520 lines
17 KiB
Python
520 lines
17 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
from typing import Optional
|
|
|
|
import torch
|
|
from pytorch_lightning.utilities.cloud_io import get_filesystem
|
|
from torch import Tensor
|
|
from torch.utils.data import Dataset
|
|
from pytorch_lightning.utilities.apply_func import apply_to_collection
|
|
from pytorch_lightning.utilities.data import get_len
|
|
from collections.abc import Iterable, Iterator, Mapping, Sequence
|
|
from typing import Any, Union
|
|
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
|
|
|
|
class TensorRunningAccum(object):
|
|
"""Tracks a running accumulation values (min, max, mean) without graph
|
|
references.
|
|
|
|
Examples:
|
|
>>> accum = TensorRunningAccum(5)
|
|
>>> accum.last(), accum.mean()
|
|
(None, None)
|
|
>>> accum.append(torch.tensor(1.5))
|
|
>>> accum.last(), accum.mean()
|
|
(tensor(1.5000), tensor(1.5000))
|
|
>>> accum.append(torch.tensor(2.5))
|
|
>>> accum.last(), accum.mean()
|
|
(tensor(2.5000), tensor(2.))
|
|
>>> accum.reset()
|
|
>>> _= [accum.append(torch.tensor(i)) for i in range(13)]
|
|
>>> accum.last(), accum.mean(), accum.min(), accum.max()
|
|
(tensor(12.), tensor(10.), tensor(8.), tensor(12.))
|
|
"""
|
|
|
|
def __init__(self, window_length: int):
|
|
self.window_length = window_length
|
|
self.memory = None
|
|
self.current_idx: int = 0
|
|
self.last_idx: Optional[int] = None
|
|
self.rotated: bool = False
|
|
|
|
def reset(self) -> None:
|
|
"""Empty the accumulator."""
|
|
self.__init__(self.window_length)
|
|
|
|
def last(self):
|
|
"""Get the last added element."""
|
|
if self.last_idx is not None:
|
|
return self.memory[self.last_idx]
|
|
|
|
def append(self, x):
|
|
"""Add an element to the accumulator."""
|
|
if self.memory is None:
|
|
self.memory = torch.zeros(self.window_length, *x.shape)
|
|
|
|
# ensure same device and type
|
|
if self.memory.device != x.device or self.memory.type() != x.type():
|
|
x = x.to(self.memory)
|
|
|
|
# store without grads
|
|
with torch.no_grad():
|
|
self.memory[self.current_idx] = x
|
|
self.last_idx = self.current_idx
|
|
|
|
# increase index
|
|
self.current_idx += 1
|
|
|
|
# reset index when hit limit of tensor
|
|
self.current_idx = self.current_idx % self.window_length
|
|
if self.current_idx == 0:
|
|
self.rotated = True
|
|
|
|
def mean(self):
|
|
"""Get mean value from stored elements."""
|
|
return self._agg_memory('mean')
|
|
|
|
def max(self):
|
|
"""Get maximal value from stored elements."""
|
|
return self._agg_memory('max')
|
|
|
|
def min(self):
|
|
"""Get minimal value from stored elements."""
|
|
return self._agg_memory('min')
|
|
|
|
def _agg_memory(self, how: str):
|
|
if self.last_idx is not None:
|
|
if self.rotated:
|
|
return getattr(self.memory, how)()
|
|
else:
|
|
return getattr(self.memory[: self.current_idx], how)()
|
|
|
|
|
|
class Accumulator(object):
|
|
def __init__(self):
|
|
self.num_values = 0
|
|
self.total = 0
|
|
|
|
def accumulate(self, x):
|
|
with torch.no_grad():
|
|
self.total += x
|
|
self.num_values += 1
|
|
|
|
def mean(self):
|
|
return self.total / self.num_values
|
|
|
|
|
|
class PredictionCollection(object):
|
|
def __init__(self, global_rank: int, world_size: int):
|
|
self.global_rank = global_rank
|
|
self.world_size = world_size
|
|
self.predictions = {}
|
|
self.num_predictions = 0
|
|
|
|
def _add_prediction(self, name, values, filename):
|
|
if filename not in self.predictions:
|
|
self.predictions[filename] = {name: values}
|
|
elif name not in self.predictions[filename]:
|
|
self.predictions[filename][name] = values
|
|
elif isinstance(values, Tensor):
|
|
self.predictions[filename][name] = torch.cat(
|
|
(self.predictions[filename][name], values)
|
|
)
|
|
elif isinstance(values, list):
|
|
self.predictions[filename][name].extend(values)
|
|
|
|
def add(self, predictions):
|
|
|
|
if predictions is None:
|
|
return
|
|
|
|
for filename, pred_dict in predictions.items():
|
|
for feature_name, values in pred_dict.items():
|
|
self._add_prediction(feature_name, values, filename)
|
|
|
|
def to_disk(self) -> None:
|
|
"""Write predictions to file(s).
