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### File: hfdocs/source/training_script.mdx
# 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://git... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/training_script.mdx", "license": "apache-2.0", "size": 6918} |
### File: hubconf.py
dependencies = ['torch']
import timm
globals().update(timm.models._registry._model_entrypoints)
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "hubconf.py", "license": "apache-2.0", "size": 96} |
### File: 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
fr... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "inference.py", "license": "apache-2.0", "size": 15985} |
### File: 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... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "onnx_export.py", "license": "apache-2.0", "size": 4430} |
### File: 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 arg... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "onnx_validate.py", "license": "apache-2.0", "size": 4544} |
### File: results/generate_csv_results.py
import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv'... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "results/generate_csv_results.py", "license": "apache-2.0", "size": 2540} |
### File: 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(ope... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "setup.py", "license": "apache-2.0", "size": 1935} |
### File: timm/__init__.py
from .version import __version__
from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/__init__.py", "license": "apache-2.0", "size": 292} |
### File: timm/data/__init__.py
from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .constants import *
from .dataset import ImageDataset, IterableImageDatas... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/__init__.py", "license": "apache-2.0", "size": 819} |
### File: timm/data/auto_augment.py
""" AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/ma... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/auto_augment.py", "license": "apache-2.0", "size": 35550} |
### File: timm/data/config.py
import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/config.py", "license": "apache-2.0", "size": 4531} |
### File: timm/data/constants.py
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGE... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/constants.py", "license": "apache-2.0", "size": 442} |
### File: timm/data/dataset.py
""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.ge... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/dataset.py", "license": "apache-2.0", "size": 5833} |
### File: timm/data/dataset_factory.py
""" Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/dataset_factory.py", "license": "apache-2.0", "size": 6989} |
### File: timm/data/dataset_info.py
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractm... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/dataset_info.py", "license": "apache-2.0", "size": 2391} |
### File: timm/data/distributed_sampler.py
import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.n... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/distributed_sampler.py", "license": "apache-2.0", "size": 5540} |
### File: timm/data/imagenet_info.py
import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/imagenet_info.py", "license": "apache-2.0", "size": 4167} |
### File: timm/data/loader.py
""" Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
imp... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/loader.py", "license": "apache-2.0", "size": 11849} |
### File: timm/data/mixup.py
""" Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyT... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/mixup.py", "license": "apache-2.0", "size": 14634} |
### File: timm/data/random_erasing.py
""" Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/random_erasing.py", "license": "apache-2.0", "size": 4964} |
### File: timm/data/readers/__init__.py
from .reader_factory import create_reader
from .img_extensions import *
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/__init__.py", "license": "apache-2.0", "size": 72} |
### File: timm/data/readers/class_map.py
import os
import pickle
def load_class_map(map_or_filename, root=''):
if isinstance(map_or_filename, dict):
assert dict, 'class_map dict must be non-empty'
return map_or_filename
class_map_path = map_or_filename
if not os.path.exists(class_map_path)... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/class_map.py", "license": "apache-2.0", "size": 895} |
### File: timm/data/readers/img_extensions.py
from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIO... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/img_extensions.py", "license": "apache-2.0", "size": 1482} |
### File: timm/data/readers/reader.py
from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index,... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader.py", "license": "apache-2.0", "size": 487} |
### File: timm/data/readers/reader_factory.py
import os
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(name, root, split='train', **kwargs):
name = name.lower()
name = name.split('/', 1)
prefix = ''
if len(name) > 1:
pref... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_factory.py", "license": "apache-2.0", "size": 1364} |
### File: timm/data/readers/reader_hfds.py
""" Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hug... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_hfds.py", "license": "apache-2.0", "size": 2431} |
### File: timm/data/readers/reader_image_folder.py
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_image_folder.py", "license": "apache-2.0", "size": 3315} |
### File: timm/data/readers/reader_image_in_tar.py
""" A dataset reader that reads tarfile based datasets
This reader can extract image samples from:
* a single tar of image files
