File size: 3,308 Bytes
186701e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
# Log Analysis
## Curve plotting
`tools/analysis_tools/analyze_logs.py` in MMDetection plots loss/mAP curves given a training log file. Run `pip install seaborn` first to install the dependency.
```shell
mim run mmdet analyze_logs plot_curve \
${LOG} \ # path of train log in json format
[--keys ${KEYS}] \ # the metric that you want to plot, default to 'bbox_mAP'
[--start-epoch ${START_EPOCH}] # the epoch that you want to start, default to 1
[--eval-interval ${EVALUATION_INTERVAL}] \ # the evaluation interval when training, default to 1
[--title ${TITLE}] \ # title of figure
[--legend ${LEGEND}] \ # legend of each plot, default to None
[--backend ${BACKEND}] \ # backend of plt, default to None
[--style ${STYLE}] \ # style of plt, default to 'dark'
[--out ${OUT_FILE}] # the path of output file
# [] stands for optional parameters, when actually entering the command line, you do not need to enter []
```
Examples:
- Plot the classification loss of some run.
```shell
mim run mmdet analyze_logs plot_curve \
yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
--keys loss_cls \
--legend loss_cls
```
<img src="https://user-images.githubusercontent.com/27466624/204747359-754555df-1f97-4d5c-87ca-9ad3a0badcce.png" width="600"/>
- Plot the classification and regression loss of some run, and save the figure to a pdf.
```shell
mim run mmdet analyze_logs plot_curve \
yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
--keys loss_cls loss_bbox \
--legend loss_cls loss_bbox \
--out losses_yolov5_s.pdf
```
<img src="https://user-images.githubusercontent.com/27466624/204748560-2d17ce4b-fb5f-4732-a962-329109e73aad.png" width="600"/>
- Compare the bbox mAP of two runs in the same figure.
```shell
mim run mmdet analyze_logs plot_curve \
yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json \
--keys bbox_mAP \
--legend yolov5_s yolov5_n \
--eval-interval 10 # Note that the evaluation interval must be the same as during training. Otherwise, it will raise an error.
```
<img src="https://user-images.githubusercontent.com/27466624/204748704-21db9f9e-386e-449c-91c7-2ce3f8b51f24.png" width="600"/>
## Compute the average training speed
```shell
mim run mmdet analyze_logs cal_train_time \
${LOG} \ # path of train log in json format
[--include-outliers] # include the first value of every epoch when computing the average time
```
Examples:
```shell
mim run mmdet analyze_logs cal_train_time \
yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json
```
The output is expected to be like the following.
```text
-----Analyze train time of yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json-----
slowest epoch 278, average time is 0.1705 s/iter
fastest epoch 300, average time is 0.1510 s/iter
time std over epochs is 0.0026
average iter time: 0.1556 s/iter
```
|