|
|
"""
|
|
for filepath, predictions in self.predictions.items():
|
|
fs = get_filesystem(filepath)
|
|
# normalize local filepaths only
|
|
if fs.protocol == "file":
|
|
filepath = os.path.realpath(filepath)
|
|
if self.world_size > 1:
|
|
stem, extension = os.path.splitext(filepath)
|
|
filepath = f"{stem}_rank_{self.global_rank}{extension}"
|
|
dirpath = os.path.split(filepath)[0]
|
|
fs.mkdirs(dirpath, exist_ok=True)
|
|
|
|
# Convert any tensor values to list
|
|
predictions = {
|
|
k: v if not isinstance(v, Tensor) else v.tolist()
|
|
for k, v in predictions.items()
|
|
}
|
|
|
|
# Check if all features for this file add up to same length
|
|
feature_lens = {k: len(v) for k, v in predictions.items()}
|
|
if len(set(feature_lens.values())) != 1:
|
|
raise ValueError(
|
|
"Mismatching feature column lengths found in stored EvalResult predictions."
|
|
)
|
|
|
|
# Switch predictions so each entry has its own dict
|
|
outputs = []
|
|
for values in zip(*predictions.values()):
|
|
output_element = {k: v for k, v in zip(predictions.keys(), values)}
|
|
outputs.append(output_element)
|
|
|
|
# Write predictions for current file to disk
|
|
with fs.open(filepath, "wb") as fp:
|
|
torch.save(outputs, fp)
|
|
|
|
|
|
class CycleIterator(object):
|
|
"""
|
|
Iterator for restarting a dataloader if it runs out of samples
|
|
"""
|
|
def __init__(self, loader: Any, length: Optional[int] = None):
|
|
"""
|
|
|
|
Args:
|
|
loader: the loader to restart for cyclic (and optionally infinite) sampling
|
|
length: the number of batches to sample (with restarted loaders if necessary) before raising StopIteration
|
|
if None: infinite
|
|
|
|
"""
|
|
if length is None:
|
|
length = float('inf')
|
|
|
|
self.length = length
|
|
self.loader = loader
|
|
self._loader_iter = None
|
|
self.counter = 0
|
|
|
|
def __iter__(self) -> Any:
|
|
"""
|
|
|
|
Creates the internal iterator and returns self
|
|
|
|
Returns:
|
|
CycleIterator: self
|
|
|
|
"""
|
|
self.counter = 0
|
|
self._loader_iter = iter(self.loader)
|
|
return self
|
|
|
|
def __next__(self) -> Any:
|
|
"""
|
|
Fetches the next batch from internal dataloader and restarts
|
|
it if necessary
|
|
|
|
Returns:
|
|
Any: the resulting batch
|
|
|
|
Raises:
|
|
StopIteration: if more then :attr:`length` batches have been returned
|
|
|
|
"""
|
|
# Note: if self.length is `inf`, then the iterator will never stop
|
|
if self.counter >= self.__len__():
|
|
raise StopIteration
|
|
|
|
try:
|
|
return next(self._loader_iter)
|
|
|
|
except StopIteration:
|
|
self._loader_iter = iter(self.loader)
|
|
return next(self._loader_iter)
|
|
|
|
finally:
|
|
self.counter += 1
|
|
|
|
def __len__(self) -> Union[int, float]:
|
|
return self.length
|
|
|
|
|
|
class CombinedDataset(object):
|
|
"""
|
|
Combine multiple datasets and compute their statistics
|
|
"""
|
|
COMPUTE_FUNCS = {'min_size': min, 'max_size_cycle': max}
|
|
|
|
def __init__(self, datasets: Union[Sequence, Mapping], mode: str = 'min_size'):
|
|
"""
|
|
|
|
Args:
|
|
datasets: a sequence/mapping datasets. Can be a collections of torch.utils.Dataset,
|
|
Iterable or even None.
|
|
mode: whether to use the minimum number of batches in all samples or the maximum
|
|
number of batches in all samples.
|
|
|
|
"""
|
|
self.datasets = datasets
|
|
if mode not in self.COMPUTE_FUNCS.keys():
|
|
raise MisconfigurationException(
|
|
f'You have selected unsupported mode "{mode}",'
|
|
f' please select one the: {list(self.COMPUTE_FUNCS.keys())}.'
|
|
)
|
|
self.mode = mode
|
|
|
|
@property
|
|
def max_len(self) -> Union[int, float]:
|
|
return self._calc_num_data(self.datasets, 'max_size_cycle')
|
|
|
|
@property
|
|
def min_len(self) -> Union[int, float]:
|
|
return self._calc_num_data(self.datasets, 'min_size')
|
|
|
|
@staticmethod
|
|
def _calc_num_data(datasets: Union[Sequence, Mapping], mode: str) -> Union[int, float]:
|
|
"""
|
|
Compute the length of `CombinedDataset` according to the `mode`.
