Python env.get_py_bin_ext方法代码示例

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_retinanet_on_dataset(num_images, output_dir, dataset):
    """
    If doing multi-gpu testing, we need to divide the data on various gpus and
    make the subprocess call for each child process that'll run test_retinanet()
    on its subset data. After all the subprocesses finish, we combine the results
    and return
    """
    # 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)

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

    # Combine the results from each subprocess now
    boxes, scores, classes, image_ids = [], [], [], []
    for det_data in outputs:
        boxes.extend(det_data['boxes'])
        scores.extend(det_data['scores'])
        classes.extend(det_data['classes'])
        image_ids.extend(det_data['ids'])
    return boxes, scores, classes, image_ids, 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(
        args, 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, args.test_net_file + 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)]
    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().
    tag = 'discovery' if 'train' in dataset_name else 'detection'
    outputs = subprocess_utils.process_in_parallel(
        tag, num_images, binary, output_dir,
        args.load_ckpt, args.load_detectron, opts
    )

    # Collate the results from each subprocess
    all_boxes = {}
    for det_data in outputs:
        all_boxes_batch = det_data['all_boxes']
        all_boxes.update(all_boxes_batch)
    if 'train' in dataset_name:
        det_file = os.path.join(output_dir, 'discovery.pkl')
    else:
        det_file = os.path.join(output_dir, 'detections.pkl')
    cfg_yaml = yaml.dump(cfg)
    save_object(
        dict(
            all_boxes=all_boxes,
            cfg=cfg_yaml
        ), det_file
    )
    logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))

    return all_boxes 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_generate_rpn_on_dataset(
    args, 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, args.test_net_file + 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)]

    # Run inference in parallel in subprocesses
    outputs = subprocess_utils.process_in_parallel(
        'rpn_proposals', num_images, binary, output_dir,
        args.load_ckpt, args.load_detectron, 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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(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)

    # 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
    )

    # 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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_generate_rpn_on_dataset(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)

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

    # 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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(
        args, 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, args.test_net_file + 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)]
    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,
        args.load_ckpt, args.load_detectron, 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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
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 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(
        args, 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, args.test_net_file + 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)]
    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,
        args.load_ckpt, args.load_detectron, args.net_name, args.mlp_head_dim, 
        args.heatmap_kernel_size, args.part_crop_size, args.use_kps17,
        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)]
    all_hois = {}
    all_losses = defaultdict(list)
    all_keyps_vcoco = [[] 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']
        all_hois = {**all_hois, **det_data['all_hois']}
        for k, v in det_data['all_losses'].items():
            all_losses[k].extend(v)

        all_keyps_vcoco_batch = det_data['all_keyps_vcoco']
        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]
            all_keyps_vcoco[cls_idx] += all_keyps_vcoco_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,
            all_hois=all_hois,
            all_keyps_vcoco=all_keyps_vcoco,
            all_losses=all_losses,
            cfg=cfg_yaml
        ), det_file
    )
    logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))

    return all_boxes, all_segms, all_keyps, all_hois, all_keyps_vcoco, all_losses 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(
        args, 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, args.test_net_file + 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)]
    if proposal_file:
        opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]
        
    if args.do_val:
        opts += ['--do_val']
    if args.use_gt_boxes:
        opts += ['--use_gt_boxes']
        
    if args.use_gt_labels:
        opts += ['--use_gt_labels']

    # 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(
        'rel_detection', num_images, binary, output_dir,
        args.load_ckpt, args.load_detectron, opts
    )

    # Collate the results from each subprocess
    all_results = []
    for det_data in outputs:
        all_results += det_data
    
    if args.use_gt_boxes:
        if args.use_gt_labels:
            det_file = os.path.join(args.output_dir, 'rel_detections_gt_boxes_prdcls.pkl')
        else:
            det_file = os.path.join(args.output_dir, 'rel_detections_gt_boxes_sgcls.pkl')
    else:
        det_file = os.path.join(args.output_dir, 'rel_detections.pkl')
    save_object(all_results, det_file)
    logger.info('Wrote rel_detections to: {}'.format(os.path.abspath(det_file)))

    return all_results 

# 需要导入模块: from utils import env [as 别名]
# 或者: from utils.env import get_py_bin_ext [as 别名]
def multi_gpu_test_net_on_dataset(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)

    # 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
    )

    # 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)]
    all_rois = []
    for det_data in outputs:
        all_rois.extend(det_data['roidb'])
        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,
            all_rois=all_rois
        ), det_file
    )
    logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))

    return all_boxes, all_segms, all_keyps