Getting Started

This page provides basic tutorials about the usage of MMSegmentation. For installation instructions, please see install.md.

Prepare datasets

It is recommended to symlink the dataset root to $MMSEGMENTATION/data. If your folder structure is different, you may need to change the corresponding paths in config files.

mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── VOCdevkit
│   │   ├── VOC2012
│   │   │   ├── JPEGImages
│   │   │   ├── SegmentationClass
│   │   │   ├── ImageSets
│   │   │   │   ├── Segmentation
│   │   ├── VOC2010
│   │   │   ├── JPEGImages
│   │   │   ├── SegmentationClassContext
│   │   │   ├── ImageSets
│   │   │   │   ├── SegmentationContext
│   │   │   │   │   ├── train.txt
│   │   │   │   │   ├── val.txt
│   │   │   ├── trainval_merged.json
│   │   ├── VOCaug
│   │   │   ├── dataset
│   │   │   │   ├── cls
│   ├── ade
│   │   ├── ADEChallengeData2016
│   │   │   ├── annotations
│   │   │   │   ├── training
│   │   │   │   ├── validation
│   │   │   ├── images
│   │   │   │   ├── training
│   │   │   │   ├── validation

Cityscapes

The data could be found here after registration.

By convention, **labelTrainIds.png are used for cityscapes training. We provided a scripts based on cityscapesscripts to generate **labelTrainIds.png.

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8

Pascal VOC

Pascal VOC 2012 could be downloaded from here. Beside, most recent works on Pascal VOC dataset usually exploit extra augmentation data, which could be found here.

If you would like to use augmented VOC dataset, please run following command to convert augmentation annotations into proper format.

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8

Please refer to concat dataset for details about how to concatenate them and train them together.

ADE20K

The training and validation set of ADE20K could be download from this link. We may also download test set from here.

Pascal Context

The training and validation set of Pascal Context could be download from here. You may also download test set from here after registration.

To split the training and validation set from original dataset, you may download trainval_merged.json from here.

If you would like to use Pascal Context dataset, please install Detail and then run the following command to convert annotations into proper format.

python tools/convert_datasets/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json

Inference with pretrained models

We provide testing scripts to evaluate a whole dataset (Cityscapes, PASCAL VOC, ADE20k, etc.), and also some high-level apis for easier integration to other projects.

Test a dataset

single GPU
single node multiple GPU
multiple node

You can use the following commands to test a dataset.

# 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., mIoU is available for all dataset. Cityscapes could be evaluated by cityscapes as well as standard mIoU metrics.
--show: If specified, segmentation 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.
--show-dir: If specified, segmentation 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.

Examples:

Assume that you have already downloaded the checkpoints to the directory checkpoints/.

Test PSPNet and visualize the results. Press any key for the next image.

python tools/test.py configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
    --show

Test PSPNet and save the painted images for latter visualization.

python tools/test.py configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
    --show-dir psp_r50_512x1024_40ki_cityscapes_results

Test PSPNet on PASCAL VOC (without saving the test results) and evaluate the mIoU.

python tools/test.py configs/pspnet/pspnet_r50-d8_512x1024_20k_voc12aug.py \
    checkpoints/pspnet_r50-d8_512x1024_20k_voc12aug_20200605_003338-c57ef100.pth \
    --eval mAP

Test PSPNet with 4 GPUs, and evaluate the standard mIoU and cityscapes metric.
```
./tools/dist_test.sh configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
    4 --out results.pkl --eval mIoU cityscapes
```
Note: There is some gap (~0.1%) between cityscapes mIoU and our mIoU. The reason is that cityscapes average each class with class size by default. We use the simple version without average for all datasets.
Test PSPNet on cityscapes test split with 4 GPUs, and generate the png files to be submit to the official evaluation server.

First, add following to config file configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py,
```
data = dict(
    test=dict(
        img_dir='leftImg8bit/test',
        ann_dir='gtFine/test'))
```
Then run test.
```
./tools/dist_test.sh configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
    4 --format-only --options "imgfile_prefix=./pspnet_test_results"
```
You will get png files under ./pspnet_test_results directory. You may run zip -r results.zip pspnet_test_results/ and submit the zip file to evaluation server.

Image demo

We provide a demo script to test a single image.

python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${DEVICE_NAME}] [--palette-thr ${PALETTE}]

Examples:

python demo/image_demo.py demo/demo.jpg configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth --device cuda:0 --palette cityscapes

High-level APIs for testing images

Here is an example of building the model and test given images.

from mmseg.apis import inference_segmentor, init_segmentor
import mmcv

config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'

# build the model from a config file and a checkpoint file
model = init_segmentor(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_segmentor(model, img)
# visualize the results in a new window
model.show_result(img, result, show=True)
# 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_segmentor(model, frame)
    model.show_result(frame, result, wait_time=1)

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

Train a model

MMSegmentation 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 some iterations, you can change the evaluation interval by adding the interval argument in the training config.

evaluation = dict(interval=4000)  # This evaluate the model per 4000 iterations.

*Important*: The default learning rate in config files is for 4 GPUs and 2 img/gpu (batch size = 4x2 = 8). Equivalently, you may also use 8 GPUs and 1 imgs/gpu since all models using cross-GPU SyncBN.

To trade speed with GPU memory, you may pass in --options model.backbone.with_cp=True to enable checkpoint in backbone.

Train with a single GPU

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

./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 iterations 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 (to continue the training process).
--load-from ${CHECKPOINT_FILE}: Load weights from a checkpoint file (to start finetuning for another task).

Difference between resume-from and load-from:

resume-from loads both the model weights and optimizer state including the iteration number.
load-from loads only the model weights, starts the training from iteration 0.

Train with multiple machines

If you run MMSegmentation on a cluster managed with slurm, you can use the script slurm_train.sh. (This script also supports single machine training.)

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

Here is an example of using 16 GPUs to train PSPNet on the dev partition.

GPUS=16 ./tools/slurm_train.sh dev pspr50 configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py /nfs/xxxx/psp_r50_512x1024_40ki_cityscapes

You can check 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. 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. Otherwise, there will be error message saying RuntimeError: Address already in use.

If you use dist_train.sh to launch training jobs, you can set the port in commands with environment variable PORT.

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 slurm_train.sh to launch training jobs, you can set the port in commands with environment variable MASTER_PORT.

MASTER_PORT=29500 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE}
MASTER_PORT=29501 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE}

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 to compute the FLOPs and params of a given model.

python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]

You will get the result like this.

==============================
Input shape: (3, 2048, 1024)
Flops: 1429.68 GMac
Params: 48.98 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.

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.

python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}

E.g.,

python tools/publish_model.py work_dirs/pspnet/latest.pth psp_r50_hszhao_200ep.pth

The final output filename will be psp_r50_512x1024_40ki_cityscapes-{hash id}.pth.

Convert to ONNX (experimental)

We provide a script to convert model to ONNX format. The converted model could be visualized by tools like Netron. Besides, we also support comparing the output results between Pytorch and ONNX model.

python tools/pytorch2onnx.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} --output-file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify]

Note: This tool is still experimental. Some customized operators are not supported for now.

Tutorials

Currently, we provide four tutorials for users to add new dataset, design data pipeline and add new modules, use training tricks. We also provide a full description about the config system.

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