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Useful Tools

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

Useful Tools Link

Model Conversion

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_8xb32-1x1x3-100e_kinetics400-rgb/latest.pth tsn_r50_1x1x3_100e_kinetics400_rgb.pth

The final output filename will be tsn_r50_8xb32-1x1x3-100e_kinetics400-rgb-{hash id}.pth.

Miscellaneous

Evaluating a metric

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

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

Check videos

tools/analysis_tools/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_tools/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]

Multi-Stream Fusion

tools/analysis_tools/report_accuracy.py uses the dumped results (by setting --dump res.pkl when testing) to fuse the multi-stream prediction scores, i.e., late fusion.

python tools/analysis_tools/report_accuracy.py [--preds ${RESULT_PKL_1 [RESULT_PKL_2 ...]}] [--coefficients ${COEFFICIENT_1 [COEFFICIENT_2, ...]}] [--apply-softmax]

Take joint-bone fusion as an example, which is a general practice in the task of skeleton-based action recognition.

python tools/analysis_tools/report_accuracy.py --preds demo/fuse/joint.pkl demo/fuse/bone.pkl --coefficients 1.0 1.0
Mean Class Accuracy: 0.9180
Top 1 Accuracy: 0.9333
Top 5 Accuracy: 0.9833