Model Analysis¶

Get Model Params & FLOPs¶

MMPose provides tools/analysis_tools/get_flops.py to get model parameters and FLOPs.

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

Description of all arguments:

CONFIG_FILE : The path of a model config file.

--shape: The input shape to the model.

--input-constructor: If specified as batch, it will generate a batch tensor to calculate FLOPs.

--batch-size：If --input-constructor is specified as batch, it will generate a random tensor with shape (batch_size, 3, **input_shape) to calculate FLOPs.

--cfg-options: If specified, the key-value pair optional cfg will be merged into config file.

Example:

python tools/analysis_tools/get_flops.py configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py

We will get the following results:

==============================
Input shape: (1, 3, 256, 192)
Flops: 7.7 GFLOPs
Params: 28.54 M
==============================

Note

This tool is still experimental and we do not guarantee that the number is absolutely correct. Some operators are not counted into FLOPs like GN and custom operators.

Log Analysis¶

MMPose provides tools/analysis_tools/analyze_logs.py to analyze the training log. The log file can be either a json file or a text file. The json file is recommended, because it is more convenient to parse and visualize.

Currently, the following functions are supported:

Plot loss/accuracy curves
Calculate training time

Plot Loss/Accuracy Curves¶

The function depends on seaborn, please install it first by running pip install seaborn.

log_curve

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

Examples:

Plot loss curve

python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_kpt --legend loss_kpt

Plot accuracy curve and export to PDF file

python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys acc_pose --out results.pdf

Plot multiple log files on the same figure

python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys loss_kpt --legend run1 run2 --title loss_kpt --out loss_kpt.png

Calculate Training Time¶

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

Examples:

python tools/analysis_tools/analyze_logs.py cal_train_time log.json

The result is as follows:

-----Analyze train time of hrnet_w32_256x192.json-----
slowest epoch 56, average time is 0.6924
fastest epoch 1, average time is 0.6502
time std over epochs is 0.0085
average iter time: 0.6688 s/iter