mirror of
https://github.com/open-mmlab/mmyolo.git
synced 2025-06-03 15:00:20 +08:00
d06de6d36f
* support compute flops * fix yolov8 error * Add FLOPs Doc * Update docs/zh_cn/recommended_topics/complexity_analysis.md Co-authored-by: Haian Huang(深度眸) <1286304229@qq.com> * Update get_flops.py * Update complexity_analysis.md * Update complexity_analysis.md * Add EN `complexity_analysis.md` * Update complexity_analysis.md * Add `complexity_analysis.md` index * Add `complexity_analysis.md` index * Update README_zh-CN.md * Update README.md * update --------- Co-authored-by: huanghaian <huanghaian@sensetime.com> Co-authored-by: Haian Huang(深度眸) <1286304229@qq.com>
121 lines
7.1 KiB
Markdown
121 lines
7.1 KiB
Markdown
# Model Complexity Analysis
|
|
|
|
We provide a `tools/analysis_tools/get_flops.py` script to help with the complexity analysis for models of MMYOLO.
|
|
Currently, it provides the interfaces to compute parameter, activation and flops of the given model,
|
|
and supports printing the related information layer-by-layer in terms of network structure or table.
|
|
|
|
The commands as follows:
|
|
|
|
```shell
|
|
python tools/analysis_tools/get_flops.py
|
|
${CONFIG_FILE} \ # config file path
|
|
[--shape ${IMAGE_SIZE}] \ # input image size (int), default 640*640
|
|
[--show-arch ${ARCH_DISPLAY}] \ # print related information by network layers
|
|
[--not-show-table ${TABLE_DISPLAY}] \ # print related information by table
|
|
[--cfg-options ${CFG_OPTIONS}] # config file option
|
|
# [] stands for optional parameter, do not type [] when actually entering the command line
|
|
```
|
|
|
|
Let's take the `rtmdet_s_syncbn_fast_8xb32-300e_coco.py` config file in RTMDet as an example to show how this script can be used:
|
|
|
|
## Usage Example 1: Print Flops, Parameters and related information by table
|
|
|
|
```shell
|
|
python tools/analysis_tools/get_flops.py configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py
|
|
```
|
|
|
|
Output:
|
|
|
|
```python
|
|
==============================
|
|
Input shape: torch.Size([640, 640])
|
|
Model Flops: 14.835G
|
|
Model Parameters: 8.887M
|
|
==============================
|
|
```
|
|
|
|
| module | #parameters or shape | #flops | #activations |
|
|
| :-------------------------------- | :------------------- | :------ | :----------: |
|
|
| model | 8.887M | 14.835G | 35.676M |
|
|
| backbone | 4.378M | 5.416G | 22.529M |
|
|
| backbone.stem | 7.472K | 0.765G | 6.554M |
|
|
| backbone.stem.0 | 0.464K | 47.514M | 1.638M |
|
|
| backbone.stem.1 | 2.336K | 0.239G | 1.638M |
|
|
| backbone.stem.2 | 4.672K | 0.478G | 3.277M |
|
|
| backbone.stage1 | 42.4K | 0.981G | 7.373M |
|
|
| backbone.stage1.0 | 18.56K | 0.475G | 1.638M |
|
|
| backbone.stage1.1 | 23.84K | 0.505G | 5.734M |
|
|
| backbone.stage2 | 0.21M | 1.237G | 4.915M |
|
|
| backbone.stage2.0 | 73.984K | 0.473G | 0.819M |
|
|
| backbone.stage2.1 | 0.136M | 0.764G | 4.096M |
|
|
| backbone.stage3 | 0.829M | 1.221G | 2.458M |
|
|
| backbone.stage3.0 | 0.295M | 0.473G | 0.41M |
|
|
| backbone.stage3.1 | 0.534M | 0.749G | 2.048M |
|
|
| backbone.stage4 | 3.29M | 1.211G | 1.229M |
|
|
