2.7 KiB
2.7 KiB
Analysis tools
Count number of parameters
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
Publish a model
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
Reproducibility
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.
Log Analysis
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