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useful_tools.md

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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}] [--eval-interval ${EVALUATION_INTERVAL}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]

loss curve image

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'.

  1. 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
  1. 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
  1. 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/analysis_tools/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 modify the test_evaluator to save the results bbox by:

  1. Find which dataset in 'configs/base/datasets' the current config corresponds to.
  2. Replace the original test_evaluator and test_dataloader with test_evaluator and test_dataloader in the comment in dataset config.
  3. Use the following command to get the results bbox and segmentation json file.
python tools/test.py \
       configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py \
       checkpoint/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
  1. Get COCO bbox error results per category , save analyze result images to the directory(In config the default directory is './work_dirs/coco_instance/test')
python tools/analysis_tools/coco_error_analysis.py \
       results.bbox.json \
       results \
       --ann=data/coco/annotations/instances_val2017.json \
  1. Get COCO segmentation error results per category , save analyze result images to the directory
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. Install TorchServe

Suppose you have a Python environment with PyTorch and MMDetection successfully installed, then you could run the following command to install TorchServe and its dependencies. For more other installation options, please refer to the quick start.

python -m pip install torchserve torch-model-archiver torch-workflow-archiver nvgpu

Note: Please refer to torchserve docker if you want to use TorchServe in docker.

2. Convert model from MMDetection to TorchServe

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

3. Start TorchServe

torchserve --start --ncs \
  --model-store ${MODEL_STORE} \
  --models  ${MODEL_NAME}.mar

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 response similar to:

[
  {
    "class_label": 16,
    "class_name": "dog",
    "bbox": [
      294.63409423828125,
      203.99111938476562,
      417.048583984375,
      281.62744140625
    ],
    "score": 0.9987992644309998
  },
  {
    "class_label": 16,
    "class_name": "dog",
    "bbox": [
      404.26019287109375,
      126.0080795288086,
      574.5091552734375,
      293.6662292480469
    ],
    "score": 0.9979367256164551
  },
  {
    "class_label": 16,
    "class_name": "dog",
    "bbox": [
      197.2144775390625,
      93.3067855834961,
      307.8505554199219,
      276.7560119628906
    ],
    "score": 0.993338406085968
  }
]

Compare results

And you can use test_torchserver.py to compare result of TorchServe and PyTorch, and visualize them.

python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}] [--score-thr ${SCORE_THR}] [--work-dir ${WORK_DIR}]

Example:

python tools/deployment/test_torchserver.py \
demo/demo.jpg \
configs/yolo/yolov3_d53_8xb8-320-273e_coco.py \
checkpoint/yolov3_d53_320_273e_coco-421362b6.pth \
yolov3 \
--work-dir ./work-dir

5. Stop TorchServe

torchserve --stop

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.

  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. Refer to mmcv.cnn.get_model_complexity_info() for details.
  3. The FLOPs of two-stage detectors is dependent on the number of proposals.

Model conversion

MMDetection model to ONNX

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

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

  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/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}]

Dataset Download

tools/misc/download_dataset.py supports downloading datasets such as COCO, VOC, and LVIS.

python tools/misc/download_dataset.py --dataset-name coco2017
python tools/misc/download_dataset.py --dataset-name voc2007
python tools/misc/download_dataset.py --dataset-name lvis

For users in China, these datasets can also be downloaded from OpenDataLab with high speed:

Benchmark

Robust Detection Benchmark

tools/analysis_tools/test_robustness.py andtools/analysis_tools/robustness_eval.py helps users to evaluate model robustness. The core idea comes from Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. For more information how to evaluate models on corrupted images and results for a set of standard models please refer to robustness_benchmarking.md.

FPS Benchmark

tools/analysis_tools/benchmark.py helps users to calculate FPS. The FPS value includes model forward and post-processing. In order to get a more accurate value, currently only supports single GPU distributed startup mode.

python -m torch.distributed.launch --nproc_per_node=1 --master_port=${PORT} tools/analysis_tools/benchmark.py \
    ${CONFIG} \
    [--checkpoint ${CHECKPOINT}] \
    [--repeat-num ${REPEAT_NUM}] \
    [--max-iter ${MAX_ITER}] \
    [--log-interval ${LOG_INTERVAL}] \
    --launcher pytorch

Examples: Assuming that you have already downloaded the Faster R-CNN model checkpoint to the directory checkpoints/.

python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/analysis_tools/benchmark.py \
       configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
       --launcher pytorch

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...]}]

Hyper-parameter Optimization

YOLO Anchor Optimization

tools/analysis_tools/optimize_anchors.py provides two method to optimize YOLO anchors.

