Python _six.int_classes方法代码示例

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def collate(self, batch):
        elem = batch[0]
        if isinstance(elem, Data):
            return Batch.from_data_list(batch, self.follow_batch)
        elif isinstance(elem, torch.Tensor):
            return default_collate(batch)
        elif isinstance(elem, float):
            return torch.tensor(batch, dtype=torch.float)
        elif isinstance(elem, int_classes):
            return torch.tensor(batch)
        elif isinstance(elem, string_classes):
            return batch
        elif isinstance(elem, container_abcs.Mapping):
            return {key: self.collate([d[key] for d in batch]) for key in elem}
        elif isinstance(elem, tuple) and hasattr(elem, '_fields'):
            return type(elem)(*(self.collate(s) for s in zip(*batch)))
        elif isinstance(elem, container_abcs.Sequence):
            return [self.collate(s) for s in zip(*batch)]

        raise TypeError('DataLoader found invalid type: {}'.format(type(elem))) 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def fast_batch_collator(batched_inputs):
    """
    A simple batch collator for most common reid tasks
    """
    elem = batched_inputs[0]
    if isinstance(elem, torch.Tensor):
        out = torch.zeros((len(batched_inputs), *elem.size()), dtype=elem.dtype)
        for i, tensor in enumerate(batched_inputs):
            out[i] += tensor
        return out

    elif isinstance(elem, container_abcs.Mapping):
        return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}

    elif isinstance(elem, float):
        return torch.tensor(batched_inputs, dtype=torch.float64)
    elif isinstance(elem, int_classes):
        return torch.tensor(batched_inputs)
    elif isinstance(elem, string_classes):
        return batched_inputs 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def concatenate_cache(batch):
    r"""Puts each data field into a tensor with outer dimension batch size"""
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        out = None
        return torch.cat(batch, 0, out=out)  # the main difference is here
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        elem = batch[0]
        if elem_type.__name__ == 'ndarray':
            # array of string classes and object
            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
                raise TypeError(error_msg_fmt.format(elem.dtype))
            return concatenate_cache([torch.from_numpy(b) for b in batch])
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], float):
        return torch.tensor(batch, dtype=torch.float64)
    elif isinstance(batch[0], int_classes):
        return torch.tensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], container_abcs.Mapping):
        return {key: concatenate_cache([d[key] for d in batch])
                for key in batch[0]}
    elif isinstance(batch[0], tuple) and hasattr(batch[0], '_fields'):
        return type(batch[0])(*(concatenate_cache(samples)
                                for samples in zip(*batch)))
    elif isinstance(batch[0], container_abcs.Sequence):  # also some diffs here
        # just unpack
        return [s_ for s in batch for s_ in s]

    raise TypeError((error_msg_fmt.format(type(batch[0])))) 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def default_collate(batch):
    "Puts each data field into a tensor with outer dimension batch size"

    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if torch.is_tensor(batch[0]):
        out = None
        if _use_shared_memory:
            # If we're in a background process, concatenate directly into a
            # shared memory tensor to avoid an extra copy
            numel = sum([x.numel() for x in batch])
            storage = batch[0].storage()._new_shared(numel)
            out = batch[0].new(storage)
        return torch.stack(batch, 0, out=out)
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        elem = batch[0]
        if elem_type.__name__ == 'ndarray':
            # array of string classes and object
            if re.search('[SaUO]', elem.dtype.str) is not None:
                raise TypeError(error_msg.format(elem.dtype))

            return torch.stack([torch.from_numpy(b) for b in batch], 0)
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], int_classes):
        return torch.LongTensor(batch)
    elif isinstance(batch[0], float):
        return torch.DoubleTensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], collections.Mapping):
        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], collections.Sequence):
        transposed = zip(*batch)
        return [default_collate(samples) for samples in transposed]

    raise TypeError((error_msg.format(type(batch[0])))) 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def default_collate(batch):
    """Puts each data field into a tensor with outer dimension batch size"""

    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        return torch.stack(batch, 0)
    elif (
        elem_type.__module__ == "numpy"
        and elem_type.__name__ != "str_"
        and elem_type.__name__ != "string_"
    ):  # pragma: no cover
        elem = batch[0]
        if elem_type.__name__ == "ndarray":
            return torch.stack([torch.from_numpy(b) for b in batch], 0)
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith("float") else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], int_classes):  # pragma: no cover
        return torch.LongTensor(batch)
    elif isinstance(batch[0], float):  # pragma: no cover
        return torch.DoubleTensor(batch)
    elif isinstance(batch[0], string_classes):  # pragma: no cover
        return batch
    elif isinstance(batch[0], container_abcs.Mapping):  # pragma: no cover
        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], container_abcs.Sequence):  # pragma: no cover
        transposed = zip(*batch)
        return [default_collate(samples) for samples in transposed]

    raise TypeError((error_msg.format(type(batch[0])))) 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def mt_collate(batch):
    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if torch.is_tensor(batch[0]):
        stacked = torch.stack(batch, 0)
        return stacked
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        elem = batch[0]
        if elem_type.__name__ == 'ndarray':
            # array of string classes and object
            if re.search('[SaUO]', elem.dtype.str) is not None:
                raise TypeError(error_msg.format(elem.dtype))
            return torch.stack([torch.from_numpy(b) for b in batch], 0)
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return __numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], int_classes):
        return torch.LongTensor(batch)
    elif isinstance(batch[0], float):
        return torch.DoubleTensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], collections.Mapping):
        return {key: mt_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], collections.Sequence):
        transposed = zip(*batch)
        return [mt_collate(samples) for samples in transposed]

