mirror of https://github.com/open-mmlab/mmselfsup.git synced 2025-06-03 14:59:38 +08:00

[Feature] Support pixel reconstruction visualization (#570 )

* refactor reconstruction visualization

* support simmim visualization

* fix reconstruction bug of MAE

* support visualization of MaskFeat

* refaction mae visualization demo

* add unit test

* fix lint and ut

* update

* add docs

* set random seed

* update

* update docstring

* add torch version check

* update

* rename

* update version

* update

* fix lint

* add docstring

* update docs

2022-12-06 19:45:01 +08:00

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Analysis tools

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

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