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# Contributing to mmdetection
All kinds of contributions are welcome, including but not limited to the following.
- Fixes (typo, bugs)
- New features and components
## Workflow
1. fork and pull the latest mmdetection
2. checkout a new branch (do not use master branch for PRs)
3. commit your changes
4. create a PR
Note
- If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
- If you are the author of some papers and would like to include your method to mmdetection,
please contact Wenwei Zhang (zwwdev[at]gmail[dot]com). We will much appreciate your contribution.
## Code style
### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
We use the following tools for linting and formatting:
- [flake8](http://flake8.pycqa.org/en/latest/): linter
- [yapf](https://github.com/google/yapf): formatter
- [isort](https://github.com/timothycrosley/isort): sort imports
Style configurations of yapf and isort can be found in [.style.yapf](.style.yapf) and [.isort.cfg](.isort.cfg).
>Before you create a PR, make sure that your code lints and is formatted by yapf.
### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
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# Getting Started
This page provides basic tutorials about the usage of MMDetection.
For installation instructions, please see [INSTALL.md](INSTALL.md).
## Inference with pretrained models
We provide testing scripts to evaluate a whole dataset (COCO, PASCAL VOC, Cityscapes, etc.),
and also some high-level apis for easier integration to other projects.
### Test a dataset
- [x] single GPU testing
- [x] multiple GPU testing
- [x] visualize detection results
You can use the following commands to test a dataset.
```shell
# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
```
Optional arguments:
- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics.
- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment, otherwise you may encounter the error like `cannot connect to X server`.
If you would like to evaluate the dataset, do not specify `--show` at the same time.
Examples:
Assume that you have already downloaded the checkpoints to the directory `checkpoints/`.
1. Test Faster R-CNN and visualize the results. Press any key for the next image.
```shell
python tools/test.py configs/faster_rcnn_r50_fpn_1x.py \
checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth \
--show
```
2. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP.
```shell
python tools/test.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \
checkpoints/SOME_CHECKPOINT.pth \
--eval mAP
```
3. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP.
```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x.py \
checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
8 --out results.pkl --eval bbox segm
```
4. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate the json file to be submit to the official evaluation server.
```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x.py \
checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
8 --format-only --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
```
You will get two json files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`.
5. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate the txt and png files to be submit to the official evaluation server.
```shell
./tools/dist_test.sh configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \
checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \
8 --format_only --options "outfile_prefix=./mask_rcnn_cityscapes_test_results"
```
The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory.
### Webcam demo
We provide a webcam demo to illustrate the results.
```shell
python demo/webcam_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--camera-id ${CAMERA-ID}] [--score-thr ${SCORE_THR}]
```
Examples:
```shell
python demo/webcam_demo.py configs/faster_rcnn_r50_fpn_1x.py \
checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth
```
### High-level APIs for testing images
#### Synchronous interface
Here is an example of building the model and test given images.
```python
from mmdet.apis import init_detector, inference_detector, show_result
import mmcv
config_file = 'configs/faster_rcnn_r50_fpn_1x.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'
# build the model from a config file and a checkpoint file
model = init_detector(config_file, checkpoint_file, device='cuda:0')
# test a single image and show the results
img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once
result = inference_detector(model, img)
# visualize the results in a new window
show_result(img, result, model.CLASSES)
# or save the visualization results to image files
show_result(img, result, model.CLASSES, out_file='result.jpg')
# test a video and show the results
video = mmcv.VideoReader('video.mp4')
for frame in video:
result = inference_detector(model, frame)
show_result(frame, result, model.CLASSES, wait_time=1)
```
A notebook demo can be found in [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb).
#### Asynchronous interface - supported for Python 3.7+
Async interface allows not to block CPU on GPU bound inference code and enables better CPU/GPU utilization for single threaded application. Inference can be done concurrently either between different input data samples or between different models of some inference pipeline.
See `tests/async_benchmark.py` to compare the speed of synchronous and asynchronous interfaces.
