repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/experimental.py | """Contains utilities to flag a feature as "experimental" in datasets."""
import warnings
from functools import wraps
from typing import Callable
def experimental(fn: Callable) -> Callable:
"""Decorator to flag a feature as experimental.
An experimental feature trigger a warning when used as it might be subj... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/metadata.py | import textwrap
from collections import Counter
from itertools import groupby
from operator import itemgetter
from pathlib import Path
from typing import Any, ClassVar, Dict, List, Optional, Tuple, Union
import yaml
from huggingface_hub import DatasetCardData
from ..config import METADATA_CONFIGS_FIELD
from ..utils.l... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/version.py | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/readme.py | # loading package files: https://stackoverflow.com/a/20885799
import importlib.resources as pkg_resources
import logging
from pathlib import Path
from typing import Any, List, Tuple
import yaml
from . import resources
from .deprecation_utils import deprecated
BASE_REF_URL = "https://github.com/huggingface/datasets/... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/beam_utils.py | import os
from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline
from .logging import get_logger
CHUNK_SIZE = 2 << 20 # 2mb
logger = get_logger(__name__)
class BeamPipeline(Pipeline):
"""Wrapper over `apache_beam.pipeline.Pipeline` for convenience"""
def is_local(se... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/__init__.py | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/download_manager.py | # deprecated, please use datasets.download.download_manager
| 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/py_utils.py | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/stratify.py | import numpy as np
def approximate_mode(class_counts, n_draws, rng):
"""Computes approximate mode of multivariate hypergeometric.
This is an approximation to the mode of the multivariate
hypergeometric given by class_counts and n_draws.
It shouldn't be off by more than one.
It is the mostly likely... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/tqdm.py | """Utility helpers to handle progress bars in `datasets`.
Example:
1. Use `datasets.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`.
2. To disable progress bars, either use `disable_progress_bars()` helper or set the
environment variable `HF_DATASETS_DISABLE_PROGRESS_BARS` to 1.
3. To r... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/hub.py | import time
from functools import partial
from huggingface_hub import HfApi, hf_hub_url
from packaging import version
from requests import ConnectionError, HTTPError
from .. import config
from . import logging
logger = logging.get_logger(__name__)
# Retry `preupload_lfs_files` in `huggingface_hub<0.20.0` on the "5... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/patching.py | from importlib import import_module
from .logging import get_logger
logger = get_logger(__name__)
class _PatchedModuleObj:
"""Set all the modules components as attributes of the _PatchedModuleObj object."""
def __init__(self, module, attrs=None):
attrs = attrs or []
if module is not None:
... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/filelock.py | # deprecated, please use the `filelock` package instead
from filelock import ( # noqa: F401 # imported for backward compatibility TODO: remove in 3.0.0
BaseFileLock,
SoftFileLock,
Timeout,
UnixFileLock,
WindowsFileLock,
)
from ._filelock import FileLock # noqa: F401 # imported for backward compa... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/_datasets_server.py | from typing import Any, Dict, List, Optional, Union
from .. import config
from ..exceptions import DatasetsError
from .file_utils import (
get_authentication_headers_for_url,
http_get,
)
from .logging import get_logger
logger = get_logger(__name__)
class DatasetsServerError(DatasetsError):
"""Dataset-s... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/doc_utils.py | from typing import Callable
def is_documented_by(function_with_docstring: Callable):
"""Decorator to share docstrings across common functions.
Args:
function_with_docstring (`Callable`): Name of the function with the docstring.
"""
def wrapper(target_function):
target_function.__doc_... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/_filelock.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import copy
import io
import json
import os
import posixpath
import re
import shutil
import sys
import time
import urllib
import warnings
... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/_dill.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/typing.py | import os
from typing import Dict, List, Tuple, TypeVar, Union
T = TypeVar("T")
ListLike = Union[List[T], Tuple[T, ...]]
