Let's write a morestrings package and use it from the hello program.
First, create a directory for the package named
$HOME/hello/morestrings, and then a file named
reverse.go in that directory with the following contents:
// Package morestrings implements additional functions to manipulate UTF-8
// encoded strings, beyond what is provided in the standard "strings" package.
package morestrings
// ReverseRunes returns its argument string reversed rune-wise left to right.
func ReverseRunes(s string) string {
r := []rune(s)
for i, j := 0, len(r)-1; i < len(r)/2; i, j = i+1, j-1 {
r[i], r[j] = r[j], r[i]
}
return string(r)
}After confirming that the morestrings package builds, let's use it
from the hello program. To do so, modify your original
$HOME/hello/hello.go to use the morestrings package:
package main
import (
"fmt"
"example/user/hello/morestrings"
)
func main() {
fmt.Println(morestrings.ReverseRunes("!oG ,olleH"))
}Let's test that the package compiles with go build:
$ cd $HOME/hello/morestrings
$ go build
$Install the hello program:
$ go install example/user/helloRunning the new version of the program, you should see a new, reversed message:
$ hello
Hello, Go!An import path can describe how to obtain the package source code using a
revision control system such as Git or Mercurial. The go tool uses
this property to automatically fetch packages from remote repositories.
For instance, to use github.com/google/go-cmp/cmp in your program:
package main
import (
"fmt"
"example/user/hello/morestrings"
"github.com/google/go-cmp/cmp"
)
func main() {
fmt.Println(morestrings.ReverseRunes("!oG ,olleH"))
fmt.Println(cmp.Diff("Hello World", "Hello Go"))
}Now that you have a dependency on an external module, you need to download that
module and record its version in your go.mod file. The go
mod tidy command adds missing module requirements for imported packages
and removes requirements on modules that aren't used anymore.
$ go mod tidy
go: finding module for package github.com/google/go-cmp/cmp
go: found github.com/google/go-cmp/cmp in github.com/google/go-cmp v0.5.4
$ go install example/user/hello
$ hello
Hello, Go!
string(
- "Hello World",
+ "Hello Go",
)
$ cat go.mod
module example/user/hello
go 1.16
require github.com/google/go-cmp v0.5.4
$Module dependencies are automatically downloaded to the pkg/mod
subdirectory of the directory indicated by the GOPATH environment
variable. The downloaded contents for a given version of a module are shared
among all other modules that require that version, so
the go command marks those files and directories as read-only. To
remove all downloaded modules, you can pass the -modcache flag
to go clean:
$ go clean -modcache
$Shortcuts
r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
To support these two classes, in `./_utils` we define many utility methods and
functions to be run in multiprocessing. E.g., the data loading worker loop is
in `./_utils/worker.py`.
"""
import os
import threading
import itertools
import warnings
import queue
from typing import Any, Callable, TypeVar, Generic, Sequence, List, Optional
import multiprocessing as python_multiprocessing
import torch
import torch.multiprocessing as multiprocessing
from torch._utils import ExceptionWrapper
from torch._six import string_classes
from . import IterableDataset, Sampler, SequentialSampler, RandomSampler, BatchSampler, Dataset
from . import _utils
T_co = TypeVar('T_co', covariant=True)
T = TypeVar('T')
_worker_init_fn_t = Callable[[int], None]
# Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that
# type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.
# See https://github.com/python/mypy/issues/3737.
_collate_fn_t = Callable[[List[T]], Any]
# This function used to be defined in this file. However, it was moved to
# _utils/collate.py. Although it is rather hard to access this from user land
# (one has to explicitly directly `import torch.utils.data.dataloader`), there
# probably is user code out there using it. This aliasing maintains BC in this
# aspect.
default_collate: _collate_fn_t = _utils.collate.default_collate
get_worker_info = _utils.worker.get_worker_info
class _DatasetKind(object):
Map = 0
Iterable = 1
@staticmethod
def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
if kind == _DatasetKind.Map:
return _utils.fetch._MapDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
else:
return _utils.fetch._IterableDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
class _InfiniteConstantSampler(Sampler):
r"""Analogous to ``itertools.repeat(None, None)``.
Used as sampler for :class:`~torch.utils.data.IterableDataset`.
Args:
data_source (Dataset): dataset to sample from
"""
def __init__(self):
super(_InfiniteConstantSampler, self).__init__(None)
def __iter__(self):
while True:
yield None
[docs]class DataLoader(Generic[T_co]):
r"""
Data loader. Combines a dataset and a sampler, and provides an iterable over
the given dataset.
The :class:`~torch.utils.data.DataLoader` supports both map-style and
iterable-style datasets with single- or multi-process loading, customizing
loading order and optional automatic batching (collation) and memory pinning.
See :py:mod:`torch.utils.data` documentation page for more details.
Args:
dataset (Dataset): dataset from which to load the data.
batch_size (int, optional): how many samples per batch to load
(default: ``1``).
shuffle (bool, optional): set to ``True`` to have the data reshuffled
at every epoch (default: ``False``).
sampler (Sampler or Iterable, optional): defines the strategy to draw
samples from the dataset. Can be any ``Iterable`` with ``__len__``
implemented. If specified, :attr:`shuffle` must not be specified.
batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
returns a batch of indices at a time. Mutually exclusive with
:attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
and :attr:`drop_last`.
num_workers (int, optional): how many subprocesses to use for data
loading. ``0`` means that the data will be loaded in the main process.
(default: ``0``)
collate_fn (callable, optional): merges a list of samples to form a
mini-batch of Tensor(s). Used when using batched loading from a
map-style dataset.
pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
into CUDA pinned memory before returning them. If your data elements
are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
see the example below.
drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If ``False`` and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: ``False``)
timeout (numeric, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative. (default: ``0``)
worker_init_fn (callable, optional): If not ``None``, this will be called on each
worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
input, after seeding and before data loading. (default: ``None``)
generator (torch.Generator, optional): If not ``None``, this RNG will be used
by RandomSampler to generate random indexes and multiprocessing to generate
`base_seed` for workers. (default: ``None``)
prefetch_factor (int, optional, keyword-only arg): Number of samples loaded
in advance by each worker. ``2`` means there will be a total of
2 * num_workers samples prefetched across all workers. (default: ``2``)
persistent_workers (bool, optional): If ``True``, the data loader will not shutdown
the worker processes after a dataset has been consumed once. This allows to
maintain the workers `Dataset` instances alive. (default: ``False``)
.. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
cannot be an unpicklable object, e.g., a lambda function. See
:ref:`multiprocessing-best-practices` on more details related
to multiprocessing in PyTorch.
.. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
it instead returns an estimate based on ``len(dataset) / batch_size``, with proper
rounding depending on :attr:`drop_last`, regardless of multi-process loading
configurations. This represents the best guess PyTorch can make because PyTorch
trusts user :attr:`dataset` code in correctly handling multi-process
loading to avoid duplicate data.
However, if sharding results in multiple workers having incomplete last batches,
this estimate can still be inaccurate, because (1) an otherwise complete batch can
be broken into multiple ones and (2) more than one batch worth of samples can be
dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
cases in general.
See `Dataset Types`_ for more details on these two types of datasets and how
:class:`~torch.utils.data.IterableDataset` interacts with
`Multi-process data loading`_.
.. warning:: See :ref:`reproducibility`, and :ref:`dataloader-workers-random-seed`, and
:ref:`data-loading-randomness` notes for random seed related questions.
"""
dataset: Dataset[T_co]
batch_size: Optional[int]
num_workers: int
pin_memory: bool
drop_last: bool
timeout: float
sampler: Sampler
prefetch_factor: int
_iterator : Optional['_BaseDataLoaderIter']
__initialized = False
def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1,
shuffle: bool = False, sampler: Optional[Sampler] = None,
batch_sampler: Optional[Sampler[Sequence]] = None,
num_workers: int = 0, collate_fn: Optional[_collate_fn_t] = None,
pin_memory: bool = False, drop_last: bool = False,
timeout: float = 0, worker_init_fn: Optional[_worker_init_fn_t] = None,
multiprocessing_context=None, generator=None,
*, prefetch_factor: int = 2,
persistent_workers: bool = False):
torch._C._log_api_usage_once("python.data_loader")
if num_workers < 0:
raise ValueError('num_workers option should be non-negative; '
'use num_workers=0 to disable multiprocessing.')
if timeout < 0:
raise ValueError('timeout option should be non-negative')
if num_workers == 0 and prefetch_factor != 2:
raise ValueError('prefetch_factor option could only be specified in multiprocessing.'
