python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
import tensorboard
from distutils.version import LooseVersion
if not hasattr(tensorboard, "__version__") or LooseVersion(
tensorboard.__version__
) < LooseVersion("1.15"):
raise ImportError("TensorBoard logging requires TensorBoard version 1.15 or above")
del LooseVersion
del tensorboard
from .writer import ... | pytorch-master | torch/utils/tensorboard/__init__.py |
import math
import numpy as np
from ._convert_np import make_np
from ._utils import make_grid
from tensorboard.compat import tf
from tensorboard.plugins.projector.projector_config_pb2 import EmbeddingInfo
def make_tsv(metadata, save_path, metadata_header=None):
if not metadata_header:
metadata = [str(x) f... | pytorch-master | torch/utils/tensorboard/_embedding.py |
from tensorboard.compat.proto.graph_pb2 import GraphDef
from tensorboard.compat.proto.node_def_pb2 import NodeDef
from tensorboard.compat.proto.versions_pb2 import VersionDef
from tensorboard.compat.proto.attr_value_pb2 import AttrValue
from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
def load_o... | pytorch-master | torch/utils/tensorboard/_onnx_graph.py |
import json
import logging
import os
from typing import Optional
import numpy as np
from google.protobuf import struct_pb2
# pylint: disable=unused-import
from six.moves import range
from tensorboard.compat.proto.summary_pb2 import HistogramProto
from tensorboard.compat.proto.summary_pb2 import Summary
from tensorboa... | pytorch-master | torch/utils/tensorboard/summary.py |
"""
This module converts objects into numpy array.
"""
import numpy as np
import torch
def make_np(x):
"""
Args:
x: An instance of torch tensor or caffe blob name
Returns:
numpy.array: Numpy array
"""
if isinstance(x, np.ndarray):
return x
if isinstance(x, str): # Caffe... | pytorch-master | torch/utils/tensorboard/_convert_np.py |
"""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... | pytorch-master | torch/utils/tensorboard/writer.py |
import copy
import logging
import os
import re
from tensorboard.compat.proto.graph_pb2 import GraphDef
from tensorboard.compat.proto.node_def_pb2 import NodeDef
from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
from builtins import bytes
from caffe2.proto import caffe2_pb2
from caffe2.python impo... | pytorch-master | torch/utils/tensorboard/_caffe2_graph.py |
import numpy as np
# Functions for converting
def figure_to_image(figures, close=True):
"""Render matplotlib figure to numpy format.
Note that this requires the ``matplotlib`` package.
Args:
figure (matplotlib.pyplot.figure) or list of figures: figure or a list of figures
close (bool): F... | pytorch-master | torch/utils/tensorboard/_utils.py |
#!/usr/bin/env python3
"""
model_dump: a one-stop shop for TorchScript model inspection.
The goal of this tool is to provide a simple way to extract lots of
useful information from a TorchScript model and make it easy for humans
to consume. It (mostly) replaces zipinfo, common uses of show_pickle,
and various ad-hoc ... | pytorch-master | torch/utils/model_dump/__init__.py |
#!/usr/bin/env python3
import sys
from . import main
sys.exit(main(sys.argv))
| pytorch-master | torch/utils/model_dump/__main__.py |
import warnings
from typing import Any, List, Optional, Set
import torch
import torch.utils.data.datapipes as dp
from torch.utils.data.graph import DataPipe, DataPipeGraph, traverse
__all__ = [
"apply_sharding",
"apply_shuffle_seed",
"apply_shuffle_settings",
"get_all_graph_pipes",
]
def get_all_g... | pytorch-master | torch/utils/data/graph_settings.py |
import io
import pickle
from torch.utils.data import IterDataPipe, MapDataPipe
from torch.utils.data._utils.serialization import DILL_AVAILABLE
from typing import Dict, List, Set, Tuple, Type, Union
__all__ = ["traverse"]
DataPipe = Union[IterDataPipe, MapDataPipe]
DataPipeGraph = Dict[int, Tuple[DataPipe, "DataPip... | pytorch-master | torch/utils/data/graph.py |
# TODO(VitalyFedyunin): Rearranging this imports leads to crash,
# need to cleanup dependencies and fix it
from torch.utils.data.sampler import (
