[Docs] Fix docs.
mzr1996
parent
702c196514
commit
6beac5060f
|
|
@ -129,7 +129,7 @@ html_theme_options = {
|
|||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
html_css_files = ['css/readthedocs.css']
|
||||
# html_js_files = ['js/custom.js']
|
||||
html_js_files = ['js/custom.js']
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
|
|
@ -236,5 +236,4 @@ def builder_inited_handler(app):
|
|||
|
||||
|
||||
def setup(app):
|
||||
app.add_js_file('./_static/js/custom.js')
|
||||
app.connect('builder-inited', builder_inited_handler)
|
||||
|
|
|
|||
|
|
@ -14,3 +14,7 @@ article.pytorch-article .section :not(dt) > code {
|
|||
background-color: #f3f4f7;
|
||||
border-radius: 5px;
|
||||
}
|
||||
|
||||
table.colwidths-auto td {
|
||||
width: 50%
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
var collapsedSections = ['Model zoo'];
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
mmcls.apis
|
||||
-------------
|
||||
.. automodule:: mmcls.apis
|
||||
:members:
|
||||
|
||||
mmcls.core
|
||||
-------------
|
||||
|
||||
evaluation
|
||||
^^^^^^^^^^
|
||||
.. automodule:: mmcls.core.evaluation
|
||||
:members:
|
||||
|
||||
mmcls.models
|
||||
---------------
|
||||
|
||||
models
|
||||
^^^^^^
|
||||
.. automodule:: mmcls.models
|
||||
:members:
|
||||
|
||||
classifiers
|
||||
^^^^^^^^^^^
|
||||
.. automodule:: mmcls.models.classifiers
|
||||
:members:
|
||||
|
||||
backbones
|
||||
^^^^^^^^^^
|
||||
.. automodule:: mmcls.models.backbones
|
||||
:members:
|
||||
|
||||
heads
|
||||
^^^^^^^^^^
|
||||
.. automodule:: mmcls.models.heads
|
||||
:members:
|
||||
|
||||
necks
|
||||
^^^^^^
|
||||
.. automodule:: mmcls.models.necks
|
||||
:members:
|
||||
|
||||
losses
|
||||
^^^^^^
|
||||
.. automodule:: mmcls.models.losses
|
||||
:members:
|
||||
|
||||
utils
|
||||
^^^^^^
|
||||
.. automodule:: mmcls.models.utils
|
||||
:members:
|
||||
|
||||
mmcls.datasets
|
||||
-----------------
|
||||
|
||||
datasets
|
||||
^^^^^^^^
|
||||
.. automodule:: mmcls.datasets
|
||||
:members:
|
||||
|
||||
pipelines
|
||||
^^^^^^^^^
|
||||
.. automodule:: mmcls.datasets.pipelines
|
||||
:members:
|
||||
|
||||
mmcls.utils
|
||||
--------------
|
||||
.. automodule:: mmcls.utils
|
||||
:members:
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.apis
|
||||
===================================
|
||||
|
||||
These are some high-level APIs for classification tasks.
|
||||
|
||||
.. contents:: mmcls.apis
|
||||
:depth: 2
|
||||
:local:
|
||||
:backlinks: top
|
||||
|
||||
.. currentmodule:: mmcls.apis
|
||||
|
||||
Train
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
init_random_seed
|
||||
set_random_seed
|
||||
train_model
|
||||
|
||||
Test
|
||||
------------------
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
single_gpu_test
|
||||
multi_gpu_test
|
||||
|
||||
Inference
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
init_model
|
||||
inference_model
|
||||
show_result_pyplot
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.core
|
||||
===================================
|
||||
|
||||
This package includes some runtime components. These components are useful in
|
||||
classification tasks but not supported by MMCV yet.
|
||||
|
||||
.. note::
|
||||
|
||||
Some components may be moved to MMCV in the future.
