r_facelib/detection/retinaface/retinaface.py

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter

from modules import shared

from r_facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
from r_facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
from r_facelib.detection.retinaface.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
                                                 py_cpu_nms)

#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
    device = torch.device('cuda')
elif torch.backends.mps.is_available():
    device = torch.device('mps')
# elif hasattr(torch,'dml'):
#     device = torch.device('dml')
elif hasattr(torch,'dml') or hasattr(torch,'privateuseone'): # AMD
    if shared.cmd_opts is not None: # A1111
        if shared.cmd_opts.device_id is not None:
            device = torch.device(f'privateuseone:{shared.cmd_opts.device_id}')
        else:
            device = torch.device('privateuseone:0')
    else:
        device = torch.device('privateuseone:0')
else:
    device = torch.device('cpu')


def generate_config(network_name):

    cfg_mnet = {
        'name': 'mobilenet0.25',
        'min_sizes': [[16, 32], [64, 128], [256, 512]],
        'steps': [8, 16, 32],
        'variance': [0.1, 0.2],
        'clip': False,
        'loc_weight': 2.0,
        'gpu_train': True,
        'batch_size': 32,
        'ngpu': 1,
        'epoch': 250,
        'decay1': 190,
        'decay2': 220,
        'image_size': 640,
        'return_layers': {
            'stage1': 1,
            'stage2': 2,
            'stage3': 3
        },
        'in_channel': 32,
        'out_channel': 64
    }

    cfg_re50 = {
        'name': 'Resnet50',
        'min_sizes': [[16, 32], [64, 128], [256, 512]],
        'steps': [8, 16, 32],
        'variance': [0.1, 0.2],
        'clip': False,
        'loc_weight': 2.0,
        'gpu_train': True,
        'batch_size': 24,
        'ngpu': 4,
        'epoch': 100,
        'decay1': 70,
        'decay2': 90,
        'image_size': 840,
        'return_layers': {
            'layer2': 1,
            'layer3': 2,
            'layer4': 3
        },
        'in_channel': 256,
        'out_channel': 256
    }

    if network_name == 'mobile0.25':
        return cfg_mnet
    elif network_name == 'resnet50':
        return cfg_re50
    else:
        raise NotImplementedError(f'network_name={network_name}')


class RetinaFace(nn.Module):

    def __init__(self, network_name='resnet50', half=False, phase='test'):
        super(RetinaFace, self).__init__()
        self.half_inference = half
        cfg = generate_config(network_name)
        self.backbone = cfg['name']

        self.model_name = f'retinaface_{network_name}'
        self.cfg = cfg
        self.phase = phase
        self.target_size, self.max_size = 1600, 2150
        self.resize, self.scale, self.scale1 = 1., None, None
        self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]]).to(device)
        self.reference = get_reference_facial_points(default_square=True)
        # Build network.
        backbone = None
        if cfg['name'] == 'mobilenet0.25':
            backbone = MobileNetV1()
            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
        elif cfg['name'] == 'Resnet50':
            import torchvision.models as models
            backbone = models.resnet50(pretrained=False)
            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])

        in_channels_stage2 = cfg['in_channel']
        in_channels_list = [
            in_channels_stage2 * 2,
            in_channels_stage2 * 4,
            in_channels_stage2 * 8,
        ]

        out_channels = cfg['out_channel']
        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 = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
        self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])

        self.to(device)
        self.eval()
        if self.half_inference:
            self.half()

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

        if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
            out = list(out.values())
        # 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)
        tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
        ldm_regressions = (torch.cat(tmp, 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

    def __detect_faces(self, inputs):
        # get scale
        height, width = inputs.shape[2:]
        self.scale = torch.tensor([width, height, width, height], dtype=torch.float32).to(device)
        tmp = [width, height, width, height, width, height, width, height, width, height]
        self.scale1 = torch.tensor(tmp, dtype=torch.float32).to(device)

        # forawrd
        inputs = inputs.to(device)
        if self.half_inference:
            inputs = inputs.half()
        loc, conf, landmarks = self(inputs)

        # get priorbox
        priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
        priors = priorbox.forward().to(device)

        return loc, conf, landmarks, priors

    # single image detection
    def transform(self, image, use_origin_size):
        # convert to opencv format
        if isinstance(image, Image.Image):
            image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
        image = image.astype(np.float32)

        # testing scale
        im_size_min = np.min(image.shape[0:2])
        im_size_max = np.max(image.shape[0:2])
        resize = float(self.target_size) / float(im_size_min)

        # prevent bigger axis from being more than max_size
        if np.round(resize * im_size_max) > self.max_size:
            resize = float(self.max_size) / float(im_size_max)
        resize = 1 if use_origin_size else resize

