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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9644184 * fix ditributed training and eval
144 lines
4.3 KiB
Python
144 lines
4.3 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import time
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from collections.abc import Sequence
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from typing import Mapping
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import numpy as np
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import torch
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from .builder import PREPROCESSORS, build_preprocessor
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@PREPROCESSORS.register_module()
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class Compose(object):
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"""Compose a data pipeline with a sequence of transforms.
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Args:
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transforms (list[dict | callable]):
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Either config dicts of transforms or transform objects.
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profiling (bool, optional): If set True, will profile and
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print preprocess time for each step.
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"""
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def __init__(self, transforms, field_name=None, profiling=False):
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assert isinstance(transforms, Sequence)
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self.profiling = profiling
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self.transforms = []
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self.field_name = field_name
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for transform in transforms:
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if isinstance(transform, dict):
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if self.field_name is None:
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transform = build_preprocessor(transform, field_name)
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else:
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# if not found key in field_name, try field_name=None(default_group)
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try:
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transform = build_preprocessor(transform, field_name)
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except KeyError:
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transform = build_preprocessor(transform, None)
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elif callable(transform):
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pass
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else:
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raise TypeError('transform must be callable or a dict, but got'
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f' {type(transform)}')
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self.transforms.append(transform)
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def __call__(self, data):
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for t in self.transforms:
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if self.profiling:
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start = time.time()
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data = t(data)
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if self.profiling:
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print(f'{t} time {time.time()-start}')
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if data is None:
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return None
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return data
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def __repr__(self):
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format_string = self.__class__.__name__ + '('
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for t in self.transforms:
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format_string += f'\n {t}'
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format_string += '\n)'
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return format_string
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def to_tensor(data):
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"""Convert objects of various python types to :obj:`torch.Tensor`.
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Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
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:class:`Sequence`, :class:`int` and :class:`float`.
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Args:
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data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
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be converted.
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"""
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if isinstance(data, torch.Tensor):
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return data
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elif isinstance(data, np.ndarray):
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return torch.from_numpy(data)
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elif isinstance(data, Sequence) and not isinstance(data, str):
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return torch.tensor(data)
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elif isinstance(data, int):
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return torch.LongTensor([data])
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elif isinstance(data, float):
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return torch.FloatTensor([data])
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else:
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raise TypeError(f'type {type(data)} cannot be converted to tensor.')
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@PREPROCESSORS.register_module()
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class ToTensor(object):
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"""Convert target object to tensor.
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Args:
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keys (Sequence[str]): Key of data to be converted to Tensor.
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Only valid when data is type of `Mapping`. If `keys` is None,
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all values of keys will be converted to tensor by default.
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"""
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def __init__(self, keys=None):
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self.keys = keys
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def __call__(self, data):
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if isinstance(data, Mapping):
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if self.keys is None:
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self.keys = list(data.keys())
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for key in self.keys:
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data[key] = to_tensor(data[key])
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else:
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data = to_tensor(data)
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return data
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def __repr__(self):
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return self.__class__.__name__ + f'(keys={self.keys})'
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@PREPROCESSORS.register_module()
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class Filter(object):
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"""This is usually the last stage of the dataloader transform.
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Only data of reserved keys will be kept and passed directly to the model, others will be removed.
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Args:
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keys (Sequence[str]): Keys of data to be reserved, others will be removed.
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"""
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def __init__(self, reserved_keys):
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self.reserved_keys = reserved_keys
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def __call__(self, data):
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assert isinstance(data, Mapping)
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reserved_data = {}
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for key in self.reserved_keys:
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reserved_data[key] = data[key]
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return reserved_data
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def __repr__(self):
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return self.__class__.__name__ + f'(keys={self.reserved_keys})'
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