|
||||
|
||||
.. contents:: mmcls.core
|
||||
:depth: 2
|
||||
:local:
|
||||
:backlinks: top
|
||||
|
||||
.. currentmodule:: mmcls.core
|
||||
|
||||
Evaluation
|
||||
------------------
|
||||
|
||||
Evaluation metrics calculation functions
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
precision
|
||||
recall
|
||||
f1_score
|
||||
precision_recall_f1
|
||||
average_precision
|
||||
mAP
|
||||
support
|
||||
average_performance
|
||||
calculate_confusion_matrix
|
||||
|
||||
Hook
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
ClassNumCheckHook
|
||||
PreciseBNHook
|
||||
CosineAnnealingCooldownLrUpdaterHook
|
||||
|
||||
|
||||
Optimizers
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
Lamb
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.datasets
|
||||
===================================
|
||||
|
||||
The ``datasets`` package contains several usual datasets for image classification tasks and some dataset wrappers.
|
||||
|
||||
.. currentmodule:: mmcls.datasets
|
||||
|
||||
Custom Dataset
|
||||
--------------
|
||||
|
||||
.. autoclass:: CustomDataset
|
||||
|
||||
ImageNet
|
||||
--------
|
||||
|
||||
.. autoclass:: ImageNet
|
||||
|
||||
.. autoclass:: ImageNet21k
|
||||
|
||||
CIFAR
|
||||
-----
|
||||
|
||||
.. autoclass:: CIFAR10
|
||||
|
||||
.. autoclass:: CIFAR100
|
||||
|
||||
MNIST
|
||||
-----
|
||||
|
||||
.. autoclass:: MNIST
|
||||
|
||||
.. autoclass:: FashionMNIST
|
||||
|
||||
VOC
|
||||
---
|
||||
|
||||
.. autoclass:: VOC
|
||||
|
||||
Base classes
|
||||
------------
|
||||
|
||||
.. autoclass:: BaseDataset
|
||||
|
||||
.. autoclass:: MultiLabelDataset
|
||||
|
||||
Dataset Wrappers
|
||||
----------------
|
||||
|
||||
.. autoclass:: ConcatDataset
|
||||
|
||||
.. autoclass:: RepeatDataset
|
||||
|
||||
.. autoclass:: ClassBalancedDataset
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.models
|
||||
===================================
|
||||
|
||||
The ``models`` package contains several sub-packages for addressing the different components of a model.
|
||||
|
||||
- :ref:`classifiers`: The top-level module which defines the whole process of a classification model.
|
||||
- :ref:`backbones`: Usually a feature extraction network, e.g., ResNet, MobileNet.
|
||||
- :ref:`necks`: The component between backbones and heads, e.g., GlobalAveragePooling.
|
||||
- :ref:`heads`: The component for specific tasks. In MMClassification, we provides heads for classification.
|
||||
- :ref:`losses`: Loss functions.
|
||||
|
||||
.. currentmodule:: mmcls.models
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
build_classifier
|
||||
build_backbone
|
||||
build_neck
|
||||
build_head
|
||||
build_loss
|
||||
|
||||
.. _classifiers:
|
||||
|
||||
Classifier
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
BaseClassifier
|
||||
ImageClassifier
|
||||
|
||||
.. _backbones:
|
||||
|
||||
Backbones
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
AlexNet
|
||||
CSPDarkNet
|
||||
CSPNet
|
||||
CSPResNeXt
|
||||
CSPResNet
|
||||
Conformer
|
||||
ConvMixer
|
||||
ConvNeXt
|
||||
DistilledVisionTransformer
|
||||
EfficientNet
|
||||
HRNet
|
||||
LeNet5
|
||||
MlpMixer
|
||||
MobileNetV2
|
||||
MobileNetV3
|
||||
PCPVT
|
||||
PoolFormer
|
||||
RegNet
|
||||
RepMLPNet
|
||||
RepVGG
|
||||
Res2Net
|
||||
ResNeSt
|
||||
ResNeXt
|
||||
ResNet
|
||||
ResNetV1c
|
||||
ResNetV1d
|
||||
ResNet_CIFAR
|
||||
SEResNeXt
|
||||
SEResNet
|
||||
SVT
|
||||
ShuffleNetV1
|
||||
ShuffleNetV2
|
||||
SwinTransformer
|
||||
T2T_ViT
|
||||
TIMMBackbone
|
||||
TNT
|
||||
VGG
|
||||
VisionTransformer
|
||||
|
||||
.. _necks:
|
||||
|
||||
Necks
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
GlobalAveragePooling
|
||||
GeneralizedMeanPooling
|
||||
HRFuseScales
|
||||
|
||||
.. _heads:
|
||||
|
||||
Heads
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
ClsHead
|
||||
LinearClsHead
|
||||
StackedLinearClsHead
|
||||
MultiLabelClsHead
|
||||
MultiLabelLinearClsHead
|
||||
VisionTransformerClsHead
|
||||
DeiTClsHead
|
||||
ConformerHead
|
||||
|
||||
.. _losses:
|
||||
|
||||
Losses
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
Accuracy
|
||||
AsymmetricLoss
|
||||
CrossEntropyLoss
|
||||
LabelSmoothLoss
|
||||
FocalLoss
|
||||
SeesawLoss
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
Batch Augmentation
|
||||
===================================
|
||||
|
||||
Batch augmentation is the augmentation which involve multiple samples, such as Mixup and CutMix.
