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Copy pathdata_helper.py
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executable file
·370 lines (328 loc) · 11.5 KB
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from typing import Iterable, Any, Optional
from collections.abc import Sequence
import numbers
import time
from threading import Thread
from queue import Queue
import numpy as np
import torch
def to_device(item: Any, device: str, ignore_keys: list = []) -> Any:
r"""
Overview:
Transfer data to certain device
Arguments:
- item (:obj:`Any`): the item to be transferred
- device (:obj:`str`): the device wanted
- ignore_keys (:obj:`list`): the keys to be ignored in transfer, defalut set to empty
Returns:
- item (:obj:`Any`): the transferred item
.. note:
Now supports item type: :obj:`torch.nn.Module`, :obj:`torch.Tensor`, :obj:`Sequence`, \
:obj:`dict`, :obj:`numbers.Integral`, :obj:`numbers.Real`, :obj:`np.ndarray`, :obj:`str` and :obj:`None`.
"""
if isinstance(item, torch.nn.Module):
return item.to(device)
elif isinstance(item, torch.Tensor):
return item.to(device)
elif isinstance(item, Sequence):
if isinstance(item, str):
return item
else:
return [to_device(t, device) for t in item]
elif isinstance(item, dict):
new_item = {}
for k in item.keys():
if k in ignore_keys:
new_item[k] = item[k]
else:
new_item[k] = to_device(item[k], device)
return new_item
elif isinstance(item, numbers.Integral) or isinstance(item, numbers.Real):
return item
elif isinstance(item, np.ndarray) or isinstance(item, np.bool_):
return item
elif item is None or isinstance(item, str):
return item
else:
raise TypeError("not support item type: {}".format(type(item)))
def to_dtype(item: Any, dtype: type) -> Any:
r"""
Overview:
Change data to certain dtype
Arguments:
- item (:obj:`Any`): the item to be dtype changed
- dtype (:obj:`type`): the type wanted
Returns:
- item (:obj:`object`): the dtype changed item
.. note:
Now supports item type: :obj:`torch.Tensor`, :obj:`Sequence`, :obj:`dict`
"""
if isinstance(item, torch.Tensor):
return item.to(dtype=dtype)
elif isinstance(item, Sequence):
return [to_dtype(t, dtype) for t in item]
elif isinstance(item, dict):
return {k: to_dtype(item[k], dtype) for k in item.keys()}
else:
raise TypeError("not support item type: {}".format(type(item)))
def to_tensor(
item: Any,
dtype: Optional[torch.dtype] = None,
ignore_keys: list = [],
transform_scalar: bool = True
) -> torch.Tensor:
r"""
Overview:
Change `numpy.ndarray`, sequence of scalars to torch.Tensor, and keep other data types unchanged.
Arguments:
- item (:obj:`Any`): the item to be changed
- dtype (:obj:`type`): the type of wanted tensor
Returns:
- item (:obj:`torch.Tensor`): the change tensor
.. note:
Now supports item type: :obj:`dict`, :obj:`list`, :obj:`tuple` and :obj:`None`
"""
def transform(d):
if dtype is None:
return torch.as_tensor(d)
else:
return torch.tensor(d, dtype=dtype)
if isinstance(item, dict):
new_data = {}
for k, v in item.items():
if k in ignore_keys:
new_data[k] = v
else:
new_data[k] = to_tensor(v, dtype, ignore_keys, transform_scalar)
return new_data
elif isinstance(item, list) or isinstance(item, tuple):
if len(item) == 0:
return None
elif isinstance(item[0], numbers.Integral) or isinstance(item[0], numbers.Real):
return transform(item)
elif hasattr(item, '_fields'): # namedtuple
return type(item)(*[to_tensor(t, dtype) for t in item])
else:
new_data = []
for t in item:
new_data.append(to_tensor(t, dtype, ignore_keys, transform_scalar))
return new_data
elif isinstance(item, np.ndarray):
if dtype is None:
if item.dtype == np.float64:
return torch.FloatTensor(item)
else:
return torch.from_numpy(item)
else:
return torch.from_numpy(item).to(dtype)
elif isinstance(item, bool) or isinstance(item, str):
return item
elif np.isscalar(item):
if transform_scalar:
if dtype is None:
return torch.as_tensor(item)
else:
return torch.as_tensor(item).to(dtype)
else:
return item
elif item is None:
return None
elif isinstance(item, torch.Tensor):
if dtype is None:
return item
else:
return item.to(dtype)
else:
raise TypeError("not support item type: {}".format(type(item)))
def to_ndarray(item: Any, dtype: np.dtype = None) -> np.ndarray:
r"""
Overview:
Change `torch.Tensor`, sequence of scalars to ndarray, and keep other data types unchanged.