BatchSampler,
RandomSampler,
Sampler,
SequentialSampler,
SubsetRandomSampler,
WeightedRandomSampler,
)
from torch.utils.data.dataset import (
Chai... | pytorch-master | torch/utils/data/__init__.py |
import bisect
import warnings
import math
from typing import (
Generic,
Iterable,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union
)
# No 'default_generator' in torch/__init__.pyi
from torch import default_generator, randperm
from torch._utils import _accumulate
from ... i... | pytorch-master | torch/utils/data/dataset.py |
import math
from typing import TypeVar, Optional, Iterator
import torch
from . import Sampler, Dataset
import torch.distributed as dist
__all__ = ["DistributedSampler", ]
T_co = TypeVar('T_co', covariant=True)
class DistributedSampler(Sampler[T_co]):
r"""Sampler that restricts data loading to a subset of the d... | pytorch-master | torch/utils/data/distributed.py |
import time
from typing import Any, List
import torch.utils.data.backward_compatibility
import torch.utils.data.graph_settings
from torch.utils.data import DataLoader, IterDataPipe, communication
from torch.utils.data.datapipes.iter import IterableWrapper
__all__ = [
"DataLoader2",
]
class _ThreadingDataLoade... | pytorch-master | torch/utils/data/dataloader_experimental.py |
import warnings
def worker_init_fn(worker_id):
warnings.warn("Usage of backward_compatibility.worker_init_fn is deprecated"
" as DataLoader automatically applies sharding in every worker")
| pytorch-master | torch/utils/data/backward_compatibility.py |
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 functools
import itertools
import... | pytorch-master | torch/utils/data/dataloader.py |
import torch
from torch import Tensor
from typing import Iterator, Iterable, Optional, Sequence, List, TypeVar, Generic, Sized, Union
__all__ = [
"BatchSampler",
"RandomSampler",
"Sampler",
"SequentialSampler",
"SubsetRandomSampler",
"WeightedRandomSampler",
]
T_co = TypeVar('T_co', covariant... | pytorch-master | torch/utils/data/sampler.py |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter to fetch
data from an iterable-style or map-style dataset. This logic is shared in both
single- and multi-processing data loading.
"""
class _BaseDatasetFetcher(object):
def __init__(self, dataset, auto_collation, collate_fn, drop_last):
... | pytorch-master | torch/utils/data/_utils/fetch.py |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
import random
import os
import queue
from dataclasses import dataclass
from torch._utils import ExceptionWrapper
from typing im... | pytorch-master | torch/utils/data/_utils/worker.py |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers to
collate samples fetched from dataset into Tensor(s).
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
`default_collate` and `default_convert` are exposed to users via 'dataloader.py'.
"""
im... | pytorch-master | torch/utils/data/_utils/collate.py |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter to put
fetched tensors into pinned memory.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import collections
import queue
import torch
from torch._six import string_classes
from . import MP_STATU... | pytorch-master | torch/utils/data/_utils/pin_memory.py |
r"""Utility classes & functions for data loading. Code in this folder is mostly
used by ../dataloder.py.
A lot of multiprocessing is used in data loading, which only supports running
functions defined in global environment (py2 can't serialize static methods).
Therefore, for code tidiness we put these functions into d... | pytorch-master | torch/utils/data/_utils/__init__.py |
r""""Signal handling for multiprocessing data loading.
NOTE [ Signal handling in multiprocessing data loading ]
In cases like DataLoader, if a worker process dies due to bus error/segfault
or just hang, the main process will hang waiting for data. This is difficult
to avoid on PyTorch side as it can be caused by limi... | pytorch-master | torch/utils/data/_utils/signal_handling.py |
try:
