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Analysis Tools (TODO)

Log Analysis
- Plot Curves
- Calculate Training Time
Result Analysis
- Evaluate Results
- View Typical Results
Model Complexity
FAQs

Log Analysis

Plot Curves

tools/analysis_tools/analyze_logs.py plots curves of given keys according to the log files.

python tools/analysis_tools/analyze_logs.py plot_curve  \
    ${JSON_LOGS}  \
    [--keys ${KEYS}]  \
    [--title ${TITLE}]  \
    [--legend ${LEGEND}]  \
    [--backend ${BACKEND}]  \
    [--style ${STYLE}]  \
    [--out ${OUT_FILE}]  \
    [--window-size ${WINDOW_SIZE}]

Description of all arguments：

json_logs : The paths of the log files, separate multiple files by spaces.
--keys : The fields of the logs to analyze, separate multiple keys by spaces. Defaults to 'loss'.
--title : The title of the figure. Defaults to use the filename.
--legend : The names of legend, the number of which must be equal to len(${JSON_LOGS}) * len(${KEYS}). Defaults to use "${JSON_LOG}-${KEYS}".
--backend : The backend of matplotlib. Defaults to auto selected by matplotlib.
--style : The style of the figure. Default to whitegrid.
--out : The path of the output picture. If not set, the figure won't be saved.
--window-size: The shape of the display window. The format should be 'W*H'. Defaults to '12*7'.

The `--style` option depends on `seaborn` package, please install it before setting it.

Examples:

Plot the loss curve in training.

python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss

Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg.

python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5  --legend top1 top5 --out results.jpg

Compare the top-1 accuracy of two log files in the same figure.

python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2

The tool will automatically select to find keys in training logs or validation logs according to the keys.
Therefore, if you add a custom evaluation metric, please also add the key to `TEST_METRICS` in this tool.

Calculate Training Time

tools/analysis_tools/analyze_logs.py can also calculate the training time according to the log files.

python tools/analysis_tools/analyze_logs.py cal_train_time \
    ${JSON_LOGS}
    [--include-outliers]

Description of all arguments:

json_logs : The paths of the log files, separate multiple files by spaces.
--include-outliers : If set, include the first iteration in each epoch (Sometimes the time of first iterations is longer).

Example:

python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json

The output is expected to be like the below.

-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json-----
slowest epoch 68, average time is 0.3818
fastest epoch 1, average time is 0.3694
time std over epochs is 0.0020
average iter time: 0.3777 s/iter

Result Analysis

With the --out argument in tools/test.py, we can save the inference results of all samples as a file. And with this result file, we can do further analysis.

Evaluate Results

tools/analysis_tools/eval_metric.py can evaluate metrics again.

python tools/analysis_tools/eval_metric.py \
      ${CONFIG} \
      ${RESULT} \
      [--metrics ${METRICS}]  \
      [--cfg-options ${CFG_OPTIONS}] \
      [--metric-options ${METRIC_OPTIONS}]

Description of all arguments:

config : The path of the model config file.
result: The Output result file in json/pickle format from tools/test.py.
--metrics : Evaluation metrics, the acceptable values depend on the dataset.
--cfg-options: If specified, the key-value pair config will be merged into the config file, for more details please refer to Learn about Configs
--metric-options: If specified, the key-value pair arguments will be passed to the metric_options argument of dataset's evaluate function.

In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "class_scores" to use this tool.

Examples:

python tools/analysis_tools/eval_metric.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py your_result.pkl --metrics accuracy --metric-options "topk=(1,5)"

View Typical Results

tools/analysis_tools/analyze_results.py can save the images with the highest scores in successful or failed prediction.

python tools/analysis_tools/analyze_results.py \
      ${CONFIG} \
      ${RESULT} \
      [--out-dir ${OUT_DIR}] \
      [--topk ${TOPK}] \
      [--cfg-options ${CFG_OPTIONS}]

Description of all arguments:

config : The path of the model config file.
result: Output result file in json/pickle format from tools/test.py.
--out_dir: Directory to store output files.
--topk: The number of images in successful or failed prediction with the highest topk scores to save. If not specified, it will be set to 20.
--cfg-options: If specified, the key-value pair config will be merged into the config file, for more details please refer to Learn about Configs

In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "pred_score", "pred_label" and "pred_class" to use this tool.

Examples:

python tools/analysis_tools/analyze_results.py \
       configs/resnet/resnet50_b32x8_imagenet.py \
       result.pkl \
       --out_dir results \
       --topk 50

Model Complexity

Get the FLOPs and params (experimental)

We provide a script adapted from fvcore to compute the FLOPs and params of a given model.

python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]