utils/face_parsing/face_parsing.py

#!/usr/bin/python
# -*- encoding: utf-8 -*-

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

from .resnet import Resnet18
from models.modules.networks import init_net


class ConvBNReLU(nn.Module):
    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_chan,
                out_chan,
                kernel_size = ks,
                stride = stride,
                padding = padding,
                bias = False)
        self.bn = nn.BatchNorm2d(out_chan)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = F.relu(self.bn(x))
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

class BiSeNetOutput(nn.Module):
    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
        super(BiSeNetOutput, self).__init__()
        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = self.conv_out(x)
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for _, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class AttentionRefinementModule(nn.Module):
    def __init__(self, in_chan, out_chan, *args, **kwargs):
        super(AttentionRefinementModule, self).__init__()
        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
        self.bn_atten = nn.BatchNorm2d(out_chan)
        self.sigmoid_atten = nn.Sigmoid()
        self.init_weight()

    def forward(self, x):
        feat = self.conv(x)
        atten = F.avg_pool2d(feat, feat.size()[2:])
        atten = self.conv_atten(atten)
        atten = self.bn_atten(atten)
        atten = self.sigmoid_atten(atten)
        out = torch.mul(feat, atten)
        return out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)


class ContextPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(ContextPath, self).__init__()
        self.resnet = Resnet18()
        self.arm16 = AttentionRefinementModule(256, 128)
        self.arm32 = AttentionRefinementModule(512, 128)
        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)

        self.init_weight()

    def forward(self, x):
        H0, W0 = x.size()[2:]
        feat8, feat16, feat32 = self.resnet(x)
        H8, W8 = feat8.size()[2:]
        H16, W16 = feat16.size()[2:]
        H32, W32 = feat32.size()[2:]

        avg = F.avg_pool2d(feat32, feat32.size()[2:])
        avg = self.conv_avg(avg)
        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')

        feat32_arm = self.arm32(feat32)
        feat32_sum = feat32_arm + avg_up
        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
        feat32_up = self.conv_head32(feat32_up)

        feat16_arm = self.arm16(feat16)
        feat16_sum = feat16_arm + feat32_up
        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
        feat16_up = self.conv_head16(feat16_up)

        return feat8, feat16_up, feat32_up  # x8, x8, x16

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for _, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


### This is not used, since I replace this with the resnet feature with the same size
class SpatialPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(SpatialPath, self).__init__()
        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
        self.init_weight()

    def forward(self, x):
        feat = self.conv1(x)
        feat = self.conv2(feat)
        feat = self.conv3(feat)
        feat = self.conv_out(feat)
        return feat

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for _, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class FeatureFusionModule(nn.Module):
    def __init__(self, in_chan, out_chan, *args, **kwargs):
        super(FeatureFusionModule, self).__init__()
        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
        self.conv1 = nn.Conv2d(out_chan,
                out_chan//4,
                kernel_size = 1,
                stride = 1,
                padding = 0,
                bias = False)
        self.conv2 = nn.Conv2d(out_chan//4,
                out_chan,
                kernel_size = 1,
                stride = 1,
                padding = 0,
                bias = False)
        self.relu = nn.ReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()
        self.init_weight()

    def forward(self, fsp, fcp):
        fcat = torch.cat([fsp, fcp], dim=1)
        feat = self.convblk(fcat)
        atten = F.avg_pool2d(feat, feat.size()[2:])
        atten = self.conv1(atten)
        atten = self.relu(atten)
        atten = self.conv2(atten)
        atten = self.sigmoid(atten)
        feat_atten = torch.mul(feat, atten)
        feat_out = feat_atten + feat
        return feat_out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for _, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class BiSeNet(nn.Module):
    def __init__(self, n_classes, *args, **kwargs):
        super(BiSeNet, self).__init__()
        self.cp = ContextPath()
        ## here self.sp is deleted
        self.ffm = FeatureFusionModule(256, 256)
        self.conv_out = BiSeNetOutput(256, 256, n_classes)
        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
        self.init_weight()

    def forward(self, x):
        H, W = x.size()[2:]
        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
        feat_fuse = self.ffm(feat_sp, feat_cp8)

        feat_out = self.conv_out(feat_fuse)
        feat_out16 = self.conv_out16(feat_cp8)
        feat_out32 = self.conv_out32(feat_cp16)

        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
        return feat_out, feat_out16, feat_out32

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
        for _, child in self.named_children():
            child_wd_params, child_nowd_params = child.get_params()
            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
                lr_mul_wd_params += child_wd_params
                lr_mul_nowd_params += child_nowd_params
            else:
                wd_params += child_wd_params
                nowd_params += child_nowd_params
        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params


class PartWeightsGenerator():
    def __init__(self, gpu_ids, DDP_device):
        super(PartWeightsGenerator, self).__init__()