NestedDataStructureLike = Union[T, List[T], Dict[str, T]]
PathLike = Union[str, bytes, os.PathLike]
| 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/extract.py | import bz2
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from ._filelock import FileLock
from .logging import get_logger
... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/tf_utils.py | # Copyright 2022 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/info_utils.py | import enum
import os
from typing import Optional
from huggingface_hub.utils import insecure_hashlib
from .. import config
from .logging import get_logger
logger = get_logger(__name__)
class VerificationMode(enum.Enum):
"""`Enum` that specifies which verification checks to run.
The default mode is `BASIC... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/utils/deprecation_utils.py | import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional
from .logging import get_logger
_emitted_deprecation_warnings = set()
logger = get_logger(__name__)
def deprecated(help_message: Optional[str] = None):
"""Decorator to mark a class or a function as depr... | 0 |
hf_public_repos/datasets/src/datasets/utils | hf_public_repos/datasets/src/datasets/utils/resources/languages.json | {
"code": "Programming language (C++, Java, Javascript, Python, etc.)",
"aa": "Afar",
"aaa": "Ghotuo",
"aab": "Alumu-Tesu",
"aac": "Ari",
"aad": "Amal",
"aae": "Arbëreshë Albanian",
"aaf": "Aranadan",
"aag": "Ambrak",
"aah": "Abu' Arapesh",
"aai": "Arifama-Miniafia",
"aak... | 0 |
hf_public_repos/datasets/src/datasets/utils | hf_public_repos/datasets/src/datasets/utils/resources/creators.json | {
"language": [
"found",
"crowdsourced",
"expert-generated",
"machine-generated",
"other"
],
"annotations": [
"found",
"crowdsourced",
"expert-generated",
"machine-generated",
"no-annotation",
"other"
]
}
| 0 |
hf_public_repos/datasets/src/datasets/utils | hf_public_repos/datasets/src/datasets/utils/resources/readme_structure.yaml | name: "" # Filename comes here
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: "Table of Contents"
allow_empty: false
allow_empty_text: false
subs... | 0 |
hf_public_repos/datasets/src/datasets/utils | hf_public_repos/datasets/src/datasets/utils/resources/multilingualities.json | {
"monolingual": "contains a single language",
"multilingual": "contains multiple languages",
"translation": "contains translated or aligned text",
"other": "other type of language distribution"
}
| 0 |
hf_public_repos/datasets/src/datasets/utils | hf_public_repos/datasets/src/datasets/utils/resources/size_categories.json | [
"unknown",
"n<1K",
"1K<n<10K",
"10K<n<100K",
"100K<n<1M",
"1M<n<10M",
"10M<n<100M",
"100M<n<1B",
"1B<n<10B",
"10B<n<100B",
"100B<n<1T",
"n>1T"
]
| 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/download/mock_download_manager.py | # Copyright 2020 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/download/download_config.py | import copy
import warnings
from dataclasses import InitVar, dataclass, field
from pathlib import Path
from typing import Any, Dict, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/download/__init__.py | __all__ = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/download/download_manager.py | # Copyright 2020 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/download/streaming_download_manager.py | import glob
import io
import os
import posixpath
import re
import tarfile
import time
import xml.dom.minidom
import zipfile
from asyncio import TimeoutError
from io import BytesIO
from itertools import chain
from pathlib import Path, PurePosixPath
from typing import Any, Callable, Dict, Generator, Iterable, List, Optio... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/__init__.py | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/formatting.py | # Copyright 2020 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/torch_formatter.py | # Copyright 2020 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/jax_formatter.py | # Copyright 2021 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/tf_formatter.py | # Copyright 2020 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/formatting/np_formatter.py | # Copyright 2020 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/dummy_data.py | import fnmatch
import json
import os
import shutil
import tempfile
import xml.etree.ElementTree as ET
from argparse import ArgumentParser
from pathlib import Path
from typing import Optional
from datasets import config
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import D... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/run_beam.py | import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List
from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datas... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/env.py | import platform
from argparse import ArgumentParser
import fsspec
import huggingface_hub
import pandas
import pyarrow
from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDatasetsC... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/convert.py | import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
HIGHLIGHT_MESSAGE_PRE = """<<<<<<< This should probably be modified because it mentions: """
HIGHLIGHT_MESSAGE_POST = """=======
>>>>>>>... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/__init__.py | from abc import ABC, abstractmethod
from argparse import ArgumentParser
class BaseDatasetsCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError()
| 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/test.py | import logging
import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator
import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import D... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/commands/datasets_cli.py | #!/usr/bin/env python
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestComm... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/filesystems/compression.py | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class BaseCompressedFileFileSystem(AbstractArchiveFileSystem):
"""Read contents of compressed file as a filesystem with one file inside."""