'let num_workers > 0 to enable multiprocessing.')
assert prefetch_factor > 0
if persistent_workers and num_workers == 0:
raise ValueError('persistent_workers option needs num_workers > 0')
self.dataset = dataset
self.num_workers = num_workers
self.prefetch_factor = prefetch_factor
self.pin_memory = pin_memory
self.timeout = timeout
self.worker_init_fn = worker_init_fn
self.multiprocessing_context = multiprocessing_context
# Arg-check dataset related before checking samplers because we want to
# tell users that iterable-style datasets are incompatible with custom
# samplers first, so that they don't learn that this combo doesn't work
# after spending time fixing the custom sampler errors.
if isinstance(dataset, IterableDataset):
self._dataset_kind = _DatasetKind.Iterable
# NOTE [ Custom Samplers and IterableDataset ]
#
# `IterableDataset` does not support custom `batch_sampler` or
# `sampler` since the key is irrelevant (unless we support
# generator-style dataset one day...).
#
# For `sampler`, we always create a dummy sampler. This is an
# infinite sampler even when the dataset may have an implemented
# finite `__len__` because in multi-process data loading, naive
# settings will return duplicated data (which may be desired), and
# thus using a sampler with length matching that of dataset will
# cause data lost (you may have duplicates of the first couple
# batches, but never see anything afterwards). Therefore,
# `Iterabledataset` always uses an infinite sampler, an instance of
# `_InfiniteConstantSampler` defined above.
#
# A custom `batch_sampler` essentially only controls the batch size.
# However, it is unclear how useful it would be since an iterable-style
# dataset can handle that within itself. Moreover, it is pointless
# in multi-process data loading as the assignment order of batches
# to workers is an implementation detail so users can not control
# how to batchify each worker's iterable. Thus, we disable this
# option. If this turns out to be useful in future, we can re-enable
# this, and support custom samplers that specify the assignments to
# specific workers.
if shuffle is not False:
raise ValueError(
"DataLoader with IterableDataset: expected unspecified "
"shuffle option, but got shuffle={}".format(shuffle))
elif sampler is not None:
# See NOTE [ Custom Samplers and IterableDataset ]
raise ValueError(
"DataLoader with IterableDataset: expected unspecified "
"sampler option, but got sampler={}".format(sampler))
elif batch_sampler is not None:
# See NOTE [ Custom Samplers and IterableDataset ]
raise ValueError(
"DataLoader with IterableDataset: expected unspecified "
"batch_sampler option, but got batch_sampler={}".format(batch_sampler))
else:
self._dataset_kind = _DatasetKind.Map
if sampler is not None and shuffle:
raise ValueError('sampler option is mutually exclusive with '
'shuffle')
if batch_sampler is not None:
# auto_collation with custom batch_sampler
if batch_size != 1 or shuffle or sampler is not None or drop_last:
raise ValueError('batch_sampler option is mutually exclusive '
'with batch_size, shuffle, sampler, and '
'drop_last')
batch_size = None
drop_last = False
elif batch_size is None:
# no auto_collation
if drop_last:
raise ValueError('batch_size=None option disables auto-batching '
'and is mutually exclusive with drop_last')
if sampler is None: # give default samplers
if self._dataset_kind == _DatasetKind.Iterable:
# See NOTE [ Custom Samplers and IterableDataset ]
sampler = _InfiniteConstantSampler()
else: # map-style
if shuffle:
sampler = RandomSampler(dataset, generator=generator)
else:
sampler = SequentialSampler(dataset)
if batch_size is not None and batch_sampler is None:
# auto_collation without custom batch_sampler
batch_sampler = BatchSampler(sampler, batch_size, drop_last)
self.batch_size = batch_size
self.drop_last = drop_last
self.sampler = sampler
self.batch_sampler = batch_sampler
self.generator = generator
if collate_fn is None:
if self._auto_collation:
collate_fn = _utils.collate.default_collate
else:
collate_fn = _utils.collate.default_convert
self.collate_fn = collate_fn
self.persistent_workers = persistent_workers
self.__initialized = True
self._IterableDataset_len_called = None # See NOTE [ IterableDataset and __len__ ]
self._iterator = None
self.check_worker_number_rationality()
torch.set_vital('Dataloader', 'enabled', 'True') # type: ignore[attr-defined]
def _get_iterator(self) -> '_BaseDataLoaderIter':
if self.num_workers == 0:
return _SingleProcessDataLoaderIter(self)
else:
self.check_worker_number_rationality()
return _MultiProcessingDataLoaderIter(self)
@property
def multiprocessing_context(self):
return self.__multiprocessing_context
@multiprocessing_context.setter
def multiprocessing_context(self, multiprocessing_context):
if multiprocessing_context is not None:
if self.num_workers > 0:
if isinstance(multiprocessing_context, string_classes):
valid_start_methods = multiprocessing.get_all_start_methods()
if multiprocessing_context not in valid_start_methods:
raise ValueError(
('multiprocessing_context option '
'should specify a valid start method in {!r}, but got '
'multiprocessing_context={!r}').format(valid_start_methods, multiprocessing_context))
# error: Argument 1 to "get_context" has incompatible type "Union[str, bytes]"; expected "str" [arg-type]
multiprocessing_context = multiprocessing.get_context(multiprocessing_context) # type: ignore[arg-type]
if not isinstance(multiprocessing_context, python_multiprocessing.context.BaseContext):
raise TypeError(('multiprocessing_context option should be a valid context '
'object or a string specifying the start method, but got '
'multiprocessing_context={}').format(multiprocessing_context))
else:
raise ValueError(('multiprocessing_context can only be used with '
'multi-process loading (num_workers > 0), but got '
'num_workers={}').format(self.num_workers))
self.__multiprocessing_context = multiprocessing_context
def __setattr__(self, attr, val):
if self.__initialized and attr in (
'batch_size', 'batch_sampler', 'sampler', 'drop_last', 'dataset', 'persistent_workers'):
raise ValueError('{} attribute should not be set after {} is '
'initialized'.format(attr, self.__class__.__name__))
super(DataLoader, self).__setattr__(attr, val)
# We quote '_BaseDataLoaderIter' since it isn't defined yet and the definition can't be moved up
# since '_BaseDataLoaderIter' references 'DataLoader'.
def __iter__(self) -> '_BaseDataLoaderIter':
# When using a single worker the returned iterator should be
# created everytime to avoid reseting its state
# However, in the case of a multiple workers iterator
# the iterator is only created once in the lifetime of the
# DataLoader object so that workers can be reused
if self.persistent_workers and self.num_workers > 0:
if self._iterator is None:
self._iterator = self._get_iterator()
else:
self._iterator._reset(self)
return self._iterator
else:
return self._get_iterator()
@property
def _auto_collation(self):
return self.batch_sampler is not None
@property
def _index_sampler(self):
# The actual sampler used for generating indices for `_DatasetFetcher`
# (see _utils/fetch.py) to read data at each time. This would be
# `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
# We can't change `.sampler` and `.batch_sampler` attributes for BC
# reasons.
if self._auto_collation:
return self.batch_sampler
else:
return self.sampler
def __len__(self) -> int:
if self._dataset_kind == _DatasetKind.Iterable:
# NOTE [ IterableDataset and __len__ ]
#
# For `IterableDataset`, `__len__` could be inaccurate when one naively
# does multi-processing data loading, since the samples will be duplicated.
# However, no real use case should be actually using that behavior, so
# it should count as a user error. We should generally trust user
# code to do the proper thing (e.g., configure each replica differently
# in `__iter__`), and give us the correct `__len__` if they choose to
# implement it (this will still throw if the dataset does not implement
# a `__len__`).
#
# To provide a further warning, we track if `__len__` was called on the
# `DataLoader`, save the returned value in `self._len_called`, and warn
# if the iterator ends up yielding more than this number of samples.
# Cannot statically verify that dataset is Sized
length = self._IterableDataset_len_called = len(self.dataset) # type: ignore[assignment, arg-type]
if self.batch_size is not None: # IterableDataset doesn't allow custom sampler or batch_sampler
from math import ceil
if self.drop_last:
length = length // self.batch_size
else:
length = ceil(length / self.batch_size)
return length
else:
return len(self._index_sampler)
def check_worker_number_rationality(self):
# This function check whether the dataloader's worker number is rational based on
# current system's resource. Current rule is that if the number of workers this
# Dataloader will create is bigger than the number of logical cpus that is allowed to
# use, than we will pop up a warning to let user pay attention.
#
# eg. If current system has 2 physical CPUs with 16 cores each. And each core support 2
# threads, then the total logical cpus here is 2 * 16 * 2 = 64. Let's say current
# DataLoader process can use half of them which is 32, then the rational max number of
# worker that initiated from this process is 32.
# Now, let's say the created DataLoader has num_works = 40, which is bigger than 32.
# So the warning message is triggered to notify the user to lower the worker number if
# necessary.
#
#
# [Note] Please note that this function repects `cpuset` only when os.sched_getaffinity is
# available (available in most of Linux system, but not OSX and Windows).
# When os.sched_getaffinity is not available, os.cpu_count() is called instead, but
# it doesn't repect cpuset.
# We don't take threading into account since each worker process is single threaded
# at this time.
#
# We don't set any threading flags (eg. OMP_NUM_THREADS, MKL_NUM_THREADS, etc)
# other than `torch.set_num_threads` to 1 in the worker process, if the passing
# in functions use 3rd party modules that rely on those threading flags to determine
# how many thread to create (eg. numpy, etc), then it is caller's responsibility to
# set those flags correctly.
def _create_warning_msg(num_worker_suggest, num_worker_created, cpuset_checked):
suggested_max_worker_msg = ((
"Our suggested max number of worker in current system is {}{}, which is smaller "
"than what this DataLoader is going to create.").format(
num_worker_suggest,
("" if cpuset_checked else " (`cpuset` is not taken into account)"))
) if num_worker_suggest is not None else (
"DataLoader is not able to compute a suggested max number of worker in current system.")
