Apart from training/testing scripts, We provide lots of useful tools under the tools/ directory.

Log Analysis

tools/analysis_tools/analyze_logs.py plots loss/mAP curves given a training log file. Run pip install seaborn first to install the dependency.

python tools/analysis_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.

python tools/analysis_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.

python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --out losses.pdf

Compare the bbox mAP of two runs in the same figure.

python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2

Compute the average training speed.

python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--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

Result Analysis

tools/analysis_tools/analyze_results.py calculates single image mAP and saves or shows the topk images with the highest and lowest scores based on prediction results.

Usage

python tools/analysis_tools/analyze_results.py \
      ${CONFIG} \
      ${PREDICTION_PATH} \
      ${SHOW_DIR} \
      [--show] \
      [--wait-time ${WAIT_TIME}] \
      [--topk ${TOPK}] \
      [--show-score-thr ${SHOW_SCORE_THR}] \
      [--cfg-options ${CFG_OPTIONS}]

Description of all arguments:

config : The path of a model config file.
prediction_path: Output result file in pickle format from tools/test.py
show_dir: Directory where painted GT and detection images will be saved
--show：Determines whether to show painted images, If not specified, it will be set to False
--wait-time: The interval of show (s), 0 is block
--topk: The number of saved images that have the highest and lowest topk scores after sorting. If not specified, it will be set to 20.
--show-score-thr: Show score threshold. If not specified, it will be set to 0.
--cfg-options: If specified, the key-value pair optional cfg will be merged into config file

Examples:

Assume that you have got result file in pickle format from tools/test.py in the path './result.pkl'.

Test Faster R-CNN and visualize the results, save images to the directory results/

python tools/analysis_tools/analyze_results.py \
       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
       result.pkl \
       results \
       --show

Test Faster R-CNN and specified topk to 50, save images to the directory results/

python tools/analysis_tools/analyze_results.py \
       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
       result.pkl \
       results \
       --topk 50

If you want to filter the low score prediction results, you can specify the show-score-thr parameter

python tools/analysis_tools/analyze_results.py \
       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
       result.pkl \
       results \
       --show-score-thr 0.3

Visualization

Visualize Datasets

tools/misc/browse_dataset.py helps the user to browse a detection dataset (both images and bounding box annotations) visually, or save the image to a designated directory.

python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}]

Visualize Models

First, convert the model to ONNX as described here. Note that currently only RetinaNet is supported, support for other models will be coming in later versions. The converted model could be visualized by tools like Netron.

Visualize Predictions

If you need a lightweight GUI for visualizing the detection results, you can refer DetVisGUI project.

Error Analysis

tools/analysis_tools/coco_error_analysis.py analyzes COCO results per category and by different criterion. It can also make a plot to provide useful information.

python tools/analysis_tools/coco_error_analysis.py ${RESULT} ${OUT_DIR} [-h] [--ann ${ANN}] [--types ${TYPES[TYPES...]}]

Example:

Assume that you have got Mask R-CNN checkpoint file in the path 'checkpoint'. For other checkpoints, please refer to our model zoo. You can use the following command to get the results bbox and segmentation json file.

# out: results.bbox.json and results.segm.json
python tools/test.py \
       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
       checkpoint/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
       --format-only \
       --options "jsonfile_prefix=./results"

Get COCO bbox error results per category , save analyze result images to the directory results/

python tools/analysis_tools/coco_error_analysis.py \
       results.bbox.json \
       results \
       --ann=data/coco/annotations/instances_val2017.json \

Get COCO segmentation error results per category , save analyze result images to the directory results/

python tools/analysis_tools/coco_error_analysis.py \
       results.segm.json \
       results \
       --ann=data/coco/annotations/instances_val2017.json \
       --types='segm'

Model Serving

In order to serve an MMDetection model with TorchServe, you can follow the steps:

1. Convert model from MMDetection to TorchServe

python tools/deployment/mmdet2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output_folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}

2. Build `mmdet-serve` docker image

DOCKER_BUILDKIT=1 docker build -t mmdet-serve:latest docker/serve/

3. Launch `mmdet-serve`

Check the official docs for running TorchServe with docker.

Example:

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 \
mmdet-serve:latest

*Note: ${MODEL_STORE} needs to be an absolute path.

Read the docs about the Inference (8080), Management (8081) and Metrics (8082) APis

4. Test deployment

curl -O 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:

[
  {
    "dog": [
      402.9117736816406,
      124.19664001464844,
      571.7910766601562,
      292.6463623046875
    ],
    "score": 0.9561963081359863
  },
  {
    "dog": [
      293.90057373046875,
      196.2908477783203,
      417.4869079589844,
      286.2522277832031
    ],
    "score": 0.9179860353469849
  },
  {
    "dog": [
      202.178466796875,
      86.3709487915039,
      311.9863586425781,
      276.28411865234375
    ],
    "score": 0.8933767080307007
  }
]

Model Complexity

tools/analysis_tools/get_flops.py is a script adapted from flops-counter.pytorch to compute the FLOPs and params of a given model.