Arguments:
- item (:obj:`object`): the item to be changed
- dtype (:obj:`type`): the type of wanted ndarray
Returns:
- item (:obj:`object`): the changed ndarray
.. note:
Now supports item type: :obj:`torch.Tensor`, :obj:`dict`, :obj:`list`, :obj:`tuple` and :obj:`None`
"""
def transform(d):
if dtype is None:
return np.array(d)
else:
return np.array(d, dtype=dtype)
if isinstance(item, dict):
new_data = {}
for k, v in item.items():
new_data[k] = to_ndarray(v, dtype)
return new_data
elif isinstance(item, list) or isinstance(item, tuple):
if len(item) == 0:
return None
elif isinstance(item[0], numbers.Integral) or isinstance(item[0], numbers.Real):
return transform(item)
elif hasattr(item, '_fields'): # namedtuple
return type(item)(*[to_ndarray(t, dtype) for t in item])
else:
new_data = []
for t in item:
new_data.append(to_ndarray(t, dtype))
return new_data
elif isinstance(item, torch.Tensor):
if dtype is None:
return item.numpy()
else:
return item.numpy().astype(dtype)
elif isinstance(item, np.ndarray):
if dtype is None:
return item
else:
return item.astype(dtype)
elif isinstance(item, bool) or isinstance(item, str):
return item
elif np.isscalar(item):
return np.array(item)
elif item is None:
return None
else:
raise TypeError("not support item type: {}".format(type(item)))
def to_list(item: Any) -> list:
r"""
Overview:
Transform `torch.Tensor`, `numpy.ndarray` to `list`, keep other data types unchanged
Arguments:
- item (:obj:`Any`): the item to be transformed
Returns:
- item (:obj:`list`): the list after transformation
.. note::
Now supports item type: :obj:`torch.Tensor`,:obj:`numpy.ndarray`, :obj:`dict`, :obj:`list`, \
:obj:`tuple` and :obj:`None`
"""
if item is None:
return item
elif isinstance(item, torch.Tensor):
return item.tolist()
elif isinstance(item, np.ndarray):
return item.tolist()
elif isinstance(item, list) or isinstance(item, tuple):
return [to_list(t) for t in item]
elif isinstance(item, dict):
return {k: to_list(v) for k, v in item.items()}
elif np.isscalar(item):
return item
else:
raise TypeError("not support item type: {}".format(type(item)))
def tensor_to_list(item):
r"""
Overview:
Transform `torch.Tensor` to `list`, keep other data types unchanged
Arguments:
- item (:obj:`Any`): the item to be transformed
Returns:
- item (:obj:`list`): the list after transformation
.. note::
Now supports item type: :obj:`torch.Tensor`, :obj:`dict`, :obj:`list`, :obj:`tuple` and :obj:`None`
"""
if item is None:
return item
elif isinstance(item, torch.Tensor):
return item.tolist()
elif isinstance(item, list) or isinstance(item, tuple):
return [tensor_to_list(t) for t in item]
elif isinstance(item, dict):
return {k: tensor_to_list(v) for k, v in item.items()}
elif np.isscalar(item):
return item
else:
raise TypeError("not support item type: {}".format(type(item)))
def same_shape(data: list) -> bool:
r"""
Overview:
Judge whether all data elements in a list have the same shape.
Arguments:
- data (:obj:`list`): the list of data
Returns:
- same (:obj:`bool`): whether the list of data all have the same shape
"""
assert (isinstance(data, list))
shapes = [t.shape for t in data]
return len(set(shapes)) == 1
class LogDict(dict):
'''
Overview:
Derived from ``dict``; Would transform ``torch.Tensor`` to ``list`` for convenient logging.
'''
def _transform(self, data):
if isinstance(data, torch.Tensor):
new_data = data.tolist()
else:
new_data = data
return new_data
def __setitem__(self, key, value):
new_value = self._transform(value)
super().__setitem__(key, new_value)
def update(self, data):
for k, v in data.items():
self.__setitem__(k, v)
def build_log_buffer():
r"""
Overview:
Builg log buffer, a subclass of dict, which can transform the input data into log format.
Returns:
- log_buffer (:obj:`LogDict`): Log buffer dict
"""
return LogDict()
class CudaFetcher(object):
"""
Overview:
Fetch data from source, and transfer it to specified device.
Interfaces:
run, close
"""
def __init__(self, data_source: Iterable, device: str, queue_size: int = 4, sleep: float = 0.1) -> None:
self._source = data_source
self._queue = Queue(maxsize=queue_size)
self._stream = torch.cuda.Stream()
self._producer_thread = Thread(target=self._producer, args=(), name='cuda_fetcher_producer')
self._sleep = sleep
self._device = device
def __next__(self) -> Any:
return self._queue.get()
def run(self) -> None:
"""
Overview:
Start `producer` thread: Keep fetching data from source,
change the device, and put into `queue` for request.
"""
self._end_flag = False
self._producer_thread.start()
def close(self) -> None:
"""
Overview:
Stop `producer` thread by setting `end_flag` to `True`.
"""
self._end_flag = True
def _producer(self) -> None:
with torch.cuda.stream(self._stream):
while not self._end_flag:
if self._queue.full():
time.sleep(self._sleep)
else:
data = next(self._source)
data = to_device(data, self._device)
self._queue.put(data)
def get_tensor_data(data: Any) -> Any:
"""
Overview:
Get pure tensor data from the given data(without disturbing grad computation graph)
"""
if isinstance(data, torch.Tensor):
return data.data.clone()
elif data is None:
return None
elif isinstance(data, Sequence):
return [get_tensor_data(d) for d in data]
elif isinstance(data, dict):
return {k: get_tensor_data(v) for k, v in data.items()}
else:
raise TypeError("not support type in get_tensor_data: {}".format(type(data)))