* a folder of multiple tarfiles containing imagefiles
* a tar of tars containing image files
Labels are based on the combined folder and/o... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_image_in_tar.py", "license": "apache-2.0", "size": 9182} |
### File: timm/data/readers/reader_image_tar.py
""" A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
imp... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_image_tar.py", "license": "apache-2.0", "size": 2644} |
### File: timm/data/readers/reader_tfds.py
""" Dataset reader that wraps TFDS datasets
Wraps many (most?) TFDS image-classification datasets
from https://github.com/tensorflow/datasets
https://www.tensorflow.org/datasets/catalog/overview#image_classification
Hacked together by / Copyright 2020 Ross Wightman
"""
impor... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_tfds.py", "license": "apache-2.0", "size": 17858} |
### File: timm/data/readers/reader_wds.py
""" Dataset reader for webdataset
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import json
import logging
import math
import os
import random
import sys
from dataclasses import dataclass
from functools import partial
from itertools import islice
from typing ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/reader_wds.py", "license": "apache-2.0", "size": 16724} |
### File: timm/data/readers/shared_count.py
from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
self.... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/readers/shared_count.py", "license": "apache-2.0", "size": 303} |
### File: timm/data/real_labels.py
""" Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as n... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/real_labels.py", "license": "apache-2.0", "size": 1800} |
### File: timm/data/tf_preprocessing.py
""" Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licen... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/tf_preprocessing.py", "license": "apache-2.0", "size": 9169} |
### File: timm/data/transforms.py
import math
import numbers
import random
import warnings
from typing import List, Sequence
import torch
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/transforms.py", "license": "apache-2.0", "size": 11992} |
### File: timm/data/transforms_factory.py
""" Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_M... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/data/transforms_factory.py", "license": "apache-2.0", "size": 9840} |
### File: timm/layers/__init__.py
from .activations import *
from .adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
from .blur_pool import BlurPool2d
from .class... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/__init__.py", "license": "apache-2.0", "size": 3740} |
### File: timm/layers/activations.py
""" Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import fu... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/activations.py", "license": "apache-2.0", "size": 4468} |
### File: timm/layers/activations_jit.py
""" Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/activations_jit.py", "license": "apache-2.0", "size": 2529} |
### File: timm/layers/activations_me.py
""" Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/activations_me.py", "license": "apache-2.0", "size": 5886} |
### File: timm/layers/adaptive_avgmax_pool.py
""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/adaptive_avgmax_pool.py", "license": "apache-2.0", "size": 6310} |
### File: timm/layers/attention_pool2d.py
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked toge... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/attention_pool2d.py", "license": "apache-2.0", "size": 4934} |
### File: timm/layers/blur_pool.py
"""
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/blur_pool.py", "license": "apache-2.0", "size": 1594} |
### File: timm/layers/bottleneck_attn.py
""" Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vas... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/bottleneck_attn.py", "license": "apache-2.0", "size": 6895} |
### File: timm/layers/cbam.py
""" CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may en... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/cbam.py", "license": "apache-2.0", "size": 4426} |
### File: timm/layers/classifier.py
""" Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/classifier.py", "license": "apache-2.0", "size": 7486} |
### File: timm/layers/cond_conv2d.py
""" PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
impo... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/cond_conv2d.py", "license": "apache-2.0", "size": 5199} |
### File: timm/layers/config.py
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_att... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/config.py", "license": "apache-2.0", "size": 4175} |
### File: timm/layers/conv2d_same.py
""" Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .config import is_exportable, is_scriptable
from .padding import pad_same, pad_same_arg, get_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/conv2d_same.py", "license": "apache-2.0", "size": 3216} |
### File: timm/layers/conv_bn_act.py
""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/conv_bn_act.py", "license": "apache-2.0", "size": 3836} |
### File: timm/layers/create_act.py
""" Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/create_act.py", "license": "apache-2.0", "size": 5320} |
### File: timm/layers/create_attn.py
""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import Gather... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/create_attn.py", "license": "apache-2.0", "size": 3514} |
### File: timm/layers/create_conv2d.py
""" Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/create_conv2d.py", "license": "apache-2.0", "size": 1622} |
### File: timm/layers/create_norm.py
""" Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import types
import functools