import dill
# XXX: By default, dill writes the Pickler dispatch table to inject its
# own logic there. This globally affects the behavior of the standard library
# pickler for any user who transitively depends on this module!
# Undo this extension to avoid altering the behavior of the pickler ... | pytorch-master | torch/utils/data/_utils/serialization.py |
import threading
import time
class LocalQueue():
ops = 0
stored = 0
uid = 0
empty = 0
def __init__(self, name='unnamed'):
self.items = []
self.name = name
self.uid = LocalQueue.uid
LocalQueue.uid += 1
def put(self, item, block=True):
LocalQueue.ops += ... | pytorch-master | torch/utils/data/communication/queue.py |
import time
import types
from torch.utils.data import IterDataPipe, communication
DEFAULT_NON_BLOCKING_SLEEP = 0.001
__all__ = [
"DataPipeBehindQueues",
"EnsureNonBlockingDataPipe",
"InvalidStateResetRequired",
"NonBlocking",
"NotAvailable",
"QueueWrapper",
"default_not_available_hook",
]... | pytorch-master | torch/utils/data/communication/iter.py |
from torch.utils.data import communication
class Protocol(object):
__slots__ = ('request_queue', 'response_queue')
def __init__(self, request_queue, response_queue):
self.request_queue = request_queue
self.response_queue = response_queue
class ProtocolClient(Protocol):
"""
Proto... | pytorch-master | torch/utils/data/communication/protocol.py |
from . import eventloop
from . import iter
from . import map
from . import messages
from . import protocol
from . import queue
| pytorch-master | torch/utils/data/communication/__init__.py |
import torch
import threading
import pickle
from torch.utils.data import IterDataPipe, communication, MapDataPipe
try:
import dill
# XXX: By default, dill writes the Pickler dispatch table to inject its
# own logic there. This globally affects the behavior of the standard library
# pickler for any use... | pytorch-master | torch/utils/data/communication/eventloop.py |
import time
import types
from torch.utils.data import communication, MapDataPipe
DEFAULT_NON_BLOCKING_SLEEP = 0.001
__all__ = [
"DataPipeBehindQueues",
"EnsureNonBlockingMapDataPipe",
"NonBlockingMap",
"NotAvailable",
"QueueWrapperForMap",
"default_not_available_hook",
]
def default_not_ava... | pytorch-master | torch/utils/data/communication/map.py |
class DataLoaderQueueMessage(object):
pass
class Request(DataLoaderQueueMessage):
pass
class Response(DataLoaderQueueMessage):
pass
class ResetIteratorRequest(Request):
pass
class ResetIteratorResponse(Response):
pass
class TerminateRequest(Request):
pass
class TerminateResponse(Resp... | pytorch-master | torch/utils/data/communication/messages.py |
import inspect
from functools import wraps
from typing import Any, Callable, Optional, Type, Union, get_type_hints
from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
from torch.utils.data.datapipes._typing import _DataPipeMeta
######################################################
# Functional ... | pytorch-master | torch/utils/data/datapipes/_decorator.py |
# Taking reference from official Python typing
# https://github.com/python/cpython/blob/master/Lib/typing.py
import collections
import functools
import numbers
import sys
from torch.utils.data.datapipes._hook_iterator import hook_iterator, _SnapshotState
from typing import (Any, Dict, Iterator, Generic, List, Set, Tu... | pytorch-master | torch/utils/data/datapipes/_typing.py |
import inspect
import functools
from enum import Enum
import torch.autograd
class _SnapshotState(Enum):
r"""
These are the snapshotting-related states that IterDataPipes can be in.
