Skip to content

GitLab

Commit 85529f35 authored by unknown's avatar unknown
Browse files

添加openmmlab测试用例

parent b21b0c01
Hide whitespace changes
Inline Side-by-side
Showing with 3948 additions and 0 deletions
openmmlab_test/mmclassification-speed-benchmark/docker/serve/Dockerfile 0 → 100644
View file @ 85529f35
ARG PYTORCH="1.6.0"
ARG CUDA="10.1"
ARG CUDNN="7"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ARG MMCV="1.3.1"
ARG MMCLS="0.12.0"
ENV PYTHONUNBUFFERED TRUE
RUN apt-get update && \
DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
ca-certificates \
g++ \
openjdk-11-jre-headless \
# MMDet Requirements
ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
&& rm -rf /var/lib/apt/lists/*
ENV PATH="/opt/conda/bin:$PATH"
RUN export FORCE_CUDA=1
# TORCHSEVER
RUN pip install torchserve torch-model-archiver
# MMLAB
RUN pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
RUN pip install mmcls==${MMCLS}
RUN useradd -m model-server \
&& mkdir -p /home/model-server/tmp
COPY entrypoint.sh /usr/local/bin/entrypoint.sh
RUN chmod +x /usr/local/bin/entrypoint.sh \
&& chown -R model-server /home/model-server
COPY config.properties /home/model-server/config.properties
RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
EXPOSE 8080 8081 8082
USER model-server
WORKDIR /home/model-server
ENV TEMP=/home/model-server/tmp
ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
CMD ["serve"]
openmmlab_test/mmclassification-speed-benchmark/docker/serve/config.properties 0 → 100644
View file @ 85529f35
inference_address=http://0.0.0.0:8080
management_address=http://0.0.0.0:8081
metrics_address=http://0.0.0.0:8082
model_store=/home/model-server/model-store
load_models=all
openmmlab_test/mmclassification-speed-benchmark/docker/serve/entrypoint.sh 0 → 100644
View file @ 85529f35
#!/bin/bash
set -e
if [[ "$1" = "serve" ]]; then
shift 1
torchserve --start --ts-config /home/model-server/config.properties
else
eval "$@"
fi
# prevent docker exit
tail -f /dev/null
openmmlab_test/mmclassification-speed-benchmark/docs/Makefile 0 → 100644
View file @ 85529f35
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
openmmlab_test/mmclassification-speed-benchmark/docs/changelog.md 0 → 100644
View file @ 85529f35
## Changelog
### v0.12.0(3/6/2021)
- Finish adding Chinese tutorials and build Chinese documentation on readthedocs.
- Update ResNeXt checkpoints and ResNet checkpoints on CIFAR.
#### New Features
- Improve and add Chinese translation of `data_pipeline.md` and `new_modules.md`. (#265)
- Build Chinese translation on readthedocs. (#267)
- Add an argument efficientnet_style to `RandomResizedCrop` and `CenterCrop`. (#268)
#### Improvements
- Only allow directory operation when rank==0 when testing. (#258)
- Fix typo in `base_head`. (#274)
- Update ResNeXt checkpoints. (#283)
#### Bug Fixes
- Add attribute `data.test` in MNIST configs. (#264)
- Download CIFAR/MNIST dataset only on rank 0. (#273)
- Fix MMCV version compatibility. (#276)
- Fix CIFAR color channels bug and update checkpoints in model zoo. (#280)
### v0.11.1(21/5/2021)
- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`.
#### New Features
- Add `dim` argument for `GlobalAveragePooling`. (#236)
- Add random noise to `RandAugment` magnitude. (#240)
- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243)
#### Improvements
- Refactor arguments passing for Heads. (#239)
- Allow more flexible `magnitude_range` in `RandAugment`. (#249)
- Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252)
#### Bug Fixes
- Fix typo in `analyze_results.py`. (#237)
- Fix typo in unittests. (#238)
- Check if specified tmpdir exists when testing to avoid deleting existing data. (#242 & #258)
- Add missing config files in `MANIFEST.in`. (#250 & #255)
- Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251)
### v0.11.0(1/5/2021)
- Support cutmix trick.
- Support random augmentation.
- Add `tools/deployment/test.py` as a ONNX runtime test tool.
- Support ViT backbone and add training configs for ViT on ImageNet.
- Add Chinese `README.md` and some Chinese tutorials.
