From a50d96f7f19431e7774b5f5e1ea71b2825e370f2 Mon Sep 17 00:00:00 2001 From: mzr1996 Date: Mon, 20 Mar 2023 15:54:22 +0800 Subject: [PATCH] Update docs. --- docker/Dockerfile | 4 +-- docs/en/conf.py | 2 +- docs/en/user_guides/config.md | 47 ++++++++++++++++++----------------- docs/zh_CN/conf.py | 2 +- docs/zh_CN/get_started.md | 2 +- 5 files changed, 29 insertions(+), 28 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index e5a03d30..a1687d60 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -16,10 +16,10 @@ RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build && apt-get clean \ && rm -rf /var/lib/apt/lists/* -# Install MMCV +# Install MIM RUN pip install openmim -# Install MMClassification +# Install MMPretrain RUN conda clean --all RUN git clone -b pretrain https://github.com/open-mmlab/mmclassification.git mmpretrain WORKDIR ./mmpretrain diff --git a/docs/en/conf.py b/docs/en/conf.py index 23d95b6e..384922ef 100644 --- a/docs/en/conf.py +++ b/docs/en/conf.py @@ -93,7 +93,7 @@ html_theme_options = { 'menu': [ { 'name': 'GitHub', - 'url': 'https://github.com/open-mmlab/mmpretrain' + 'url': 'https://github.com/open-mmlab/mmclassification/tree/pretrain' }, { 'name': 'Colab Tutorials', diff --git a/docs/en/user_guides/config.md b/docs/en/user_guides/config.md index 6ad4f193..460b43c1 100644 --- a/docs/en/user_guides/config.md +++ b/docs/en/user_guides/config.md @@ -16,7 +16,7 @@ To manage various configurations in a deep-learning experiment, we use a kind of these configurations. This config system has a modular and inheritance design, and more details can be found in {external+mmengine:doc}`the tutorial in MMEngine `. -Usually, we use python files as config file. All configuration files are placed under the [`configs`](https://github.com/open-mmlab/mmpretrain/tree/main/configs) folder, and the directory structure is as follows: +Usually, we use python files as config file. All configuration files are placed under the [`configs`](https://github.com/open-mmlab/mmclassification/tree/pretrain/configs) folder, and the directory structure is as follows: ```text MMPretrain/ @@ -38,20 +38,20 @@ MMPretrain/ If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config. -This article mainly explains the structure of configuration files, and how to modify it based on the existing configuration files. We will take [ResNet50 config file](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnet/resnet50_8xb32_in1k.py) as an example and explain it line by line. +This article mainly explains the structure of configuration files, and how to modify it based on the existing configuration files. We will take [ResNet50 config file](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnet/resnet50_8xb32_in1k.py) as an example and explain it line by line. ## Config Structure There are four kinds of basic component files in the `configs/_base_` folders, namely： -- [models](https://github.com/open-mmlab/mmpretrain/tree/main/configs/_base_/models) -- [datasets](https://github.com/open-mmlab/mmpretrain/tree/main/configs/_base_/datasets) -- [schedules](https://github.com/open-mmlab/mmpretrain/tree/main/configs/_base_/schedules) -- [runtime](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/default_runtime.py) +- [models](https://github.com/open-mmlab/mmclassification/tree/pretrain/configs/_base_/models) +- [datasets](https://github.com/open-mmlab/mmclassification/tree/pretrain/configs/_base_/datasets) +- [schedules](https://github.com/open-mmlab/mmclassification/tree/pretrain/configs/_base_/schedules) +- [runtime](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/default_runtime.py) We call all the config files in the `_base_` folder as _primitive_ config files. You can easily build your training config file by inheriting some primitive config files. -For easy understanding, we use [ResNet50 config file](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnet/resnet50_8xb32_in1k.py) as an example and comment on each line. +For easy understanding, we use [ResNet50 config file](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnet/resnet50_8xb32_in1k.py) as an example and comment on each line. ```python _base_ = [ # This config file will inherit