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
_NORM_MAP = dict(
batchnor... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/create_norm.py", "license": "apache-2.0", "size": 1740} |
### File: timm/layers/create_norm_act.py
""" NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/create_norm_act.py", "license": "apache-2.0", "size": 3748} |
### File: timm/layers/drop.py
""" DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/drop.py", "license": "apache-2.0", "size": 6872} |
### File: timm/layers/eca.py
"""
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/eca.py", "license": "apache-2.0", "size": 6386} |
### File: timm/layers/evo_norm.py
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
edito... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/evo_norm.py", "license": "apache-2.0", "size": 13862} |
### File: timm/layers/fast_norm.py
""" 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing im... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/fast_norm.py", "license": "apache-2.0", "size": 4008} |
### File: timm/layers/filter_response_norm.py
""" Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils impor... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/filter_response_norm.py", "license": "apache-2.0", "size": 2540} |
### File: timm/layers/format.py
from enum import Enum
from typing import Union
import torch
class Format(str, Enum):
NCHW = 'NCHW'
NHWC = 'NHWC'
NCL = 'NCL'
NLC = 'NLC'
FormatT = Union[str, Format]
def get_spatial_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NLC:
dim = (1... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/format.py", "license": "apache-2.0", "size": 1109} |
### File: timm/layers/gather_excite.py
""" Gather-Excite Attention Block
Paper: `Gather-Excite: Exploiting Feature Context in CNNs` - https://arxiv.org/abs/1810.12348
Official code here, but it's only partial impl in Caffe: https://github.com/hujie-frank/GENet
I've tried to support all of the extent both w/ and w/o ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/gather_excite.py", "license": "apache-2.0", "size": 3824} |
### File: timm/layers/global_context.py
""" Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
""... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/global_context.py", "license": "apache-2.0", "size": 2445} |
### File: timm/layers/grn.py
""" Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching e... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/grn.py", "license": "apache-2.0", "size": 1319} |
### File: timm/layers/halo_attn.py
""" Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon S... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/halo_attn.py", "license": "apache-2.0", "size": 10662} |
### File: timm/layers/helpers.py
""" Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
ret... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/helpers.py", "license": "apache-2.0", "size": 1053} |
### File: timm/layers/inplace_abn.py
import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/inplace_abn.py", "license": "apache-2.0", "size": 3374} |
### File: timm/layers/interpolate.py
""" Interpolation helpers for timm layers
RegularGridInterpolator from https://github.com/sbarratt/torch_interpolations
Copyright Shane Barratt, Apache 2.0 license
"""
import torch
from itertools import product
class RegularGridInterpolator:
""" Interpolate data defined on a ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/interpolate.py", "license": "apache-2.0", "size": 2439} |
### File: timm/layers/lambda_layer.py
""" Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
Th... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/lambda_layer.py", "license": "apache-2.0", "size": 5941} |
### File: timm/layers/linear.py
""" Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/linear.py", "license": "apache-2.0", "size": 743} |
### File: timm/layers/median_pool.py
""" Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/median_pool.py", "license": "apache-2.0", "size": 1737} |
### File: timm/layers/mixed_conv2d.py
""" PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/mixed_conv2d.py", "license": "apache-2.0", "size": 1843} |
### File: timm/layers/ml_decoder.py
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/ml_decoder.py", "license": "apache-2.0", "size": 7008} |
### File: timm/layers/mlp.py
""" MLP module w/ dropout and configurable activation layer
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vis... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/mlp.py", "license": "apache-2.0", "size": 8495} |
### File: timm/layers/non_local_attn.py
""" Bilinear-Attention-Transform and Non-Local Attention
Paper: `Non-Local Neural Networks With Grouped Bilinear Attentional Transforms`
- https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_202... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/non_local_attn.py", "license": "apache-2.0", "size": 6218} |
### File: timm/layers/norm.py
""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.funct... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/norm.py", "license": "apache-2.0", "size": 6040} |
### File: timm/layers/norm_act.py
""" Normalization + Activation Layers
Provides Norm+Act fns for standard PyTorch norm layers such as
* BatchNorm
* GroupNorm
* LayerNorm
This allows swapping with alternative layers that are natively both norm + act such as
* EvoNorm (evo_norm.py)
* FilterResponseNorm (filter_respons... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/norm_act.py", "license": "apache-2.0", "size": 17418} |
### File: timm/layers/padding.py
""" Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
import torch
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/padding.py", "license": "apache-2.0", "size": 2877} |
### File: timm/layers/patch_dropout.py
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/patch_dropout.py", "license": "apache-2.0", "size": 1741} |
