模型复杂度分析¶
计算FLOPs 和参数数量（实验性的）¶
我们根据 fvcore 提供了一个脚本用于计算给定模型的 FLOPs 和参数量。
python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
所有参数说明：
config: 配置文件的路径。--shape: 输入尺寸，支持单值或者双值， 如：--shape 256、--shape 224 256。默认为224 224。
你将获得如下结果：
==============================
Input shape: (3, 224, 224)
Flops: 17.582G
Params: 91.234M
Activation: 23.895M
==============================
同时，你会得到每层的详细复杂度信息，如下所示:
| module | #parameters or shape | #flops | #activations |
|:------------------------------------------|:-----------------------|:----------|:---------------|
| model | 91.234M | 17.582G | 23.895M |
| backbone | 85.799M | 17.582G | 23.895M |
| backbone.cls_token | (1, 1, 768) | | |
| backbone.pos_embed | (1, 197, 768) | | |
| backbone.patch_embed.projection | 0.591M | 0.116G | 0.151M |
| backbone.patch_embed.projection.weight | (768, 3, 16, 16) | | |
| backbone.patch_embed.projection.bias | (768,) | | |
| backbone.layers | 85.054M | 17.466G | 23.744M |
| backbone.layers.0 | 7.088M | 1.455G | 1.979M |
| backbone.layers.1 | 7.088M | 1.455G | 1.979M |
| backbone.layers.2 | 7.088M | 1.455G | 1.979M |
| backbone.layers.3 | 7.088M | 1.455G | 1.979M |
| backbone.layers.4 | 7.088M | 1.455G | 1.979M |
| backbone.layers.5 | 7.088M | 1.455G | 1.979M |
| backbone.layers.6 | 7.088M | 1.455G | 1.979M |
| backbone.layers.7 | 7.088M | 1.455G | 1.979M |
| backbone.layers.8 | 7.088M | 1.455G | 1.979M |
| backbone.layers.9 | 7.088M | 1.455G | 1.979M |
| backbone.layers.10 | 7.088M | 1.455G | 1.979M |
| backbone.layers.11 | 7.088M | 1.455G | 1.979M |
| backbone.ln1 | 1.536K | 0.756M | 0 |
| backbone.ln1.weight | (768,) | | |
| backbone.ln1.bias | (768,) | | |
| head.layers | 5.435M | | |
| head.layers.pre_logits | 2.362M | | |
| head.layers.pre_logits.weight | (3072, 768) | | |
| head.layers.pre_logits.bias | (3072,) | | |
| head.layers.head | 3.073M | | |
| head.layers.head.weight | (1000, 3072) | | |
| head.layers.head.bias | (1000,) | | |
警告
警告
此工具仍处于试验阶段，我们不保证该数字正确无误。您最好将结果用于简单比较，但在技术报告或论文中采用该结果之前，请仔细检查。
FLOPs 与输入的尺寸有关，而参数量与输入尺寸无关。默认输入尺寸为 (1, 3, 224, 224)
一些运算不会被计入 FLOPs 的统计中，例如某些自定义运算。详细信息请参考
fvcore.nn.flop_count._DEFAULT_SUPPORTED_OPS。