|
||||
|
||||
In MMClassification, these batch augmentation is used as a part of :ref:`classifiers`. A typical usage is as below:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
model = dict(
|
||||
backbone = ...,
|
||||
neck = ...,
|
||||
head = ...,
|
||||
train_cfg=dict(augments=[
|
||||
dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes),
|
||||
dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes),
|
||||
]))
|
||||
)
|
||||
|
||||
.. currentmodule:: mmcls.models.utils.augment
|
||||
|
||||
Mixup
|
||||
-----
|
||||
.. autoclass:: BatchMixupLayer
|
||||
|
||||
CutMix
|
||||
------
|
||||
.. autoclass:: BatchCutMixLayer
|
||||
|
||||
ResizeMix
|
||||
---------
|
||||
.. autoclass:: BatchResizeMixLayer
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.models.utils
|
||||
===================================
|
||||
|
||||
This package includes some helper functions and common components used in various networks.
|
||||
|
||||
.. contents:: mmcls.models.utils
|
||||
:depth: 2
|
||||
:local:
|
||||
:backlinks: top
|
||||
|
||||
.. currentmodule:: mmcls.models.utils
|
||||
|
||||
Common Components
|
||||
------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
InvertedResidual
|
||||
SELayer
|
||||
ShiftWindowMSA
|
||||
MultiheadAttention
|
||||
ConditionalPositionEncoding
|
||||
|
||||
Helper Functions
|
||||
------------------
|
||||
|
||||
channel_shuffle
|
||||
^^^^^^^^^^^^^^^
|
||||
.. autofunction:: channel_shuffle
|
||||
|
||||
make_divisible
|
||||
^^^^^^^^^^^^^^
|
||||
.. autofunction:: make_divisible
|
||||
|
||||
to_ntuple
|
||||
^^^^^^^^^^^^^^
|
||||
.. autofunction:: to_ntuple
|
||||
.. autofunction:: to_2tuple
|
||||
.. autofunction:: to_3tuple
|
||||
.. autofunction:: to_4tuple
|
||||
|
||||
is_tracing
|
||||
^^^^^^^^^^^^^^
|
||||
.. autofunction:: is_tracing
|
||||
|
|
@ -0,0 +1,171 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
Data Transformations
|
||||
***********************************
|
||||
|
||||
In MMClassification, the data preparation and the dataset is decomposed. The
|
||||
datasets only define how to get samples' basic information from the file
|
||||
system. These basic information includes the ground-truth label and raw images
|
||||
data / the paths of images.
|
||||
|
||||
To prepare the inputs data, we need to do some transformations on these basic
|
||||
information. These transformations includes loading, preprocessing and
|
||||
formatting. And a series of data transformations makes up a data pipeline.