```python
import asyncio
import torch
from mmdet.apis import init_detector, async_inference_detector, show_result
from mmdet.utils.contextmanagers import concurrent
async def main():
config_file = 'configs/faster_rcnn_r50_fpn_1x.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'
device = 'cuda:0'
model = init_detector(config_file, checkpoint=checkpoint_file, device=device)
# queue is used for concurrent inference of multiple images
streamqueue = asyncio.Queue()
# queue size defines concurrency level
streamqueue_size = 3
for _ in range(streamqueue_size):
streamqueue.put_nowait(torch.cuda.Stream(device=device))
# test a single image and show the results
img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once
async with concurrent(streamqueue):
result = await async_inference_detector(model, img)
# visualize the results in a new window
show_result(img, result, model.CLASSES)
# or save the visualization results to image files
show_result(img, result, model.CLASSES, out_file='result.jpg')
asyncio.run(main())
```
## Train a model
MMDetection 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.
```
**\*Important\***: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16).
According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu.
### Train with a single GPU
```shell
python tools/train.py ${CONFIG_FILE}
```
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:
- `--validate` (**strongly recommended**): Perform evaluation at every k (default value is 1, which can be modified like [this](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn_r50_fpn_1x.py#L174)) epochs during the training.
- `--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 MMDetection 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
./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [${GPUS}]
```
Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition.
```shell
./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x.py /nfs/xxxx/mask_rcnn_r50_fpn_1x 16
```
You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/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 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} 4
CUDA_VISIBLE_DEVICES=4,5,6,7 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} 4
```
## Useful tools
We provide lots of useful tools under `tools/` directory.
### Analyze logs
You can plot loss/mAP curves given a training log file. Run `pip install seaborn` first to install the dependency.
![loss curve image](../demo/loss_curve.png)
```shell
python tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
```
Examples:
- Plot the classification loss of some run.
```shell
python tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls
```
- Plot the classification and regression loss of some run, and save the figure to a pdf.
```shell
python tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_reg --out losses.pdf
```
- Compare the bbox mAP of two runs in the same figure.
```shell
python tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2
```
You can also compute the average training speed.
```shell
python tools/analyze_logs.py cal_train_time ${CONFIG_FILE} [--include-outliers]
```
The output is expected to be like the following.
```
-----Analyze train time of work_dirs/some_exp/20190611_192040.log.json-----
slowest epoch 11, average time is 1.2024
fastest epoch 1, average time is 1.1909
time std over epochs is 0.0028
average iter time: 1.1959 s/iter
```
### 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, 1280, 800)
Flops: 239.32 GMac
Params: 37.74 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, 1280, 800).
(2) Some operators are not counted into FLOPs like GN and custom operators.
You can add support for new operators by modifying [`mmdet/utils/flops_counter.py`](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/utils/flops_counter.py).
(3) The FLOPs of two-stage detectors is dependent on the number of proposals.
### 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 and
(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/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth
```
The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`.
### Test the robustness of detectors
Please refer to [ROBUSTNESS_BENCHMARKING.md](ROBUSTNESS_BENCHMARKING.md).
## How-to
### Use my own datasets
The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC).
Here we show an example of adding a custom dataset of 5 classes, assuming it is also in COCO format.
In `mmdet/datasets/my_dataset.py`:
```python
from .coco import CocoDataset
from .registry import DATASETS
@DATASETS.register_module
class MyDataset(CocoDataset):
CLASSES = ('a', 'b', 'c', 'd', 'e')
```
In `mmdet/datasets/__init__.py`:
```python
from .my_dataset import MyDataset
```
Then you can use `MyDataset` in config files, with the same API as CocoDataset.
It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.
Actually, we define a simple annotation format and all existing datasets are
processed to be compatible with it, either online or offline.
The annotation of a dataset is a list of dict, each dict corresponds to an image.
There are 3 field `filename` (relative path), `width`, `height` for testing,
and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields:
`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide
annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore`
to cover them.
Here is an example.
```
[
{
'filename': 'a.jpg',
'width': 1280,
'height': 720,
'ann': {
'bboxes': <np.ndarray, float32> (n, 4),
'labels': <np.ndarray, int64> (n, ),
'bboxes_ignore': <np.ndarray, float32> (k, 4),
'labels_ignore': <np.ndarray, int64> (k, ) (optional field)
}
},
...
