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transform_test = transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((args.img_h,args.img_w)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
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])
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end = time.time()
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def extract_gall_feat(gall_loader):
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net.eval()
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print ('Extracting Gallery Feature...')
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start = time.time()
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ptr = 0
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gall_feat_pool = np.zeros((ngall, pool_dim))
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with torch.no_grad():
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for batch_idx, data in enumerate(gall_loader):
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input = data['img']
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batch_num = input.size(0)
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input = Variable(input.cuda())
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feat_pool = net(input, input, test_mode[0], use_cmalign=False)['feat4_p_norm']
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gall_feat_pool[ptr:ptr+batch_num,: ] = feat_pool.detach().cpu().numpy()
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ptr = ptr + batch_num
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print('Extracting Time:\t {:.3f}'.format(time.time()-start))
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return gall_feat_pool
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def extract_query_feat(query_loader):
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net.eval()
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print ('Extracting Query Feature...')
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start = time.time()
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ptr = 0
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query_feat_pool = np.zeros((nquery, pool_dim))
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with torch.no_grad():
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for batch_idx, data in enumerate(query_loader):
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input = data['img']
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batch_num = input.size(0)
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input = Variable(input.cuda())
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feat_pool = net(input, input, test_mode[1], use_cmalign=False)['feat4_p_norm']
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query_feat_pool[ptr:ptr+batch_num,: ] = feat_pool.detach().cpu().numpy()
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ptr = ptr + batch_num
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print('Extracting Time:\t {:.3f}'.format(time.time()-start))
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return query_feat_pool
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if dataset == 'sysu':
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print('==> Resuming from checkpoint..')
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model_path = osp.join(args.save_path, args.exp_name)
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model_path = osp.join(model_path, 'checkpoints/{}.t'.format(args.model_name))
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if os.path.isfile(model_path):
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print('==> loading checkpoint {} from {}'.format(args.model_name, args.exp_name))
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checkpoint = torch.load(model_path)
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net.load_state_dict(checkpoint['net'])
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print('==> loaded checkpoint')
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else:
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print('==> checkpoint {} is not found'.format(args.model_name))
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# testing set
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query_img, query_label, query_cam = process_query_sysu(data_path, mode=args.mode)
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gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=args.mode, trial=0)
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nquery = len(query_label)
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ngall = len(gall_label)
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print("Dataset statistics:")
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print(" ------------------------------")
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print(" subset | # ids | # images")
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print(" ------------------------------")
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print(" query | {:5d} | {:8d}".format(len(np.unique(query_label)), nquery))
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print(" gallery | {:5d} | {:8d}".format(len(np.unique(gall_label)), ngall))
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print(" ------------------------------")
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queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
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query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
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print('Data Loading Time:\t {:.3f}'.format(time.time() - end))
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query_feat_pool = extract_query_feat(query_loader)
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for trial in range(10):
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gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=args.mode, trial=trial)
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trial_gallset = TestData(gall_img, gall_label, transform=transform_test, img_size=(args.img_w, args.img_h))
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trial_gall_loader = data.DataLoader(trial_gallset, batch_size=args.test_batch, shuffle=False, num_workers=4)
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gall_feat_pool = extract_gall_feat(trial_gall_loader)
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distmat_pool = np.matmul(query_feat_pool, np.transpose(gall_feat_pool))
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cmc_pool, mAP_pool, mINP_pool = eval_sysu(-distmat_pool, query_label, gall_label, query_cam, gall_cam)
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if trial == 0:
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all_cmc_pool = cmc_pool
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all_mAP_pool = mAP_pool
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all_mINP_pool = mINP_pool
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else:
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all_cmc_pool = all_cmc_pool + cmc_pool
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all_mAP_pool = all_mAP_pool + mAP_pool
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all_mINP_pool = all_mINP_pool + mINP_pool
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print('Test Trial: {}'.format(trial))
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print(
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'POOL: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
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cmc_pool[0], cmc_pool[4], cmc_pool[9], cmc_pool[19], mAP_pool, mINP_pool))
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