Spaces:
Sleeping
Sleeping
File size: 7,218 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
# 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：
```shell
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
```shell
python tools/analysis_tools/get_flops.py configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py
```
输出如下：
```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 |
## 样例 2：以网络结构形式逐层展示模型复杂度信息
```shell
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：
```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
)
```
|