]
```
There are two ways to work with custom datasets.
- online conversion
You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods
`load_annotations(self, ann_file)` and `get_ann_info(self, idx)`,
like [CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py) and [VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py).
- offline conversion
You can convert the annotation format to the expected format above and save it to
a pickle or json file, like [pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/convert_datasets/pascal_voc.py).
Then you can simply use `CustomDataset`.
### Customize optimizer
An example of customized optimizer `CopyOfSGD` is defined in `mmdet/core/optimizer/copy_of_sgd.py`.
More generally, a customized optimizer could be defined as following.
In `mmdet/core/optimizer/my_optimizer.py`:
```python
from .registry import OPTIMIZERS
from torch.optim import Optimizer
@OPTIMIZERS.register_module
class MyOptimizer(Optimizer):
```
In `mmdet/core/optimizer/__init__.py`:
```python
from .my_optimizer import MyOptimizer
```
Then you can use `MyOptimizer` in `optimizer` field of config files.
### Develop new components
We basically categorize model components into 4 types.
- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet.
- neck: the component between backbones and heads, e.g., FPN, PAFPN.
- head: the component for specific tasks, e.g., bbox prediction and mask prediction.
- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align.
Here we show how to develop new components with an example of MobileNet.
1. Create a new file `mmdet/models/backbones/mobilenet.py`.
```python
import torch.nn as nn
from ..registry import BACKBONES
@BACKBONES.register_module
class MobileNet(nn.Module):
def __init__(self, arg1, arg2):
pass
def forward(self, x): # should return a tuple
pass
def init_weights(self, pretrained=None):
pass
```
2. Import the module in `mmdet/models/backbones/__init__.py`.
```python
from .mobilenet import MobileNet
```
3. Use it in your config file.
```python
model = dict(
...
backbone=dict(
type='MobileNet',
arg1=xxx,
arg2=xxx),
...
```
For more information on how it works, you can refer to [TECHNICAL_DETAILS.md](TECHNICAL_DETAILS.md) (TODO).
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## Installation
### Requirements
- Linux (Windows is not officially supported)
- Python 3.5+
- PyTorch 1.1 or higher
- CUDA 9.0 or higher
- NCCL 2
- GCC 4.9 or higher
- [mmcv](https://github.com/open-mmlab/mmcv)
We have tested the following versions of OS and softwares:
- OS: Ubuntu 16.04/18.04 and CentOS 7.2
- CUDA: 9.0/9.2/10.0/10.1
- NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
- GCC(G++): 4.9/5.3/5.4/7.3
### Install mmdetection
a. Create a conda virtual environment and activate it.
```shell
conda create -n open-mmlab python=3.7 numba=0.45.1 -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
```
c. Clone the mmdetection repository.
```shell
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
```
d. Install build requirements and then install mmdetection.
(We install pycocotools via the github repo instead of pypi because the pypi version is old and not compatible with the latest numpy.)
```shell
pip install -r requirements/build.txt
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
pip install -v -e . # or "python setup.py develop"
```
e. Clone the MMDetection3D repository.
```shell
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
```
f. Install build requirements and then install MMDetection3D.
```shell
pip install -r requirements/build.txt
pip install -v -e . # or "python setup.py develop"
```
Note:
1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
2. Following the above instructions, mmdetection 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).
3. If you would like to use `opencv-python-headless` instead of `opencv-python`,
you can install it before installing MMCV.
4. Some dependencies are optional. Simply running `pip install -v -e .` will only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
### Another option: Docker Image
We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image.
```shell
# build an image with PyTorch 1.1, CUDA 10.0 and CUDNN 7.5
docker build -t mmdetection docker/
```
### Prepare datasets
It is recommended to symlink the dataset root to `$MMDETECTION/data`.
If your folder structure is different, you may need to change the corresponding paths in config files.