#### New Features
- Support cutmix trick. (#198)
- Add `simplify` option in `pytorch2onnx.py`. (#200)
- Support random augmentation. (#201)
- Add config and checkpoint for training ResNet on CIFAR-100. (#208)
- Add `tools/deployment/test.py` as a ONNX runtime test tool. (#212)
- Support ViT backbone and add training configs for ViT on ImageNet. (#214)
- Add finetuning configs for ViT on ImageNet. (#217)
- Add `device` option to support training on CPU. (#219)
- Add Chinese `README.md` and some Chinese tutorials. (#221)
- Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (#225)
- Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230)
#### Improvements
- Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (#203)
- Add `cal_acc` option in `ClsHead`. (#206)
- Check `CLASSES` in checkpoint to avoid unexpected key error. (#207)
- Check mmcv version when importing mmcls to ensure compatibility. (#209)
- Update `CONTRIBUTING.md` to align with that in MMCV. (#210)
- Change tags to html comments in configs README.md. (#226)
- Clean codes in ViT backbone. (#227)
- Reformat `pytorch2onnx.md` tutorial. (#229)
- Update `setup.py` to support MMCLI. (#232)
#### Bug Fixes
- Fix missing `cutmix_prob` in ViT configs. (#220)
- Fix backend for resize in ResNeXt configs. (#222)
### v0.10.0(1/4/2021)
- Support AutoAugmentation
- Add tutorials for installation and usage.
#### New Features
- Add `Rotate` pipeline for data augmentation. (#167)
- Add `Invert` pipeline for data augmentation. (#168)
- Add `Color` pipeline for data augmentation. (#171)
- Add `Solarize` and `Posterize` pipeline for data augmentation. (#172)
- Support fp16 training. (#178)
- Add tutorials for installation and basic usage of MMClassification.(#176)
- Support `AutoAugmentation`, `AutoContrast`, `Equalize`, `Contrast`, `Brightness` and `Sharpness` pipelines for data augmentation. (#179)
#### Improvements
- Support dynamic shape export to onnx. (#175)
- Release training configs and update model zoo for fp16 (#184)
- Use MMCV's EvalHook in MMClassification (#182)
#### Bug Fixes
- Fix wrong naming in vgg config (#181)
### v0.9.0(1/3/2021)
- Implement mixup trick.
- Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python.
#### New Features
- Implement mixup and provide configs of training ResNet50 using mixup. (#160)
- Add `Shear` pipeline for data augmentation. (#163)
- Add `Translate` pipeline for data augmentation. (#165)
- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153)
#### Improvements
- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (#158)
- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py`, matching the behavior of other open-mmlab projects. (#162)
#### Bug Fixes
- Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161)
### v0.8.0(31/1/2021)
- Support multi-label task.
- Support more flexible metrics settings.
- Fix bugs.
#### New Features
- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123)
- Add BCE loss for multi-label task. (#130)
- Add focal loss for multi-label task. (#131)
- Support PASCAL VOC 2007 dataset for multi-label task. (#134)
- Add asymmetric loss for multi-label task. (#132)
- Add analyze_results.py to select images for success/fail demonstration. (#142)
- Support new metric that calculates the total number of occurrences of each label. (#143)
- Support class-wise evaluation results. (#143)
- Add thresholds in eval_metrics. (#146)
- Add heads and a baseline config for multilabel task. (#145)
#### Improvements
- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135)
- Add tags in README.md. (#137)
- Fix optional issues in docstring. (#138)
- Update stat.py to classify papers. (#139)
- Fix mismatched columns in README.md. (#150)
- Fix test.py to support more evaluation metrics. (#155)
#### Bug Fixes
- Fix bug in VGG weight_init. (#140)
- Fix bug in 2 ResNet configs in which outdated heads were used. (#147)
- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop`. (#151)
- Fix missing `meta_keys` in `Collect`. (#149 & #152)
### v0.7.0(31/12/2020)
- Add more evaluation metrics.
- Fix bugs.
#### New Features
- Remove installation of MMCV from requirements. (#90)
- Add 3 evaluation metrics: precision, recall and F-1 score. (#93)
- Allow config override during testing and inference with `--options`. (#91 & #96)
#### Improvements
- Use `build_runner` to make runners more flexible. (#54)
- Support to get category ids in `BaseDataset`. (#72)
- Allow `CLASSES` override during `BaseDateset` initialization. (#85)
- Allow input image as ndarray during inference. (#87)
- Optimize MNIST config. (#98)
- Add config links in model zoo documentation. (#99)
- Use functions from MMCV to collect environment. (#103)
- Refactor config files so that they are now categorized by methods. (#116)
- Add README in config directory. (#117)
- Add model statistics. (#119)
- Refactor documentation in consistency with other MM repositories. (#126)
#### Bug Fixes
- Add missing `CLASSES` argument to dataset wrappers. (#66)
- Fix slurm evaluation error during training. (#69)
- Resolve error caused by shape in `Accuracy`. (#104)
- Fix bug caused by extremely insufficient data in distributed sampler.(#108)
- Fix bug in `gpu_ids` in distributed training. (#107)
- Fix bug caused by extremely insufficient data in collect results during testing (#114)
### v0.6.0(11/10/2020)
- Support new method: ResNeSt and VGG.
