retina_face.py

import torch
import torch.nn as nn
import torchvision.models.detection.backbone_utils as backbone_utils
import torchvision.models.resnet as resnet
import torchvision.models._utils as _utils
import torch.nn.functional as F
from collections import OrderedDict
import os

from mobile_v1 import MobileNetV1,FPN,SSH



class ClassHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(ClassHead,self).__init__()
        self.num_anchors = num_anchors
        self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0)
        # self.output_act = nn.LogSoftmax(dim=-1)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()
        # b, h, w, c = out.shape
        # out = out.view(b, h, w, self.num_anchors, 2)
        # out = self.output_act(out)

        return out.view(out.shape[0], -1, 2)

class BboxHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(BboxHead,self).__init__()
        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()

        return out.view(out.shape[0], -1, 4)

class LandmarkHead(nn.Module):
    def __init__(self,inchannels=512,num_anchors=3):
        super(LandmarkHead,self).__init__()
        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0)

    def forward(self,x):
        out = self.conv1x1(x)
        out = out.permute(0,2,3,1).contiguous()

        return out.view(out.shape[0], -1, 10)

class RetinaFace(nn.Module):
    def __init__(self, phase = 'train', net = 'mnet0.25', return_layers = {'stage1': 1, 'stage2': 2, 'stage3': 3}):
        super(RetinaFace,self).__init__()
        self.phase = phase
        backbone = None
        if net == 'mnet0.25':
            backbone = MobileNetV1()
            if True:
                current_dir = os.getcwd()
                model_best_path = os.path.join(current_dir , "model_best.pth.tar")
                checkpoint = torch.load(model_best_path, map_location=torch.device('cpu'))
                from collections import OrderedDict
                new_state_dict = OrderedDict()
                for k, v in checkpoint['state_dict'].items():
                    name = k[7:]  # remove module.
                    new_state_dict[name] = v
                # load params
                backbone.load_state_dict(new_state_dict)

        self.body = _utils.IntermediateLayerGetter(backbone, return_layers)
        in_channels_stage2 = 32
        in_channels_list = [
            in_channels_stage2 * 2,
            in_channels_stage2 * 4,
            in_channels_stage2 * 8,
        ]
        out_channels = 64
        self.fpn = FPN(in_channels_list,out_channels)
        self.ssh1 = SSH(out_channels, out_channels)
        self.ssh2 = SSH(out_channels, out_channels)
        self.ssh3 = SSH(out_channels, out_channels)

        self.ClassHead = self._make_class_head()
        self.BboxHead = self._make_bbox_head()
        self.LandmarkHead = self._make_landmark_head()

    def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        classhead = nn.ModuleList()
        for i in range(fpn_num):
            classhead.append(ClassHead(inchannels,anchor_num))
        return classhead

    def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        bboxhead = nn.ModuleList()
        for i in range(fpn_num):
            bboxhead.append(BboxHead(inchannels,anchor_num))
        return bboxhead

    def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
        landmarkhead = nn.ModuleList()
        for i in range(fpn_num):
            landmarkhead.append(LandmarkHead(inchannels,anchor_num))
        return landmarkhead

    def forward(self,inputs):
        out = self.body(inputs)

        # FPN
        fpn = self.fpn(out)

        # SSH
        feature1 = self.ssh1(fpn[0])
        feature2 = self.ssh2(fpn[1])
        feature3 = self.ssh3(fpn[2])
        features = [feature1, feature2, feature3]

        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)

        if self.phase == 'train':
            output = (bbox_regressions, classifications, ldm_regressions)
        else:
            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
        return output