warn_msg = (
"This DataLoader will create {} worker processes in total. {} "
"Please be aware that excessive worker creation might get DataLoader running slow or even freeze, "
"lower the worker number to avoid potential slowness/freeze if necessary.").format(
num_worker_created,
suggested_max_worker_msg)
return warn_msg
if not self.num_workers or self.num_workers == 0:
return
# try to compute a suggested max number of worker based on system's resource
max_num_worker_suggest = None
cpuset_checked = False
if hasattr(os, 'sched_getaffinity'):
try:
max_num_worker_suggest = len(os.sched_getaffinity(0))
cpuset_checked = True
except Exception:
pass
if max_num_worker_suggest is None:
# os.cpu_count() could return Optional[int]
# get cpu count first and check None in order to satify mypy check
cpu_count = os.cpu_count()
if cpu_count is not None:
max_num_worker_suggest = cpu_count
if max_num_worker_suggest is None:
warnings.warn(_create_warning_msg(
max_num_worker_suggest,
self.num_workers,
cpuset_checked))
return
if self.num_workers > max_num_worker_suggest:
warnings.warn(_create_warning_msg(
max_num_worker_suggest,
self.num_workers,
cpuset_checked))
class _BaseDataLoaderIter(object):
def __init__(self, loader: DataLoader) -> None:
self._dataset = loader.dataset
self._dataset_kind = loader._dataset_kind
self._IterableDataset_len_called = loader._IterableDataset_len_called
self._auto_collation = loader._auto_collation
self._drop_last = loader.drop_last
self._index_sampler = loader._index_sampler
self._num_workers = loader.num_workers
self._prefetch_factor = loader.prefetch_factor
self._pin_memory = loader.pin_memory and torch.cuda.is_available()
self._timeout = loader.timeout
self._collate_fn = loader.collate_fn
self._sampler_iter = iter(self._index_sampler)
self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
self._persistent_workers = loader.persistent_workers
self._num_yielded = 0
self._profile_name = "enumerate(DataLoader)#{}.__next__".format(self.__class__.__name__)
def __iter__(self) -> '_BaseDataLoaderIter':
return self
def _reset(self, loader, first_iter=False):
self._sampler_iter = iter(self._index_sampler)
self._num_yielded = 0
self._IterableDataset_len_called = loader._IterableDataset_len_called
def _next_index(self):
return next(self._sampler_iter) # may raise StopIteration
def _next_data(self):
raise NotImplementedError
def __next__(self) -> Any:
with torch.autograd.profiler.record_function(self._profile_name):
if self._sampler_iter is None:
self._reset()
data = self._next_data()
self._num_yielded += 1
if self._dataset_kind == _DatasetKind.Iterable and \
self._IterableDataset_len_called is not None and \
self._num_yielded > self._IterableDataset_len_called:
warn_msg = ("Length of IterableDataset {} was reported to be {} (when accessing len(dataloader)), but {} "
"samples have been fetched. ").format(self._dataset, self._IterableDataset_len_called,
self._num_yielded)
if self._num_workers > 0:
warn_msg += ("For multiprocessing data-loading, this could be caused by not properly configuring the "
"IterableDataset replica at each worker. Please see "
"https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples.")
warnings.warn(warn_msg)
return data
next = __next__ # Python 2 compatibility
def __len__(self) -> int:
return len(self._index_sampler)
def __getstate__(self):
# TODO: add limited pickling support for sharing an iterator
# across multiple threads for HOGWILD.
# Probably the best way to do this is by moving the sample pushing
# to a separate thread and then just sharing the data queue
# but signalling the end is tricky without a non-blocking API
raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
def __init__(self, loader):
super(_SingleProcessDataLoaderIter, self).__init__(loader)
assert self._timeout == 0
assert self._num_workers == 0
self._dataset_fetcher = _DatasetKind.create_fetcher(
self._dataset_kind, self._dataset, self._auto_collation, self._collate_fn, self._drop_last)
def _next_data(self):
index = self._next_index() # may raise StopIteration
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
if self._pin_memory:
data = _utils.pin_memory.pin_memory(data)
return data
class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
r"""Iterates once over the DataLoader's dataset, as specified by the sampler"""