- Support new dataset: CIFAR10.
- Provide new tools to do model inference, model conversion from pytorch to onnx.
#### New Features
- Add model inference. (#16)
- Add pytorch2onnx. (#20)
- Add PIL backend for transform `Resize`. (#21)
- Add ResNeSt. (#25)
- Add VGG and its pretained models. (#27)
- Add CIFAR10 configs and models. (#38)
- Add albumentations transforms. (#45)
- Visualize results on image demo. (#58)
#### Improvements
- Replace urlretrieve with urlopen in dataset.utils. (#13)
- Resize image according to its short edge. (#22)
- Update ShuffleNet config. (#31)
- Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33)
#### Bug Fixes
- Fix init_weights in `shufflenet_v2.py`. (#29)
- Fix the parameter `size` in test_pipeline. (#30)
- Fix the parameter in cosine lr schedule. (#32)
- Fix the convert tools for mobilenet_v2. (#34)
- Fix crash in CenterCrop transform when image is greyscale (#40)
- Fix outdated configs. (#53)
openmmlab_test/mmclassification-speed-benchmark/docs/conf.py 0 → 100644
View file @ 85529f35
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import subprocess
import sys
sys.path.insert(0, os.path.abspath('..'))
# -- Project information -----------------------------------------------------
project = 'MMClassification'
copyright = '2020, OpenMMLab'
author = 'MMClassification Authors'
version_file = '../mmcls/version.py'
def get_version():
with open(version_file, 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']
# The full version, including alpha/beta/rc tags
release = get_version()
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'recommonmark',
'sphinx_markdown_tables',
]
autodoc_mock_imports = ['mmcls.version']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
# -- Options for HTML output -------------------------------------------------
source_suffix = {
'.rst': 'restructuredtext',
'.md': 'markdown',
}
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
master_doc = 'index'
def builder_inited_handler(app):
subprocess.run(['./stat.py'])
def setup(app):
app.connect('builder-inited', builder_inited_handler)
openmmlab_test/mmclassification-speed-benchmark/docs/getting_started.md 0 → 100644
View file @ 85529f35
# Getting Started
This page provides basic tutorials about the usage of MMClassification.
## Prepare datasets
It is recommended to symlink the dataset root to `$MMCLASSIFICATION/data`.
If your folder structure is different, you may need to change the corresponding paths in config files.
```
mmclassification
├── mmcls
├── tools
├── configs
├── docs
├── data
│ ├── imagenet
│ │ ├── meta
│ │ ├── train
│ │ ├── val
│ ├── cifar
│ │ ├── cifar-10-batches-py
│ ├── mnist
│ │ ├── train-images-idx3-ubyte
│ │ ├── train-labels-idx1-ubyte
│ │ ├── t10k-images-idx3-ubyte
│ │ ├── t10k-labels-idx1-ubyte
```
For ImageNet, it has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). It can be accessed with the following steps.
1. Register an account and login to the [download page](http://www.image-net.org/download-images).
2. Find download links for ILSVRC2012 and download the following two files
- ILSVRC2012_img_train.tar (~138GB)
- ILSVRC2012_img_val.tar (~6.3GB)
3. Untar the downloaded files
4. Download meta data using this [script](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
For MNIST, CIFAR10 and CIFAR100, the datasets will be downloaded and unzipped automatically if they are not found.
For using custom datasets, please refer to [Tutorials 2: Adding New Dataset](tutorials/new_dataset.md).
## Inference with pretrained models
We provide scripts to inference a single image, inference a dataset and test a dataset (e.g., ImageNet).
### Inference a single image
```shell
python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE}
```
### Inference and test a dataset
- single GPU
- single node multiple GPU
- multiple node
You can use the following commands to infer a dataset.
```shell
# single-gpu
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
# multi-gpu
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--metrics ${METRICS}] [--out ${RESULT_FILE}]
# multi-node in slurm environment
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] --launcher slurm
```
Optional arguments:
- `RESULT_FILE`: Filename of the output results. If not specified, the results will not be saved to a file. Support formats include json, yaml and pickle.
- `METRICS`：Items to be evaluated on the results, like accuracy, precision, recall, etc.
Examples:
Assume that you have already downloaded the checkpoints to the directory `checkpoints/`.
Infer ResNet-50 on ImageNet validation set to get predicted labels and their corresponding predicted scores.
```shell
python tools/test.py configs/imagenet/resnet50_batch256.py checkpoints/xxx.pth --out result.pkl
```
## Train a model
MMClassification implements distributed training and non-distributed training,
which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.
