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# Init the test dataloader.
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test_dataloader = torch.utils.data.DataLoader(
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test_dataset,
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batch_size=1,
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shuffle=True,
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num_workers=4,
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collate_fn=collate_fn,
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)
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dataset_name = '{}_sem_seg_{}'.format(cfg.data.dataset_name, cfg.test.split)
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metadata = MetadataCatalog.get(dataset_name)
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result_dir = os.path.join(output_dir, '{:0>4}_eval_on_{}'.format(stats.epoch, cfg.data.dataset_name))
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print(result_dir)
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evaluator = SemSegEvaluator(dataset_name, output_dir=result_dir)
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evaluator.reset()
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pth_list = os.listdir(result_dir)
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conf_matrix_list = []
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predictions = []
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depth_metrics = {
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'absolute': [],
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'absrel': [],
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'thres 1.25': [],
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'thres 1.25^2': [],
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'thres 1.25^3': [],
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}
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print("loading results...")
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for result_fname in tqdm(pth_list):
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if not result_fname.startswith('results_'):
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continue
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f = open(os.path.join(result_dir, result_fname), 'rb')
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gpu_results = pickle.load(f)
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conf_matrix_list.append(gpu_results['conf_matrix'])
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predictions.append(gpu_results['predictions'])
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gpu_depth_metrics = gpu_results['depth_metrics']
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print("aggregating...")
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evaluator._predictions = list(itertools.chain(*predictions))
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conf_matrix = np.zeros_like(conf_matrix_list[0])
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for gpu_conf_matrix in conf_matrix_list:
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conf_matrix += gpu_conf_matrix
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evaluator._conf_matrix = conf_matrix
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print("[detectron2 evaluation]")
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results = evaluator.evaluate()
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print("treating objects as a single class")
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print("[object and stuff evaluation]")
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# build a new conf matrix
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# stuff object
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# -----------------
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# stuff | | |
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# | ------|-------|
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# object | | |
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# -----------------
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conf_matrix_os = np.zeros_like(conf_matrix[:-1, :-1])
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stuff_list = [0, 1, 21]
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for gt_idx in range(conf_matrix_os.shape[0]):
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for pred_idx in range(conf_matrix_os.shape[1]):
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if gt_idx in stuff_list and pred_idx in stuff_list: # stuff tp
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conf_matrix_os[0][0] += conf_matrix[gt_idx][pred_idx]
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elif gt_idx in stuff_list and pred_idx not in stuff_list:
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conf_matrix_os[0][1] += conf_matrix[gt_idx][pred_idx]
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elif gt_idx not in stuff_list and pred_idx not in stuff_list:
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conf_matrix_os[1][1] += conf_matrix[gt_idx][pred_idx]
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elif gt_idx not in stuff_list and pred_idx in stuff_list:
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conf_matrix_os[1][0] += conf_matrix[gt_idx][pred_idx]
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else:
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raise ValueError("should not reach this point")
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evaluator._conf_matrix[:-1, :-1] = conf_matrix_os
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results = evaluator.evaluate()
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print(results)
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print("[depth metrics]")
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depth_metrics = {
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'all': {
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'absolute': {'cnt': 0, 'total': 0},
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'absrel': {'cnt': 0, 'total': 0},
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'thres 1.25': {'cnt': 0, 'total': 0},
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'thres 1.25^2': {'cnt': 0, 'total': 0},
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'thres 1.25^3': {'cnt': 0, 'total': 0},
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},
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'stuff': {
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'absolute': {'cnt': 0, 'total': 0},
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'absrel': {'cnt': 0, 'total': 0},
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'thres 1.25': {'cnt': 0, 'total': 0},
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'thres 1.25^2': {'cnt': 0, 'total': 0},
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'thres 1.25^3': {'cnt': 0, 'total': 0},
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},
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'object': {
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'absolute': {'cnt': 0, 'total': 0},
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'absrel': {'cnt': 0, 'total': 0},
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'thres 1.25': {'cnt': 0, 'total': 0},
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'thres 1.25^2': {'cnt': 0, 'total': 0},
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'thres 1.25^3': {'cnt': 0, 'total': 0},
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},
|
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