使用fvcore计算Pytorch中模型的参数数量以及FLOPs
fvcore是Facebook开源的一个轻量级的核心库，它提供了各种计算机视觉框架中常见且基本的功能。其中就包括了统计模型的参数以及FLOPs等。
fvcore is a light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks
项目开源地址：
https://github.com/facebookresearch/fvcore
在python环境中安装fvcore
pip install fvcore
示例：
假设我需要计算以下resnet50的参数数量以及FLOPs参数。
import torch
from torchvision.models import resnet50
from fvcore.nn import FlopCountAnalysis, parameter_count_table
# 创建resnet50网络
model = resnet50(num_classes=1000)
# 创建输入网络的tensor
tensor = (torch.rand(1, 3, 224, 224),)
# 分析FLOPs
flops = FlopCountAnalysis(model, tensor)
print("FLOPs: ", flops.total())
# 分析parameters
print(parameter_count_table(model))
终端输出结果如下，FLOPs为4089184256，模型参数数量约为25.6M（这里的参数数量和我自己计算的有些出入，主要是在BN模块中，这里只计算了beta和gamma两个训练参数，没有统计moving_mean和moving_var两个参数），具体可以看下我在官方提的issue。
通过终端打印的信息我们可以发现在计算FLOPs时并没有包含BN层，池化层还有普通的add操作（我发现计算FLOPs时并没有统一的规定，在github上看的计算FLOPs项目基本每个都不同，但计算出来的结果大同小异）。
Skipped operation aten::batch_norm 53 time(s)
Skipped operation aten::max_pool2d 1 time(s)
Skipped operation aten::add_ 16 time(s)
Skipped operation aten::adaptive_avg_pool2d 1 time(s)
FLOPs: 4089184256
| name | #elements or shape |
|:-----------------------|:---------------------|
| model | 25.6M |
| conv1 | 9.4K |
| conv1.weight | (64, 3, 7, 7) |
| bn1 | 0.1K |
| bn1.weight | (64,) |
| bn1.bias | (64,) |
| layer1 | 0.2M |
| layer1.0 | 75.0K |
| layer1.0.conv1 | 4.1K |
| layer1.0.bn1 | 0.1K |
| layer1.0.conv2 | 36.9K |
| layer1.0.bn2 | 0.1K |
| layer1.0.conv3 | 16.4K |
| layer1.0.bn3 | 0.5K |
| layer1.0.downsample | 16.9K |
| layer1.1 | 70.4K |
| layer1.1.conv1 | 16.4K |
| layer1.1.bn1 | 0.1K |
| layer1.1.conv2 | 36.9K |
| layer1.1.bn2 | 0.1K |
| layer1.1.conv3 | 16.4K |
| layer1.1.bn3 | 0.5K |
| layer1.2 | 70.4K |
| layer1.2.conv1 | 16.4K |
| layer1.2.bn1 | 0.1K |
| layer1.2.conv2 | 36.9K |
| layer1.2.bn2 | 0.1K |
| layer1.2.conv3 | 16.4K |
| layer1.2.bn3 | 0.5K |
| layer2 | 1.2M |
| layer2.0 | 0.4M |
| layer2.0.conv1 | 32.8K |
| layer2.0.bn1 | 0.3K |
| layer2.0.conv2 | 0.1M |
| layer2.0.bn2 | 0.3K |
| layer2.0.conv3 | 65.5K |
| layer2.0.bn3 | 1.0K |
| layer2.0.downsample | 0.1M |
| layer2.1 | 0.3M |
| layer2.1.conv1 | 65.5K |
| layer2.1.bn1 | 0.3K |
| layer2.1.conv2 | 0.1M |
| layer2.1.bn2 | 0.3K |
| layer2.1.conv3 | 65.5K |
| layer2.1.bn3 | 1.0K |
| layer2.2 | 0.3M |
| layer2.2.conv1 | 65.5K |
| layer2.2.bn1 | 0.3K |
| layer2.2.conv2 | 0.1M |
| layer2.2.bn2 | 0.3K |
| layer2.2.conv3 | 65.5K |
| layer2.2.bn3 | 1.0K |
| layer2.3 | 0.3M |
| layer2.3.conv1 | 65.5K |
| layer2.3.bn1 | 0.3K |
| layer2.3.conv2 | 0.1M |
| layer2.3.bn2 | 0.3K |
| layer2.3.conv3 | 65.5K |
| layer2.3.bn3 | 1.0K |
| layer3 | 7.1M |
| layer3.0 | 1.5M |
| layer3.0.conv1 | 0.1M |
| layer3.0.bn1 | 0.5K |
| layer3.0.conv2 | 0.6M |
| layer3.0.bn2 | 0.5K |
| layer3.0.conv3 | 0.3M |
| layer3.0.bn3 | 2.0K |
| layer3.0.downsample | 0.5M |
| layer3.1 | 1.1M |
| layer3.1.conv1 | 0.3M |
| layer3.1.bn1 | 0.5K |
| layer3.1.conv2 | 0.6M |
| layer3.1.bn2 | 0.5K |
| layer3.1.conv3 | 0.3M |
| layer3.1.bn3 | 2.0K |
| layer3.2 | 1.1M |
| layer3.2.conv1 | 0.3M |
| layer3.2.bn1 | 0.5K |
| layer3.2.conv2 | 0.6M |
| layer3.2.bn2 | 0.5K |
| layer3.2.conv3 | 0.3M |
| layer3.2.bn3 | 2.0K |
| layer3.3 | 1.1M |
| layer3.3.conv1 | 0.3M |
| layer3.3.bn1 | 0.5K |
| layer3.3.conv2 | 0.6M |
| layer3.3.bn2 | 0.5K |
| layer3.3.conv3 | 0.3M |
| layer3.3.bn3 | 2.0K |
| layer3.4 | 1.1M |
| layer3.4.conv1 | 0.3M |
| layer3.4.bn1 | 0.5K |
| layer3.4.conv2 | 0.6M |
| layer3.4.bn2 | 0.5K |
| layer3.4.conv3 | 0.3M |
| layer3.4.bn3 | 2.0K |
| layer3.5 | 1.1M |
| layer3.5.conv1 | 0.3M |
| layer3.5.bn1 | 0.5K |
| layer3.5.conv2 | 0.6M |
| layer3.5.bn2 | 0.5K |
| layer3.5.conv3 | 0.3M |
| layer3.5.bn3 | 2.0K |
| layer4 | 15.0M |
| layer4.0 | 6.0M |
| layer4.0.conv1 | 0.5M |
| layer4.0.bn1 | 1.0K |
| layer4.0.conv2 | 2.4M |
| layer4.0.bn2 | 1.0K |
| layer4.0.conv3 | 1.0M |
| layer4.0.bn3 | 4.1K |
| layer4.0.downsample | 2.1M |
| layer4.1 | 4.5M |
| layer4.1.conv1 | 1.0M |
| layer4.1.bn1 | 1.0K |
| layer4.1.conv2 | 2.4M |
| layer4.1.bn2 | 1.0K |
| layer4.1.conv3 | 1.0M |
| layer4.1.bn3 | 4.1K |
| layer4.2 | 4.5M |
| layer4.2.conv1 | 1.0M |
| layer4.2.bn1 | 1.0K |
| layer4.2.conv2 | 2.4M |
| layer4.2.bn2 | 1.0K |
| layer4.2.conv3 | 1.0M |
| layer4.2.bn3 | 4.1K |
| fc | 2.0M |
| fc.weight | (1000, 2048) |
| fc.bias | (1000,) |
Process finished with exit code 0
更多使用方法，可以去原项目中查看使用文档。