num_class, head_size):
        super(MultitaskHead, self).__init__()

        m = int(input_channels / 4)
        heads = []
        for output_channels in sum(head_size, []):
            heads.append(
                nn.Sequential(
                    nn.Conv2d(input_channels, m, kernel_size=3, padding=1),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(m, output_channels, kernel_size=1),
                )
            )
        self.heads = nn.ModuleList(heads)
        assert num_class == sum(sum(head_size, []))

    def forward(self, x):
        return torch.cat([head(x) for head in self.heads], dim=1)


def build_hg():
    inplanes = 64
    num_feats = 256 //2
    depth =  4
    num_stacks = 2
    num_blocks = 1
    head_size = [[2], [2]]

    out_feature_channels = 256

    num_class = sum(sum(head_size, []))
    model = HourglassNet(
        block=Bottleneck2D,
        inplanes = inplanes,
        num_feats= num_feats,
        depth=depth,
        head=lambda c_in, c_out: MultitaskHead(c_in, c_out, head_size=head_size),
        num_stacks = num_stacks,
        num_blocks = num_blocks,
        num_classes = num_class)

    model.out_feature_channels = out_feature_channels

    return model