f.write('{:s}\n'.format(event[0][0] + '/' + im_name + '.jpg')) f.write('{:d}\n'.format(det.shape[0])) for i in range(det.shape[0]): xmin = det[i][0] ymin = det[i][1] xmax = det[i][2] ymax = det[i][3] score = det[i][4] f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format( xmin, ymin, (xmax - xmin + 1), (ymax - ymin + 1), score)) if __name__ == '__main__': # net and model if args.net == "mv2": net = S3FD_MV2(phase='test', size=None, num_classes=2) # initialize detector elif args.net == "FairNAS_A": net = S3FD_FairNAS_A(phase='test', size=None, num_classes=2) elif args.net == "FairNAS_B": net = S3FD_FairNAS_B(phase='test', size=None, num_classes=2) net = load_model(net, args.trained_model) net.eval() print('Finished loading model!') print(net) print(args.cuda) if args.cuda: net = net.cuda() cudnn.benchmark = True else: net = net.cpu() """Detect object classes in an image using pre-computed object proposals."""
sys.stdout = Logger(os.path.join(args.save_folder, 'log.txt')) # TensorBoardX summary = TensorboardSummary(args.save_folder) writer = summary.create_summary() img_dim = 640 rgb_means = (104, 117, 123) num_classes = 2 batch_size = args.batch_size weight_decay = args.weight_decay gamma = args.gamma momentum = args.momentum if args.net == 'vgg16': net = S3FD('train', img_dim, num_classes) elif args.net == 'mv2': net = S3FD_MV2('train', img_dim, num_classes) print("Printing net...") print(net) '''if os.path.isfile(args.pretrained): vgg_weights = torch.load(args.pretrained) print('Loading VGG network...') net.vgg.load_state_dict(vgg_weights)''' if args.resume_net is not None: print('Loading resume network...') state_dict = torch.load(args.resume_net) # create new OrderedDict that does not contain `module.` from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): head = k[:7]