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1: Inference and train with existing models and standard datasets

MMDetection provides hundreds of existing and existing detection models in Model Zoo), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these existing models and standard datasets, including:

  • Use existing models to inference on given images.
  • Test existing models on standard datasets.
  • Train predefined models on standard datasets.

Inference with existing models

By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and existing model parameters are save in a checkpoint file.

To start with, we recommend Faster RCNN with this configuration file and this checkpoint file. It is recommended to download the checkpoint file to checkpoints directory.

High-level APIs for inference

MMDetection provide high-level Python APIs for inference on images. Here is an example of building the model and inference on given images or videos.

from mmdet.apis import init_detector, inference_detector
import mmcv

# Specify the path to model config and checkpoint file
config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.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
model.show_result(img, result)
# or save the visualization results to image files
model.show_result(img, result, 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)
    model.show_result(frame, result, wait_time=1)

A notebook demo can be found in demo/inference_demo.ipynb.

Note: inference_detector only supports single-image inference for now.

Asynchronous interface - supported for Python 3.7+

For Python 3.7+, MMDetection also supports async interfaces. By utilizing CUDA streams, it 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.

import asyncio
import torch
from mmdet.apis import init_detector, async_inference_detector
from mmdet.utils.contextmanagers import concurrent

async def main():
    config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
    checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.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
    model.show_result(img, result)
    # or save the visualization results to image files
    model.show_result(img, result, out_file='result.jpg')


asyncio.run(main())

Demos

We also provide two demo scripts, implemented with high-level APIs and supporting functionality codes. Source codes are available here.

Image demo

This script performs inference on a single image.

python demo/image_demo.py \
    ${IMAGE_FILE} \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    [--device ${GPU_ID}] \
    [--score-thr ${SCORE_THR}]

Examples:

python demo/image_demo.py demo/demo.jpg \
    configs/faster_rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
    --device cpu

Webcam demo

This is a live demo from a webcam.

python demo/webcam_demo.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    [--device ${GPU_ID}] \
    [--camera-id ${CAMERA-ID}] \
    [--score-thr ${SCORE_THR}]

Examples:

python demo/webcam_demo.py \
    configs/faster_rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth

Test existing models on standard datasets

To evaluate a model's accuracy, one usually tests the model on some standard datasets. MMDetection supports multiple public datasets including COCO, Pascal VOC, CityScapes, and more. This section will show how to test existing models on supported datasets.

Prepare datasets

Public datasets like Pascal VOC and COCO are available from official websites or mirrors. It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to $MMDETECTION/data as below. 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 need to be converted into the coco format using tools/convert_datasets/cityscapes.py:

pip install cityscapesscripts

python tools/convert_datasets/cityscapes.py \
    ./data/cityscapes \
    --nproc 8 \
    --out-dir ./data/cityscapes/annotations

TODO: CHANGE TO THE NEW PATH

Test existing models

We provide testing scripts for evaluating an existing model on the whole dataset (COCO, PASCAL VOC, Cityscapes, etc.). The following testing environments are supported:

  • single GPU
  • single node multiple GPUs
  • multiple nodes

Choose the proper script to perform testing depending on the testing environment.

# single-gpu testing
python tools/test.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    [--out ${RESULT_FILE}] \
    [--eval ${EVAL_METRICS}] \
    [--show]

# multi-gpu testing
bash tools/dist_test.sh \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    ${GPU_NUM} \
    [--out ${RESULT_FILE}] \
    [--eval ${EVAL_METRICS}]

tools/dist_test.sh also supports multi-node testing, but relies on PyTorch's launch utility.

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 an error like cannot connect to X server.
  • --show-dir: If specified, detection results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option.
  • --show-score-thr: If specified, detections with scores below this threshold will be removed.
  • --cfg-options: if specified, the key-value pair optional cfg will be merged into config file
  • --eval-options: if specified, the key-value pair optional eval cfg will be kwargs for dataset.evaluate() function, it's only for evaluation

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. Config and checkpoint files are available here.

    python tools/test.py \
    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
    --show

  2. Test Faster R-CNN and save the painted images for future visualization. Config and checkpoint files are available here.

    python tools/test.py \
    configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
    --show-dir faster_rcnn_r50_fpn_1x_results

  3. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP. Config and checkpoint files are available here.

    python tools/test.py \
    configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \
    checkpoints/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth \
    --eval mAP

  4. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP. Config and checkpoint files are available here.

    ./tools/dist_test.sh \
    configs/mask_rcnn_r50_fpn_1x_coco.py \
    checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
    8 \
    --out results.pkl \
    --eval bbox segm

  5. Test Mask R-CNN with 8 GPUs, and evaluate the classwise bbox and mask AP. Config and checkpoint files are available here.