|
|
|
|
Args:
|
|
datasets: a sequence/mapping datasets. Can be a collections of torch.utils.data.Dataset,
|
|
Iterable or even None.
|
|
mode: Determine `CombinedDataset`'s length is the maximum or minimum of
|
|
the datasets.
|
|
|
|
Returns:
|
|
length: the length of `CombinedDataset`
|
|
|
|
"""
|
|
if mode not in CombinedDataset.COMPUTE_FUNCS.keys():
|
|
raise MisconfigurationException(f"Invalid Mode: {mode}")
|
|
|
|
# extract the lengths
|
|
all_lengths = apply_to_collection(datasets, (Dataset, Iterable, type(None)), get_len,
|
|
wrong_dtype=(Sequence, Mapping))
|
|
|
|
compute_func = CombinedDataset.COMPUTE_FUNCS[mode]
|
|
|
|
if isinstance(all_lengths, (int, float)):
|
|
length = all_lengths
|
|
|
|
elif isinstance(all_lengths, Mapping):
|
|
length = compute_func(all_lengths.values())
|
|
|
|
elif isinstance(all_lengths, Sequence):
|
|
length = compute_func(all_lengths)
|
|
|
|
return length
|
|
|
|
def __len__(self) -> int:
|
|
"""Return the minimum length of the datasets."""
|
|
return self._calc_num_data(self.datasets, self.mode)
|
|
|
|
|
|
class CombinedLoader(object):
|
|
"""
|
|
Combines different dataloaders and allows sampling in parallel.
|
|
|
|
Supported modes are 'min_size', which raises StopIteration after the shortest loader
|
|
(the one with the lowest number of batches) is done, and 'max_size_cycle` which raises
|
|
StopIteration after the longest loader (the one with most batches) is done, while cycling
|
|
through the shorter loaders.
|
|
|
|
Examples:
|
|
>>> loaders = {'a': torch.utils.data.DataLoader(range(6), batch_size=4),
|
|
... 'b': torch.utils.data.DataLoader(range(15), batch_size=5)}
|
|
>>> combined_loader = CombinedLoader(loaders, 'max_size_cycle')
|
|
>>> for item in combined_loader:
|
|
... print(item)
|
|
{'a': tensor([0, 1, 2, 3]), 'b': tensor([0, 1, 2, 3, 4])}
|
|
{'a': tensor([4, 5]), 'b': tensor([5, 6, 7, 8, 9])}
|
|
{'a': tensor([0, 1, 2, 3]), 'b': tensor([10, 11, 12, 13, 14])}
|
|
>>> combined_loader = CombinedLoader(loaders, 'min_size')
|
|
>>> for item in combined_loader:
|
|
... print(item)
|
|
{'a': tensor([0, 1, 2, 3]), 'b': tensor([0, 1, 2, 3, 4])}
|
|
{'a': tensor([4, 5]), 'b': tensor([5, 6, 7, 8, 9])}
|
|
|
|
"""
|
|
SUPPORTED_MODES = ('min_size', 'max_size_cycle')
|
|
|
|
def __init__(self, loaders: Any, mode: str = 'min_size'):
|
|
"""
|
|
|
|
Args:
|
|
loaders: the loaders to sample from. Can be all kind of collection
|
|
mode: the mode. Supported are 'min_size' which stops if the shortest loader is exhausted and
|
|
'max_size_cycle' which stops if the longest loader is exhausted and cycles through the smaller ones.
|
|
|
|
"""
|
|
self.loaders = loaders
|
|
|
|
datasets = apply_to_collection(self.loaders, Iterable, getattr, 'dataset', None,
|
|
wrong_dtype=(Sequence, Mapping))
|
|
# could be multiple datasets, but use self.dataset to follow the name convention in DataLoader
|
|
self.dataset = CombinedDataset(datasets, mode)
|
|
|
|
if mode not in self.SUPPORTED_MODES:
|
|
raise MisconfigurationException(f"Invalid Mode: {mode}")
|
|
|
|
self.mode = mode
|
|
|
|
if self.mode == 'max_size_cycle':
|
|
self._wrap_loaders_max_size_cycle()
|
|
|
|
@property
|
|
def sampler(self) -> Union[Iterable, Sequence, Mapping]:
|
|
"""Return a collections of samplers extracting from loaders."""