### File: timm/layers/patch_embed.py
""" Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked toge... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/patch_embed.py", "license": "apache-2.0", "size": 9598} |
### File: timm/layers/pool2d_same.py
""" AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/pool2d_same.py", "license": "apache-2.0", "size": 3045} |
### File: timm/layers/pos_embed.py
""" Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
_logger = logging.getLogger(__name__)
def... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/pos_embed.py", "license": "apache-2.0", "size": 2584} |
### File: timm/layers/pos_embed_rel.py
""" Relative position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
import os
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .interpolate import RegularGridInterpolat... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/pos_embed_rel.py", "license": "apache-2.0", "size": 19449} |
### File: timm/layers/pos_embed_sincos.py
""" Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from .trace_utils import _assert
def pixel_freq_b... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/pos_embed_sincos.py", "license": "apache-2.0", "size": 14452} |
### File: timm/layers/selective_kernel.py
""" Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv_bn_act import ConvNormActAa
from .helpers import make_divis... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/selective_kernel.py", "license": "apache-2.0", "size": 5387} |
### File: timm/layers/separable_conv.py
""" Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from to... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/separable_conv.py", "license": "apache-2.0", "size": 2620} |
### File: timm/layers/space_to_depth.py
import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/space_to_depth.py", "license": "apache-2.0", "size": 1775} |
### File: timm/layers/split_attn.py
""" Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/split_attn.py", "license": "apache-2.0", "size": 3076} |
### File: timm/layers/split_batchnorm.py
""" Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-l... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/split_batchnorm.py", "license": "apache-2.0", "size": 3441} |
### File: timm/layers/squeeze_excite.py
""" Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Sq... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/squeeze_excite.py", "license": "apache-2.0", "size": 4327} |
### File: timm/layers/std_conv.py
""" Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.105... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/std_conv.py", "license": "apache-2.0", "size": 5887} |
### File: timm/layers/trace_utils.py
try:
from torch import _assert
except ImportError:
def _assert(condition: bool, message: str):
assert condition, message
def _float_to_int(x: float) -> int:
"""
Symbolic tracing helper to substitute for inbuilt `int`.
Hint: Inbuilt `int` can't accept an... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/trace_utils.py", "license": "apache-2.0", "size": 335} |
### File: timm/layers/weight_init.py
import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/layers/weight_init.py", "license": "apache-2.0", "size": 4765} |
### File: timm/loss/__init__.py
from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
from .binary_cross_entropy import BinaryCrossEntropy
from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from .jsd import JsdCrossEntropy
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/loss/__init__.py", "license": "apache-2.0", "size": 245} |
### File: timm/loss/asymmetric_loss.py
import torch
import torch.nn as nn
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
super(AsymmetricLossMultiLabel, self).__init__()
self.gamma_neg = ga... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/loss/asymmetric_loss.py", "license": "apache-2.0", "size": 3343} |
### File: timm/loss/binary_cross_entropy.py
""" Binary Cross Entropy w/ a few extras
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryCrossEntropy(nn.Module):
""" BCE with optional one-hot from dense ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/loss/binary_cross_entropy.py", "license": "apache-2.0", "size": 2030} |
### File: timm/loss/cross_entropy.py
""" Cross Entropy w/ smoothing or soft targets
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropy(nn.Module):
""" NLL loss with label smoothing.
"""
def __init__(se... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/loss/cross_entropy.py", "license": "apache-2.0", "size": 1145} |
### File: timm/loss/jsd.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from .cross_entropy import LabelSmoothingCrossEntropy
class JsdCrossEntropy(nn.Module):
""" Jensen-Shannon Divergence + Cross-Entropy Loss
Based on impl here: https://github.com/google-research/augmix/blob/master/i... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/loss/jsd.py", "license": "apache-2.0", "size": 1595} |
### File: timm/models/__init__.py
from .beit import *
from .byoanet import *
from .byobnet import *
from .cait import *
from .coat import *
from .convit import *
from .convmixer import *
from .convnext import *
from .crossvit import *
from .cspnet import *
from .davit import *
from .deit import *
from .densenet import ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/models/__init__.py", "license": "apache-2.0", "size": 3246} |
### File: timm/models/_builder.py
import dataclasses
import logging
import os
from copy import deepcopy
from typing import Optional, Dict, Callable, Any, Tuple
from torch import nn as nn
from torch.hub import load_state_dict_from_url
from timm.models._features import FeatureListNet, FeatureHookNet
from timm.models._f... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "timm/models/_builder.py", "license": "apache-2.0", "size": 18037} |
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