    ./tools/dist_test.sh \
    configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
    checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
    8 \
    --out results.pkl \
    --eval bbox segm \
    --options "classwise=True"

  6. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files for submitting to the official evaluation server. Config and checkpoint files are available here.

    ./tools/dist_test.sh \
    configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
    checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
    8 \
    -format-only \
    --options "jsonfile_prefix=./mask_rcnn_test-dev_results"

    This command generates two JSON files mask_rcnn_test-dev_results.bbox.json and mask_rcnn_test-dev_results.segm.json.

  7. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate txt and png files for submitting to the official evaluation server. Config and checkpoint files are available here.

    ./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 "txtfile_prefix=./mask_rcnn_cityscapes_test_results"

    The generated png and txt would be under ./mask_rcnn_cityscapes_test_results directory.

Batch Inference

MMDetection supports inference with a single image or batched images in test mode. By default, we use single-image inference and you can use batch inference by modifying samples_per_gpu in the config of test data. You can do that either by modifying the config as below.

data = dict(train=dict(...), val=dict(...), test=dict(samples_per_gpu=2, ...))

Or you can set it through --cfg-options as --cfg-options data.test.samples_per_gpu=2

Deprecated ImageToTensor

In test mode, ImageToTensor pipeline is deprecated, it's replaced by DefaultFormatBundle that recommended to manually replace it in the test data pipeline in your config file. examples:

# use ImageToTensor (deprecated)
pipelines = [
   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', mean=[0, 0, 0], std=[1, 1, 1]),
           dict(type='Pad', size_divisor=32),
           dict(type='ImageToTensor', keys=['img']),
           dict(type='Collect', keys=['img']),
       ])
   ]

# manually replace ImageToTensor to DefaultFormatBundle (recommended)
pipelines = [
   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', mean=[0, 0, 0], std=[1, 1, 1]),
           dict(type='Pad', size_divisor=32),
           dict(type='DefaultFormatBundle'),
           dict(type='Collect', keys=['img']),
       ])
   ]

Train predefined models on standard datasets

MMDetection also provides out-of-the-box tools for training detection models. This section will show how to train predefined models (under configs) on standard datasets i.e. COCO.

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, 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 imgs/gpu and lr=0.08 for 16 GPUs * 4 imgs/gpu.

Prepare datasets

Training requires preparing datasets too. See section Prepare datasets above for details.

Note: Currently, the config files under configs/cityscapes use COCO pretrained weights to initialize. You could download the existing models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training.

Training on a single GPU

We provide tools/train.py to launch training jobs on a single GPU. The basic usage is as follows.

python tools/train.py \
    ${CONFIG_FILE} \
    [optional arguments]

During training, log files and checkpoints will be saved to the working directory, which is specified by work_dir in the config file or via CLI argument --work-dir.

By default, the model is evaluated on the validation set every epoch, the evaluation interval can be specified in the config file as shown below.

# evaluate the model every 12 epoch.
evaluation = dict(interval=12)

This tool accepts several optional arguments, including:

  • --no-validate (not suggested): Disable evaluation during training.
  • --work-dir ${WORK_DIR}: Override the working directory.
  • --resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
  • --options 'Key=value': Overrides other settings in the used config.

Note:

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.

Training on multiple GPUs

We provide tools/dist_train.sh to launch training on multiple GPUs. The basic usage is as follows.

bash ./tools/dist_train.sh \
    ${CONFIG_FILE} \
    ${GPU_NUM} \
    [optional arguments]

Optional arguments remain the same as stated above.

Launch multiple jobs simultaneously

If you would like to 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.

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

Training on multiple nodes

MMDetection relies on torch.distributed package for distributed training. Thus, as a basic usage, one can launch distributed training via PyTorch's launch utility.

Manage jobs with Slurm

Slurm is a good job scheduling system for computing clusters. On a cluster managed by Slurm, you can use slurm_train.sh to spawn training jobs. It supports both single-node and multi-node training.

The basic usage is as follows.

[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}

Below is an example of using 16 GPUs to train Mask R-CNN on a Slurm partition named dev, and set the work-dir to some shared file systems.

GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x

You can check the source code to review full arguments and environment variables.

When using Slurm, the port option need to be set in one of the following ways:

  1. Set the port through --options. This is more recommended since it does not change the original configs.

    CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'

  2. Modify the config files to set different communication ports.

    In config1.py, set

    dist_params = dict(backend='nccl', port=29500)

    In config2.py, set

    dist_params = dict(backend='nccl', port=29501)

    Then you can launch two jobs with config1.py and config2.py.

    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}