```
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── cityscapes
│ │ ├── annotations
│ │ ├── leftImg8bit
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── gtFine
│ │ │ ├── train
│ │ │ ├── val
│ ├── VOCdevkit
│ │ ├── VOC2007
│ │ ├── VOC2012
```
The cityscapes annotations have to be converted into the coco format using `tools/convert_datasets/cityscapes.py`:
```shell
pip install cityscapesscripts
python tools/convert_datasets/cityscapes.py ./data/cityscapes --nproc 8 --out_dir ./data/cityscapes/annotations
```
Current the config files in `cityscapes` use COCO pre-trained weights to initialize.
You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training.
### A from-scratch setup script
Here is a full script for setting up mmdetection with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT).
```shell
conda create -n open-mmlab python=3.7 numba=0.45.1 -y
conda activate open-mmlab
conda install -c pytorch pytorch torchvision -y
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
pip install -v -e .
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -r requirements/build.txt
pip install -v -e .
mkdir data
ln -s $COCO_ROOT data
```
### Using multiple MMDetection3D versions
If there are more than one mmdetection on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.
Another way is to insert the following code to the main scripts (`train.py`, `test.py` or any other scripts you run)
```python
import os.path as osp
import sys
sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
```
Or run the following command in the terminal of corresponding folder to temporally use the current one.
```shell
export PYTHONPATH=`pwd`:$PYTHONPATH
```
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# 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)
docs/ROBUSTNESS_BENCHMARKING.md 0 → 100644
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# Corruption Benchmarking
## Introduction
We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484).
This page provides basic tutorials how to use the benchmark.
```
@article{michaelis2019winter,
title={Benchmarking Robustness in Object Detection:
Autonomous Driving when Winter is Coming},
author={Michaelis, Claudio and Mitzkus, Benjamin and
Geirhos, Robert and Rusak, Evgenia and
Bringmann, Oliver and Ecker, Alexander S. and
Bethge, Matthias and Brendel, Wieland},
journal={arXiv:1907.07484},
year={2019}
}
```
![image corruption example](../demo/corruptions_sev_3.png)
## About the benchmark
To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark)
The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally
published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich.
The image corruption functions are included in this library but can be installed separately using:
```shell
pip install imagecorruptions
```
Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images.
We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library,
which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions).
## Inference with pretrained models
We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark.
### Test a dataset
- [x] single GPU testing
- [ ] multiple GPU testing
- [ ] visualize detection results
You can use the following commands to test a models performance under the 15 corruptions used in the benchmark.
```shell
# single-gpu testing
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
```
Alternatively different group of corruptions can be selected.
```shell
# noise
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise
# blur
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur
# wetaher
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather
# digital
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital
```
Or a costom set of corruptions e.g.:
```shell
# gaussian noise, zoom blur and snow
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow
```
Finally the corruption severities to evaluate can be chosen.
Severity 0 corresponds to clean data and the effect increases from 1 to 5.
```shell
# severity 1
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1
# severities 0,2,4
python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4
```
## Results for modelzoo models
The results on COCO 2017val are shown in the below table.
Model | Backbone | Style | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % |
:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:|
Faster R-CNN | R-50-FPN | pytorch | 1x | 36.3 | 18.2 | 50.2 | - | - | - |
Faster R-CNN | R-101-FPN | pytorch | 1x | 38.5 | 20.9 | 54.2 | - | - | - |
Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1 | 22.3 | 55.5 | - | - | - |
Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3 | 23.4 | 56.6 | - | - | - |
Faster R-CNN | R-50-FPN-DCN | pytorch | 1x | 40.0 | 22.4 | 56.1 | - | - | - |
Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7 | 61.6 | - | - | - |
Mask R-CNN | R-50-FPN | pytorch | 1x | 37.3 | 18.7 | 50.1 | 34.2 | 16.8 | 49.1 |
Mask R-CNN | R-50-FPN-DCN | pytorch | 1x | 41.1 | 23.3 | 56.7 | 37.2 | 20.7 | 55.7 |
Cascade R-CNN | R-50-FPN | pytorch | 1x | 40.4 | 20.1 | 49.7 | - | - | - |
Cascade Mask R-CNN | R-50-FPN | pytorch | 1x| 41.2 | 20.7 | 50.2 | 35.7 | 17.6 | 49.3 |
RetinaNet | R-50-FPN | pytorch | 1x | 35.6 | 17.8 | 50.1 | - | - | - |
Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8 | 28.1 | 64.0 |
Results may vary slightly due to the stochastic application of the corruptions.
docs/TECHNICAL_DETAILS.md 0 → 100644
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# Technical Details
In this section, we will introduce the main units of training a detector:
data pipeline, model and iteration pipeline.