|
||||
Therefore, you can find the a ``pipeline`` argument in the configs of dataset,
|
||||
for example:
|
||||
|
||||
.. code:: python
|
||||
|
||||
img_norm_cfg = dict(
|
||||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
||||
train_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(type='RandomResizedCrop', size=224),
|
||||
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
dict(type='ImageToTensor', keys=['img']),
|
||||
dict(type='ToTensor', keys=['gt_label']),
|
||||
dict(type='Collect', keys=['img', 'gt_label'])
|
||||
]
|
||||
test_pipeline = [
|
||||
dict(type='LoadImageFromFile'),
|
||||
dict(type='Resize', size=256),
|
||||
dict(type='CenterCrop', crop_size=224),
|
||||
dict(type='Normalize', **img_norm_cfg),
|
||||
dict(type='ImageToTensor', keys=['img']),
|
||||
dict(type='Collect', keys=['img'])
|
||||
]
|
||||
|
||||
data = dict(
|
||||
train=dict(..., pipeline=train_pipeline),
|
||||
val=dict(..., pipeline=test_pipeline),
|
||||
test=dict(..., pipeline=test_pipeline),
|
||||
)
|
||||
|
||||
Every item of a pipeline list is one of the following data transformations class. And if you want to add a custom data transformation class, the tutorial :doc:`Custom Data Pipelines </tutorials/data_pipeline>` will help you.
|
||||
|
||||
.. contents:: mmcls.datasets.pipelines
|
||||
:depth: 2
|
||||
:local:
|
||||
:backlinks: top
|
||||
|
||||
.. currentmodule:: mmcls.datasets.pipelines
|
||||
|
||||
Loading
|
||||
=======
|
||||
|
||||
LoadImageFromFile
|
||||
---------------------
|
||||
.. autoclass:: LoadImageFromFile
|
||||
|
||||
Preprocessing and Augmentation
|
||||
==============================
|
||||
|
||||
CenterCrop
|
||||
---------------------
|
||||
.. autoclass:: CenterCrop
|
||||
|
||||
Lighting
|
||||
---------------------
|
||||
.. autoclass:: Lighting
|
||||
|
||||
Normalize
|
||||
---------------------
|
||||
.. autoclass:: Normalize
|
||||
|
||||
Pad
|
||||
---------------------
|
||||
.. autoclass:: Pad
|
||||
|
||||
Resize
|
||||
---------------------
|
||||
.. autoclass:: Resize
|
||||
|
||||
RandomCrop
|
||||
---------------------
|
||||
.. autoclass:: RandomCrop
|
||||
|
||||
RandomErasing
|
||||
---------------------
|
||||
.. autoclass:: RandomErasing
|
||||
|
||||
RandomFlip
|
||||
---------------------
|
||||
.. autoclass:: RandomFlip
|
||||
|
||||
RandomGrayscale
|
||||
---------------------
|
||||
.. autoclass:: RandomGrayscale
|
||||
|
||||
RandomResizedCrop
|
||||
---------------------
|
||||
.. autoclass:: RandomResizedCrop
|
||||
|
||||
ColorJitter
|
||||
---------------------
|
||||
.. autoclass:: ColorJitter
|
||||
|
||||
|
||||
Composed Augmentation
|
||||
---------------------
|
||||
Composed augmentation is a kind of methods which compose a series of data
|
||||
augmentation transformations, such as ``AutoAugment`` and ``RandAugment``.
|
||||
|
||||
.. autoclass:: AutoAugment
|
||||
|
||||
.. autoclass:: RandAugment
|
||||
|
||||
In composed augmentation, we need to specify several data transformations or
|
||||
several groups of data transformations (The ``policies`` argument) as the
|
||||
random sampling space. These data transformations are chosen from the below
|
||||
table. In addition, we provide some preset policies in `this folder`_.
|
||||
|
||||
.. _this folder: https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/datasets/pipelines
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
AutoContrast
|
||||
Brightness
|
||||
ColorTransform
|
||||
Contrast
|
||||
Cutout
|
||||
Equalize
|
||||
Invert
|
||||
Posterize
|
||||
Rotate
|
||||
Sharpness
|
||||
Shear
|
||||
Solarize
|
||||
SolarizeAdd
|
||||
Translate
|
||||
|
||||
Formatting
|
||||
==========
|
||||
|
||||
Collect
|
||||
---------------------
|
||||
.. autoclass:: Collect
|
||||
|
||||
ImageToTensor
|
||||
---------------------
|
||||
.. autoclass:: ImageToTensor
|
||||
|
||||
ToNumpy
|
||||
---------------------
|
||||
.. autoclass:: ToNumpy
|
||||
|
||||
ToPIL
|
||||
---------------------
|
||||
.. autoclass:: ToPIL
|
||||
|
||||
ToTensor
|
||||
---------------------
|
||||
.. autoclass:: ToTensor
|
||||
|
||||
Transpose
|
||||
---------------------
|
||||
.. autoclass:: Transpose
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
.. role:: hidden
|
||||
:class: hidden-section
|
||||
|
||||
mmcls.utils
|
||||
===================================
|
||||
|
||||
These are some useful help function in the ``utils`` package.