`NotStarted` - allows you to restore a snapshot and create an iterator without reset
`Restored` - cannot restore again, ... | pytorch-master | torch/utils/data/datapipes/_hook_iterator.py |
import functools
import pickle
from typing import Dict, Callable, Optional, TypeVar, Generic, Iterator
from torch.utils.data.datapipes._typing import _DataPipeMeta, _IterDataPipeMeta
from torch.utils.data.datapipes._hook_iterator import _SnapshotState
from torch.utils.data.datapipes.utils.common import (
_deprecat... | pytorch-master | torch/utils/data/datapipes/datapipe.py |
from . import iter
from . import map
from . import dataframe
| pytorch-master | torch/utils/data/datapipes/__init__.py |
import os
import pathlib
from typing import Any, Dict, List, Set, Tuple, Union
def materialize_lines(lines: List[str], indentation: int) -> str:
output = ""
new_line_with_indent = "\n" + " " * indentation
for i, line in enumerate(lines):
if i != 0:
output += new_line_with_indent
... | pytorch-master | torch/utils/data/datapipes/gen_pyi.py |
from torch.utils.data.datapipes.dataframe.dataframes import (
CaptureDataFrame, DFIterDataPipe,
)
from torch.utils.data.datapipes.dataframe.datapipes import (
DataFramesAsTuplesPipe,
)
__all__ = ['CaptureDataFrame', 'DFIterDataPipe', 'DataFramesAsTuplesPipe']
# Please keep this list sorted
assert __all__ == s... | pytorch-master | torch/utils/data/datapipes/dataframe/__init__.py |
import random
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
__all__ = [
"ConcatDataFramesPipe",
"DataFramesAsTuplesPipe",
"... | pytorch-master | torch/utils/data/datapipes/dataframe/datapipes.py |
_pandas = None
_WITH_PANDAS = None
def _try_import_pandas() -> bool:
try:
import pandas # type: ignore[import]
global _pandas
_pandas = pandas
return True
except ImportError:
return False
# pandas used only for prototyping, will be shortly replaced with TorchArrow
de... | pytorch-master | torch/utils/data/datapipes/dataframe/dataframe_wrapper.py |
from torch.utils.data.datapipes.datapipe import DataChunk
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
__all__ = ["DataChunkDF", ]
class DataChunkDF(DataChunk):
"""
DataChunkDF iterating over individual items inside of DataFrame containers,
to access DataFrames... | pytorch-master | torch/utils/data/datapipes/dataframe/structures.py |
from typing import Any, Dict, List
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
from torch.utils.data.datapipes.dataframe.structures import DataChunkDF
# TODO(VitalyFedyunin): Add error when two different traces get... | pytorch-master | torch/utils/data/datapipes/dataframe/dataframes.py |
from io import IOBase
from typing import Iterable, Tuple, Optional
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.utils.common import get_file_binaries_from_pathnames, _deprecation_warning
__all__ = [
... | pytorch-master | torch/utils/data/datapipes/iter/fileopener.py |
import functools
from collections import namedtuple
from typing import Callable, Iterator, Sized, TypeVar, Optional, Union, Any, Dict, List
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data._utils.collate import default_collate
from torch.utils.data.datapipes.dataframe import... | pytorch-master | torch/utils/data/datapipes/iter/callable.py |
from collections import defaultdict
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe, DataChunk
from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
from typing import Any, Callable, DefaultDict, Iterator, List, Optio... | pytorch-master | torch/utils/data/datapipes/iter/grouping.py |
from torch.utils.data.datapipes.iter.utils import (
IterableWrapperIterDataPipe as IterableWrapper,
)
from torch.utils.data.datapipes.iter.callable import (
CollatorIterDataPipe as Collator,
MapperIterDataPipe as Mapper,
)
from torch.utils.data.datapipes.iter.combinatorics import (
SamplerIterDataPipe a... | pytorch-master | torch/utils/data/datapipes/iter/__init__.py |
from typing import Callable, Iterator, Optional, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
from torch.utils.data.datapipes.utils.common impor... | pytorch-master | torch/utils/data/datapipes/iter/selecting.py |
import warnings
from collections import deque
from typing import Any, Callable, Iterator, List, Optional, Sized, Tuple, TypeVar, Deque
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes._hook_iterator import _SnapshotState
from torch.utils.data.datapipes.datapipe imp... | pytorch-master | torch/utils/data/datapipes/iter/combining.py |
from typing import Iterator, List, Sequence, Union
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.iter import IterableWrapper
from torch.utils.data.datapipes.utils.common import get_file_pathnames_from... | pytorch-master | torch/utils/data/datapipes/iter/filelister.py |
import copy
import warnings
from torch.utils.data.datapipes.datapipe import IterDataPipe
__all__ = ["IterableWrapperIterDataPipe", ]
class IterableWrapperIterDataPipe(IterDataPipe):
r"""
Wraps an iterable object to create an IterDataPipe.