| backbone.stage4.0 | 1.181M | 0.472G | 0.205M |
|
|
| backbone.stage4.1 | 0.657M | 0.263G | 0.307M |
|
|
| backbone.stage4.2 | 1.452M | 0.476G | 0.717M |
|
|
| neck | 3.883M | 4.366G | 8.141M |
|
|
| neck.reduce_layers.2 | 0.132M | 52.634M | 0.102M |
|
|
| neck.reduce_layers.2.conv | 0.131M | 52.429M | 0.102M |
|
|
| neck.reduce_layers.2.bn | 0.512K | 0.205M | 0 |
|
|
| neck.top_down_layers | 0.491M | 1.23G | 4.506M |
|
|
| neck.top_down_layers.0 | 0.398M | 0.638G | 1.638M |
|
|
| neck.top_down_layers.1 | 92.608K | 0.593G | 2.867M |
|
|
| neck.downsample_layers | 0.738M | 0.472G | 0.307M |
|
|
| neck.downsample_layers.0 | 0.148M | 0.236G | 0.205M |
|
|
| neck.downsample_layers.1 | 0.59M | 0.236G | 0.102M |
|
|
| neck.bottom_up_layers | 1.49M | 0.956G | 2.15M |
|
|
| neck.bottom_up_layers.0 | 0.3M | 0.48G | 1.434M |
|
|
| neck.bottom_up_layers.1 | 1.19M | 0.476G | 0.717M |
|
|
| neck.out_layers | 1.033M | 1.654G | 1.075M |
|
|
| neck.out_layers.0 | 0.148M | 0.945G | 0.819M |
|
|
| neck.out_layers.1 | 0.295M | 0.472G | 0.205M |
|
|
| neck.out_layers.2 | 0.59M | 0.236G | 51.2K |
|
|
| neck.upsample_layers | | 1.229M | 0 |
|
|
| neck.upsample_layers.0 | | 0.41M | 0 |
|
|
| neck.upsample_layers.1 | | 0.819M | 0 |
|
|
| bbox_head.head_module | 0.625M | 5.053G | 5.006M |
|
|
| bbox_head.head_module.cls_convs | 0.296M | 2.482G | 2.15M |
|
|
| bbox_head.head_module.cls_convs.0 | 0.295M | 2.481G | 2.15M |
|
|
| bbox_head.head_module.cls_convs.1 | 0.512K | 0.819M | 0 |
|
|
| bbox_head.head_module.cls_convs.2 | 0.512K | 0.205M | 0 |
|
|
| bbox_head.head_module.reg_convs | 0.296M | 2.482G | 2.15M |
|
|
| bbox_head.head_module.reg_convs.0 | 0.295M | 2.481G | 2.15M |
|
|
| bbox_head.head_module.reg_convs.1 | 0.512K | 0.819M | 0 |
|
|
| bbox_head.head_module.reg_convs.2 | 0.512K | 0.205M | 0 |
|
|
| bbox_head.head_module.rtm_cls | 30.96K | 86.016M | 0.672M |
|
|
| bbox_head.head_module.rtm_cls.0 | 10.32K | 65.536M | 0.512M |
|
|
| bbox_head.head_module.rtm_cls.1 | 10.32K | 16.384M | 0.128M |
|
|
| bbox_head.head_module.rtm_cls.2 | 10.32K | 4.096M | 32K |
|
|
| bbox_head.head_module.rtm_reg | 1.548K | 4.301M | 33.6K |
|
|
| bbox_head.head_module.rtm_reg.0 | 0.516K | 3.277M | 25.6K |
|
|
| bbox_head.head_module.rtm_reg.1 | 0.516K | 0.819M | 6.4K |
|
|
| bbox_head.head_module.rtm_reg.2 | 0.516K | 0.205M | 1.6K |
|
|
|
|
## Usage Example 2: Print related information by network layers
|
|
|
|
```shell
|
|
python tools/analysis_tools/get_flops.py configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py --show-arch
|
|
```
|
|
|
|
Due to the complex structure of RTMDet, the output is long.
|
|
The following shows only the output from bbox_head.head_module.rtm_reg section:
|
|
|
|
```python
|
|
(rtm_reg): ModuleList(
|
|
#params: 1.55K, #flops: 4.3M, #acts: 33.6K
|
|
(0): Conv2d(
|
|
128, 4, kernel_size=(1, 1), stride=(1, 1)
|
|
#params: 0.52K, #flops: 3.28M, #acts: 25.6K
|
|
)
|
|
(1): Conv2d(
|
|
128, 4, kernel_size=(1, 1), stride=(1, 1)
|
|
#params: 0.52K, #flops: 0.82M, #acts: 6.4K
|
|
)
|
|
(2): Conv2d(
|
|
128, 4, kernel_size=(1, 1), stride=(1, 1)
|
|
#params: 0.52K, #flops: 0.2M, #acts: 1.6K
|
|
)
|
|
```
|