# NOTE [ Data Loader Multiprocessing Shutdown Logic ]
#
# Preliminary:
#
# Our data model looks like this (queues are indicated with curly brackets):
#
# main process ||
# | ||
# {index_queue} ||
# | ||
# worker processes || DATA
# | ||
# {worker_result_queue} || FLOW
# | ||
# pin_memory_thread of main process || DIRECTION
# | ||
# {data_queue} ||
# | ||
# data output \/
#
# P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
# `pin_memory=False`.
#
#
# Terminating multiprocessing logic requires very careful design. In
# particular, we need to make sure that
#
# 1. The iterator gracefully exits the workers when its last reference is
# gone or it is depleted.
#
# In this case, the workers should be gracefully exited because the
# main process may still need to continue to run, and we want cleaning
# up code in the workers to be executed (e.g., releasing GPU memory).
# Naturally, we implement the shutdown logic in `__del__` of
# DataLoaderIterator.
#
# We delay the discussion on the logic in this case until later.
#
# 2. The iterator exits the workers when the loader process and/or worker
# processes exits normally or with error.
#
# We set all workers and `pin_memory_thread` to have `daemon=True`.
#
# You may ask, why can't we make the workers non-daemonic, and
# gracefully exit using the same logic as we have in `__del__` when the
# iterator gets deleted (see 1 above)?
#
# First of all, `__del__` is **not** guaranteed to be called when
# interpreter exits. Even if it is called, by the time it executes,
# many Python core library resources may alreay be freed, and even
# simple things like acquiring an internal lock of a queue may hang.
# Therefore, in this case, we actually need to prevent `__del__` from
# being executed, and rely on the automatic termination of daemonic
# children.
#
# Thus, we register an `atexit` hook that sets a global flag
# `_utils.python_exit_status`. Since `atexit` hooks are executed in the
# reverse order of registration, we are guaranteed that this flag is
# set before library resources we use are freed (which, at least in
# CPython, is done via an `atexit` handler defined in
# `multiprocessing/util.py`
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/util.py#L320-L362
# registered when an object requiring this mechanism is first
# created, e.g., `mp.Queue`
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/context.py#L100-L103
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/queues.py#L29
# )
#
# So in `__del__`, we check if `_utils.python_exit_status` is set or
# `None` (freed), and perform no-op if so.
#
# However, simply letting library clean-up codes run can also be bad,
# because such codes (i.e., `multiprocessing.util._exit_function()`)
# include join putting threads for `mp.Queue`, which can be blocking.
# Hence, the main process putting threads are called with
# `cancel_join_thread` at creation. See later section
# [ 3b. A process won't hang when putting into a queue; ]
# for more details.
#
# Here are two example cases where library clean-up codes can run
# before `__del__` is called:
#
# 1. If we hold onto a reference to the iterator, it more often
# than not tries to do `multiprocessing` library cleaning before
# clearing the alive referenced objects (https://github.com/pytorch/pytorch/issues/48666)
# and thus prevents our cleaning-up code to run first.
#
# 2. A similar issue araises when a `DataLoader` is used in a subprocess.
# When a process ends, it shuts the all its daemonic children
# down with a SIGTERM (instead of joining them without a timeout).
# Simiarly for threads, but by a different mechanism. This fact,
# together with a few implementation details of multiprocessing, forces
# us to make workers daemonic. All of our problems arise when a
# DataLoader is used in a subprocess, and are caused by multiprocessing
# code which looks more or less like this:
#
# try:
# your_function_using_a_dataloader()
# finally:
# multiprocessing.util._exit_function()
#
# The joining/termination mentioned above happens inside
# `_exit_function()`. Now, if `your_function_using_a_dataloader()`
# throws, the stack trace stored in the exception will prevent the
# frame which uses `DataLoaderIter` to be freed. If the frame has any
# reference to the `DataLoaderIter` (e.g., in a method of the iter),
# its `__del__`, which starts the shutdown procedure, will not be
# called. That, in turn, means that workers aren't notified. Attempting
# to join in `_exit_function` will then result in a hang.
#
# For context, `_exit_function` is also registered as an `atexit` call.
# So it is unclear to me (@ssnl) why this is needed in a finally block.
# The code dates back to 2008 and there is no comment on the original
# PEP 371 or patch https://bugs.python.org/issue3050 (containing both
# the finally block and the `atexit` registration) that explains this.
#
#
# Finally, another choice is to just shutdown workers with logic in 1
# above whenever we see an error in `next`. This isn't ideal because
# a. It prevents users from using try-catch to resume data loading.
# b. It doesn't prevent hanging if users have references to the
# iterator.
#
# 3. All processes exit if any of them die unexpectedly by fatal signals.
#
# As shown above, the workers are set as daemonic children of the main
# process. However, automatic cleaning-up of such child processes only
# happens if the parent process exits gracefully (e.g., not via fatal
# signals like SIGKILL). So we must ensure that each process will exit
# even the process that should send/receive data to/from it were
# killed, i.e.,
#
# a. A process won't hang when getting from a queue.
#
# Even with carefully designed data dependencies (i.e., a `put()`
# always corresponding to a `get()`), hanging on `get()` can still
# happen when data in queue is corrupted (e.g., due to
# `cancel_join_thread` or unexpected exit).
#
# For child exit, we set a timeout whenever we try to get data
# from `data_queue`, and check the workers' status on each timeout
# and error.
# See `_DataLoaderiter._get_batch()` and
# `_DataLoaderiter._try_get_data()` for details.
#
# Additionally, for child exit on non-Windows platforms, we also
# register a SIGCHLD handler (which is supported on Windows) on
# the main process, which checks if any of the workers fail in the
# (Python) handler. This is more efficient and faster in detecting
# worker failures, compared to only using the above mechanism.
# See `DataLoader.cpp` and `_utils/signal_handling.py` for details.
#
# For `.get()` calls where the sender(s) is not the workers, we
# guard them with timeouts, and check the status of the sender
# when timeout happens:
# + in the workers, the `_utils.worker.ManagerWatchdog` class
# checks the status of the main process.
# + if `pin_memory=True`, when getting from `pin_memory_thread`,
# check `pin_memory_thread` status periodically until `.get()`
# returns or see that `pin_memory_thread` died.
#
# b. A process won't hang when putting into a queue;
#
# We use `mp.Queue` which has a separate background thread to put
# objects from an unbounded buffer array. The background thread is
# daemonic and usually automatically joined when the process
# *exits*.
#
# In case that the receiver has ended abruptly while
# reading from the pipe, the join will hang forever. The usual
# solution for this in Python is calling `q.cancel_join_thread`,
# which prevents automatically joining it when finalizing
# (exiting).
#
# Nonetheless, `cancel_join_thread` must only be called when the
# queue is **not** going to be read from or write into by another
# process, because it may hold onto a lock or leave corrupted data
# in the queue, leading other readers/writers to hang.
#
# Hence,
# + For worker processes, we only do so (for their output
# queues, i.e., `worker_result_queue`) before exiting.
# + For `pin_memory_thread`, its output queue `data_queue` is a
# `queue.Queue` that does blocking `put` if the queue is full.
# So there is no above problem, but as a result, in
# `_pin_memory_loop`, we do need to wrap the `put` in a loop
# that breaks not only upon success, but also when the main
# process stops reading, i.e., is shutting down.
# + For loader process, we `cancel_join_thread()` for all
# `_index_queues` because the whole purpose of workers and
# `pin_memory_thread` is to serve the loader process. If
# loader process is already exiting, we don't really care if
# the queues are corrupted.
#
#
# Now let's get back to 1:
# how we gracefully exit the workers when the last reference to the
# iterator is gone.
#
# To achieve this, we implement the following logic along with the design
# choices mentioned above:
#
# `workers_done_event`:
# A `multiprocessing.Event` shared among the main process and all worker
# processes. This is used to signal the workers that the iterator is
# shutting down. After it is set, they will not send processed data to
# queues anymore, and only wait for the final `None` before exiting.
# `done_event` isn't strictly needed. I.e., we can just check for `None`
# from the input queue, but it allows us to skip wasting resources
# processing data if we are already shutting down.
#
# `pin_memory_thread_done_event`:
# A `threading.Event` for a similar purpose to that of
# `workers_done_event`, but is for the `pin_memory_thread`. The reason
# that separate events are needed is that `pin_memory_thread` reads from
# the output queue of the workers. But the workers, upon seeing that
# `workers_done_event` is set, only wants to see the final `None`, and is
# not required to flush all data in the output queue (e.g., it may call
# `cancel_join_thread` on that queue if its `IterableDataset` iterator
# happens to exhaust coincidentally, which is out of the control of the
# main process). Thus, since we will exit `pin_memory_thread` before the
# workers (see below), two separete events are used.
#
# NOTE: In short, the protocol is that the main process will set these
# `done_event`s and then the corresponding processes/threads a `None`,
# and that they may exit at any time after receiving the `None`.
#
# NOTE: Using `None` as the final signal is valid, since normal data will
# always be a 2-tuple with the 1st element being the index of the data
# transferred (different from dataset index/key), and the 2nd being
# either the dataset key or the data sample (depending on which part
# of the data model the queue is at).
#
# [ worker processes ]
# While loader process is alive:
# Get from `index_queue`.
# If get anything else,
# Check `workers_done_event`.
# If set, continue to next iteration
# i.e., keep getting until see the `None`, then exit.
# Otherwise, process data:
# If is fetching from an `IterableDataset` and the iterator
# is exhausted, send an `_IterableDatasetStopIteration`
# object to signal iteration end. The main process, upon
# receiving such an object, will send `None` to this
# worker and not use the corresponding `index_queue`
# anymore.
# If timed out,
# No matter `workers_done_event` is set (still need to see `None`)
# or not, must continue to next iteration.
# (outside loop)
# If `workers_done_event` is set, (this can be False with `IterableDataset`)
# `data_queue.cancel_join_thread()`. (Everything is ending here:
# main process won't read from it;
# other workers will also call
# `cancel_join_thread`.)
#
# [ pin_memory_thread ]
# # No need to check main thread. If this thread is alive, the main loader
# # thread must be alive, because this thread is set as daemonic.
# While `pin_memory_thread_done_event` is not set:
# Get from `index_queue`.
# If timed out, continue to get in the next iteration.
# Otherwise, process data.
# While `pin_memory_thread_done_event` is not set:
# Put processed data to `data_queue` (a `queue.Queue` with blocking put)
# If timed out, continue to put in the next iteration.
# Otherwise, break, i.e., continuing to the out loop.
#
# NOTE: we don't check the status of the main thread because
# 1. if the process is killed by fatal signal, `pin_memory_thread`
# ends.
# 2. in other cases, either the cleaning-up in __del__ or the
# automatic exit of daemonic thread will take care of it.
# This won't busy-wait either because `.get(timeout)` does not
# busy-wait.
#
# [ main process ]
# In the DataLoader Iter's `__del__`
# b. Exit `pin_memory_thread`
# i. Set `pin_memory_thread_done_event`.
# ii Put `None` in `worker_result_queue`.
# iii. Join the `pin_memory_thread`.
# iv. `worker_result_queue.cancel_join_thread()`.
#
# c. Exit the workers.
# i. Set `workers_done_event`.
# ii. Put `None` in each worker's `index_queue`.
# iii. Join the workers.
# iv. Call `.cancel_join_thread()` on each worker's `index_queue`.
#
# NOTE: (c) is better placed after (b) because it may leave corrupted
# data in `worker_result_queue`, which `pin_memory_thread`
# reads from, in which case the `pin_memory_thread` can only
# happen at timeing out, which is slow. Nonetheless, same thing
# happens if a worker is killed by signal at unfortunate times,
# but in other cases, we are better off having a non-corrupted
# `worker_result_queue` for `pin_memory_thread`.
#
# NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)
# can be omitted
#
# NB: `done_event`s isn't strictly needed. E.g., we can just check for
# `None` from `index_queue`, but it allows us to skip wasting resources
# processing indices already in `index_queue` if we are already shutting
# down.
def __init__(self, loader):
super(_MultiProcessingDataLoaderIter, self).__init__(loader)
assert self._num_workers > 0
assert self._prefetch_factor > 0
if loader.multiprocessing_context is None:
multiprocessing_context = multiprocessing
else:
multiprocessing_context = loader.multiprocessing_context
self._worker_init_fn = loader.worker_init_fn
self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))
# No certainty which module multiprocessing_context is
self._worker_result_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]
self._worker_pids_set = False
self._shutdown = False
self._workers_done_event = multiprocessing_context.Event()
self._index_queues = []
self._workers = []
for i in range(self._num_workers):
# No certainty which module multiprocessing_context is
index_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]
# Need to `cancel_join_thread` here!
# See sections (2) and (3b) above.
index_queue.cancel_join_thread()
w = multiprocessing_context.Process(
target=_utils.worker._worker_loop,
args=(self._dataset_kind, self._dataset, index_queue,
self._worker_result_queue, self._workers_done_event,
self._auto_collation, self._collate_fn, self._drop_last,
self._base_seed, self._worker_init_fn, i, self._num_workers,
self._persistent_workers))
w.daemon = True
# NB: Process.start() actually take some time as it needs to
# start a process and pass the arguments over via a pipe.
# Therefore, we only add a worker to self._workers list after
# it started, so that we do not call .join() if program dies
# before it starts, and __del__ tries to join but will get:
# AssertionError: can only join a started process.
w.start()
self._index_queues.append(index_queue)
self._workers.append(w)
if self._pin_memory:
self._pin_memory_thread_done_event = threading.Event()
# Queue is not type-annotated
self._data_queue = queue.Queue() # type: ignore[var-annotated]
pin_memory_thread = threading.Thread(
target=_utils.pin_memory._pin_memory_loop,
args=(self._worker_result_queue, self._data_queue,
torch.cuda.current_device(),
self._pin_memory_thread_done_event))
pin_memory_thread.daemon = True
pin_memory_thread.start()
# Similar to workers (see comment above), we only register
# pin_memory_thread once it is started.
self._pin_memory_thread = pin_memory_thread
else:
self._data_queue = self._worker_result_queue
# .pid can be None only before process is spawned (not the case, so ignore)
_utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers)) # type: ignore[misc]
_utils.signal_handling._set_SIGCHLD_handler()
self._worker_pids_set = True
self._reset(loader, first_iter=True)
def _reset(self, loader, first_iter=False):
super()._reset(loader, first_iter)
self._send_idx = 0 # idx of the next task to be sent to workers
self._rcvd_idx = 0 # idx of the next task to be returned in __next__
# information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).
# map: task idx => - (worker_id,) if data isn't fetched (outstanding)
# \ (worker_id, data) if data is already fetched (out-of-order)
self._task_info = {}
self._tasks_outstanding = 0 # always equal to count(v for v in task_info.values() if len(v) == 1)