All outputs (log files and checkpoints) will be saved to the working directory,
which is specified by `work_dir` in the config file.
By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by adding the interval argument in the training config.
```python
evaluation = dict(interval=12) # This evaluate the model per 12 epoch.
```
### Train with a single GPU
```shell
python tools/train.py ${CONFIG_FILE} [optional arguments]
```
If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`.
### Train with multiple GPUs
```shell
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
```
Optional arguments are:
- `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1) epochs during the training. To disable this behavior, use `--no-validate`.
- `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file.
- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
Difference between `resume-from` and `load-from`:
`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.
### Train with multiple machines
If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
```shell
[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
```
You can check [slurm_train.sh](https://github.com/open-mmlab/mmclassification/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
If you have just multiple machines connected with ethernet, you can refer to
PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
Usually it is slow if you do not have high speed networking like InfiniBand.
### Launch multiple jobs on a single machine
If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,
you need to specify different ports (29500 by default) for each job to avoid communication conflict.
If you use `dist_train.sh` to launch training jobs, you can set the port in commands.
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
```
If you use launch training jobs with Slurm, you need to modify the config files (usually the 6th line from the bottom in config files) to set different communication ports.
In `config1.py`,
```python
dist_params = dict(backend='nccl', port=29500)
```
In `config2.py`,
```python
dist_params = dict(backend='nccl', port=29501)
```
Then you can launch two jobs with `config1.py` ang `config2.py`.
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}
```
## Useful tools
We provide lots of useful tools under `tools/` directory.
### Get the FLOPs and params (experimental)
We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
```shell
python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
```
You will get the result like this.
```
==============================
Input shape: (3, 224, 224)
Flops: 4.12 GFLOPs
Params: 25.56 M
==============================
```
**Note**: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.
(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
(2) Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
### Publish a model
Before you upload a model to AWS, you may want to
(1) convert model weights to CPU tensors
(2) delete the optimizer states
(3) compute the hash of the checkpoint file and append the hash id to the filename.
```shell
python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
```
E.g.,
```shell
python tools/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50_20200708.pth
```
The final output filename will be `imagenet_resnet50_20200708-{hash id}.pth`.
## Tutorials
Currently, we provide five tutorials for users.
- [finetune models](tutorials/finetune.md)
- [add new dataset](tutorials/new_dataset.md)
- [design data pipeline](tutorials/data_pipeline.md)
- [add new modules](tutorials/new_modules.md).
openmmlab_test/mmclassification-speed-benchmark/docs/index.rst 0 → 100644
View file @ 85529f35
Welcome to MMClassification's documentation!
==========================================
You can switch between Chinese and English documents in the lower-left corner of the layout.
您可以在页面左下角切换中英文文档。
.. toctree::
:maxdepth: 2
:caption: Get Started
install.md
getting_started.md
.. toctree::
:maxdepth: 2
:caption: Model zoo
modelzoo_statistics.md
.. toctree::
:maxdepth: 2
:caption: Tutorials
tutorials/finetune.md
tutorials/new_dataset.md
tutorials/data_pipeline.md
tutorials/new_modules.md
.. toctree::
:maxdepth: 2
:caption: Useful Tools and Scripts
tutorials/pytorch2onnx.md
tutorials/onnx2tensorrt.md
.. toctree::
:caption: Language Switch
switch_language.md
Indices and tables
==================
* :ref:`genindex`
* :ref:`search`
openmmlab_test/mmclassification-speed-benchmark/docs/install.md 0 → 100644
View file @ 85529f35
## Installation
### Requirements
- Python 3.6+
- PyTorch 1.3+
- [MMCV](https://github.com/open-mmlab/mmcv)
The compatible MMClassification and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.
| MMClassification version | MMCV version |
|:-------------------:|:-------------------:|
| master | mmcv>=1.3.1, <=1.5.0 |
| 0.12.0 | mmcv>=1.3.1, <=1.5.0 |
| 0.11.1 | mmcv>=1.3.1, <=1.5.0 |
| 0.11.0 | mmcv>=1.3.0 |
| 0.10.0 | mmcv>=1.3.0 |
| 0.9.0 | mmcv>=1.1.4 |
| 0.8.0 | mmcv>=1.1.4 |
| 0.7.0 | mmcv>=1.1.4 |
| 0.6.0 | mmcv>=1.1.4 |
### Install MMClassification
a. Create a conda virtual environment and activate it.
```shell
conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab
```
b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g.,
```shell
conda install pytorch torchvision -c pytorch
```
Note: Make sure that your compilation CUDA version and runtime CUDA version match.
You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
`E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install
PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.
