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Showing content from https://mmselfsup.readthedocs.io/en/latest/user_guides/analysis_tools.html below:

Analysis tools — MMSelfSup 1.0.0 documentation

• Analysis tools

  • Count number of parameters

  • Publish a model

  • Reproducibility

  • Log Analysis

python tools/analysis_tools/count_parameters.py ${CONFIG_FILE}

An example:

python tools/analysis_tools/count_parameters.py configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py

Before you publish a model, you may want to

• Convert model weights to CPU tensors.

• Delete the optimizer states.

• 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}

An example:

python tools/model_converters/publish_model.py YOUR/PATH/epoch_100.pth YOUR/PATH/epoch_100_output.pth

If you want to make your performance exactly reproducible, please set --cfg-options randomness.deterministic=True to train the final model. Note that this will switch off torch.backends.cudnn.benchmark and slow down the training speed.

tools/analysis_tools/analyze_logs.py plots loss/lr 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_dense --legend loss_dense
  

• 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_dense loss_single --out losses.pdf
  

• Compare the loss of two runs in the same figure.

  python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys loss --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
  

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