|
||||
|
||||
.. contents:: mmcls.utils
|
||||
:depth: 1
|
||||
:local:
|
||||
:backlinks: top
|
||||
|
||||
.. currentmodule:: mmcls.utils
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated
|
||||
:nosignatures:
|
||||
|
||||
collect_env
|
||||
get_root_logger
|
||||
load_json_log
|
||||
setup_multi_processes
|
||||
|
|
@ -44,6 +44,8 @@ release = get_version()
|
|||
# ones.
|
||||
extensions = [
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.autosummary',
|
||||
'sphinx.ext.intersphinx',
|
||||
'sphinx.ext.napoleon',
|
||||
'sphinx.ext.viewcode',
|
||||
'sphinx_markdown_tables',
|
||||
|
|
@ -127,6 +129,7 @@ html_theme_options = {
|
|||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
html_css_files = ['css/readthedocs.css']
|
||||
html_js_files = ['js/custom.js']
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
|
|
@ -204,6 +207,15 @@ StandaloneHTMLBuilder.supported_image_types = [
|
|||
# Ignore >>> when copying code
|
||||
copybutton_prompt_text = r'>>> |\.\.\. '
|
||||
copybutton_prompt_is_regexp = True
|
||||
# Auto-generated header anchors
|
||||
myst_heading_anchors = 3
|
||||
# Configuration for intersphinx
|
||||
intersphinx_mapping = {
|
||||
'python': ('https://docs.python.org/3', None),
|
||||
'numpy': ('https://numpy.org/doc/stable', None),
|
||||
'torch': ('https://pytorch.org/docs/stable/', None),
|
||||
'mmcv': ('https://mmcv.readthedocs.io/en/master/', None),
|
||||
}
|
||||
|
||||
|
||||
def builder_inited_handler(app):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,2 @@
|
|||
[html writers]
|
||||
table_style: colwidths-auto
|
||||
|
|
@ -59,7 +59,14 @@ You can switch between Chinese and English documentation in the lower-left corne
|
|||
.. toctree::
|
||||
:caption: API 参考文档
|
||||
|
||||
api.rst
|
||||
mmcls.apis <api/apis>
|
||||
mmcls.core <api/core>
|
||||
mmcls.models <api/models>
|
||||
mmcls.models.utils <api/models.utils>
|
||||
mmcls.datasets <api/datasets>
|
||||
数据转换 <api/transforms>
|
||||
批数据增强 <api/models.utils.augment>
|
||||
mmcls.utils <api/utils>
|
||||
|
||||
|
||||
.. toctree::
|
||||
|
|
|
|||
|
|
@ -2,13 +2,12 @@
|
|||
|
||||
<!-- TOC -->
|
||||
|
||||
- [ONNX 转 TensorRT（试验性的）](#onnx-tensorrt)
|
||||
- [如何将模型从 ONNX 转换到 TensorRT](#id1)
|
||||
- [准备工作](#id2)
|
||||
- [使用方法](#id3)
|
||||
- [支持转换至 TensorRT 的模型列表](#tensorrt)
|
||||
- [提示](#id4)
|
||||
- [常见问题](#id5)
|
||||
- [如何将模型从 ONNX 转换到 TensorRT](#如何将模型从-onnx-转换到-tensorrt)
|
||||
- [准备工作](#准备工作)
|
||||
- [使用方法](#使用方法)
|
||||
- [支持转换至 TensorRT 的模型列表](#支持转换至-tensorrt-的模型列表)
|
||||
- [提示](#提示)
|
||||
- [常见问题](#常见问题)