        # init face parsing network
        self.net = BiSeNet(n_classes=19)
        self.net = init_net(self.net, gpu_ids=gpu_ids, DDP_device=DDP_device)
        if isinstance(self.net, torch.nn.DataParallel) or isinstance(self.net, torch.nn.parallel.DistributedDataParallel):
            self.net = self.net.module
        cur_folder = os.path.split(__file__)[0]
        self.net.load_state_dict(torch.load(os.path.join(cur_folder, 'face_parsing.pth'), map_location=lambda storage, loc: storage))
        self.net.eval()

        # init some tensors
        self.mu = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
        self.sigma = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
        self.gauss_kernel = torch.tensor([1,4,7,4,1,4,16,26,16,4,7,26,41,26,7,4,16,26,16,4,1,4,7,4,1],).view(1, 1, 5, 5) / 273.0

        if len(gpu_ids) > 0 or not DDP_device is None:
            device = DDP_device if not DDP_device is None else gpu_ids[0]
            self.mu = self.mu.to(device)
            self.sigma = self.sigma.to(device)
            self.gauss_kernel = self.gauss_kernel.to(device)

        # init attributes list
        self.atts_list = [
            'background', #0
            'skin', #1
            'left_brow', #2
            'right_brow', #3
            'left_eye', #4
            'right_eye', #5
            'eye_glasses', #6
            'left_ear', #7
            'right_ear', #8
            'ear_rings', #9
            'nose', #10
            'teeth', #11
            'upper_lip', #12
            'lower_lip', #13
            'neck', #14
            'necklace', #15
            'cloth', #16
            'hair', #17
            'hat', #18
        ]

    def generate_masks(self, img):
        with torch.no_grad():
            _, _, h, w = img.size()
            img_512 = F.interpolate(img, size=512, mode='bilinear')
            pred_512 = self.net((img_512 * 0.5 + 0.5 - self.mu) / self.sigma)[0]
            pred = F.interpolate(pred_512, size=(h, w), mode='bilinear')
            pred = pred.argmax(1, keepdim=True)

            skin_mask = torch.zeros_like(pred).float()
            skin_mask[pred == 1] = 1.0
            skin_mask[pred == 7] = 1.0
            skin_mask[pred == 8] = 1.0
            skin_mask[pred == 10] = 1.0
            skin_mask[pred == 14] = 1.0
            skin_mask = F.conv2d(skin_mask, self.gauss_kernel, padding=2)

            eye_mask = torch.zeros_like(pred).float()
            eye_mask[pred == 4] = 1.0
            eye_mask[pred == 5] = 1.0
            eye_mask = F.conv2d(eye_mask, self.gauss_kernel, padding=2)

            mouth_mask = torch.zeros_like(pred).float()
            mouth_mask[pred == 11] = 1.0
            mouth_mask[pred == 12] = 1.0
            mouth_mask[pred == 13] = 1.0
            mouth_mask = F.conv2d(mouth_mask, self.gauss_kernel, padding=2)

            hair_mask = torch.zeros_like(pred).float()
            hair_mask[pred == 17] = 1.0
            hair_mask = F.conv2d(hair_mask, self.gauss_kernel, padding=2)

            return skin_mask, eye_mask, mouth_mask, hair_mask


    def generate_weights(self, img, weights_dict, blur=True):
        with torch.no_grad():
            _, _, h, w = img.size()
            img_512 = F.interpolate(img, size=512, mode='bilinear')
            pred_512 = self.net((img_512 * 0.5 + 0.5 - self.mu) / self.sigma)[0]
            pred = F.interpolate(pred_512, size=(h, w), mode='bilinear')
            pred = pred.argmax(1, keepdim=True)
            weights = torch.ones_like(pred).float()
            for idx, att in enumerate(self.atts_list):
                if att in weights_dict:
                    weights[pred == idx] = weights_dict[att]
            if blur:
                return F.conv2d(weights, self.gauss_kernel, padding=2)
            else:
                return weights


class GradWeightFunc(torch.autograd.Function):

    @staticmethod
    def forward(ctx, img, weights):
        ctx.param = (weights, )
        return img

    @staticmethod
    def backward(ctx, grad):
        weights = ctx.param[0]
        return weights * grad, None


class GradWeightLayer(nn.Module):

    def __init__(self):
        super(GradWeightLayer, self).__init__()

    def forward(self, img, weights):
        return GradWeightFunc.apply(img, weights)