## Data pipeline
Following typical conventions, we use `Dataset` and `DataLoader` for data loading
with multiple workers. `Dataset` returns a dict of data items corresponding
the arguments of models' forward method.
Since the data in object detection may not be the same size (image size, gt bbox size, etc.),
we introduce a new `DataContainer` type in MMCV to help collect and distribute
data of different size.
See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details.
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.
We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
![pipeline figure](../demo/data_pipeline.png)
The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.
Here is an pipeline example for Faster R-CNN.
```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='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
```
For each operation, we list the related dict fields that are added/updated/removed.
### Data loading
`LoadImageFromFile`
- add: img, img_shape, ori_shape
`LoadAnnotations`
- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields
`LoadProposals`
- add: proposals
### Pre-processing
`Resize`
- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
- update: img, img_shape, *bbox_fields, *mask_fields, *seg_fields
`RandomFlip`
- add: flip
- update: img, *bbox_fields, *mask_fields, *seg_fields
`Pad`
- add: pad_fixed_size, pad_size_divisor
- update: img, pad_shape, *mask_fields, *seg_fields
`RandomCrop`
- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields
`Normalize`
- add: img_norm_cfg
- update: img
`SegRescale`
- update: gt_semantic_seg
`PhotoMetricDistortion`
- update: img
`Expand`
- update: img, gt_bboxes
`MinIoURandomCrop`
- update: img, gt_bboxes, gt_labels
`Corrupt`
- update: img
### Formatting
`ToTensor`
- update: specified by `keys`.
`ImageToTensor`
- update: specified by `keys`.
`Transpose`
- update: specified by `keys`.
`ToDataContainer`
- update: specified by `fields`.
`DefaultFormatBundle`
- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg
`Collect`
- add: img_meta (the keys of img_meta is specified by `meta_keys`)
- remove: all other keys except for those specified by `keys`
### Test time augmentation
`MultiScaleFlipAug`
## Model
In MMDetection, model components are basically categorized as 4 types.
- backbone: usually a FCN network to extract feature maps, e.g., ResNet.
- neck: the part between backbones and heads, e.g., FPN, ASPP.
- head: the part for specific tasks, e.g., bbox prediction and mask prediction.
- roi extractor: the part for extracting features from feature maps, e.g., RoI Align.
We also write implement some general detection pipelines with the above components,
such as `SingleStageDetector` and `TwoStageDetector`.
### Build a model with basic components
Following some basic pipelines (e.g., two-stage detectors), the model structure
can be customized through config files with no pains.
If we want to implement some new components, e.g, the path aggregation
FPN structure in [Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534), there are two things to do.
1. create a new file in `mmdet/models/necks/pafpn.py`.
```python
from ..registry import NECKS
@NECKS.register
class PAFPN(nn.Module):
def __init__(self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
add_extra_convs=False):
pass
def forward(self, inputs):
# implementation is ignored
pass
```
2. Import the module in `mmdet/models/necks/__init__.py`.
```python
from .pafpn import PAFPN
```
2. modify the config file from
```python
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5)
```
to
```python
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5)
```
We will release more components (backbones, necks, heads) for research purpose.
### Write a new model
To write a new detection pipeline, you need to inherit from `BaseDetector`,
which defines the following abstract methods.
- `extract_feat()`: given an image batch of shape (n, c, h, w), extract the feature map(s).
- `forward_train()`: forward method of the training mode
- `simple_test()`: single scale testing without augmentation
- `aug_test()`: testing with augmentation (multi-scale, flip, etc.)