# A list of booleans representing whether each worker still has work to
# do, i.e., not having exhausted its iterable dataset object. It always
# contains all `True`s if not using an iterable-style dataset
# (i.e., if kind != Iterable).
# Not that this indicates that a worker still has work to do *for this epoch*.
# It does not mean that a worker is dead. In case of `_persistent_workers`,
# the worker will be reset to available in the next epoch.
self._workers_status = [True for i in range(self._num_workers)]
# We resume the prefetching in case it was enabled
if not first_iter:
for idx in range(self._num_workers):
self._index_queues[idx].put(_utils.worker._ResumeIteration())
resume_iteration_cnt = self._num_workers
while resume_iteration_cnt > 0:
return_idx, return_data = self._get_data()
if isinstance(return_idx, _utils.worker._ResumeIteration):
assert return_data is None
resume_iteration_cnt -= 1
# prime the prefetch loop
for _ in range(self._prefetch_factor * self._num_workers):
self._try_put_index()
def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):
# Tries to fetch data from `self._data_queue` once for a given timeout.
# This can also be used as inner loop of fetching without timeout, with
# the sender status as the loop condition.
#
# This raises a `RuntimeError` if any worker died expectedly. This error
# can come from either the SIGCHLD handler in `_utils/signal_handling.py`
# (only for non-Windows platforms), or the manual check below on errors
# and timeouts.
#
# Returns a 2-tuple:
# (bool: whether successfully get data, any: data if successful else None)
try:
data = self._data_queue.get(timeout=timeout)
return (True, data)
except Exception as e:
# At timeout and error, we manually check whether any worker has
# failed. Note that this is the only mechanism for Windows to detect
# worker failures.
failed_workers = []
for worker_id, w in enumerate(self._workers):
if self._workers_status[worker_id] and not w.is_alive():
failed_workers.append(w)
self._mark_worker_as_unavailable(worker_id)
if len(failed_workers) > 0:
pids_str = ', '.join(str(w.pid) for w in failed_workers)
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
if isinstance(e, queue.Empty):
return (False, None)
import tempfile
import errno
try:
# Raise an exception if we are this close to the FDs limit.
# Apparently, trying to open only one file is not a sufficient
# test.
# See NOTE [ DataLoader on Linux and open files limit ]
fds_limit_margin = 10
fs = [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]
except OSError as e:
if e.errno == errno.EMFILE:
raise RuntimeError(
"Too many open files. Communication with the"
" workers is no longer possible. Please increase the"
" limit using `ulimit -n` in the shell or change the"
" sharing strategy by calling"
" `torch.multiprocessing.set_sharing_strategy('file_system')`"
" at the beginning of your code") from None
raise
# NOTE [ DataLoader on Linux and open files limit ]
#
# On Linux when DataLoader is used with multiprocessing we pass the data between
# the root process and the workers through SHM files. We remove those files from
# the filesystem as soon as they are created and keep them alive by
# passing around their file descriptors through AF_UNIX sockets. (See
# docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in
# the wiki (https://github.com/pytorch/pytorch/wiki).)
#
# This sometimes leads us to exceeding the open files limit. When that happens,
# and the offending file descriptor is coming over a socket, the `socket` Python
# package silently strips the file descriptor from the message, setting only the
# `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that
# it _indicates that some control data were discarded due to lack of space in
# the buffer for ancillary data_). This might reflect the C implementation of
# AF_UNIX sockets.
#
# This behaviour can be reproduced with the script and instructions at the
# bottom of this note.
#
# When that happens, the standard Python `multiprocessing` (and not
# `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`
#
# Sometimes, instead of the FD being stripped, you may get an `OSError:
# Too many open files`, both in the script below and in DataLoader. However,
# this is rare and seems to be nondeterministic.
#
#
# #!/usr/bin/env python3
# import sys
# import socket
# import os
# import array
# import shutil
# import socket
#
#
# if len(sys.argv) != 4:
# print("Usage: ", sys.argv[0], " tmp_dirname iteration (send|recv)")
# sys.exit(1)
#
# if __name__ == '__main__':
# dirname = sys.argv[1]
# sock_path = dirname + "/sock"
# iterations = int(sys.argv[2])
# def dummy_path(i):
# return dirname + "/" + str(i) + ".dummy"
#
#
# if sys.argv[3] == 'send':
# while not os.path.exists(sock_path):
# pass
# client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
# client.connect(sock_path)
# for i in range(iterations):
# fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)
# ancdata = array.array('i', [fd])
# msg = bytes([i % 256])
# print("Sending fd ", fd, " (iteration #", i, ")")
# client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])
#
#
# else:
# assert sys.argv[3] == 'recv'
#
# if os.path.exists(dirname):
# raise Exception("Directory exists")
#
# os.mkdir(dirname)
#
# print("Opening socket...")
# server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
# server.bind(sock_path)
#
# print("Listening...")
# for i in range(iterations):
# a = array.array('i')
# msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))
# assert(len(ancdata) == 1)
# cmsg_level, cmsg_type, cmsg_data = ancdata[0]
# a.frombytes(cmsg_data)
# print("Received fd ", a[0], " (iteration #", i, ")")
#
# shutil.rmtree(dirname)
#
# Steps to reproduce:
#
# 1. Run two shells and set lower file descriptor limit in the receiving one:
# (shell1) ulimit -n 1020
# (shell2) ulimit -n 1022
#
# 2. Run the script above with the `recv` option in the first shell
# (shell1) ./test_socket.py sock_tmp 1017 recv
#
# 3. Run the script with the `send` option in the second shell:
# (shell2) ./test_socket.py sock_tmp 1017 send
def _get_data(self):
# Fetches data from `self._data_queue`.
#
# We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,
# which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`
# in a loop. This is the only mechanism to detect worker failures for
# Windows. For other platforms, a SIGCHLD handler is also used for
# worker failure detection.
#
# If `pin_memory=True`, we also need check if `pin_memory_thread` had
# died at timeouts.
if self._timeout > 0:
success, data = self._try_get_data(self._timeout)
if success:
return data
else:
raise RuntimeError('DataLoader timed out after {} seconds'.format(self._timeout))
elif self._pin_memory:
while self._pin_memory_thread.is_alive():
success, data = self._try_get_data()
if success:
return data
else:
# while condition is false, i.e., pin_memory_thread died.
raise RuntimeError('Pin memory thread exited unexpectedly')
# In this case, `self._data_queue` is a `queue.Queue`,. But we don't
# need to call `.task_done()` because we don't use `.join()`.
else:
while True:
success, data = self._try_get_data()
if success:
return data
def _next_data(self):
while True:
# If the worker responsible for `self._rcvd_idx` has already ended
# and was unable to fulfill this task (due to exhausting an `IterableDataset`),
# we try to advance `self._rcvd_idx` to find the next valid index.
#
# This part needs to run in the loop because both the `self._get_data()`
# call and `_IterableDatasetStopIteration` check below can mark
# extra worker(s) as dead.
while self._rcvd_idx < self._send_idx:
info = self._task_info[self._rcvd_idx]
worker_id = info[0]
if len(info) == 2 or self._workers_status[worker_id]: # has data or is still active
break
del self._task_info[self._rcvd_idx]
self._rcvd_idx += 1
else:
# no valid `self._rcvd_idx` is found (i.e., didn't break)
if not self._persistent_workers:
self._shutdown_workers()
raise StopIteration
# Now `self._rcvd_idx` is the batch index we want to fetch
# Check if the next sample has already been generated
if len(self._task_info[self._rcvd_idx]) == 2:
data = self._task_info.pop(self._rcvd_idx)[1]
return self._process_data(data)
assert not self._shutdown and self._tasks_outstanding > 0
idx, data = self._get_data()
self._tasks_outstanding -= 1
if self._dataset_kind == _DatasetKind.Iterable:
# Check for _IterableDatasetStopIteration
if isinstance(data, _utils.worker._IterableDatasetStopIteration):
if self._persistent_workers:
self._workers_status[data.worker_id] = False
else:
self._mark_worker_as_unavailable(data.worker_id)
self._try_put_index()
continue
if idx != self._rcvd_idx:
# store out-of-order samples
self._task_info[idx] += (data,)
else:
del self._task_info[idx]
return self._process_data(data)
def _try_put_index(self):
assert self._tasks_outstanding < self._prefetch_factor * self._num_workers
try:
index = self._next_index()
except StopIteration:
return
for _ in range(self._num_workers): # find the next active worker, if any
worker_queue_idx = next(self._worker_queue_idx_cycle)
if self._workers_status[worker_queue_idx]:
break
else:
# not found (i.e., didn't break)
return
self._index_queues[worker_queue_idx].put((self._send_idx, index))
self._task_info[self._send_idx] = (worker_queue_idx,)
self._tasks_outstanding += 1
self._send_idx += 1
def _process_data(self, data):
self._rcvd_idx += 1
self._try_put_index()
if isinstance(data, ExceptionWrapper):
data.reraise()
return data
def _mark_worker_as_unavailable(self, worker_id, shutdown=False):
# Mark a worker as having finished its work e.g., due to
# exhausting an `IterableDataset`. This should be used only when this
# `_MultiProcessingDataLoaderIter` is going to continue running.
assert self._workers_status[worker_id] or (self._persistent_workers and shutdown)
# Signal termination to that specific worker.
q = self._index_queues[worker_id]
# Indicate that no more data will be put on this queue by the current
# process.
q.put(None)
