Apart from training/testing scripts, We provide lots of useful tools under the tools/ directory.
Useful Tools Link¶
Log Analysis¶
tools/analysis/analyze_logs.py plots loss/top-k acc curves given a training log file. Run pip install seaborn first to install the dependency.
python tools/analysis/analyze_logs.py plot_curve ${JSON_LOGS} [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
Examples:
Plot the classification loss of some run.
python tools/analysis/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls
Plot the top-1 acc and top-5 acc of some run, and save the figure to a pdf.
python tools/analysis/analyze_logs.py plot_curve log.json --keys top1_acc top5_acc --out results.pdf
Compare the top-1 acc of two runs in the same figure.
python tools/analysis/analyze_logs.py plot_curve log1.json log2.json --keys top1_acc --legend run1 run2
You can also compute the average training speed.
python tools/analysis/analyze_logs.py cal_train_time ${JSON_LOGS} [--include-outliers]
Compute the average training speed for a config file.
python tools/analysis/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json
The output is expected to be like the following.
-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json----- slowest epoch 60, average time is 0.9736 fastest epoch 18, average time is 0.9001 time std over epochs is 0.0177 average iter time: 0.9330 s/iter
Model Complexity¶
/tools/analysis/get_flops.py is a script adapted from flops-counter.pytorch to compute the FLOPs and params of a given model.
python tools/analysis/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
We will get the result like this
==============================
Input shape: (1, 3, 32, 340, 256)
Flops: 37.1 GMac
Params: 28.04 M
==============================
Note
This tool is still experimental and we do not guarantee that the number is absolutely correct. You may 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, 340, 256) for 2D recognizer, (1, 3, 32, 340, 256) for 3D recognizer.
(2) Some operators are not counted into FLOPs like GN and custom operators. Refer to mmcv.cnn.get_model_complexity_info() for details.
Model Conversion¶
MMAction2 model to ONNX (experimental)¶
/tools/deployment/pytorch2onnx.py is a script to convert model to ONNX format.
It also supports comparing the output results between Pytorch and ONNX model for verification.
Run pip install onnx onnxruntime first to install the dependency.
Please note that a softmax layer could be added for recognizers by --softmax option, in order to get predictions in range [0, 1].
For recognizers, please run:
python tools/deployment/pytorch2onnx.py $CONFIG_PATH $CHECKPOINT_PATH --shape $SHAPE --verify
For localizers, please run:
python tools/deployment/pytorch2onnx.py $CONFIG_PATH $CHECKPOINT_PATH --is-localizer --shape $SHAPE --verify
Prepare a model for publishing¶
tools/deployment/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. (3) compute the hash of the checkpoint file and append the hash id to the filename.
python tools/deployment/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
E.g.,
python tools/deployment/publish_model.py work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/latest.pth tsn_r50_1x1x3_100e_kinetics400_rgb.pth
The final output filename will be tsn_r50_1x1x3_100e_kinetics400_rgb-{hash id}.pth.
Model Serving¶
In order to serve an MMAction2 model with TorchServe, you can follow the steps:
1. Convert model from MMAction2 to TorchServe¶
python tools/deployment/mmaction2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output_folder ${MODEL_STORE} \
--model-name ${MODEL_NAME} \
--label-file ${LABLE_FILE}
2. Build mmaction-serve docker image¶
DOCKER_BUILDKIT=1 docker build -t mmaction-serve:latest docker/serve/
3. Launch mmaction-serve¶
Check the official docs for running TorchServe with docker.
Example:
docker run --rm \
--cpus 8 \
--gpus device=0 \
-p8080:8080 -p8081:8081 -p8082:8082 \
--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
mmaction-serve:latest
Note: ${MODEL_STORE} needs to be an absolute path. Read the docs about the Inference (8080), Management (8081) and Metrics (8082) APis
4. Test deployment¶
# Assume you are under the directory `mmaction2`
curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.mp4
You should obtain a response similar to:
{
"arm wrestling": 1.0,
"rock scissors paper": 4.962051880497143e-10,
"shaking hands": 3.9761663406245873e-10,
"massaging feet": 1.1924419784925533e-10,
"stretching leg": 1.0601879096849842e-10
}
Miscellaneous¶
Evaluating a metric¶
tools/analysis/eval_metric.py evaluates certain metrics of the results saved in a file according to a config file.
The saved result file is created on tools/test.py by setting the arguments --out ${RESULT_FILE} to indicate the result file,
which stores the final output of the whole model.
python tools/analysis/eval_metric.py ${CONFIG_FILE} ${RESULT_FILE} [--eval ${EVAL_METRICS}] [--cfg-options ${CFG_OPTIONS}] [--eval-options ${EVAL_OPTIONS}]
Print the entire config¶
tools/analysis/print_config.py prints the whole config verbatim, expanding all its imports.
python tools/analysis/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]
Check videos¶
tools/analysis/check_videos.py uses specified video encoder to iterate all samples that are specified by the input configuration file, looks for invalid videos (corrupted or missing), and saves the corresponding file path to the output file. Please note that after deleting invalid videos, users need to regenerate the video file list.
python tools/analysis/check_videos.py ${CONFIG} [-h] [--options OPTIONS [OPTIONS ...]] [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] [--output-file OUTPUT_FILE] [--split SPLIT] [--decoder DECODER] [--num-processes NUM_PROCESSES] [--remove-corrupted-videos]