from __future__ import print_function import os import argparse import torch import torch.backends.cudnn as cudnn import numpy as np from data import cfg_mnet, cfg_re50 from layers.functions.prior_box import PriorBox from utils.nms.py_cpu_nms import py_cpu_nms import cv2 from models.retinaface import RetinaFace from utils.box_utils import decode, decode_landm from utils.timer import Timer parser = argparse.ArgumentParser(description='Retinaface') parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth', type=str, help='Trained state_dict file path to open') parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') parser.add_argument('--save_folder', default='eval/', type=str, help='Dir to save results') parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference') parser.add_argument('--dataset', default='FDDB', type=str, choices=['FDDB'], help='dataset') parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold') parser.add_argument('--top_k', default=5000, type=int, help='top_k') parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold') parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k') parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results') parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold') args = parser.parse_args() def check_keys(model, pretrained_state_dict): ckpt_keys = set(pretrained_state_dict.keys()) model_keys = set(model.state_dict().keys()) used_pretrained_keys = model_keys & ckpt_keys unused_pretrained_keys = ckpt_keys - model_keys missing_keys = model_keys - ckpt_keys print('Missing keys:{}'.format(len(missing_keys))) print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) print('Used keys:{}'.format(len(used_pretrained_keys))) assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' return True def remove_prefix(state_dict, prefix): ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' print('remove prefix \'{}\''.format(prefix)) f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x return {f(key): value for key, value in state_dict.items()} def load_model(model, pretrained_path, load_to_cpu): print('Loading pretrained model from {}'.format(pretrained_path)) if load_to_cpu: pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) else: device = torch.cuda.current_device() pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) if "state_dict" in pretrained_dict.keys(): pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') else: pretrained_dict = remove_prefix(pretrained_dict, 'module.') check_keys(model, pretrained_dict) model.load_state_dict(pretrained_dict, strict=False) return model if __name__ == '__main__': torch.set_grad_enabled(False) cfg = None if args.network == "mobile0.25": cfg = cfg_mnet elif args.network == "resnet50": cfg = cfg_re50 # net and model net = RetinaFace(cfg=cfg, phase = 'test') net = load_model(net, args.trained_model, args.cpu) net.eval() print('Finished loading model!') print(net) cudnn.benchmark = True device = torch.device("cpu" if args.cpu else "cuda") net = net.to(device) # save file if not os.path.exists(args.save_folder): os.makedirs(args.save_folder) fw = open(os.path.join(args.save_folder, args.dataset + '_dets.txt'), 'w') # testing dataset testset_folder = os.path.join('data', args.dataset, 'images/') testset_list = os.path.join('data', args.dataset, 'img_list.txt') with open(testset_list, 'r') as fr: test_dataset = fr.read().split() num_images = len(test_dataset) # testing scale resize = 1 _t = {'forward_pass': Timer(), 'misc': Timer()} # testing begin for i, img_name in enumerate(test_dataset): image_path = testset_folder + img_name + '.jpg' img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR) img = np.float32(img_raw) if resize != 1: img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) im_height, im_width, _ = img.shape scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) img -= (104, 117, 123) img = img.transpose(2, 0, 1) img = torch.from_numpy(img).unsqueeze(0) img = img.to(device) scale = scale.to(device) _t['forward_pass'].tic() loc, conf, landms = net(img) # forward pass _t['forward_pass'].toc() _t['misc'].tic() priorbox = PriorBox(cfg, image_size=(im_height, im_width)) priors = priorbox.forward() priors = priors.to(device) prior_data = priors.data boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) boxes = boxes * scale / resize boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2]]) scale1 = scale1.to(device) landms = landms * scale1 / resize landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > args.confidence_threshold)[0] boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS # order = scores.argsort()[::-1][:args.top_k] order = scores.argsort()[::-1] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) keep = py_cpu_nms(dets, args.nms_threshold) dets = dets[keep, :] landms = landms[keep] # keep top-K faster NMS # dets = dets[:args.keep_top_k, :] # landms = landms[:args.keep_top_k, :] dets = np.concatenate((dets, landms), axis=1) _t['misc'].toc() # save dets if args.dataset == "FDDB": fw.write('{:s}\n'.format(img_name)) fw.write('{:.1f}\n'.format(dets.shape[0])) for k in range(dets.shape[0]): xmin = dets[k, 0] ymin = dets[k, 1] xmax = dets[k, 2] ymax = dets[k, 3] score = dets[k, 4] w = xmax - xmin + 1 h = ymax - ymin + 1 # fw.write('{:.3f} {:.3f} {:.3f} {:.3f} {:.10f}\n'.format(xmin, ymin, w, h, score)) fw.write('{:d} {:d} {:d} {:d} {:.10f}\n'.format(int(xmin), int(ymin), int(w), int(h), score)) print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time)) # show image if args.save_image: for b in dets: if b[4] < args.vis_thres: continue text = "{:.4f}".format(b[4]) b = list(map(int, b)) cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2) cx = b[0] cy = b[1] + 12 cv2.putText(img_raw, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255)) # landms cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4) cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4) cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4) cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4) cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4) # save image if not os.path.exists("./results/"): os.makedirs("./results/") name = "./results/" + str(i) + ".jpg" cv2.imwrite(name, img_raw) fw.close()