```shell
conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
```
`E.g.2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install
PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
```shell
conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
```
If you build PyTorch from source instead of installing the prebuilt pacakge,
you can use more CUDA versions such as 9.0.
c. Clone the mmclassification repository.
```shell
git clone https://github.com/open-mmlab/mmclassification.git
cd mmclassification
```
d. Install build requirements and then install mmclassification.
```shell
pip install -e . # or "python setup.py develop"
```
Note:
1. Following the above instructions, mmclassification is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
2. If you would like to use `opencv-python-headless` instead of `opencv-python`,
you can install it before installing [mmcv](https://github.com/open-mmlab/mmcv).
### Using multiple MMClassification versions
The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMClassification in the current directory.
To use the default MMClassification installed in the environment rather than that you are working with, you can remove the following line in those scripts
```shell
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
```
openmmlab_test/mmclassification-speed-benchmark/docs/model_zoo.md 0 → 100644
View file @ 85529f35
# Model Zoo
## ImageNet
ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.
| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) |
| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) |
| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.log.json) |
| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) |
| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) |
| ResNeSt-50* | 27.48 | 5.41 | 81.13 | 95.59 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) &#124; [log]() |
| ResNeSt-101* | 48.28 | 10.27 | 82.32 | 96.24 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) &#124; [log]() |
| ResNeSt-200* | 70.2 | 17.53 | 82.41 | 96.22 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) &#124; [log]() |
| ResNeSt-269* | 110.93 | 22.58 | 82.70 | 96.28 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) &#124; [log]() |
| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) |
| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) |
| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) |
| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) |
| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) |
| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x8d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) |
| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) |
| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |
| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
| ViT-B/16* | 86.86 | 33.03 | 84.20 | 97.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch16_384.pth) &#124; [log]() |
| ViT-B/32* | 88.3 | 8.56 | 81.73 | 96.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch32_384.pth) &#124; [log]() |
| ViT-L/16* | 304.72 | 116.68 | 85.08 | 97.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch16_384.pth) &#124; [log]() |
| ViT-L/32* | 306.63 | 29.66 | 81.52 | 96.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch32_384.pth) &#124; [log]() |
Models with * are converted from other repos, others are trained by ourselves.
## CIFAR10
| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
|:---------------------:|:---------:|:--------:|:---------:|:--------:|:--------:|
| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) |
| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) |
| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) |
| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) |
| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) |
openmmlab_test/mmclassification-speed-benchmark/docs/stat.py 0 → 100644
View file @ 85529f35
#!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
num_configs = 0
for f in files:
url = osp.dirname(f.replace('../', url_prefix))
with open(f, 'r') as content_file:
content = content_file.read()
title = content.split('\n')[0].replace('# ', '').strip()
ckpts = set(x.lower().strip()
for x in re.findall(r'\[model\]\((https?.*)\)', content))
if len(ckpts) == 0:
continue
_papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
num_ckpts += len(ckpts)
titles.append(title)
statsmsg = f"""
\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
"""
stats.append((paper, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
msglist = '\n'.join(x for _, _, x in stats)
papertypes, papercounts = np.unique([t for t, _ in allpapers],
return_counts=True)
countstr = '\n'.join(
[f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
modelzoo = f"""
# Model Zoo Statistics
* Number of papers: {len(set(titles))}
{countstr}
* Number of checkpoints: {num_ckpts}
{msglist}
"""
with open('modelzoo_statistics.md', 'w') as f:
f.write(modelzoo)
openmmlab_test/mmclassification-speed-benchmark/docs/switch_language.md 0 → 100644
View file @ 85529f35
## <a href='https://mmclassification.readthedocs.io/en/latest/'>English</a>
## <a href='https://mmclassification.readthedocs.io/zh_CN/latest/'>简体中文</a>
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/MMClassification_Tutorial.ipynb 0 → 100644
View file @ 85529f35
This source diff could not be displayed because it is too large. You can view the blob instead.
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/data_pipeline.md 0 → 100644
View file @ 85529f35
# Tutorial 3: Custom Data Pipelines
## Design of Data pipelines
Following typical conventions, we use `Dataset` and `DataLoader` for data loading
with multiple workers. Indexing `Dataset` returns a dict of data items corresponding to
the arguments of models forward method.
The data preparation pipeline and the dataset is decomposed. Usually a dataset
defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.
A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.
The operations are categorized into data loading, pre-processing and formatting.
Here is an pipeline example for ResNet-50 training on ImageNet.
```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'])
]
```
For each operation, we list the related dict fields that are added/updated/removed.
At the end of the pipeline, we use `Collect` to only retain the necessary items for forward computation.
### Data loading
`LoadImageFromFile`
- add: img, img_shape, ori_shape
By default, `LoadImageFromFile` loads images from disk but it may lead to IO bottleneck for efficient small models.