[TwoStageDetector](https://github.com/hellock/mmdetection/blob/master/mmdet/models/detectors/two_stage.py)
is a good example which shows how to do that.
## Iteration pipeline
We adopt distributed training for both single machine and multiple machines.
Supposing that the server has 8 GPUs, 8 processes will be started and each process runs on a single GPU.
Each process keeps an isolated model, data loader, and optimizer.
Model parameters are only synchronized once at the beginning.
After a forward and backward pass, gradients will be allreduced among all GPUs,
and the optimizer will update model parameters.
Since the gradients are allreduced, the model parameter stays the same for all processes after the iteration.
## Other information
For more information, please refer to our [technical report](https://arxiv.org/abs/1906.07155).
docs/conf.py 0 → 100644
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# 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 sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'MMDetection'
copyright = '2018-2020, OpenMMLab'
author = 'OpenMMLab'
# The full version, including alpha/beta/rc tags
release = '1.0.0'
# -- 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 = ['torch', 'torchvision', 'mmcv']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
source_suffix = {
'.rst': 'restructuredtext',
'.md': 'markdown',
}
# The master toctree document.
master_doc = 'index'
# 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 -------------------------------------------------
# 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']
docs/index.rst 0 → 100644
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Welcome to MMDetection's documentation!
=======================================
.. toctree::
:maxdepth: 2
INSTALL.md
GETTING_STARTED.md
MODEL_ZOO.md
TECHNICAL_DETAILS.md
CHANGELOG.md
Indices and tables
==================
* :ref:`genindex`
* :ref:`search`
docs/make.bat 0 → 100644
View file @ d1aac35d
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=_build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
docs/requirements.txt 0 → 100644
View file @ d1aac35d
recommonmark
sphinx
sphinx_markdown_tables
sphinx_rtd_theme
mmdet3d/__init__.py 0 → 100644
View file @ d1aac35d
from .version import __version__, short_version
__all__ = ['__version__', 'short_version']
mmdet3d/apis/__init__.py 0 → 100644
View file @ d1aac35d
from .train import train_detector
__all__ = [
'train_detector',
]
mmdet3d/apis/train.py 0 → 100644
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import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner
from mmdet3d.core import build_optimizer
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet.apis.train import parse_losses
from mmdet.core import (DistEvalHook, DistOptimizerHook, EvalHook,
Fp16OptimizerHook)
from mmdet.utils import get_root_logger
def batch_processor(model, data, train_mode):
"""Process a data batch.
This method is required as an argument of Runner, which defines how to
process a data batch and obtain proper outputs. The first 3 arguments of
batch_processor are fixed.
Args:
model (nn.Module): A PyTorch model.
data (dict): The data batch in a dict.
train_mode (bool): Training mode or not. It may be useless for some
models.
Returns:
dict: A dict containing losses and log vars.
"""
losses = model(**data)
loss, log_vars = parse_losses(losses)
if 'img_meta' in data:
num_samples = len(data['img_meta'].data)
else:
num_samples = len(data['img'].data)
outputs = dict(loss=loss, log_vars=log_vars, num_samples=num_samples)
return outputs
def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
logger = get_root_logger(cfg.log_level)
# start training
if distributed:
_dist_train(
model,
dataset,
cfg,
validate=validate,
logger=logger,
timestamp=timestamp,
meta=meta)
else:
_non_dist_train(
model,
dataset,
cfg,
validate=validate,
logger=logger,
timestamp=timestamp,
meta=meta)
def _dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None,
meta=None):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
dist=True,
seed=cfg.seed) for ds in dataset
]
# put model on gpus
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(
model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=True,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
runner.register_hook(DistEvalHook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None,
meta=None):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False,
seed=cfg.seed) for ds in dataset
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(
model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
runner.register_hook(EvalHook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
mmdet3d/core/__init__.py 0 → 100644
View file @ d1aac35d
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .optimizer import * # noqa: F401, F403
from .post_processing import * # noqa: F401, F403
from .utils import * # noqa: F401, F403
# from .voxel import * # noqa: F401, F403
mmdet3d/core/anchor/__init__.py 0 → 100644
View file @ d1aac35d
from .anchor_generator import (AlignedAnchorGeneratorRange, AnchorGenerator,
AnchorGeneratorRange)
__all__ = [
'AnchorGenerator', 'anchor_inside_flags', 'images_to_levels', 'unmap',
'AlignedAnchorGeneratorRange', 'AnchorGeneratorRange',
'build_anchor_generator'
]
def build_anchor_generator(cfg, **kwargs):
from . import anchor_generator
import mmcv
if isinstance(cfg, dict):
return mmcv.runner.obj_from_dict(
cfg, anchor_generator, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
mmdet3d/core/anchor/anchor_generator.py 0 → 100644
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This diff is collapsed.