# Note that we don't actually join the worker here, nor do we remove the
# worker's pid from C side struct because (1) joining may be slow, and
# (2) since we don't join, the worker may still raise error, and we
# prefer capturing those, rather than ignoring them, even though they
# are raised after the worker has finished its job.
# Joinning is deferred to `_shutdown_workers`, which it is called when
# all workers finish their jobs (e.g., `IterableDataset` replicas) or
# when this iterator is garbage collected.
self._workers_status[worker_id] = False
assert self._workers_done_event.is_set() == shutdown
def _shutdown_workers(self):
# Called when shutting down this `_MultiProcessingDataLoaderIter`.
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on
# the logic of this function.
python_exit_status = _utils.python_exit_status
if python_exit_status is True or python_exit_status is None:
# See (2) of the note. If Python is shutting down, do no-op.
return
# Normal exit when last reference is gone / iterator is depleted.
# See (1) and the second half of the note.
if not self._shutdown:
self._shutdown = True
try:
# Normal exit when last reference is gone / iterator is depleted.
# See (1) and the second half of the note.
# Exit `pin_memory_thread` first because exiting workers may leave
# corrupted data in `worker_result_queue` which `pin_memory_thread`
# reads from.
if hasattr(self, '_pin_memory_thread'):
# Use hasattr in case error happens before we set the attribute.
self._pin_memory_thread_done_event.set()
# Send something to pin_memory_thread in case it is waiting
# so that it can wake up and check `pin_memory_thread_done_event`
self._worker_result_queue.put((None, None))
self._pin_memory_thread.join()
self._worker_result_queue.cancel_join_thread()
self._worker_result_queue.close()
# Exit workers now.
self._workers_done_event.set()
for worker_id in range(len(self._workers)):
# Get number of workers from `len(self._workers)` instead of
# `self._num_workers` in case we error before starting all
# workers.
# If we are using workers_status with persistent_workers
# we have to shut it down because the worker is paused
if self._persistent_workers or self._workers_status[worker_id]:
self._mark_worker_as_unavailable(worker_id, shutdown=True)
for w in self._workers:
# We should be able to join here, but in case anything went
# wrong, we set a timeShortcuts
"""Provides an API for writing protocol buffers to event files to be
consumed by TensorBoard for visualization."""
import os
import time
import torch
from tensorboard.compat import tf
from tensorboard.compat.proto.event_pb2 import SessionLog
from tensorboard.compat.proto.event_pb2 import Event
from tensorboard.compat.proto import event_pb2
from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
from tensorboard.summary.writer.event_file_writer import EventFileWriter
from ._convert_np import make_np
from ._embedding import (
make_mat, make_sprite, make_tsv, write_pbtxt, get_embedding_info,
)
from ._onnx_graph import load_onnx_graph
from ._pytorch_graph import graph
from ._utils import figure_to_image
from .summary import (
scalar, histogram, histogram_raw, image, audio, text,
pr_curve, pr_curve_raw, video, custom_scalars, image_boxes, mesh, hparams
)
class FileWriter(object):
"""Writes protocol buffers to event files to be consumed by TensorBoard.
The `FileWriter` class provides a mechanism to create an event file in a
given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
"""
def __init__(self, log_dir, max_queue=10, flush_secs=120, filename_suffix=''):
"""Creates a `FileWriter` and an event file.
On construction the writer creates a new event file in `log_dir`.
The other arguments to the constructor control the asynchronous writes to
the event file.
Args:
log_dir: A string. Directory where event file will be written.
max_queue: Integer. Size of the queue for pending events and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs: Number. How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix: A string. Suffix added to all event filenames
in the log_dir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
"""
# Sometimes PosixPath is passed in and we need to coerce it to
# a string in all cases
# TODO: See if we can remove this in the future if we are
# actually the ones passing in a PosixPath
log_dir = str(log_dir)
self.event_writer = EventFileWriter(
log_dir, max_queue, flush_secs, filename_suffix)
def get_logdir(self):
"""Returns the directory where event file will be written."""
return self.event_writer.get_logdir()
def add_event(self, event, step=None, walltime=None):
"""Adds an event to the event file.
Args:
event: An `Event` protocol buffer.
step: Number. Optional global step value for training process
to record with the event.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time()) seconds after epoch
"""
event.wall_time = time.time() if walltime is None else walltime
if step is not None:
# Make sure step is converted from numpy or other formats
# since protobuf might not convert depending on version
event.step = int(step)
self.event_writer.add_event(event)
def add_summary(self, summary, global_step=None, walltime=None):
"""Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` protocol buffer
and adds it to the event file.
Args:
summary: A `Summary` protocol buffer.
global_step: Number. Optional global step value for training process
to record with the summary.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time()) seconds after epoch
"""
event = event_pb2.Event(summary=summary)
self.add_event(event, global_step, walltime)
def add_graph(self, graph_profile, walltime=None):
"""Adds a `Graph` and step stats protocol buffer to the event file.
Args:
graph_profile: A `Graph` and step stats protocol buffer.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time()) seconds after epoch
"""
graph = graph_profile[0]
stepstats = graph_profile[1]
event = event_pb2.Event(graph_def=graph.SerializeToString())
self.add_event(event, None, walltime)
trm = event_pb2.TaggedRunMetadata(
tag='step1', run_metadata=stepstats.SerializeToString())
event = event_pb2.Event(tagged_run_metadata=trm)
self.add_event(event, None, walltime)
def add_onnx_graph(self, graph, walltime=None):
"""Adds a `Graph` protocol buffer to the event file.
Args:
graph: A `Graph` protocol buffer.
walltime: float. Optional walltime to override the default (current)
_get_file_writerfrom time.time())
"""
event = event_pb2.Event(graph_def=graph.SerializeToString())
self.add_event(event, None, walltime)
def flush(self):
"""Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
"""
self.event_writer.flush()
def close(self):
"""Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
"""
self.event_writer.close()
def reopen(self):
"""Reopens the EventFileWriter.
Can be called after `close()` to add more events in the same directory.
The events will go into a new events file.
Does nothing if the EventFileWriter was not closed.
"""
self.event_writer.reopen()
[docs]class SummaryWriter(object):
"""Writes entries directly to event files in the log_dir to be
consumed by TensorBoard.
The `SummaryWriter` class provides a high-level API to create an event file
in a given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
"""
[docs] def __init__(self, log_dir=None, comment='', purge_step=None, max_queue=10,
flush_secs=120, filename_suffix=''):
"""Creates a `SummaryWriter` that will write out events and summaries
to the event file.
Args:
log_dir (string): Save directory location. Default is
runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
Use hierarchical folder structure to compare
between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
for each new experiment to compare across them.
comment (string): Comment log_dir suffix appended to the default
``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
purge_step (int):
When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
any events whose global_step larger or equal to :math:`T` will be
purged and hidden from TensorBoard.
Note that crashed and resumed experiments should have the same ``log_dir``.
max_queue (int): Size of the queue for pending events and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs (int): How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix (string): Suffix added to all event filenames in
the log_dir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
Examples::
from torch.utils.tensorboard import SummaryWriter
# create a summary writer with automatically generated folder name.
writer = SummaryWriter()
# folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
# create a summary writer using the specified folder name.
writer = SummaryWriter("my_experiment")
# folder location: my_experiment
# create a summary writer with comment appended.
writer = SummaryWriter(comment="LR_0.1_BATCH_16")
# folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
"""
torch._C._log_api_usage_once("tensorboard.create.summarywriter")
if not log_dir:
import socket
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join(
'runs', current_time + '_' + socket.gethostname() + comment)
self.log_dir = log_dir
self.purge_step = purge_step
self.max_queue = max_queue
self.flush_secs = flush_secs
self.filename_suffix = filename_suffix
# Initialize the file writers, but they can be cleared out on close
# and recreated later as needed.
self.file_writer = self.all_writers = None
self._get_file_writer()
# Create default bins for histograms, see generate_testdata.py in tensorflow/tensorboard
v = 1E-12
buckets = []
neg_buckets = []
while v < 1E20:
buckets.append(v)
neg_buckets.append(-v)
v *= 1.1
self.default_bins = neg_buckets[::-1] + [0] + buckets
def _check_caffe2_blob(self, item):
"""
Caffe2 users have the option of passing a string representing the name of
a blob in the workspace instead of passing the actual Tensor/array containing
the numeric values. Thus, we need to check if we received a string as input
instead of an actual Tensor/array, and if so, we need to fetch the Blob
from the workspace corresponding to that name. Fetching can be done with the
following:
from caffe2.python import workspace (if not already imported)
workspace.FetchBlob(blob_name)
workspace.FetchBlobs([blob_name1, blob_name2, ...])
"""
return isinstance(item, str)
def _get_file_writer(self):
"""Returns the default FileWriter instance. Recreates it if closed."""
if self.all_writers is None or self.file_writer is None:
self.file_writer = FileWriter(self.log_dir, self.max_queue,
self.flush_secs, self.filename_suffix)
self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
if self.purge_step is not None:
most_recent_step = self.purge_step
self.file_writer.add_event(
Event(step=most_recent_step, file_version='brain.Event:2'))
self.file_writer.add_event(
Event(step=most_recent_step, session_log=SessionLog(status=SessionLog.START)))
self.purge_step = None
return self.file_writer
def get_logdir(self):
"""Returns the directory where event files will be written."""
return self.log_dir
[docs] def add_hparams(
self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None
):
"""Add a set of hyperparameters to be compared in TensorBoard.