root_marker = ""
... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/filesystems/__init__.py | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from ..utils.deprecation_utils import deprecated
from . import compression
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _h... | 0 |
hf_public_repos/datasets/src/datasets | hf_public_repos/datasets/src/datasets/filesystems/s3filesystem.py | import s3fs
from ..utils.deprecation_utils import deprecated
@deprecated("Use s3fs.S3FileSystem instead.")
class S3FileSystem(s3fs.S3FileSystem):
"""
`datasets.filesystems.S3FileSystem` is a subclass of [`s3fs.S3FileSystem`](https://s3fs.readthedocs.io/en/latest/api.html).
Users can use this class to ac... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/distributed_train.sh | #!/bin/bash
NUM_PROC=$1
shift
torchrun --nproc_per_node=$NUM_PROC train.py "$@"
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/mkdocs.yml | site_name: 'Pytorch Image Models'
site_description: 'Pretained Image Recognition Models'
repo_name: 'rwightman/pytorch-image-models'
repo_url: 'https://github.com/rwightman/pytorch-image-models'
nav:
- index.md
- models.md
- ... | models/*.md
- results.md
- scripts.md
- training_hparam_examples.md
- featu... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/benchmark.py | #!/usr/bin/env python3
""" Model Benchmark Script
An inference and train step benchmark script for timm models.
Hacked together by Ross Wightman (https://github.com/rwightman)
"""
import argparse
import csv
import json
import logging
import time
from collections import OrderedDict
from contextlib import suppress
from... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/requirements.txt | torch>=1.7
torchvision
pyyaml
huggingface_hub
safetensors>=0.2 | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/avg_checkpoints.py | #!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/onnx_export.py | """ ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/pyproject.toml | [tool.pytest.ini_options]
markers = [
"base: marker for model tests using the basic setup",
"cfg: marker for model tests checking the config",
"torchscript: marker for model tests using torchscript",
"features: marker for model tests checking feature extraction",
"fxforward: marker for model tests u... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/requirements-docs.txt | mkdocs
mkdocs-material
mkdocs-redirects
mdx_truly_sane_lists
mkdocs-awesome-pages-plugin
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/MANIFEST.in | include timm/models/_pruned/*.txt
include timm/data/_info/*.txt
include timm/data/_info/*.json
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/onnx_validate.py | """ ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
im... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/README.md | # PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resourc... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/validate.py | #!/usr/bin/env python3
""" ImageNet Validation Script
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good perform... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/setup.py | """ Setup
"""
from setuptools import setup, find_packages
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
exec(open('timm/version.py'... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/requirements-dev.txt | pytest
pytest-timeout
pytest-xdist
pytest-forked
expecttest
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/CONTRIBUTING.md | *This guideline is very much a work-in-progress.*
Contributions to `timm` for code, documentation, tests are more than welcome!
There haven't been any formal guidelines to date so please bear with me, and feel free to add to this guide.
# Coding style
Code linting and auto-format (black) are not currently in place ... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/train.py | #!/usr/bin/env python3
""" ImageNet Training Script
This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it al... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/bulk_runner.py | #!/usr/bin/env python3
""" Bulk Model Script Runner
Run validation or benchmark script in separate process for each model
Benchmark all 'vit*' models:
python bulk_runner.py --model-list 'vit*' --results-file vit_bench.csv benchmark.py --amp -b 512
Validate all models:
python bulk_runner.py --model-list all --resul... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/inference.py | #!/usr/bin/env python3
"""PyTorch Inference Script
An example inference script that outputs top-k class ids for images in a folder into a csv.
