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if args.cfg_file is not None:
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cfg_from_file(args.cfg_file)
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if args.set_cfgs is not None:
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cfg_from_list(args.set_cfgs)
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print('Using config:')
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pprint.pprint(cfg)
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cfg.TRAIN.USE_FLIPPED = False
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imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False)
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imdb._devkit_path = imdb._devkit_path + '/VOC2007'
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imdb.competition_mode(on=True)
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print('{:d} roidb entries'.format(len(roidb)))
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input_dir = args.load_dir + "/" + args.arch + "/" + args.net + "/" + args.dataset
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if not os.path.exists(input_dir):
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raise Exception('There is no input directory for loading network from ' + input_dir)
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load_name = '/home/user/JISOO/R-FCN.pytorch-master/save/rfcn/res101/pascal_voc_0712_semi/faster_rcnn_1_28_10021.pth'
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#os.path.join(input_dir,
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# 'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, #args.checkpoint))
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# initilize the network here.
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if args.net == 'vgg16':
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fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic)
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elif args.net == 'res101':
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fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
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elif args.net == 'res50':
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fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
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elif args.net == 'res152':
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fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic)
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else:
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print("network is not defined")
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pdb.set_trace()
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fasterRCNN.create_architecture()
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print("load checkpoint %s" % (load_name))
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checkpoint = torch.load(load_name)
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fasterRCNN.load_state_dict(checkpoint['model'])
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if 'pooling_mode' in checkpoint.keys():
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cfg.POOLING_MODE = checkpoint['pooling_mode']
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print('load model successfully!')
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# initilize the tensor holder here.
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im_data = torch.FloatTensor(1)
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im_info = torch.FloatTensor(1)
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num_boxes = torch.LongTensor(1)
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gt_boxes = torch.FloatTensor(1)
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# ship to cuda
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if args.cuda:
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im_data = im_data.cuda()
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im_info = im_info.cuda()
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num_boxes = num_boxes.cuda()
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gt_boxes = gt_boxes.cuda()
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# make variable
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im_data = Variable(im_data, volatile=True)
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im_info = Variable(im_info, volatile=True)
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num_boxes = Variable(num_boxes, volatile=True)
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gt_boxes = Variable(gt_boxes, volatile=True)
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if args.cuda:
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cfg.CUDA = True
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if args.cuda:
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fasterRCNN.cuda()
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start = time.time()
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max_per_image = 100
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vis = args.vis
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if vis:
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thresh = 0.05
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else:
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thresh = 0.0
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save_name = 'faster_rcnn_10'
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num_images = len(imdb.image_index)
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all_boxes = [[[] for _ in xrange(num_images)]
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for _ in xrange(imdb.num_classes)]
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output_dir = get_output_dir(imdb, save_name)
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dataset = roibatchLoader(roidb, ratio_list, ratio_index, args.batch_size, \
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imdb.num_classes, training=False, normalize = False)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
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shuffle=False, num_workers=0,
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pin_memory=True)
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data_iter = iter(dataloader)
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_t = {'im_detect': time.time(), 'misc': time.time()}
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det_file = os.path.join(output_dir, 'detections.pkl')
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fasterRCNN.eval()
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empty_array = np.transpose(np.array([[],[],[],[],[]]), (1,0))
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