Args:
hparam_dict (dict): Each key-value pair in the dictionary is the
name of the hyper parameter and it's corresponding value.
The type of the value can be one of `bool`, `string`, `float`,
`int`, or `None`.
metric_dict (dict): Each key-value pair in the dictionary is the
name of the metric and it's corresponding value. Note that the key used
here should be unique in the tensorboard record. Otherwise the value
you added by ``add_scalar`` will be displayed in hparam plugin. In most
cases, this is unwanted.
hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
contains names of the hyperparameters and all discrete values they can hold
run_name (str): Name of the run, to be included as part of the logdir.
If unspecified, will use current timestamp.
Examples::
from torch.utils.tensorboard import SummaryWriter
with SummaryWriter() as w:
for i in range(5):
w.add_hparams({'lr': 0.1*i, 'bsize': i},
{'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
Expected result:
.. image:: _static/img/tensorboard/add_hparam.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
if type(hparam_dict) is not dict or type(metric_dict) is not dict:
raise TypeError('hparam_dict and metric_dict should be dictionary.')
exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
if not run_name:
run_name = str(time.time())
logdir = os.path.join(self._get_file_writer().get_logdir(), run_name)
with SummaryWriter(log_dir=logdir) as w_hp:
w_hp.file_writer.add_summary(exp)
w_hp.file_writer.add_summary(ssi)
w_hp.file_writer.add_summary(sei)
for k, v in metric_dict.items():
w_hp.add_scalar(k, v)
[docs] def add_scalar(
self,
tag,
scalar_value,
global_step=None,
walltime=None,
new_style=False,
double_precision=False,
):
"""Add scalar data to summary.
Args:
tag (string): Data identifier
scalar_value (float or string/blobname): Value to save
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
with seconds after epoch of event
new_style (boolean): Whether to use new style (tensor field) or old
style (simple_value field). New style could lead to faster data loading.
Examples::
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
x = range(100)
for i in x:
writer.add_scalar('y=2x', i * 2, i)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_scalar.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
if self._check_caffe2_blob(scalar_value):
from caffe2.python import workspace
scalar_value = workspace.FetchBlob(scalar_value)
summary = scalar(
tag, scalar_value, new_style=new_style, double_precision=double_precision
)
self._get_file_writer().add_summary(summary, global_step, walltime)
[docs] def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None):
"""Adds many scalar data to summary.
Args:
main_tag (string): The parent name for the tags
tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
r = 5
for i in range(100):
writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
'xcosx':i*np.cos(i/r),
'tanx': np.tan(i/r)}, i)
writer.close()
# This call adds three values to the same scalar plot with the tag
# 'run_14h' in TensorBoard's scalar section.
Expected result:
.. image:: _static/img/tensorboard/add_scalars.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_scalars")
walltime = time.time() if walltime is None else walltime
fw_logdir = self._get_file_writer().get_logdir()
for tag, scalar_value in tag_scalar_dict.items():
fw_tag = fw_logdir + "/" + main_tag.replace("/", "_") + "_" + tag
assert self.all_writers is not None
if fw_tag in self.all_writers.keys():
fw = self.all_writers[fw_tag]
else:
fw = FileWriter(fw_tag, self.max_queue, self.flush_secs,
self.filename_suffix)
self.all_writers[fw_tag] = fw
if self._check_caffe2_blob(scalar_value):
from caffe2.python import workspace
scalar_value = workspace.FetchBlob(scalar_value)
fw.add_summary(scalar(main_tag, scalar_value),
global_step, walltime)
[docs] def add_histogram(self, tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None):
"""Add histogram to summary.
Args:
tag (string): Data identifier
values (torch.Tensor, numpy.array, or string/blobname): Values to build histogram
global_step (int): Global step value to record
bins (string): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
for i in range(10):
x = np.random.random(1000)
writer.add_histogram('distribution centers', x + i, i)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_histogram.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
if self._check_caffe2_blob(values):
from caffe2.python import workspace
values = workspace.FetchBlob(values)
if isinstance(bins, str) and bins == 'tensorflow':
bins = self.default_bins
self._get_file_writer().add_summary(
histogram(tag, values, bins, max_bins=max_bins), global_step, walltime)
def add_histogram_raw(self, tag, min, max, num, sum, sum_squares,
bucket_limits, bucket_counts, global_step=None,
walltime=None):
"""Adds histogram with raw data.
Args:
tag (string): Data identifier
min (float or int): Min value
max (float or int): Max value
num (int): Number of values
sum (float or int): Sum of all values
sum_squares (float or int): Sum of squares for all values
bucket_limits (torch.Tensor, numpy.array): Upper value per bucket.
The number of elements of it should be the same as `bucket_counts`.
bucket_counts (torch.Tensor, numpy.array): Number of values per bucket
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
dummy_data = []
for idx, value in enumerate(range(50)):
dummy_data += [idx + 0.001] * value
bins = list(range(50+2))
bins = np.array(bins)
values = np.array(dummy_data).astype(float).reshape(-1)
counts, limits = np.histogram(values, bins=bins)
sum_sq = values.dot(values)
writer.add_histogram_raw(
tag='histogram_with_raw_data',
min=values.min(),
max=values.max(),
num=len(values),
sum=values.sum(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(),
bucket_counts=counts.tolist(),
global_step=0)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_histogram_raw.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_histogram_raw")
if len(bucket_limits) != len(bucket_counts):
raise ValueError('len(bucket_limits) != len(bucket_counts), see the document.')
self._get_file_writer().add_summary(
histogram_raw(tag,
min,
max,
num,
sum,
sum_squares,
bucket_limits,
bucket_counts),
global_step,
walltime)
[docs] def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats='CHW'):
"""Add image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
img = np.zeros((3, 100, 100))
img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC = np.zeros((100, 100, 3))
img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
writer = SummaryWriter()
writer.add_image('my_image', img, 0)
# If you have non-default dimension setting, set the dataformats argument.
writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_image.png
:scale: 50 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_image")
if self._check_caffe2_blob(img_tensor):
from caffe2.python import workspace
img_tensor = workspace.FetchBlob(img_tensor)
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
[docs] def add_images(self, tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW'):
"""Add batched image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
dataformats (string): Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.
Shape:
img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
accepted. e.g. NCHW or NHWC.
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
img_batch = np.zeros((16, 3, 100, 100))
for i in range(16):
img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
writer = SummaryWriter()
writer.add_images('my_image_batch', img_batch, 0)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_images.png
:scale: 30 %
"""
torch._C._log_api_usage_once("tensorboard.logging.add_images")
if self._check_caffe2_blob(img_tensor):
from caffe2.python import workspace
img_tensor = workspace.FetchBlob(img_tensor)
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
def add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None,
walltime=None, rescale=1, dataformats='CHW', labels=None):
"""Add image and draw bounding boxes on the image.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)
box should be represented as [x1, y1, x2, y2].
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
rescale (float): Optional scale override
dataformats (string): Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.
labels (list of string): The label to be shown for each bounding box.
Shape:
img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.
e.g. CHW or HWC
box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4, where N is the number of
boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax).
"""
torch._C._log_api_usage_once("tensorboard.logging.add_image_with_boxes")
if self._check_caffe2_blob(img_tensor):
from caffe2.python import workspace
img_tensor = workspace.FetchBlob(img_tensor)
if self._check_caffe2_blob(box_tensor):
from caffe2.python import workspace
box_tensor = workspace.FetchBlob(box_tensor)
if labels is not None:
if isinstance(labels, str):
labels = [labels]
if len(labels) != box_tensor.shape[0]:
labels = None
self._get_file_writer().add_summary(image_boxes(
tag, img_tensor, box_tensor, rescale=rescale, dataformats=dataformats, labels=labels), global_step, walltime)
[docs] def add_figure(self, tag, figure, global_step=None, close=True, walltime=None):
"""Render matplotlib figure into an image and add it to summary.
Note that this requires the ``matplotlib`` package.
Args:
tag (string): Data identifier
figure (matplotlib.pyplot.figure) or list of figures: Figure or a list of figures
global_step (int): Global step value to record
close (bool): Flag to automatically close the figure
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
"""
torch._C._log_api_usage_once("tensorboard.logging.add_figure")
if isinstance(figure, list):
self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='NCHW')
else:
self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='CHW')
[docs] def add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None):
"""Add video data to summary.
Note that this requires the ``moviepy`` package.
Args:
tag (string): Data identifier
vid_tensor (torch.Tensor): Video data
global_step (int): Global step value to record
fps (float or int): Frames per second
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
"""
torch._C._log_api_usage_once("tensorboard.logging.add_video")
self._get_file_writer().add_summary(
video(tag, vid_tensor, fps), global_step, walltime)
[docs] def add_audio(self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None):
"""Add audio data to summary.
Args:
tag (string): Data identifier
snd_tensor (torch.Tensor): Sound data
global_step (int): Global step value to record
sample_rate (int): sample rate in Hz
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].
"""
torch._C._log_api_usage_once("tensorboard.logging.add_audio")
if self._check_caffe2_blob(snd_tensor):
from caffe2.python import workspace
snd_tensor = workspace.FetchBlob(snd_tensor)
self._get_file_writer().add_summary(
audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime)
[docs] def add_text(self, tag, text_string, global_step=None, walltime=None):
"""Add text data to summary.