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

You will get the results like this.

==============================
Input shape: (3, 1280, 800)
Flops: 239.32 GFLOPs
Params: 37.74 M
==============================

Note: This tool is still experimental and we do not guarantee that the number is absolutely correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.

FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 1280, 800).
Some operators are not counted into FLOPs like GN and custom operators. Refer to mmcv.cnn.get_model_complexity_info() for details.
The FLOPs of two-stage detectors is dependent on the number of proposals.

Model conversion

MMDetection model to ONNX (experimental)

We provide a script to convert model to ONNX format. We also support comparing the output results between Pytorch and ONNX model for verification.

python tools/deployment/pytorch2onnx.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --output_file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify]

Note: This tool is still experimental. Some customized operators are not supported for now. For a detailed description of the usage and the list of supported models, please refer to pytorch2onnx.

MMDetection 1.x model to MMDetection 2.x

tools/model_converters/upgrade_model_version.py upgrades a previous MMDetection checkpoint to the new version. Note that this script is not guaranteed to work as some breaking changes are introduced in the new version. It is recommended to directly use the new checkpoints.

python tools/model_converters/upgrade_model_version.py ${IN_FILE} ${OUT_FILE} [-h] [--num-classes NUM_CLASSES]

RegNet model to MMDetection

tools/model_converters/regnet2mmdet.py convert keys in pycls pretrained RegNet models to MMDetection style.

python tools/model_converters/regnet2mmdet.py ${SRC} ${DST} [-h]

Detectron ResNet to Pytorch

tools/model_converters/detectron2pytorch.py converts keys in the original detectron pretrained ResNet models to PyTorch style.

python tools/model_converters/detectron2pytorch.py ${SRC} ${DST} ${DEPTH} [-h]

Prepare a model for publishing

tools/model_converters/publish_model.py helps users to prepare their model for publishing.

Before you upload a model to AWS, you may want to

convert model weights to CPU tensors
delete the optimizer states and
compute the hash of the checkpoint file and append the hash id to the filename.

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

E.g.,

python tools/model_converters/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.

Dataset Conversion

tools/data_converters/ contains tools to convert the Cityscapes dataset and Pascal VOC dataset to the COCO format.

python tools/dataset_converters/cityscapes.py ${CITYSCAPES_PATH} [-h] [--img-dir ${IMG_DIR}] [--gt-dir ${GT_DIR}] [-o ${OUT_DIR}] [--nproc ${NPROC}]
python tools/dataset_converters/pascal_voc.py ${DEVKIT_PATH} [-h] [-o ${OUT_DIR}]

Miscellaneous

Evaluating a metric

tools/analysis_tools/eval_metric.py evaluates certain metrics of a pkl result file according to a config file.

python tools/analysis_tools/eval_metric.py ${CONFIG} ${PKL_RESULTS} [-h] [--format-only] [--eval ${EVAL[EVAL ...]}]
                      [--cfg-options ${CFG_OPTIONS [CFG_OPTIONS ...]}]
                      [--eval-options ${EVAL_OPTIONS [EVAL_OPTIONS ...]}]

Print the entire config

tools/misc/print_config.py prints the whole config verbatim, expanding all its imports.

python tools/misc/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]

Test the robustness of detectors

Please refer to robustness_benchmarking.md.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

useful_tools.md

useful_tools.md

Log Analysis

Result Analysis

Visualization

Visualize Datasets

Visualize Models

Visualize Predictions

Error Analysis

Model Serving

1. Convert model from MMDetection to TorchServe

2. Build `mmdet-serve` docker image

3. Launch `mmdet-serve`

4. Test deployment

Model Complexity

Model conversion

MMDetection model to ONNX (experimental)

MMDetection 1.x model to MMDetection 2.x

RegNet model to MMDetection

Detectron ResNet to Pytorch

Prepare a model for publishing

Dataset Conversion

Miscellaneous

Evaluating a metric

Print the entire config

Test the robustness of detectors

Files

useful_tools.md

Latest commit

History

useful_tools.md

File metadata and controls

Log Analysis

Result Analysis

Visualization

Visualize Datasets

Visualize Models

Visualize Predictions

Error Analysis

Model Serving

1. Convert model from MMDetection to TorchServe

2. Build mmdet-serve docker image

3. Launch mmdet-serve

4. Test deployment

Model Complexity

Model conversion

MMDetection model to ONNX (experimental)

MMDetection 1.x model to MMDetection 2.x

RegNet model to MMDetection

Detectron ResNet to Pytorch

Prepare a model for publishing

Dataset Conversion

Miscellaneous

Evaluating a metric

Print the entire config

Test the robustness of detectors

2. Build `mmdet-serve` docker image

3. Launch `mmdet-serve`