all config files in `_base_`. @@ -72,29 +72,30 @@ This primitive config file includes a dict variable `model`, which mainly includ - For image classification tasks, it's usually `ImageClassifier` You can find more details in the [API documentation](mmpretrain.models.classifiers). - For self-supervised leanrning, there are several `SelfSupervisors`, such as `MoCoV2`, `BEiT`, `MAE`, etc. You can find more details in the [API documentation](mmpretrain.models.selfsup). - For image retrieval tasks, it's usually `ImageToImageRetriever` You can find more details in the [API documentation](mmpretrain.models.retrievers). + +Usually, we use the `type` field to specify the class of the component and use other fields to pass +the initialization arguments of the class. The {external+mmengine:doc}`registry tutorial ` describes it in detail. + +Here, we use the config fields of [`ImageClassifier`](mmpretrain.models.ImageClassifier) as an example to +describe the below initialization arguments: + - `backbone`: The settings of the backbone. The backbone is the main network to extract features of the inputs, like `ResNet`, `Swin Transformer`, `Vision Transformer` etc. All available backbones can be found in the [API documentation](mmpretrain.models.backbones). - For self-supervised leanrning, some of the backbones are re-implemented, you can find more details in the [API documentation](mmpretrain.models.selfsup). - `neck`: The settings of the neck. The neck is the intermediate module to connect the backbone and the head, like `GlobalAveragePooling`. All available necks can be found in the [API documentation](mmpretrain.models.necks). -- `head`: The settings of the task head. The head is the task-related component to do the final - classification. All available heads can be found in the [API documentation](mmpretrain.models.heads). +- `head`: The settings of the task head. The head is the task-related component to do a specified task, like image classification or self-supervised training. All available heads can be found in the [API documentation](mmpretrain.models.heads). - `loss`: The loss function to optimize, like `CrossEntropyLoss`, `LabelSmoothLoss`, `PixelReconstructionLoss` and etc. All available losses can be found in the [API documentation](mmpretrain.models.losses). - `data_preprocessor`: The component before the model forwarding to preprocess the inputs. See the [documentation](mmpretrain.models.utils.data_preprocessor) for more details. -- `train_cfg`: The extra settings of the model during training. In MMCLS, we mainly use it to specify batch augmentation settings, like `Mixup` and `CutMix`. See the [documentation](mmpretrain.models.utils.batch_augments) for more details. +- `train_cfg`: The extra settings of `ImageClassifier` during training. In `ImageClassifier`, we mainly use it to specify batch augmentation settings, like `Mixup` and `CutMix`. See the [documentation](mmpretrain.models.utils.batch_augments) for more details. -```{note} -Usually, we use the `type` field to specify the class of the component and use other fields to pass -the initialization arguments of the class. The {external+mmengine:doc}`registry tutorial ` describes it in detail. -``` - -Following is the model primitive config of the ResNet50 config file in [`configs/_base_/models/resnet50.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/models/resnet50.py): +Following is the model primitive config of the ResNet50 config file in [`configs/_base_/models/resnet50.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/models/resnet50.py): ```python model = dict( - type='ImageClassifier', # The type of the main model (classifier). + type='ImageClassifier', # The type of the main model (here is for image classification task). backbone=dict( type='ResNet', # The type of the backbone module. # All fields except `type` come from the __init__ method of class `ResNet` - # and you can find them from https://mmpretrain.readthedocs.io/en/main/api/generated/mmpretrain.models.ResNet.html + # and you can find them from https://mmclassification.readthedocs.io/en/pretrain/api/generated/mmpretrain.models.backbones.ResNet.html depth=50, num_stages=4, out_indices=(3, ), @@ -104,7 +105,7 @@ model = dict( head=dict( type='LinearClsHead', # The type of the classification head module. # All fields except `type` come from the __init__ method of class `LinearClsHead` - # and you can find them from https://mmpretrain.readthedocs.io/en/main/api/generated/mmpretrain.models.LinearClsHead.html + # and you can find them from https://mmclassification.readthedocs.io/en/pretrain/api/generated/mmpretrain.models.heads.LinearClsHead.html num_classes=1000, in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), @@ -126,7 +127,7 @@ This primitive config file includes information to construct the dataloader and - `type`: The type of the dataset, we support `CustomDataset`, `ImageNet` and many other datasets, refer to [documentation](mmpretrain.datasets). - `pipeline`: The data transform pipeline. You can find how to design a pipeline in [this tutorial](https://mmpretrain.readthedocs.io/en/1.x/tutorials/data_pipeline.html). -Following is the data primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/datasets/imagenet_bs32.py)： +Following is the data primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/datasets/imagenet_bs32.py)： ```python dataset_type = 'ImageNet' @@ -204,7 +205,7 @@ test loops: - `param_scheduler`: Optimizer parameters policy. You can use it to specify learning rate and momentum curves during training. See the {external+mmengine:doc}`documentation ` in MMEngine for more details. - `train_cfg | val_cfg | test_cfg`: The settings of the training, validation and test loops, refer to the relevant {external+mmengine:doc}`MMEngine documentation `. -Following is the schedule primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/datasets/imagenet_bs32.py)： +Following is the schedule primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/datasets/imagenet_bs32.py)： ```python optim_wrapper = dict( @@ -234,7 +235,7 @@ auto_scale_lr = dict(base_batch_size=256) This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc. -Here is the runtime primitive config file ['configs/_base_/default_runtime.py'](https://github.com/open-mmlab/mmpretrain/blob/main/configs/_base_/default_runtime.py) file used by almost all configs: +Here is the runtime primitive config file ['configs/_base_/default_runtime.py'](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/_base_/default_runtime.py) file used by almost all configs: ```python # defaults to use registries in mmpretrain @@ -382,7 +383,7 @@ param_scheduler = dict(type='CosineAnnealingLR', by_epoch=True, _delete_=True) Sometimes, you may refer to some fields in the `_base_` config, to avoid duplication of definitions. You can refer to {external+mmengine:doc}`MMEngine ` for some more instructions. -The following is an example of using auto augment in the training data preprocessing pipeline, refer to [`configs/resnest/resnest50_32xb64_in1k.py`](https://github.com/open-mmlab/mmpretrain/blob/main/configs/resnest/resnest50_32xb64_in1k.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `_base_.auto_increasing_policies` to reference the variables in the primitive config: +The following is an example of using auto augment in the training data preprocessing pipeline, refer to [`configs/resnest/resnest50_32xb64_in1k.py`](https://github.com/open-mmlab/mmclassification/blob/pretrain/configs/resnest/resnest50_32xb64_in1k.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `_base_.auto_increasing_policies` to reference the variables in the primitive config: ```python _base_ = [ diff --git a/docs/zh_CN/conf.py b/docs/zh_CN/conf.py index 3555b0f8..8e211dae 100644 --- a/docs/zh_CN/conf.py +++ b/docs/zh_CN/conf.py @@ -93,7 +93,7 @@ html_theme_options = { 'menu': [ { 'name': 'GitHub', - 'url': 'https://github.com/open-mmlab/mmpretrain' + 'url': 'https://github.com/open-mmlab/mmclassification/tree/pretrain' }, { 'name': 'Colab 教程', diff --git a/docs/zh_CN/get_started.md b/docs/zh_CN/get_started.md index 088172a0..974242cd 100644 --- a/docs/zh_CN/get_started.md +++ b/docs/zh_CN/get_started.md @@ -67,7 +67,7 @@ pip install -U openmim && mim install -e . 直接使用 mim 安装即可。 ```shell -pip install -U openmim && mim install "mmpretrain>=1.0rc0" +pip install -U openmim && mim install "mmpretrain>=1.0rc5" ``` ```{note}