mmdet3d/core/bbox/__init__.py 0 → 100644
View file @ d1aac35d
from . import box_torch_ops
from .assigners import AssignResult, BaseAssigner, MaxIoUAssigner
from .coders import ResidualCoder
# from .bbox_target import bbox_target
from .geometry import (bbox_overlaps_2d, bbox_overlaps_3d,
bbox_overlaps_nearest_3d)
from .samplers import (BaseSampler, CombinedSampler,
InstanceBalancedPosSampler, IoUBalancedNegSampler,
PseudoSampler, RandomSampler, SamplingResult)
from .transforms import delta2bbox # bbox2result_kitti,
from .transforms import (bbox2delta, bbox2result_coco, bbox2roi, bbox_flip,
bbox_mapping, bbox_mapping_back,
boxes3d_to_bev_torch_lidar, distance2bbox, roi2bbox)
from .assign_sampling import ( # isort:skip, avoid recursive imports
build_bbox_coder, # temporally settings
assign_and_sample, build_assigner, build_sampler)
__all__ = [
'BaseAssigner',
'MaxIoUAssigner',
'AssignResult',
'BaseSampler',
'PseudoSampler',
'RandomSampler',
'InstanceBalancedPosSampler',
'IoUBalancedNegSampler',
'CombinedSampler',
'SamplingResult',
'bbox2delta',
'delta2bbox',
'bbox_flip',
'bbox_mapping',
'bbox_mapping_back',
'bbox2roi',
'roi2bbox',
'bbox2result_coco',
'distance2bbox', # 'bbox2result_kitti',
'build_assigner',
'build_sampler',
'assign_and_sample',
'bbox_overlaps_2d',
'bbox_overlaps_3d',
'bbox_overlaps_nearest_3d',
'box_torch_ops',
'build_bbox_coder',
'ResidualCoder',
'boxes3d_to_bev_torch_lidar'
]
mmdet3d/core/bbox/assign_sampling.py 0 → 100644
View file @ d1aac35d
import mmcv
from . import assigners, coders, samplers
def build_assigner(cfg, **kwargs):
if isinstance(cfg, assigners.BaseAssigner):
return cfg
elif isinstance(cfg, dict):
return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
def build_bbox_coder(cfg, **kwargs):
if isinstance(cfg, coders.ResidualCoder):
return cfg
elif isinstance(cfg, dict):
return mmcv.runner.obj_from_dict(cfg, coders, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
def build_sampler(cfg, **kwargs):
if isinstance(cfg, samplers.BaseSampler):
return cfg
elif isinstance(cfg, dict):
return mmcv.runner.obj_from_dict(cfg, samplers, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
def assign_and_sample(bboxes, gt_bboxes, gt_bboxes_ignore, gt_labels, cfg):
bbox_assigner = build_assigner(cfg.assigner)
bbox_sampler = build_sampler(cfg.sampler)
assign_result = bbox_assigner.assign(bboxes, gt_bboxes, gt_bboxes_ignore,
gt_labels)
sampling_result = bbox_sampler.sample(assign_result, bboxes, gt_bboxes,
gt_labels)
return assign_result, sampling_result
mmdet3d/core/bbox/assigners/__init__.py 0 → 100644
View file @ d1aac35d
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
from .max_iou_assigner import MaxIoUAssigner
__all__ = [
'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult'
]
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