|
|
return apply_to_collection(self.loaders, Iterable, getattr, 'sampler', None,
|
|
wrong_dtype=(Sequence, Mapping))
|
|
|
|
def _wrap_loaders_max_size_cycle(self) -> Any:
|
|
"""
|
|
Wraps all loaders to make sure they are cycled until the longest loader is exhausted
|
|
|
|
Returns:
|
|
Any: the wrapped loaders
|
|
|
|
"""
|
|
all_lengths = apply_to_collection(self.loaders, Iterable, get_len,
|
|
wrong_dtype=(Sequence, Mapping))
|
|
|
|
if isinstance(all_lengths, (int, float)):
|
|
length = all_lengths
|
|
|
|
elif isinstance(all_lengths, Mapping):
|
|
length = max(all_lengths.values())
|
|
|
|
elif isinstance(all_lengths, Sequence):
|
|
length = max(all_lengths)
|
|
|
|
if isinstance(self.loaders, Mapping):
|
|
self.loaders = type(self.loaders)({k: CycleIterator(v, length=length)
|
|
for k, v in self.loaders.items()})
|
|
|
|
elif isinstance(self.loaders, Sequence):
|
|
self.loaders = type(self.loaders)([
|
|
CycleIterator(v, length=length) for v in self.loaders
|
|
])
|
|
|
|
# dataloaders are iterable but not sequence
|
|
elif isinstance(self.loaders, Iterable):
|
|
# only one dataloader, just keep it the same.
|
|
pass
|
|
else:
|
|
raise ValueError(f'Invalid Datatype for loaders: {type(self.loaders).__name__}')
|
|
|
|
def __iter__(self) -> Any:
|
|
"""
|
|
Create and return an iterator, `CombinedLoaderIterator`, for the combined loader.
|
|
"""
|
|
return CombinedLoaderIterator(self.loaders)
|
|
|
|
@staticmethod
|
|
def _calc_num_batches(loaders: Any) -> Union[int, float]:
|
|
"""
|
|
Compute the length (aka the number of batches) of `CombinedLoader`.
|
|
|
|
Args:
|
|
loaders: a collections of loaders.
|
|
|
|
Returns:
|
|
length: the minimum length of loaders
|
|
|
|
"""
|
|
all_lengths = apply_to_collection(loaders, Iterable, get_len,
|
|
wrong_dtype=(Sequence, Mapping))
|
|
|
|
if isinstance(all_lengths, (int, float)):
|
|
return all_lengths
|
|
|
|
elif isinstance(all_lengths, Mapping):
|
|
return min(all_lengths.values())
|
|
|
|
elif isinstance(all_lengths, Sequence):
|
|
return min(all_lengths)
|
|
|
|
raise TypeError(f'Got Type {type(all_lengths).__name__}, but expected one of Sequence, int or Mapping')
|
|
|
|
def __len__(self) -> int:
|
|
return self._calc_num_batches(self.loaders)
|
|
|
|
|
|
class CombinedLoaderIterator(object):
|
|
"""
|
|
Custom Iterator returning data from multple loaders, and allows sampling in parallel
|
|
"""
|
|
def __init__(self, loaders: Any):
|
|
"""
|
|
|
|
Args:
|
|
loaders: the loaders to sample from. Can be all kind of collection
|
|
|
|
"""
|
|
self.loaders = loaders
|
|
self._loader_iters = None
|
|
|
|
@property
|
|
def loader_iters(self) -> Any:
|
|
"""
|
|
Get the `_loader_iters` and create one if it is None.
|
|
"""
|
|
if self._loader_iters is None:
|
|
self._loader_iters = self.create_loader_iters(self.loaders)
|
|
|
|
return self._loader_iters
|
|
|
|
def __iter__(self) -> Any:
|
|
return self
|
|
|
|
def __next__(self) -> Any:
|
|
"""
|
|
Fetches the next batch from multiple data loaders
|
|
|
|
Returns:
|
|
Any: a collections of batch data
|
|
|
|
"""
|
|
return self.request_next_batch(self.loader_iters)
|
|
|
|
@staticmethod
|
|
def request_next_batch(loader_iters: Union[Iterator, Sequence, Mapping]) -> Any:
|
|
"""
|
|
Return the batch of data from multiple iterators.
|
|
|
|
Args:
|
|
loader_iters: a collections of iterators
|
|
|
|
Returns
|
|
Any: a collections of batch data
|
|
|
|
"""
|
|
return apply_to_collection(loader_iters, Iterator, next)
|
|
|
|
@staticmethod
|
|
def create_loader_iters(
|
|
loaders: Union[Any, Iterator, Sequence, Mapping]
|
|
) -> Union[Any, Iterator, Sequence, Mapping]:
|
|
"""
|
|
Create and return a collection of iterators from loaders.
|
|
|
|
Args:
|
|
loaders: a collections of loaders
|
|
|
|
Returns
|
|
a collections of iterators
|
|
|
|
"""
|
|
# dataloaders are Iterable but not Sequences. Need this to specifically exclude sequences
|
|
return apply_to_collection(loaders, Iterable, iter, wrong_dtype=(Sequence, Mapping))
|