    return batch 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def __init__(self, sampler, batch_size, drop_last):
        if not isinstance(sampler, torch_sampler.Sampler):
            raise ValueError("sampler should be an instance of "
                             "torch.utils.data.Sampler, but got sampler={}"
                             .format(sampler))
        if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
                batch_size <= 0:
            raise ValueError("batch_size should be a positive integeral value, "
                             "but got batch_size={}".format(batch_size))
        if not isinstance(drop_last, bool):
            raise ValueError("drop_last should be a boolean value, but got "
                             "drop_last={}".format(drop_last))
        self.sampler = sampler
        self.batch_size = batch_size
        self.drop_last = drop_last 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def __init__(self, weights, num_samples=None, replacement=True):
        # NOTE: Adapted `WeightedRandomSampler` to accept `num_samples=0` and `num_samples=None`.
        if num_samples is None:
            num_samples = len(weights)
        if not isinstance(num_samples, _int_classes) or isinstance(num_samples, bool) or \
                num_samples < 0:
            raise ValueError("num_samples should be a positive integer "
                             "value, but got num_samples={}".format(num_samples))
        if not isinstance(replacement, bool):
            raise ValueError("replacement should be a boolean value, but got "
                             "replacement={}".format(replacement))
        self.weights = torch.as_tensor(weights, dtype=torch.double)
        self.num_samples = num_samples
        self.replacement = replacement 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def collate(batch, *, root=True):
    "Puts each data field into a tensor with outer dimension batch size"

    if len(batch) == 0:
        return batch

    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if torch.is_tensor(batch[0]):
        return default_collate(batch)
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        return default_collate(batch)
    elif isinstance(batch[0], int_classes):
        return batch
    elif isinstance(batch[0], float):
        return batch
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], CameraIntrinsics):
        return batch
    elif isinstance(batch[0], Mapping):
        if root:
            return {key: collate([d[key] for d in batch], root=False) for key in batch[0]}
        else:
            return batch
    elif isinstance(batch[0], Sequence):
        return [collate(e, root=False) for e in batch]

    raise TypeError((error_msg.format(type(batch[0])))) 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def __init__(self, weights, num_samples, replacement=True):
        if not isinstance(num_samples, _int_classes) or isinstance(num_samples, bool) or \
                num_samples <= 0:
            raise ValueError("num_samples should be a positive integeral "
                             "value, but got num_samples={}".format(num_samples))
        if not isinstance(replacement, bool):
            raise ValueError("replacement should be a boolean value, but got "
                             "replacement={}".format(replacement))
        self.weights = weights / weights.sum()
        self.num_samples = num_samples
        self.replacement = replacement 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def __init__(self, sampler, batch_size, drop_last, cfg):
        if not isinstance(sampler, Sampler):
            raise ValueError(
                "sampler should be an instance of "
                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
            )
        if (
            not isinstance(batch_size, _int_classes)
            or isinstance(batch_size, bool)
            or batch_size <= 0
        ):
            raise ValueError(
                "batch_size should be a positive integer value, "
                "but got batch_size={}".format(batch_size)
            )
        if not isinstance(drop_last, bool):
            raise ValueError(
                "drop_last should be a boolean value, but got "
                "drop_last={}".format(drop_last)
            )
        self.sampler = sampler
        self.drop_last = drop_last

        bs_factor = [
            int(
                round(
                    (
                        float(cfg.DATA.TRAIN_CROP_SIZE)
                        / (s * cfg.MULTIGRID.DEFAULT_S)
                    )
                    ** 2
                )
            )
            for s in cfg.MULTIGRID.SHORT_CYCLE_FACTORS
        ]

        self.batch_sizes = [
            batch_size * bs_factor[0],
            batch_size * bs_factor[1],
            batch_size,
        ] 

# 需要导入模块: from torch import _six [as 别名]
# 或者: from torch._six import int_classes [as 别名]
def __init__(self, sampler, batch_size, max_iteration=100000000, drop_last=True):
        """
        数据加载,默认循环加载1亿次,几近无限迭代.
        每次迭代输出一个批次的数据.
        :param sampler:         采样器，传入 不同采样器 实现 不同的采样策略，    RandomSampler随机采样，SequentialSampler顺序采样
        :param batch_size:      批次大小
        :param max_iteration:   迭代次数
        :param drop_last:       是否弃掉最后的不够一批次的数据。True则弃掉；False保留，并返回，但是这一批次会小于指定批次大小。
        """
        if not isinstance(sampler, Sampler):
            raise ValueError("sampler should be an instance of "
                             "torch.utils.data.Sampler, but got sampler={}"
                             .format(sampler))
        if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
                batch_size <= 0:
            raise ValueError("batch_size should be a positive integer value, "
                             "but got batch_size={}".format(batch_size))
        if not isinstance(max_iteration, _int_classes) or isinstance(max_iteration, bool) or \
                max_iteration <= 0:
            raise ValueError("max_iter should be a positive integer value, "
                             "but got max_iter={}".format(max_iteration))

        if not isinstance(drop_last, bool):
            raise ValueError("drop_last should be a boolean value, but got "
                             "drop_last={}".format(drop_last))
        self.sampler = sampler
        self.batch_size = batch_size
        self.max_iteration = max_iteration
        self.drop_last = drop_last