Args:
iterable: Iterable object to be wrapped into an Ite... | pytorch-master | torch/utils/data/datapipes/iter/utils.py |
import random
import torch
from torch.utils.data import Sampler, SequentialSampler
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from typing import Dict, Iterator, List, Optional, Sized, Tuple, Type, TypeVar
__all__ = [
"SamplerI... | pytorch-master | torch/utils/data/datapipes/iter/combinatorics.py |
from typing import Tuple
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
__all__ = ["StreamReaderIterDataPipe", ]
@functional_datapipe('read_from_stream')
class StreamReaderIterDataPipe(IterDataPipe[Tuple[str, bytes]]):
r"""
G... | pytorch-master | torch/utils/data/datapipes/iter/streamreader.py |
from io import BufferedIOBase
from typing import Any, Callable, Iterable, Iterator, Sized, Tuple
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.utils.common import _deprecation_warning
from torch.utils.d... | pytorch-master | torch/utils/data/datapipes/iter/routeddecoder.py |
# This file takes partial of the implementation from NVIDIA's webdataset at here:
# https://github.com/tmbdev/webdataset/blob/master/webdataset/autodecode.py
import io
import json
import os.path
import pickle
import tempfile
import torch
from torch.utils.data.datapipes.utils.common import StreamWrapper
__all__ = [
... | pytorch-master | torch/utils/data/datapipes/utils/decoder.py |
pytorch-master | torch/utils/data/datapipes/utils/__init__.py | |
import fnmatch
import inspect
import os
import warnings
from io import IOBase
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
from torch.utils.data._utils.serialization import DILL_AVAILABLE
__all__ = [
"StreamWrapper",
"get_file_binaries_from_pa... | pytorch-master | torch/utils/data/datapipes/utils/common.py |
from torch.utils.data.datapipes._hook_iterator import _SnapshotState
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.graph_settings import apply_shuffle_seed
# TODO: Caveats
# 1. Caller (either the ReadingService or DataLoader) must pass in the initial RNG
# 2. `in_batch_shuffle... | pytorch-master | torch/utils/data/datapipes/utils/snapshot.py |
from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
from typing import Callable, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
__all__ = ["MapperMapDataPipe", "default_fn"]
T_co = TypeVar('T_co', covariant... | pytorch-master | torch/utils/data/datapipes/map/callable.py |
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe, DataChunk
from typing import List, Optional, Sized, TypeVar
__all__ = ["BatcherMapDataPipe", ]
T = TypeVar('T')
@functional_datapipe('batch')
class BatcherMapDataPipe(MapDataPipe[DataCh... | pytorch-master | torch/utils/data/datapipes/map/grouping.py |
# Functional DataPipe
from torch.utils.data.datapipes.map.callable import MapperMapDataPipe as Mapper
from torch.utils.data.datapipes.map.combinatorics import ShufflerMapDataPipe as Shuffler
from torch.utils.data.datapipes.map.combining import (
ConcaterMapDataPipe as Concater,
ZipperMapDataPipe as Zipper
)
fro... | pytorch-master | torch/utils/data/datapipes/map/__init__.py |
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
from typing import Sized, Tuple, TypeVar
__all__ = ["ConcaterMapDataPipe", "ZipperMapDataPipe"]
T_co = TypeVar('T_co', covariant=True)
@functional_datapipe('concat')
class ConcaterMapDat... | pytorch-master | torch/utils/data/datapipes/map/combining.py |
import copy
import warnings
from torch.utils.data.datapipes.datapipe import MapDataPipe
__all__ = ["SequenceWrapperMapDataPipe", ]
class SequenceWrapperMapDataPipe(MapDataPipe):
r"""
Wraps a sequence object into a MapDataPipe.
Args:
sequence: Sequence object to be wrapped into an MapDataPipe
... | pytorch-master | torch/utils/data/datapipes/map/utils.py |
import random
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
from typing import Iterator, List, Optional, TypeVar
__all__ = ["ShufflerMapDataPipe", ]
T_co = TypeVar('T_co', covariant=True)
@functional_datapipe('shuffle')
class Shuf... | pytorch-master | torch/utils/data/datapipes/map/combinatorics.py |
pytorch-master | torch/contrib/__init__.py | |
import time
from collections import defaultdict
from functools import partial
from typing import DefaultDict
import torch
# Unfortunately it doesn't seem as if there was any way to get TensorBoard to do
# anything without having TF installed, and so this file has a hard dependency on it
# as well. It really is a deb... | pytorch-master | torch/contrib/_tensorboard_vis.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/observer.py`, while adding an import statement
here.
"""
fro... | pytorch-master | torch/quantization/observer.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fuse_modules.py`, while adding an import statement
here.
