Python env.get_py_bin_ext方法代碼示例

本文整理匯總了Python中detectron.utils.env.get_py_bin_ext方法的典型用法代碼示例。如果您正苦於以下問題：Python env.get_py_bin_ext方法的具體用法？Python env.get_py_bin_ext怎麽用？Python env.get_py_bin_ext使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類detectron.utils.env的用法示例。

在下文中一共展示了env.get_py_bin_ext方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: multi_gpu_generate_rpn_on_dataset

# 需要導入模塊: from detectron.utils import env [as 別名]
# 或者: from detectron.utils.env import get_py_bin_ext [as 別名]
def multi_gpu_generate_rpn_on_dataset(
    weights_file, dataset_name, _proposal_file_ignored, num_images, output_dir
):
    """Multi-gpu inference on a dataset."""
    # Retrieve the test_net binary path
    binary_dir = envu.get_runtime_dir()
    binary_ext = envu.get_py_bin_ext()
    binary = os.path.join(binary_dir, 'test_net' + binary_ext)
    assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)

    # Pass the target dataset via the command line
    opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
    opts += ['TEST.WEIGHTS', weights_file]

    # Run inference in parallel in subprocesses
    outputs = subprocess_utils.process_in_parallel(
        'rpn_proposals', num_images, binary, output_dir, opts
    )

    # Collate the results from each subprocess
    boxes, scores, ids = [], [], []
    for rpn_data in outputs:
        boxes += rpn_data['boxes']
        scores += rpn_data['scores']
        ids += rpn_data['ids']
    rpn_file = os.path.join(output_dir, 'rpn_proposals.pkl')
    cfg_yaml = envu.yaml_dump(cfg)
    save_object(
        dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
    )
    logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
    return boxes, scores, ids, rpn_file

開發者ID:yihui-he，項目名稱:KL-Loss，代碼行數:34，代碼來源:rpn_generator.py

示例2: multi_gpu_generate_rpn_on_dataset

# 需要導入模塊: from detectron.utils import env [as 別名]
# 或者: from detectron.utils.env import get_py_bin_ext [as 別名]
def multi_gpu_generate_rpn_on_dataset(
    weights_file, dataset_name, _proposal_file_ignored, num_images, output_dir
):
    """Multi-gpu inference on a dataset."""
    # Retrieve the test_net binary path
    binary_dir = envu.get_runtime_dir()
    binary_ext = envu.get_py_bin_ext()
    binary = os.path.join(binary_dir, 'test_net' + binary_ext)
    assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)

    # Pass the target dataset via the command line
    opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
    opts += ['TEST.WEIGHTS', weights_file]

    # Run inference in parallel in subprocesses
    outputs = subprocess_utils.process_in_parallel(
        'rpn_proposals', num_images, binary, output_dir, opts
    )

    # Collate the results from each subprocess
    boxes, scores, ids = [], [], []
    for rpn_data in outputs:
        boxes += rpn_data['boxes']
        scores += rpn_data['scores']
        ids += rpn_data['ids']
    rpn_file = os.path.join(output_dir, 'rpn_proposals.pkl')
    cfg_yaml = yaml.dump(cfg)
    save_object(
        dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
    )
    logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
    return boxes, scores, ids, rpn_file

開發者ID:fyangneil，項目名稱:Clustered-Object-Detection-in-Aerial-Image，代碼行數:34，代碼來源:rpn_generator.py

示例3: multi_gpu_test_net_on_dataset

# 需要導入模塊: from detectron.utils import env [as 別名]
# 或者: from detectron.utils.env import get_py_bin_ext [as 別名]
def multi_gpu_test_net_on_dataset(
    weights_file, dataset_name, proposal_file, num_images, output_dir
):
    """Multi-gpu inference on a dataset."""
    binary_dir = envu.get_runtime_dir()
    binary_ext = envu.get_py_bin_ext()
    binary = os.path.join(binary_dir, 'test_net' + binary_ext)
    assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)

    # Pass the target dataset and proposal file (if any) via the command line
    opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
    opts += ['TEST.WEIGHTS', weights_file]
    if proposal_file:
        opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]

    # Run inference in parallel in subprocesses
    # Outputs will be a list of outputs from each subprocess, where the output
    # of each subprocess is the dictionary saved by test_net().
    outputs = subprocess_utils.process_in_parallel(
        'detection', num_images, binary, output_dir, opts
    )

    # Collate the results from each subprocess
    all_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    all_segms = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    all_keyps = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    for det_data in outputs:
        all_boxes_batch = det_data['all_boxes']
        all_segms_batch = det_data['all_segms']
        all_keyps_batch = det_data['all_keyps']
        for cls_idx in range(1, cfg.MODEL.NUM_CLASSES):
            all_boxes[cls_idx] += all_boxes_batch[cls_idx]
            all_segms[cls_idx] += all_segms_batch[cls_idx]
            all_keyps[cls_idx] += all_keyps_batch[cls_idx]
    det_file = os.path.join(output_dir, 'detections.pkl')
    cfg_yaml = envu.yaml_dump(cfg)
    save_object(
        dict(
            all_boxes=all_boxes,
            all_segms=all_segms,
            all_keyps=all_keyps,
            cfg=cfg_yaml
        ), det_file
    )
    logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))

    return all_boxes, all_segms, all_keyps

開發者ID:yihui-he，項目名稱:KL-Loss，代碼行數:49，代碼來源:test_engine.py

示例4: multi_gpu_test_net_on_dataset

# 需要導入模塊: from detectron.utils import env [as 別名]
# 或者: from detectron.utils.env import get_py_bin_ext [as 別名]
def multi_gpu_test_net_on_dataset(
    weights_file, dataset_name, proposal_file, num_images, output_dir
):
    """Multi-gpu inference on a dataset."""
    binary_dir = envu.get_runtime_dir()
    binary_ext = envu.get_py_bin_ext()
    binary = os.path.join(binary_dir, 'test_net' + binary_ext)
    assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)

    # Pass the target dataset and proposal file (if any) via the command line
    opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
    opts += ['TEST.WEIGHTS', weights_file]
    if proposal_file:
        opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]

    # Run inference in parallel in subprocesses
    # Outputs will be a list of outputs from each subprocess, where the output
    # of each subprocess is the dictionary saved by test_net().
    outputs = subprocess_utils.process_in_parallel(
        'detection', num_images, binary, output_dir, opts
    )

    # Collate the results from each subprocess
    all_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    all_segms = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    all_keyps = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
    for det_data in outputs:
        all_boxes_batch = det_data['all_boxes']
        all_segms_batch = det_data['all_segms']
        all_keyps_batch = det_data['all_keyps']
        for cls_idx in range(1, cfg.MODEL.NUM_CLASSES):
            all_boxes[cls_idx] += all_boxes_batch[cls_idx]
            all_segms[cls_idx] += all_segms_batch[cls_idx]
            all_keyps[cls_idx] += all_keyps_batch[cls_idx]
    det_file = os.path.join(output_dir, 'detections.pkl')
    cfg_yaml = yaml.dump(cfg)
    save_object(
        dict(
            all_boxes=all_boxes,
            all_segms=all_segms,
            all_keyps=all_keyps,
            cfg=cfg_yaml
        ), det_file
    )
    logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))

    return all_boxes, all_segms, all_keyps

開發者ID:fyangneil，項目名稱:Clustered-Object-Detection-in-Aerial-Image，代碼行數:49，代碼來源:test_engine.py

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