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import argparse
import json
import logging
import os
import time
from contextlib import su... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/setup.cfg | [dist_conda]
conda_name_differences = 'torch:pytorch'
channels = pytorch
noarch = True
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/hubconf.py | dependencies = ['torch']
import timm
globals().update(timm.models._registry._model_entrypoints)
| 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/model-index.yml | Import:
- ./docs/models/*.md
Library:
Name: PyTorch Image Models
Headline: PyTorch image models, scripts, pretrained weights
Website: https://rwightman.github.io/pytorch-image-models/
Repository: https://github.com/rwightman/pytorch-image-models
Docs: https://rwightman.github.io/pytorch-image-models/
README... | 0 |
hf_public_repos | hf_public_repos/pytorch-image-models/clean_checkpoint.py | #!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_utils.py | from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.we... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_layers.py | import torch
import torch.nn as nn
from timm.layers import create_act_layer, set_layer_config
import importlib
import os
torch_backend = os.environ.get('TORCH_BACKEND')
if torch_backend is not None:
importlib.import_module(torch_backend)
torch_device = os.environ.get('TORCH_DEVICE', 'cpu')
class MLP(nn.Module):... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_optim.py | """ Optimzier Tests
These tests were adapted from PyTorch' optimizer tests.
"""
import math
import pytest
import functools
from copy import deepcopy
import torch
from torch.testing._internal.common_utils import TestCase
from torch.nn import Parameter
from timm.scheduler import PlateauLRScheduler
from timm.optim imp... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_models.py | """Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/scripts.md | # Scripts
A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release.
The training and validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples). I have added signi... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/results.md | # Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results).
## Self-trained Weights
The table below includes ImageNe... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/index.md | # Getting Started
## Welcome
Welcome to the `timm` documentation, a lean set of docs that covers the basics of `timm`.
For a more comprehensive set of docs (currently under development), please visit [timmdocs](http://timm.fast.ai) by [Aman Arora](https://github.com/amaarora).
## Install
The library can be install... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/models.md | # Model Summaries
The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.
Most included models have pretrained weights. The weights are either:
1. ... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/archived_changes.md | # Archived Changes
### Nov 22, 2021
* A number of updated weights anew new model defs
* `eca_halonext26ts` - 79.5 @ 256
* `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
* `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, ... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/changes.md | # Recent Changes
### Aug 29, 2022
* MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this:
* `maxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.6 @ 320 (T)
### Aug 26, 2022
* CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) `timm` or... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/training_hparam_examples.md | # Training Examples
## EfficientNet-B2 with RandAugment - 80.4 top-1, 95.1 top-5
These params are for dual Titan RTX cards with NVIDIA Apex installed:
`./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 -... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/docs/feature_extraction.md | # Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring ... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/vision-transformer.md | # Vision Transformer (ViT)
The **Vision Transformer** is a model for image classification that employs a Transformer-like architecture over patches of the image. This includes the use of [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention), [Scaled Dot-Product Attention](https://paperswithcod... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/ssl-resnext.md | # SSL ResNeXT
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) ... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/ssl-resnet.md | # SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/gloun-inception-v3.md | # (Gluon) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswit... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/fbnet.md | # FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/ensemble-adversarial.md | # # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception arch... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/advprop.md | # AdvProp (EfficientNet)
**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
The w... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/gloun-resnet.md | # (Gluon) ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residu... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/tf-mixnet.md | # (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The weights from this model were ported from [Tenso... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/noisy-student.md | # Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:
1. train a teacher model on labeled images
2.... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/swsl-resnet.md | # SWSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual ... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/resnext.md | # ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$,... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/inception-resnet-v2.md | # Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
## How do I... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/legacy-se-resnet.md | # (Legacy) SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this mod... | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/.pages | title: Model Pages | 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/models/ecaresnet.md | # ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | 0 |
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