AdaptiveWingLoss/core/models.py

import math

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

from AdaptiveWingLoss.core.coord_conv import CoordConvTh


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


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 ConvBlock(nn.Module):
    def __init__(self, in_planes, out_planes):
        super(ConvBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1)
        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1)

        if in_planes != out_planes:
            self.downsample = nn.Sequential(
                nn.BatchNorm2d(in_planes),
                nn.ReLU(True),
                nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False),
            )
        else:
            self.downsample = None

    def forward(self, x):
        residual = x

        out1 = self.bn1(x)
        out1 = F.relu(out1, True)
        out1 = self.conv1(out1)

        out2 = self.bn2(out1)
        out2 = F.relu(out2, True)
        out2 = self.conv2(out2)

        out3 = self.bn3(out2)
        out3 = F.relu(out3, True)
        out3 = self.conv3(out3)

        out3 = torch.cat((out1, out2, out3), 1)

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

        out3 += residual

        return out3


class HourGlass(nn.Module):
    def __init__(self, num_modules, depth, num_features, first_one=False):
        super(HourGlass, self).__init__()
        self.num_modules = num_modules
        self.depth = depth
        self.features = num_features
        self.coordconv = CoordConvTh(
            x_dim=64,
            y_dim=64,
            with_r=True,
            with_boundary=True,
            in_channels=256,
            first_one=first_one,
            out_channels=256,
            kernel_size=1,
            stride=1,
            padding=0,
        )
        self._generate_network(self.depth)

    def _generate_network(self, level):
        self.add_module("b1_" + str(level), ConvBlock(256, 256))

        self.add_module("b2_" + str(level), ConvBlock(256, 256))

        if level > 1:
            self._generate_network(level - 1)
        else:
            self.add_module("b2_plus_" + str(level), ConvBlock(256, 256))

        self.add_module("b3_" + str(level), ConvBlock(256, 256))

    def _forward(self, level, inp):
        # Upper branch
        up1 = inp
        up1 = self._modules["b1_" + str(level)](up1)

        # Lower branch
        low1 = F.avg_pool2d(inp, 2, stride=2)
        low1 = self._modules["b2_" + str(level)](low1)

        if level > 1:
            low2 = self._forward(level - 1, low1)
        else:
            low2 = low1
            low2 = self._modules["b2_plus_" + str(level)](low2)

        low3 = low2
        low3 = self._modules["b3_" + str(level)](low3)

        up2 = F.upsample(low3, scale_factor=2, mode="nearest")

        return up1 + up2

    def forward(self, x, heatmap):
        x, last_channel = self.coordconv(x, heatmap)
        return self._forward(self.depth, x), last_channel


class FAN(nn.Module):
    def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68):
        super(FAN, self).__init__()
        self.num_modules = num_modules
        self.gray_scale = gray_scale
        self.end_relu = end_relu
        self.num_landmarks = num_landmarks

        # Base part
        if self.gray_scale:
            self.conv1 = CoordConvTh(
                x_dim=256,
                y_dim=256,
                with_r=True,
                with_boundary=False,
                in_channels=3,
                out_channels=64,
                kernel_size=7,
                stride=2,
                padding=3,
            )
        else:
            self.conv1 = CoordConvTh(
                x_dim=256,
                y_dim=256,
                with_r=True,
                with_boundary=False,
                in_channels=3,
                out_channels=64,
                kernel_size=7,
                stride=2,
                padding=3,
            )
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = ConvBlock(64, 128)
        self.conv3 = ConvBlock(128, 128)
        self.conv4 = ConvBlock(128, 256)

        # Stacking part
        for hg_module in range(self.num_modules):
            if hg_module == 0:
                first_one = True
            else:
                first_one = False
            self.add_module("m" + str(hg_module), HourGlass(1, 4, 256, first_one))
            self.add_module("top_m_" + str(hg_module), ConvBlock(256, 256))
            self.add_module("conv_last" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
            self.add_module("bn_end" + str(hg_module), nn.BatchNorm2d(256))
            self.add_module("l" + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0))

            if hg_module < self.num_modules - 1:
                self.add_module("bl" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
                self.add_module(
                    "al" + str(hg_module), nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0)
                )

    def forward(self, x):
        x, _ = self.conv1(x)
        x = F.relu(self.bn1(x), True)
        # x = F.relu(self.bn1(self.conv1(x)), True)
        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
        x = self.conv3(x)
        x = self.conv4(x)

        previous = x

        outputs = []
        boundary_channels = []
        tmp_out = None
        for i in range(self.num_modules):
            hg, boundary_channel = self._modules["m" + str(i)](previous, tmp_out)

            ll = hg
            ll = self._modules["top_m_" + str(i)](ll)

            ll = F.relu(self._modules["bn_end" + str(i)](self._modules["conv_last" + str(i)](ll)), True)

            # Predict heatmaps
            tmp_out = self._modules["l" + str(i)](ll)
            if self.end_relu:
                tmp_out = F.relu(tmp_out)  # HACK: Added relu
            outputs.append(tmp_out)
            boundary_channels.append(boundary_channel)

            if i < self.num_modules - 1:
                ll = self._modules["bl" + str(i)](ll)
                tmp_out_ = self._modules["al" + str(i)](tmp_out)
                previous = previous + ll + tmp_out_

        return outputs, boundary_channels