One is k-means anchor cluster which refers from darknet.

python tools/analysis_tools/optimize_anchors.py ${CONFIG} --algorithm k-means --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} --output-dir ${OUTPUT_DIR}

Another is using differential evolution to optimize anchors.

python tools/analysis_tools/optimize_anchors.py ${CONFIG} --algorithm differential_evolution --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} --output-dir ${OUTPUT_DIR}

E.g.,

python tools/analysis_tools/optimize_anchors.py configs/yolo/yolov3_d53_8xb8-320-273e_coco.py --algorithm differential_evolution --input-shape 608 608 --device cuda --output-dir work_dirs

You will get:

loading annotations into memory...
Done (t=9.70s)
creating index...
index created!
2021-07-19 19:37:20,951 - mmdet - INFO - Collecting bboxes from annotation...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 117266/117266, 15874.5 task/s, elapsed: 7s, ETA:     0s

2021-07-19 19:37:28,753 - mmdet - INFO - Collected 849902 bboxes.
differential_evolution step 1: f(x)= 0.506055
differential_evolution step 2: f(x)= 0.506055
......

differential_evolution step 489: f(x)= 0.386625
2021-07-19 19:46:40,775 - mmdet - INFO Anchor evolution finish. Average IOU: 0.6133754253387451
2021-07-19 19:46:40,776 - mmdet - INFO Anchor differential evolution result:[[10, 12], [15, 30], [32, 22], [29, 59], [61, 46], [57, 116], [112, 89], [154, 198], [349, 336]]
2021-07-19 19:46:40,798 - mmdet - INFO Result saved in work_dirs/anchor_optimize_result.json

Confusion Matrix

A confusion matrix is a summary of prediction results.

tools/analysis_tools/confusion_matrix.py can analyze the prediction results and plot a confusion matrix table.

First, run tools/test.py to save the .pkl detection results.

Then, run

python tools/analysis_tools/confusion_matrix.py ${CONFIG}  ${DETECTION_RESULTS}  ${SAVE_DIR} --show

And you will get a confusion matrix like this:

confusion_matrix_example

COCO Separated & Occluded Mask Metric

Detecting occluded objects still remains a challenge for state-of-the-art object detectors. We implemented the metric presented in paper A Tri-Layer Plugin to Improve Occluded Detection to calculate the recall of separated and occluded masks.

There are two ways to use this metric:

Offline evaluation

We provide a script to calculate the metric with a dumped prediction file.

First, use the tools/test.py script to dump the detection results:

python tools/test.py ${CONFIG} ${MODEL_PATH} --out results.pkl

Then, run the tools/analysis_tools/coco_occluded_separated_recall.py script to get the recall of separated and occluded masks:

python tools/analysis_tools/coco_occluded_separated_recall.py results.pkl --out occluded_separated_recall.json

The output should be like this:

loading annotations into memory...
Done (t=0.51s)
creating index...
index created!
processing detection results...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 109.3 task/s, elapsed: 46s, ETA:     0s
computing occluded mask recall...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5550/5550, 780.5 task/s, elapsed: 7s, ETA:     0s
COCO occluded mask recall: 58.79%
COCO occluded mask success num: 3263
computing separated mask recall...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3522/3522, 778.3 task/s, elapsed: 5s, ETA:     0s
COCO separated mask recall: 31.94%
COCO separated mask success num: 1125

+-----------+--------+-------------+
| mask type | recall | num correct |
+-----------+--------+-------------+
| occluded  | 58.79% | 3263        |
| separated | 31.94% | 1125        |
+-----------+--------+-------------+
Evaluation results have been saved to occluded_separated_recall.json.

Online evaluation

We implement CocoOccludedSeparatedMetric which inherits from the CocoMetic. To evaluate the recall of separated and occluded masks during training, just replace the evaluator metric type with 'CocoOccludedSeparatedMetric' in your config:

val_evaluator = dict(
    type='CocoOccludedSeparatedMetric',  # modify this
    ann_file=data_root + 'annotations/instances_val2017.json',
    metric=['bbox', 'segm'],
    format_only=False)
test_evaluator = val_evaluator

Please cite the paper if you use this metric:

@article{zhan2022triocc,
    title={A Tri-Layer Plugin to Improve Occluded Detection},
    author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew},
    journal={British Machine Vision Conference},
    year={2022}
}