Args:
tag (string): Data identifier
text_string (string): String to save
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
writer.add_text('lstm', 'This is an lstm', 0)
writer.add_text('rnn', 'This is an rnn', 10)
"""
torch._C._log_api_usage_once("tensorboard.logging.add_text")
self._get_file_writer().add_summary(
text(tag, text_string), global_step, walltime)
def add_onnx_graph(self, prototxt):
torch._C._log_api_usage_once("tensorboard.logging.add_onnx_graph")
self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt))
[docs] def add_graph(self, model, input_to_model=None, verbose=False, use_strict_trace=True):
"""Add graph data to summary.
Args:
model (torch.nn.Module): Model to draw.
input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of
variables to be fed.
verbose (bool): Whether to print graph structure in console.
use_strict_trace (bool): Whether to pass keyword argument `strict` to
`torch.jit.trace`. Pass False when you want the tracer to
record your mutable container types (list, dict)
"""
torch._C._log_api_usage_once("tensorboard.logging.add_graph")
if hasattr(model, 'forward'):
# A valid PyTorch model should have a 'forward' method
self._get_file_writer().add_graph(graph(model, input_to_model, verbose, use_strict_trace))
else:
# Caffe2 models do not have the 'forward' method
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from ._caffe2_graph import (
model_to_graph_def, nets_to_graph_def, protos_to_graph_def
)
if isinstance(model, list):
if isinstance(model[0], core.Net):
current_graph = nets_to_graph_def(model)
elif isinstance(model[0], caffe2_pb2.NetDef):
current_graph = protos_to_graph_def(model)
else:
# Handles cnn.CNNModelHelper, model_helper.ModelHelper
current_graph = model_to_graph_def(model)
event = event_pb2.Event(
graph_def=current_graph.SerializeToString())
self._get_file_writer().add_event(event)
@staticmethod
def _encode(rawstr):
# I'd use urllib but, I'm unsure about the differences from python3 to python2, etc.
retval = rawstr
retval = retval.replace("%", "%%%02x" % (ord("%")))
retval = retval.replace("/", "%%%02x" % (ord("/")))
retval = retval.replace("\\", "%%%02x" % (ord("\\")))
return retval
[docs] def add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None):
"""Add embedding projector data to summary.
Args:
mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point
metadata (list): A list of labels, each element will be convert to string
label_img (torch.Tensor): Images correspond to each data point
global_step (int): Global step value to record
tag (string): Name for the embedding
Shape:
mat: :math:`(N, D)`, where N is number of data and D is feature dimension
label_img: :math:`(N, C, H, W)`
Examples::
import keyword
import torch
meta = []
while len(meta)<100:
meta = meta+keyword.kwlist # get some strings
meta = meta[:100]
for i, v in enumerate(meta):
meta[i] = v+str(i)
label_img = torch.rand(100, 3, 10, 32)
for i in range(100):
label_img[i]*=i/100.0
writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
writer.add_embedding(torch.randn(100, 5), label_img=label_img)
writer.add_embedding(torch.randn(100, 5), metadata=meta)
"""
torch._C._log_api_usage_once("tensorboard.logging.add_embedding")
mat = make_np(mat)
if global_step is None:
global_step = 0
# clear pbtxt?
# Maybe we should encode the tag so slashes don't trip us up?
# I don't think this will mess us up, but better safe than sorry.
subdir = "%s/%s" % (str(global_step).zfill(5), self._encode(tag))
save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
fs = tf.io.gfile.get_filesystem(save_path)
if fs.exists(save_path):
if fs.isdir(save_path):
print(
'warning: Embedding dir exists, did you set global_step for add_embedding()?')
else:
raise Exception("Path: `%s` exists, but is a file. Cannot proceed." % save_path)
else:
fs.makedirs(save_path)
if metadata is not None:
assert mat.shape[0] == len(
metadata), '#labels should equal with #data points'
make_tsv(metadata, save_path, metadata_header=metadata_header)
if label_img is not None:
assert mat.shape[0] == label_img.shape[0], '#images should equal with #data points'
make_sprite(label_img, save_path)
assert mat.ndim == 2, 'mat should be 2D, where mat.size(0) is the number of data points'
make_mat(mat, save_path)
# Filesystem doesn't necessarily have append semantics, so we store an
# internal buffer to append to and re-write whole file after each
# embedding is added
if not hasattr(self, "_projector_config"):
self._projector_config = ProjectorConfig()
embedding_info = get_embedding_info(
metadata, label_img, fs, subdir, global_step, tag)
self._projector_config.embeddings.extend([embedding_info])
from google.protobuf import text_format
config_pbtxt = text_format.MessageToString(self._projector_config)
write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt)
[docs] def add_pr_curve(self, tag, labels, predictions, global_step=None,
num_thresholds=127, weights=None, walltime=None):
"""Adds precision recall curve.
Plotting a precision-recall curve lets you understand your model's
performance under different threshold settings. With this function,
you provide the ground truth labeling (T/F) and prediction confidence
(usually the output of your model) for each target. The TensorBoard UI
will let you choose the threshold interactively.
Args:
tag (string): Data identifier
labels (torch.Tensor, numpy.array, or string/blobname):
Ground truth data. Binary label for each element.
predictions (torch.Tensor, numpy.array, or string/blobname):
The probability that an element be classified as true.
Value should be in [0, 1]
global_step (int): Global step value to record
num_thresholds (int): Number of thresholds used to draw the curve.
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
labels = np.random.randint(2, size=100) # binary label
predictions = np.random.rand(100)
writer = SummaryWriter()
writer.add_pr_curve('pr_curve', labels, predictions, 0)
writer.close()
"""
torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve")
labels, predictions = make_np(labels), make_np(predictions)
self._get_file_writer().add_summary(
pr_curve(tag, labels, predictions, num_thresholds, weights),
global_step, walltime)
def add_pr_curve_raw(self, tag, true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
global_step=None,
num_thresholds=127,
weights=None,
walltime=None):
"""Adds precision recall curve with raw data.
Args:
tag (string): Data identifier
true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts
false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts
true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts
false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts
precision (torch.Tensor, numpy.array, or string/blobname): precision
recall (torch.Tensor, numpy.array, or string/blobname): recall
global_step (int): Global step value to record
num_thresholds (int): Number of thresholds used to draw the curve.
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
"""
torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve_raw")
self._get_file_writer().add_summary(
pr_curve_raw(tag,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds,
weights),
global_step,
walltime)
def add_custom_scalars_multilinechart(self, tags, category='default', title='untitled'):
"""Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument
is *tags*.
Args:
tags (list): list of tags that have been used in ``add_scalar()``
Examples::
writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
"""
torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars_multilinechart")
layout = {category: {title: ['Multiline', tags]}}
self._get_file_writer().add_summary(custom_scalars(layout))
def add_custom_scalars_marginchart(self, tags, category='default', title='untitled'):
"""Shorthand for creating marginchart. Similar to ``add_custom_scalars()``, but the only necessary argument
is *tags*, which should have exactly 3 elements.
Args:
tags (list): list of tags that have been used in ``add_scalar()``
Examples::
writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
"""
torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars_marginchart")
assert len(tags) == 3
layout = {category: {title: ['Margin', tags]}}
self._get_file_writer().add_summary(custom_scalars(layout))
[docs] def add_custom_scalars(self, layout):
"""Create special chart by collecting charts tags in 'scalars'. Note that this function can only be called once
for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called
before or after the training loop.
Args:
layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary
{chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type
(one of **Multiline** or **Margin**) and the second element should be a list containing the tags
you have used in add_scalar function, which will be collected into the new chart.
Examples::
layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
'USA':{ 'dow':['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
'nasdaq':['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
writer.add_custom_scalars(layout)
"""
torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars")
self._get_file_writer().add_summary(custom_scalars(layout))
[docs] def add_mesh(self, tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None):
"""Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js,
so it allows users to interact with the rendered object. Besides the basic definitions
such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for
advanced usage.
Args:
tag (string): Data identifier
vertices (torch.Tensor): List of the 3D coordinates of vertices.
colors (torch.Tensor): Colors for each vertex
faces (torch.Tensor): Indices of vertices within each triangle. (Optional)
config_dict: Dictionary with ThreeJS classes names and configuration.
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)
colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
Examples::
from torch.utils.tensorboard import SummaryWriter
vertices_tensor = torch.as_tensor([
[1, 1, 1],
[-1, -1, 1],
[1, -1, -1],
[-1, 1, -1],
], dtype=torch.float).unsqueeze(0)
colors_tensor = torch.as_tensor([
[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 0, 255],
], dtype=torch.int).unsqueeze(0)
faces_tensor = torch.as_tensor([
[0, 2, 3],
[0, 3, 1],
[0, 1, 2],
[1, 3, 2],
], dtype=torch.int).unsqueeze(0)
writer = SummaryWriter()
writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
writer.close()
"""
torch._C._log_api_usage_once("tensorboard.logging.add_mesh")
self._get_file_writer().add_summary(mesh(tag, vertices, colors, faces, config_dict), global_step, walltime)
[docs] def flush(self):
"""Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
"""
if self.all_writers is None:
return
for writer in self.all_writers.values():
writer.flush()
[docs] def close(self):
if self.all_writers is None:
return # ignore double close
for writer in self.all_writers.values():
writer.flush()
writer.close()
self.file_writer = self.all_writers = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()Recommend
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