Various backends are supported by mmcv to accelerate this process. For example, if the training machines have setup
[memcached](https://memcached.org/), we can revise the config as follows.
```
memcached_root = '/mnt/xxx/memcached_client/'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args=dict(
backend='memcached',
server_list_cfg=osp.join(memcached_root, 'server_list.conf'),
client_cfg=osp.join(memcached_root, 'client.conf'))),
]
```
More supported backends can be found in [mmcv.fileio.FileClient](https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py).
### Pre-processing
`Resize`
- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
- update: img, img_shape
`RandomFlip`
- add: flip, flip_direction
- update: img
`RandomCrop`
- update: img, pad_shape
`Normalize`
- add: img_norm_cfg
- update: img
### Formatting
`ToTensor`
- update: specified by `keys`.
`ImageToTensor`
- update: specified by `keys`.
`Collect`
- remove: all other keys except for those specified by `keys`
## Extend and use custom pipelines
1. Write a new pipeline in any file, e.g., `my_pipeline.py`, and place it in
the folder `mmcls/datasets/pipelines/`. The pipeline class needs to override
the `__call__` method which takes a dict as input and returns a dict.
```python
from mmcls.datasets import PIPELINES
@PIPELINES.register_module()
class MyTransform(object):
def __call__(self, results):
# apply transforms on results['img']
return results
```
2. Import the new class in `mmcls/datasets/pipelines/__init__.py`.
```python
...
from .my_pipeline import MyTransform
__all__ = [
..., 'MyTransform'
]
```
3. Use it in config files.
```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='MyTransform'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
dict(type='Collect', keys=['img', 'gt_label'])
]
```
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/finetune.md 0 → 100644
View file @ 85529f35
# Tutorial 1: Finetuning Models
Classification models pre-trained on the ImageNet dataset has been demonstrated to be effective for other datasets and other downstream tasks.
This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
There are two steps to finetune a model on a new dataset.
- Add support for the new dataset following [Tutorial 2: Adding New Dataset](new_dataset.md).
- Modify the configs as will be discussed in this tutorial.
Take the finetuning on CIFAR10 Dataset as an example, the users need to modify five parts in the config.
## Inherit base configs
To reuse the common parts among different configs, we support inheriting configs from multiple existing configs. To finetune a ResNet-50 model, the new config needs to inherit
`_base_/models/resnet50.py` to build the basic structure of the model. To use the CIFAR10 Dataset, the new config can also simply inherit `_base_/datasets/cifar10.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`.
```python
_base_ = [
'../_base_/models/resnet50.py',
'../_base_/datasets/cifar10.py', '../_base_/default_runtime.py'
]
```
Besides, users can also choose to write the whole contents rather than use inheritance, e.g. `configs/mnist/lenet5.py`.
## Modify head
Then the new config needs to modify the head according to the class numbers of the new datasets. By only changing `num_classes` in the head, the weights of the pre-trained models are mostly reused except the final prediction head.
```python
_base_ = ['./resnet50.py']
model = dict(
pretrained=None,
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
```
## Modify dataset
The users may also need to prepare the dataset and write the configs about dataset. We currently support MNIST, CIFAR and ImageNet Dataset.
For fintuning on CIFAR10, its original input size is 32 and thus we should resize it to 224, to fit the input size of models pretrained on ImageNet.
```python
_base_ = ['./cifar10.py']
img_norm_cfg = dict(
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
to_rgb=True)
train_pipeline = [
dict(type='RandomCrop', size=32, padding=4),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dict(type='Resize', size=224)
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='Resize', size=224)
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
```
## Modify training schedule
The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs.
```python
# optimizer
# lr is set for a batch size of 128
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
step=[15])
runner = dict(type='EpochBasedRunner', max_epochs=200)
log_config = dict(interval=100)
```
## Use pre-trained model
To use the pre-trained model, the new config add the link of pre-trained models in the `load_from`. The users might need to download the model weights before training to avoid the download time during training.
```python
load_from = 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmclassification/models/tbd.pth' # noqa
```
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/model_serving.md 0 → 100644
View file @ 85529f35
# Model Serving
In order to serve an `MMClassification` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps:
## 1. Convert model from MMClassification to TorchServe
```shell
python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}
```
**Note**: ${MODEL_STORE} needs to be an absolute path to a folder.
## 2. Build `mmcls-serve` docker image
```shell
docker build -t mmcls-serve:latest docker/serve/
```
## 3. Run `mmcls-serve`
Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment).
In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in CPU.
