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
        )