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
|
|
|
|||
|
|
@ -2,13 +2,12 @@
|
|||
|
||||
<!-- TOC -->
|
||||
|
||||
- [Pytorch 转 ONNX （试验性的）](#pytorch-onnx)
|
||||
- [如何将模型从 PyTorch 转换到 ONNX](#id1)
|
||||
- [准备工作](#id2)
|
||||
- [使用方法](#id3)
|
||||
- [支持导出至 ONNX 的模型列表](#onnx)
|
||||
- [提示](#id4)
|
||||
- [常见问题](#id5)
|
||||
- [如何将模型从 PyTorch 转换到 ONNX](#如何将模型从-pytorch-转换到-onnx)
|
||||
- [准备工作](#准备工作)
|
||||
- [使用方法](#使用方法)
|
||||
- [支持导出至 ONNX 的模型列表](#支持导出至-onnx-的模型列表)
|
||||
- [提示](#提示)
|
||||
- [常见问题](#常见问题)
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
|
|
|
|||
|
|
@ -2,11 +2,10 @@
|
|||
|
||||
<!-- TOC -->
|
||||
|
||||
- [Pytorch 转 TorchScript （试验性的）](#pytorch-torchscript)
|
||||
- [如何将 PyTorch 模型转换至 TorchScript](#id1)
|
||||
- [使用方法](#id2)
|
||||
- [提示](#id3)
|
||||
- [常见问题](#id4)
|
||||
- [如何将 PyTorch 模型转换至 TorchScript](#如何将-pytorch-模型转换至-torchscript)
|
||||
- [使用方法](#使用方法)
|
||||
- [提示](#提示)
|
||||
- [常见问题](#常见问题)
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
|
|
|
|||
|
|
@ -7,14 +7,8 @@
|
|||
- [定制工作流](#定制工作流)
|
||||
- [钩子](#钩子)
|
||||
- [默认训练钩子](#默认训练钩子)
|
||||
- [权重文件钩子（CheckpointHook）](#权重文件钩子（checkpointhook）)
|
||||
- [日志钩子（LoggerHooks）](#日志钩子（loggerhooks）)
|
||||
- [验证钩子（EvalHook）](#验证钩子（evalhook）)
|
||||
- [使用内置钩子](#使用内置钩子)
|
||||
- [自定义钩子](#自定义钩子)
|
||||
- [1. 创建一个新钩子](#1.-创建一个新钩子)
|
||||
- [2. 注册新钩子](#2.-注册新钩子)
|
||||
- [3. 修改配置](#3.-修改配置)
|
||||
- [常见问题](#常见问题)
|
||||
|
||||
<!-- TOC -->
|
||||
|
|
@ -183,7 +177,7 @@ custom_hooks = [
|
|||
|
||||
## 自定义钩子
|
||||
|
||||
### 1. 创建一个新钩子
|
||||
### 创建一个新钩子
|
||||
|
||||
这里举一个在 MMClassification 中创建一个新钩子，并在训练中使用它的示例：
|
||||
|
||||
|
|
@ -218,7 +212,7 @@ class MyHook(Hook):
|
|||
|
||||
根据钩子的功能，用户需要指定钩子在训练的每个阶段将要执行的操作，比如 `before_run`，`after_run`，`before_epoch`，`after_epoch`，`before_iter` 和 `after_iter`。
|
||||
|
||||
### 2. 注册新钩子
|
||||
### 注册新钩子
|
||||
|
||||
之后，需要导入 `MyHook`。假设该文件在 `mmcls/core/utils/my_hook.py`，有两种办法导入它：
|
||||
|
||||
|
|
@ -238,7 +232,7 @@ __all__ = ['MyHook']
|
|||
custom_imports = dict(imports=['mmcls.core.utils.my_hook'], allow_failed_imports=False)
|
||||
```
|
||||
|
||||
### 3. 修改配置
|
||||
### 修改配置
|
||||
|
||||
```python
|
||||
custom_hooks = [
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
docutils==0.16.0
|
||||
docutils==0.17.1
|
||||
myst-parser
|
||||
git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
|
||||
sphinx==4.0.2
|
||||
-e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
|
||||
sphinx==4.5.0
|
||||
sphinx-copybutton
|
||||
sphinx_markdown_tables
|
||||
|
|
|
|||
Loading…
Reference in New Issue