        # resize
        if resize != 1:
            image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)

        # convert to torch.tensor format
        # image -= (104, 117, 123)
        image = image.transpose(2, 0, 1)
        image = torch.from_numpy(image).unsqueeze(0)

        return image, resize

    def detect_faces(
        self,
        image,
        conf_threshold=0.8,
        nms_threshold=0.4,
        use_origin_size=True,
    ):
        """
        Params:
            imgs: BGR image
        """
        image, self.resize = self.transform(image, use_origin_size)
        image = image.to(device)
        if self.half_inference:
            image = image.half()
        image = image - self.mean_tensor

        loc, conf, landmarks, priors = self.__detect_faces(image)

        boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
        boxes = boxes * self.scale / self.resize
        boxes = boxes.cpu().numpy()

        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]

        landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
        landmarks = landmarks * self.scale1 / self.resize
        landmarks = landmarks.cpu().numpy()

        # ignore low scores
        inds = np.where(scores > conf_threshold)[0]
        boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]

        # sort
        order = scores.argsort()[::-1]
        boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]

        # do NMS
        bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
        keep = py_cpu_nms(bounding_boxes, nms_threshold)
        bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
        # self.t['forward_pass'].toc()
        # print(self.t['forward_pass'].average_time)
        # import sys
        # sys.stdout.flush()
        return np.concatenate((bounding_boxes, landmarks), axis=1)

    def __align_multi(self, image, boxes, landmarks, limit=None):

        if len(boxes) < 1:
            return [], []

        if limit:
            boxes = boxes[:limit]
            landmarks = landmarks[:limit]

        faces = []
        for landmark in landmarks:
            facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]

            warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
            faces.append(warped_face)

        return np.concatenate((boxes, landmarks), axis=1), faces

    def align_multi(self, img, conf_threshold=0.8, limit=None):

        rlt = self.detect_faces(img, conf_threshold=conf_threshold)
        boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]

        return self.__align_multi(img, boxes, landmarks, limit)

    # batched detection
    def batched_transform(self, frames, use_origin_size):
        """
        Arguments:
            frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
                type=np.float32, BGR format).
            use_origin_size: whether to use origin size.
        """
        from_PIL = True if isinstance(frames[0], Image.Image) else False

        # convert to opencv format
        if from_PIL:
            frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
            frames = np.asarray(frames, dtype=np.float32)

        # testing scale
        im_size_min = np.min(frames[0].shape[0:2])
        im_size_max = np.max(frames[0].shape[0:2])
        resize = float(self.target_size) / float(im_size_min)

        # prevent bigger axis from being more than max_size
        if np.round(resize * im_size_max) > self.max_size:
            resize = float(self.max_size) / float(im_size_max)
        resize = 1 if use_origin_size else resize

        # resize
        if resize != 1:
            if not from_PIL:
                frames = F.interpolate(frames, scale_factor=resize)
            else:
                frames = [
                    cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
                    for frame in frames
                ]

        # convert to torch.tensor format
        if not from_PIL:
            frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
        else:
            frames = frames.transpose((0, 3, 1, 2))
            frames = torch.from_numpy(frames)

        return frames, resize

    def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
        """
        Arguments:
            frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
                type=np.uint8, BGR format).
            conf_threshold: confidence threshold.
            nms_threshold: nms threshold.
            use_origin_size: whether to use origin size.
        Returns:
            final_bounding_boxes: list of np.array ([n_boxes, 5],
                type=np.float32).
            final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
        """
        # self.t['forward_pass'].tic()
        frames, self.resize = self.batched_transform(frames, use_origin_size)
        frames = frames.to(device)
        frames = frames - self.mean_tensor

        b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)

        final_bounding_boxes, final_landmarks = [], []

        # decode
        priors = priors.unsqueeze(0)
        b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
        b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
        b_conf = b_conf[:, :, 1]

        # index for selection
        b_indice = b_conf > conf_threshold

        # concat
        b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()

        for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):

            # ignore low scores
            pred, landm = pred[inds, :], landm[inds, :]
            if pred.shape[0] == 0:
                final_bounding_boxes.append(np.array([], dtype=np.float32))
                final_landmarks.append(np.array([], dtype=np.float32))
                continue

            # sort
            # order = score.argsort(descending=True)
            # box, landm, score = box[order], landm[order], score[order]

            # to CPU
            bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()

            # NMS
            keep = py_cpu_nms(bounding_boxes, nms_threshold)
            bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]

            # append
            final_bounding_boxes.append(bounding_boxes)
            final_landmarks.append(landmarks)
        # self.t['forward_pass'].toc(average=True)
        # self.batch_time += self.t['forward_pass'].diff
        # self.total_frame += len(frames)
        # print(self.batch_time / self.total_frame)

        return final_bounding_boxes, final_landmarks