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:
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
python tools/analysis_tools/get_flops.py configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py
Output:
==============================
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
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:
(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
)