Example:
```shell
docker run --rm \
--cpus 8 \
--gpus device=0 \
-p8080:8080 -p8081:8081 -p8082:8082 \
--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
mmcls-serve:latest
```
[Read the docs](https://github.com/pytorch/serve/blob/072f5d088cce9bb64b2a18af065886c9b01b317b/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis
## 4. Test deployment
```shell
curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg
curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg
```
You should obtain a respose similar to:
```json
{
"pred_label": 245,
"pred_score": 0.5536593794822693,
"pred_class": "French bulldog"
}
```
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/new_dataset.md 0 → 100644
View file @ 85529f35
# Tutorial 2: Adding New Dataset
## Customize datasets by reorganizing data
### Reorganize dataset to existing format
The simplest way is to convert your dataset to existing dataset formats (ImageNet).
For training, it differentiates classes by folders. The directory of training data is as follows:
```
imagenet
├── ...
├── train
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ...
│ ├── ...
│ ├── n15075141
│ │ ├── n15075141_999.JPEG
│ │ ├── n15075141_9993.JPEG
│ │ ├── ...
```
For validation, we provide a annotation list. Each line of the list contrains a filename and its corresponding ground-truth labels. The format is as follows:
```
ILSVRC2012_val_00000001.JPEG 65
ILSVRC2012_val_00000002.JPEG 970
ILSVRC2012_val_00000003.JPEG 230
ILSVRC2012_val_00000004.JPEG 809
ILSVRC2012_val_00000005.JPEG 516
```
Note: The value of ground-truth labels should fall in range `[0, num_classes - 1]`.
### An example of customized dataset
You can write a new Dataset class inherited from `BaseDataset`, and overwrite `load_annotations(self)`,
like [CIFAR10](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/cifar.py) and [ImageNet](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/imagenet.py).
Typically, this function returns a list, where each sample is a dict, containing necessary data informations, e.g., `img` and `gt_label`.
Assume we are going to implement a `Filelist` dataset, which takes filelists for both training and testing. The format of annotation list is as follows:
```
000001.jpg 0
000002.jpg 1
```
We can create a new dataset in `mmcls/datasets/filelist.py` to load the data.
```python
import mmcv
import numpy as np
from .builder import DATASETS
from .base_dataset import BaseDataset
@DATASETS.register_module()
class Filelist(BaseDataset):
def load_annotations(self):
assert isinstance(self.ann_file, str)
data_infos = []
with open(self.ann_file) as f:
samples = [x.strip().split(' ') for x in f.readlines()]
for filename, gt_label in samples:
info = {'img_prefix': self.data_prefix}
info['img_info'] = {'filename': filename}
info['gt_label'] = np.array(gt_label, dtype=np.int64)
data_infos.append(info)
return data_infos
```
And add this dataset class in `mmcls/datasets/__init__.py`
```python
from .base_dataset import BaseDataset
...
from .filelist import Filelist
__all__ = [
'BaseDataset', ... ,'Filelist'
]
```
Then in the config, to use `Filelist` you can modify the config as the following
```python
train = dict(
type='Filelist',
ann_file = 'image_list.txt',
pipeline=train_pipeline
)
```
## Customize datasets by mixing dataset
MMClassification also supports to mix dataset for training.
Currently it supports to concat and repeat datasets.
### Repeat dataset
We use `RepeatDataset` as wrapper to repeat the dataset. For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following
```python
dataset_A_train = dict(
type='RepeatDataset',
times=N,
dataset=dict( # This is the original config of Dataset_A
type='Dataset_A',
...
pipeline=train_pipeline
)
)
```
### Class balanced dataset
We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category
frequency. The dataset to repeat needs to instantiate function `self.get_cat_ids(idx)`
to support `ClassBalancedDataset`.
For example, to repeat `Dataset_A` with `oversample_thr=1e-3`, the config looks like the following
```python
dataset_A_train = dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict( # This is the original config of Dataset_A
type='Dataset_A',
...
pipeline=train_pipeline
)
)
```
You may refer to [source code](../../mmcls/datasets/dataset_wrappers.py) for details.
openmmlab_test/mmclassification-speed-benchmark/docs/tutorials/new_modules.md 0 → 100644
View file @ 85529f35
# Tutorial 4: Adding New Modules
## Develop new components
We basically categorize model components into 3 types.
- backbone: usually an feature extraction network, e.g., ResNet, MobileNet.
- neck: the component between backbones and heads, e.g., GlobalAveragePooling.
- head: the component for specific tasks, e.g., classification or regression.
### Add new backbones
Here we show how to develop new components with an example of ResNet_CIFAR.
As the input size of CIFAR is 32x32, this backbone replaces the `kernel_size=7, stride=2` to `kernel_size=3, stride=1` and remove the MaxPooling after stem, to avoid forwarding small feature maps to residual blocks.
It inherits from ResNet and only modifies the stem layers.
