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模型复杂度分析

我们提供了 tools/analysis_tools/get_flops.py 脚本来帮助进行 MMYOLO 系列中所有模型的复杂度分析。目前支持计算并输出给定模型的 parameters, activation 以及 flops；同时支持以网络结构或表格的形式打印输出每一层网络的复杂度信息。

调用命令如下：

python tools/analysis_tools/get_flops.py
    ${CONFIG_FILE} \                           # 配置文件路径
    [--shape ${IMAGE_SIZE}] \                  # 输入图像大小（int），默认取 640*640
    [--show-arch ${ARCH_DISPLAY}] \            # 以网络结构形式逐层展示复杂度信息
    [--not-show-table ${TABLE_DISPLAY}] \      # 以表格形式逐层展示复杂度信息
    [--cfg-options ${CFG_OPTIONS}]             # 配置文件参数修改选项
# [] 代表可选参数，实际输入命令行时，不用输入 []

接下来以 RTMDet 中的 rtmdet_s_syncbn_fast_8xb32-300e_coco.py 配置文件为例，详细展示该脚本的几种使用情形：

样例 1: 打印模型的 Flops 和 Parameters，并以表格形式展示每层网络复杂度

python tools/analysis_tools/get_flops.py  configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py

输出如下：

==============================
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

样例 2：以网络结构形式逐层展示模型复杂度信息

python tools/analysis_tools/get_flops.py  configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py --show-arch

由于该网络结构复杂，输出较长。以下仅展示 bbox_head.head_module.rtm_reg 部分的输出：

(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
        )