"""... | pytorch-master | torch/quantization/fuse_modules.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantization_mappings.py`, while adding an import statement
... | pytorch-master | torch/quantization/quantization_mappings.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize.py`, while adding an import statement
here.
"""
fr... | pytorch-master | torch/quantization/quantize.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/ns/_numeric_suite.py`, while adding an import statement
here.
"""
from t... | pytorch-master | torch/quantization/_numeric_suite.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fake_quantize.py`, while adding an import statement
here.
""... | pytorch-master | torch/quantization/fake_quantize.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/qconfig.py`, while adding an import statement
here.
"""
from... | pytorch-master | torch/quantization/qconfig.py |
from .quantize import * # noqa: F403
from .observer import * # noqa: F403
from .qconfig import * # noqa: F403
from .fake_quantize import * # noqa: F403
from .fuse_modules import fuse_modules
from .stubs import * # noqa: F403
from .quant_type import * # noqa: F403
from .quantize_jit import * # noqa: F403
# from .... | pytorch-master | torch/quantization/__init__.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/stubs.py`, while adding an import statement
here.
"""
from ... | pytorch-master | torch/quantization/stubs.py |
# flake8: noqa: F401
r"""
Utils shared by different modes of quantization (eager/graph)
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantizati... | pytorch-master | torch/quantization/utils.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fuser_method_mappings.py`, while adding an import statement
... | pytorch-master | torch/quantization/fuser_method_mappings.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize_jit.py`, while adding an import statement
here.
"""... | pytorch-master | torch/quantization/quantize_jit.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quant_type.py`, while adding an import statement
here.
"""
... | pytorch-master | torch/quantization/quant_type.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize_fx.py`, while adding an import statement
here.
"""
... | pytorch-master | torch/quantization/quantize_fx.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/ns/_numeric_suite_fx.py`, while adding an import statement
here.
"""
fro... | pytorch-master | torch/quantization/_numeric_suite_fx.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/graph_module.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/fusion_patterns.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/_equalize.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/quantization_types.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/convert.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/__init__.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/utils.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/pattern_utils.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/fuse.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/match_utils.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/prepare.py |
# flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | pytorch-master | torch/quantization/fx/quantization_patterns.py |
import torch
from torch._C import _add_docstr, _special # type: ignore[attr-defined]
from torch._torch_docs import common_args, multi_dim_common
__all__ = [
'airy_ai',
'bessel_j0',
'bessel_j1',
'bessel_y0',
'bessel_y1',
'chebyshev_polynomial_t',
'chebyshev_polynomial_u',
'chebyshev_pol... | pytorch-master | torch/special/__init__.py |
"""
This module contains tensor creation utilities.
"""
import torch
from typing import Optional, List, Tuple, Union, cast
import math
import collections.abc
# Used by make_tensor for generating complex tensor.
complex_to_corresponding_float_type_map = {torch.complex32: torch.float16,
... | pytorch-master | torch/testing/_creation.py |
"""This module exists since the `torch.testing` exposed a lot of stuff that shouldn't have been public. Although this
was never documented anywhere, some other internal FB projects as well as downstream OSS projects might use this. Thus,
we don't internalize without warning, but still go through a deprecation cycle.
""... | pytorch-master | torch/testing/_deprecated.py |
from ._comparison import assert_close
from torch._C import FileCheck
from ._creation import make_tensor
from ._deprecated import * # noqa: F403
| pytorch-master | torch/testing/__init__.py |
"""This module exist to be able to deprecate functions publicly without doing so internally. The deprecated
public versions are defined in torch.testing._deprecated and exposed from torch.testing. The non-deprecated internal
versions should be imported from torch.testing._internal
"""
from typing import List
import t... | pytorch-master | torch/testing/_legacy.py |
import abc
import cmath
import collections.abc
import contextlib
from typing import NoReturn, Callable, Sequence, List, Union, Optional, Type, Tuple, Any, Collection
import torch
try:
import numpy as np
NUMPY_AVAILABLE = True
except ModuleNotFoundError:
NUMPY_AVAILABLE = False
class ErrorMeta(Exception... | pytorch-master | torch/testing/_comparison.py |
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