Python mmcv.is_str方法代碼示例

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.

    Args:
        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
            be converted.
    """

    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError(f'type {type(data)} cannot be converted to tensor.') 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def _build_module(cfg, registry, default_args):
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        if obj_type not in registry.module_dict:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
        obj_type = registry.module_dict[obj_type]
    elif not isinstance(obj_type, type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data))) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def build_from_cfg(cfg, registry, default_args=None):
    """ build a module from config dict
    Args:
        cfg (dict): Config dict. It should at least contain the key "type".
        registry (:obj:`Registry`): The registry to search the type from.
        default_args (dict, optional): Default initialization arguments.
    Returns:
        obj: The constructed object.
    """
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        obj_type = registry.get(obj_type)
        if obj_type is None:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
    elif not inspect.isclass(obj_type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.
    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data))) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def albu_builder(self, cfg):
        """Import a module from albumentations.

        It inherits some of :func:`build_from_cfg` logic.

        Args:
            cfg (dict): Config dict. It should at least contain the key "type".

        Returns:
            obj: The constructed object.
        """

        assert isinstance(cfg, dict) and 'type' in cfg
        args = cfg.copy()

        obj_type = args.pop('type')
        if mmcv.is_str(obj_type):
            if albumentations is None:
                raise RuntimeError('albumentations is not installed')
            obj_cls = getattr(albumentations, obj_type)
        elif inspect.isclass(obj_type):
            obj_cls = obj_type
        else:
            raise TypeError(
                f'type must be a str or valid type, but got {type(obj_type)}')

        if 'transforms' in args:
            args['transforms'] = [
                self.albu_builder(transform)
                for transform in args['transforms']
            ]

        return obj_cls(**args) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def get_classes(dataset):
    """Get class names of a dataset."""
    alias2name = {}
    for name, aliases in dataset_aliases.items():
        for alias in aliases:
            alias2name[alias] = name

    if mmcv.is_str(dataset):
        if dataset in alias2name:
            labels = eval(alias2name[dataset] + '_classes()')
        else:
            raise ValueError(f'Unrecognized dataset: {dataset}')
    else:
        raise TypeError(f'dataset must a str, but got {type(dataset)}')
    return labels 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def coco_eval_with_return(result_files,
                          result_types,
                          coco,
                          max_dets=(100, 300, 1000)):
    for res_type in result_types:
        assert res_type in ['proposal', 'bbox', 'segm', 'keypoints']

    if mmcv.is_str(coco):
        coco = COCO(coco)
    assert isinstance(coco, COCO)

    eval_results = {}
    for res_type in result_types:
        result_file = result_files[res_type]
        assert result_file.endswith('.json')

        coco_dets = coco.loadRes(result_file)
        img_ids = coco.getImgIds()
        iou_type = 'bbox' if res_type == 'proposal' else res_type
        cocoEval = COCOeval(coco, coco_dets, iou_type)
        cocoEval.params.imgIds = img_ids
        if res_type == 'proposal':
            cocoEval.params.useCats = 0
            cocoEval.params.maxDets = list(max_dets)
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()
        if res_type == 'segm' or res_type == 'bbox':
            metric_names = [
                'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10',
                'AR100', 'ARs', 'ARm', 'ARl'
            ]
            eval_results[res_type] = {
                metric_names[i]: cocoEval.stats[i]
                for i in range(len(metric_names))
            }
        else:
            eval_results[res_type] = cocoEval.stats

    return eval_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def build_from_cfg(cfg, registry, default_args=None):
    """Build a module from config dict.

    Args:
        cfg (dict): Config dict. It should at least contain the key "type".
        registry (:obj:`Registry`): The registry to search the type from.
        default_args (dict, optional): Default initialization arguments.

    Returns:
        obj: The constructed object.
    """
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        obj_type = registry.get(obj_type)
        if obj_type is None:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
    elif not inspect.isclass(obj_type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def get_classes(dataset):
    """Get class names of a dataset."""
    alias2name = {}
    for name, aliases in dataset_aliases.items():
        for alias in aliases:
            alias2name[alias] = name

    if mmcv.is_str(dataset):
        if dataset in alias2name:
            labels = eval(alias2name[dataset] + '_classes()')
        else:
            raise ValueError('Unrecognized dataset: {}'.format(dataset))
    else:
        raise TypeError('dataset must a str, but got {}'.format(type(dataset)))
    return labels 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import is_str [as 別名]
def coco_eval(result_file, result_types, coco, max_dets=(100, 300, 1000)):
    for res_type in result_types:
        assert res_type in [
            'proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'
        ]

    if mmcv.is_str(coco):
        coco = COCO(coco)
    assert isinstance(coco, COCO)

    if result_types == ['proposal_fast']:
        ar = fast_eval_recall(result_file, coco, np.array(max_dets))
        for i, num in enumerate(max_dets):
            print('AR@{}\t= {:.4f}'.format(num, ar[i]))
        return

    assert result_file.endswith('.json')
    coco_dets = coco.loadRes(result_file)

    img_ids = coco.getImgIds()
    for res_type in result_types:
        iou_type = 'bbox' if res_type == 'proposal' else res_type
        cocoEval = COCOeval(coco, coco_dets, iou_type)
        cocoEval.params.imgIds = img_ids
        if res_type == 'proposal':
            cocoEval.params.useCats = 0
            cocoEval.params.maxDets = list(max_dets)
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()