1. Create a new file `mmcls/models/backbones/resnet_cifar.py`.
```python
import torch.nn as nn
from ..builder import BACKBONES
from .resnet import ResNet
@BACKBONES.register_module()
class ResNet_CIFAR(ResNet):
"""ResNet backbone for CIFAR.
short description of the backbone
Args:
depth(int): Network depth, from {18, 34, 50, 101, 152}.
...
"""
def __init__(self, depth, deep_stem, **kwargs):
# call ResNet init
super(ResNet_CIFAR, self).__init__(depth, deep_stem=deep_stem, **kwargs)
# other specific initialization
assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem'
def _make_stem_layer(self, in_channels, base_channels):
# override ResNet method to modify the network structure
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
base_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, base_channels, postfix=1)
self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x): # should return a tuple
pass # implementation is ignored
def init_weights(self, pretrained=None):
pass # override ResNet init_weights if necessary
def train(self, mode=True):
pass # override ResNet train if necessary
```
2. Import the module in `mmcls/models/backbones/__init__.py`.
```python
...
from .resnet_cifar import ResNet_CIFAR
__all__ = [
..., 'ResNet_CIFAR'
]
```
3. Use it in your config file.
```python
model = dict(
...
backbone=dict(
type='ResNet_CIFAR',
depth=18,
other_arg=xxx),
...
```
### Add new necks
Here we take `GlobalAveragePooling` as an example. It is a very simple neck without any arguments.
To add a new neck, we mainly implement the `forward` function, which applies some operation on the output from backbone and forward the results to head.
1. Create a new file in `mmcls/models/necks/gap.py`.
```python
import torch.nn as nn
from ..builder import NECKS
@NECKS.register_module()
class GlobalAveragePooling(nn.Module):
def __init__(self):
self.gap = nn.AdaptiveAvgPool2d((1, 1))
def forward(self, inputs):
# we regard inputs as tensor for simplicity
outs = self.gap(inputs)
outs = outs.view(inputs.size(0), -1)
return outs
```
2. Import the module in `mmcls/models/necks/__init__.py`.
```python
...
from .gap import GlobalAveragePooling
__all__ = [
..., 'GlobalAveragePooling'
]
```
3. Modify the config file.
```python
model = dict(
neck=dict(type='GlobalAveragePooling'),
)
```
### Add new heads
Here we show how to develop a new head with the example of `LinearClsHead` as the following.
To implement a new head, basically we need to implement `forward_train`, which takes the feature maps from necks or backbones as input and compute loss based on ground-truth labels.
1. Create a new file in `mmcls/models/heads/linear_head.py`.
```python
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class LinearClsHead(ClsHead):
def __init__(self,
num_classes,
in_channels,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, )):
super(LinearClsHead, self).__init__(loss=loss, topk=topk)
self.in_channels = in_channels
self.num_classes = num_classes
if self.num_classes <= 0:
raise ValueError(
f'num_classes={num_classes} must be a positive integer')
self._init_layers()
def _init_layers(self):
self.fc = nn.Linear(self.in_channels, self.num_classes)
def init_weights(self):
normal_init(self.fc, mean=0, std=0.01, bias=0)
def forward_train(self, x, gt_label):
cls_score = self.fc(x)
losses = self.loss(cls_score, gt_label)
return losses
```
2. Import the module in `mmcls/models/heads/__init__.py`.
```python
...
from .linear_head import LinearClsHead
__all__ = [
..., 'LinearClsHead'
]
```
3. Modify the config file.
Together with the added GlobalAveragePooling neck, an entire config for a model is as follows.
```python
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
```
### Add new loss
To add a new loss function, we mainly implement the `forward` function in the loss module.
In addition, it is helpful to leverage the decorator `weighted_loss` to weight the loss for each element.
Assuming that we want to mimic a probablistic distribution generated from another classification model, we implement a L1Loss to fulfil the purpose as below.
1. Create a new file in `mmcls/models/losses/l1_loss.py`.
```python
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def l1_loss(pred, target):
assert pred.size() == target.size() and target.numel() > 0
loss = torch.abs(pred - target)
return loss
@LOSSES.register_module()
class L1Loss(nn.Module):
def __init__(self, reduction='mean', loss_weight=1.0):
super(L1Loss, self).__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
loss = self.loss_weight * l1_loss(
pred, target, weight, reduction=reduction, avg_factor=avg_factor)
return loss
```
2. Import the module in `mmcls/models/losses/__init__.py`.
```python
...
from .l1_loss import L1Loss, l1_loss
__all__ = [
..., 'L1Loss', 'l1_loss'
]
```
3. Modify loss field in the config.
```python
loss=dict(type='L1Loss', loss_weight=1.0))
```
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment