【MMEngine】用MMEngine自带函数get_model_complexity_info计算模型每层参数量、参数总量和计算量

FLOPS：（floating-point operations per second）的缩写。“每秒浮点运算次数”，“每秒峰值速度”是“每秒所执行的浮点运算次数”。它常被用来估算电脑的执行效能，尤其是在使用到大量浮点运算的科学计算领域中。正因为FLOPS字尾的那个S，代表秒，而不是复数，所以不能省略掉。
activations：所有卷积层的输出feature map的尺寸，用于度量网络的复杂度，虽然activations不是衡量网络复杂性的通用标准，但activations可能会严重影响内存受限硬件加速器(例如GPU、TPU)上的运行时间，activations与推理时间的正相关性比FLOPs更强。

🌱🌱定义模型

????????以resnet50为例，首先定义模型，python代码如下：

import torch.nn.functional as F
import torchvision
from mmengine.model import BaseModel

class MMResNet50(BaseModel):
    def __init__(self):
        super().__init__()
        self.resnet = torchvision.models.resnet50()

    def forward(self, imgs, labels=None, mode='tensor'):
        x = self.resnet(imgs)
        if mode == 'loss':
            return {'loss': F.cross_entropy(x, labels)}
        elif mode == 'predict':
            return x, labels
        elif mode == 'tensor':
            return x

🌲🌲统计计算量和参数量

????????使用mmengine自带的get_model_complexity_info函数即可实现统计模型计算量和参数量的目的。python代码如下：

from mmengine.analysis import get_model_complexity_info

input_shape = (3, 224, 224)
model = MMResNet50()
analysis_results = get_model_complexity_info(model, input_shape)

🎅🎅以表格形式显示每层输出

????????可以选择以表格形式和模型结构两种形式进行计算量和参数量的输出，首先以表格形式展示，python代码如下：

print(analysis_results['out_table'])

📌输出

????????可以看到resnet50模型总的参数量为25.557M，计算量flops为4.145G，网络复杂度为11.115M。

+------------------------+----------------------+------------+--------------+
| module                 | #parameters or shape | #flops     | #activations |
+------------------------+----------------------+------------+--------------+
| resnet                 | 25.557M              | 4.145G     | 11.115M      |
|  conv1                 |  9.408K              |  0.118G    |  0.803M      |
|   conv1.weight         |   (64, 3, 7, 7)      |            |              |
|  bn1                   |  0.128K              |  4.014M    |  0           |
|   bn1.weight           |   (64,)              |            |              |
|   bn1.bias             |   (64,)              |            |              |
|  layer1                |  0.216M              |  0.69G     |  4.415M      |
|   layer1.0             |   75.008K            |   0.241G   |   2.007M     |
|    layer1.0.conv1      |    4.096K            |    12.845M |    0.201M    |
|    layer1.0.bn1        |    0.128K            |    1.004M  |    0         |
|    layer1.0.conv2      |    36.864K           |    0.116G  |    0.201M    |
|    layer1.0.bn2        |    0.128K            |    1.004M  |    0         |
|    layer1.0.conv3      |    16.384K           |    51.38M  |    0.803M    |
|    layer1.0.bn3        |    0.512K            |    4.014M  |    0         |
|    layer1.0.downsample |    16.896K           |    55.394M |    0.803M    |
|   layer1.1             |   70.4K              |   0.224G   |   1.204M     |
|    layer1.1.conv1      |    16.384K           |    51.38M  |    0.201M    |
|    layer1.1.bn1        |    0.128K            |    1.004M  |    0         |
|    layer1.1.conv2      |    36.864K           |    0.116G  |    0.201M    |
|    layer1.1.bn2        |    0.128K            |    1.004M  |    0         |
|    layer1.1.conv3      |    16.384K           |    51.38M  |    0.803M    |
|    layer1.1.bn3        |    0.512K            |    4.014M  |    0         |
|   layer1.2             |   70.4K              |   0.224G   |   1.204M     |
|    layer1.2.conv1      |    16.384K           |    51.38M  |    0.201M    |
|    layer1.2.bn1        |    0.128K            |    1.004M  |    0         |
|    layer1.2.conv2      |    36.864K           |    0.116G  |    0.201M    |
|    layer1.2.bn2        |    0.128K            |    1.004M  |    0         |
|    layer1.2.conv3      |    16.384K           |    51.38M  |    0.803M    |
|    layer1.2.bn3        |    0.512K            |    4.014M  |    0         |
|  layer2                |  1.22M               |  1.043G    |  3.111M      |
|   layer2.0             |   0.379M             |   0.379G   |   1.305M     |
|    layer2.0.conv1      |    32.768K           |    0.103G  |    0.401M    |
|    layer2.0.bn1        |    0.256K            |    2.007M  |    0         |
|    layer2.0.conv2      |    0.147M            |    0.116G  |    0.1M      |
|    layer2.0.bn2        |    0.256K            |    0.502M  |    0         |
|    layer2.0.conv3      |    65.536K           |    51.38M  |    0.401M    |
|    layer2.0.bn3        |    1.024K            |    2.007M  |    0         |
|    layer2.0.downsample |    0.132M            |    0.105G  |    0.401M    |
|   layer2.1             |   0.28M              |   0.221G   |   0.602M     |
|    layer2.1.conv1      |    65.536K           |    51.38M  |    0.1M      |
|    layer2.1.bn1        |    0.256K            |    0.502M  |    0         |
|    layer2.1.conv2      |    0.147M            |    0.116G  |    0.1M      |
|    layer2.1.bn2        |    0.256K            |    0.502M  |    0         |
|    layer2.1.conv3      |    65.536K           |    51.38M  |    0.401M    |
|    layer2.1.bn3        |    1.024K            |    2.007M  |    0         |
|   layer2.2             |   0.28M              |   0.221G   |   0.602M     |
|    layer2.2.conv1      |    65.536K           |    51.38M  |    0.1M      |
|    layer2.2.bn1        |    0.256K            |    0.502M  |    0         |
|    layer2.2.conv2      |    0.147M            |    0.116G  |    0.1M      |
|    layer2.2.bn2        |    0.256K            |    0.502M  |    0         |
|    layer2.2.conv3      |    65.536K           |    51.38M  |    0.401M    |
|    layer2.2.bn3        |    1.024K            |    2.007M  |    0         |
|   layer2.3             |   0.28M              |   0.221G   |   0.602M     |
|    layer2.3.conv1      |    65.536K           |    51.38M  |    0.1M      |
|    layer2.3.bn1        |    0.256K            |    0.502M  |    0         |
|    layer2.3.conv2      |    0.147M            |    0.116G  |    0.1M      |
|    layer2.3.bn2        |    0.256K            |    0.502M  |    0         |
|    layer2.3.conv3      |    65.536K           |    51.38M  |    0.401M    |
|    layer2.3.bn3        |    1.024K            |    2.007M  |    0         |
|  layer3                |  7.098M              |  1.475G    |  2.158M      |
|   layer3.0             |   1.512M             |   0.376G   |   0.652M     |
|    layer3.0.conv1      |    0.131M            |    0.103G  |    0.201M    |
|    layer3.0.bn1        |    0.512K            |    1.004M  |    0         |
|    layer3.0.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.0.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.0.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.0.bn3        |    2.048K            |    1.004M  |    0         |
|    layer3.0.downsample |    0.526M            |    0.104G  |    0.201M    |
|   layer3.1             |   1.117M             |   0.22G    |   0.301M     |
|    layer3.1.conv1      |    0.262M            |    51.38M  |    50.176K   |
|    layer3.1.bn1        |    0.512K            |    0.251M  |    0         |
|    layer3.1.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.1.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.1.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.1.bn3        |    2.048K            |    1.004M  |    0         |
|   layer3.2             |   1.117M             |   0.22G    |   0.301M     |
|    layer3.2.conv1      |    0.262M            |    51.38M  |    50.176K   |
|    layer3.2.bn1        |    0.512K            |    0.251M  |    0         |
|    layer3.2.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.2.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.2.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.2.bn3        |    2.048K            |    1.004M  |    0         |
|   layer3.3             |   1.117M             |   0.22G    |   0.301M     |
|    layer3.3.conv1      |    0.262M            |    51.38M  |    50.176K   |
|    layer3.3.bn1        |    0.512K            |    0.251M  |    0         |
|    layer3.3.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.3.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.3.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.3.bn3        |    2.048K            |    1.004M  |    0         |
|   layer3.4             |   1.117M             |   0.22G    |   0.301M     |
|    layer3.4.conv1      |    0.262M            |    51.38M  |    50.176K   |
|    layer3.4.bn1        |    0.512K            |    0.251M  |    0         |
|    layer3.4.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.4.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.4.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.4.bn3        |    2.048K            |    1.004M  |    0         |
|   layer3.5             |   1.117M             |   0.22G    |   0.301M     |
|    layer3.5.conv1      |    0.262M            |    51.38M  |    50.176K   |
|    layer3.5.bn1        |    0.512K            |    0.251M  |    0         |
|    layer3.5.conv2      |    0.59M             |    0.116G  |    50.176K   |
|    layer3.5.bn2        |    0.512K            |    0.251M  |    0         |
|    layer3.5.conv3      |    0.262M            |    51.38M  |    0.201M    |
|    layer3.5.bn3        |    2.048K            |    1.004M  |    0         |
|  layer4                |  14.965M             |  0.812G    |  0.627M      |
|   layer4.0             |   6.04M              |   0.374G   |   0.326M     |
|    layer4.0.conv1      |    0.524M            |    0.103G  |    0.1M      |
|    layer4.0.bn1        |    1.024K            |    0.502M  |    0         |
|    layer4.0.conv2      |    2.359M            |    0.116G  |    25.088K   |
|    layer4.0.bn2        |    1.024K            |    0.125M  |    0         |
|    layer4.0.conv3      |    1.049M            |    51.38M  |    0.1M      |
|    layer4.0.bn3        |    4.096K            |    0.502M  |    0         |
|    layer4.0.downsample |    2.101M            |    0.103G  |    0.1M      |
|   layer4.1             |   4.463M             |   0.219G   |   0.151M     |
|    layer4.1.conv1      |    1.049M            |    51.38M  |    25.088K   |
|    layer4.1.bn1        |    1.024K            |    0.125M  |    0         |
|    layer4.1.conv2      |    2.359M            |    0.116G  |    25.088K   |
|    layer4.1.bn2        |    1.024K            |    0.125M  |    0         |
|    layer4.1.conv3      |    1.049M            |    51.38M  |    0.1M      |
|    layer4.1.bn3        |    4.096K            |    0.502M  |    0         |
|   layer4.2             |   4.463M             |   0.219G   |   0.151M     |
|    layer4.2.conv1      |    1.049M            |    51.38M  |    25.088K   |
|    layer4.2.bn1        |    1.024K            |    0.125M  |    0         |
|    layer4.2.conv2      |    2.359M            |    0.116G  |    25.088K   |
|    layer4.2.bn2        |    1.024K            |    0.125M  |    0         |
|    layer4.2.conv3      |    1.049M            |    51.38M  |    0.1M      |
|    layer4.2.bn3        |    4.096K            |    0.502M  |    0         |
|  fc                    |  2.049M              |  2.048M    |  1K          |
|   fc.weight            |   (1000, 2048)       |            |              |
|   fc.bias              |   (1000,)            |            |              |
|  avgpool               |                      |  0.1M      |  0           |
+------------------------+----------------------+------------+--------------+

🌟🌟以网络结构形式显示每层输出

????????mmengine支持表格形式和模型结构两种形式进行计算量和参数量的输出，以网络结构形式输出的python代码如下：

print(analysis_results['out_arch'])

📌输出

MMResNet50(
#params: 25.56M, #flops: 4.14G, #acts: 11.11M
(data_preprocessor): BaseDataPreprocessor(#params: 0, #flops: N/A, #acts: N/A)
(resnet): ResNet(
    #params: 25.56M, #flops: 4.14G, #acts: 11.11M
    (conv1): Conv2d(
    3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
    #params: 9.41K, #flops: 0.12G, #acts: 0.8M
    )
    (bn1): BatchNorm2d(
    64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
    #params: 0.13K, #flops: 4.01M, #acts: 0
    )
    (relu): ReLU(inplace=True)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
    #params: 0.22M, #flops: 0.69G, #acts: 4.42M
    (0): Bottleneck(
        #params: 75.01K, #flops: 0.24G, #acts: 2.01M
        (conv1): Conv2d(
        64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 4.1K, #flops: 12.85M, #acts: 0.2M
        )
        (bn1): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 36.86K, #flops: 0.12G, #acts: 0.2M
        )
        (bn2): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 16.38K, #flops: 51.38M, #acts: 0.8M
        )
        (bn3): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 4.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
        #params: 16.9K, #flops: 55.39M, #acts: 0.8M
        (0): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            #params: 16.38K, #flops: 51.38M, #acts: 0.8M
        )
        (1): BatchNorm2d(
            256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
            #params: 0.51K, #flops: 4.01M, #acts: 0
        )
        )
    )
    (1): Bottleneck(
        #params: 70.4K, #flops: 0.22G, #acts: 1.2M
        (conv1): Conv2d(
        256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 16.38K, #flops: 51.38M, #acts: 0.2M
        )
        (bn1): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 36.86K, #flops: 0.12G, #acts: 0.2M
        )
        (bn2): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 16.38K, #flops: 51.38M, #acts: 0.8M
        )
        (bn3): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 4.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
        #params: 70.4K, #flops: 0.22G, #acts: 1.2M
        (conv1): Conv2d(
        256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 16.38K, #flops: 51.38M, #acts: 0.2M
        )
        (bn1): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 36.86K, #flops: 0.12G, #acts: 0.2M
        )
        (bn2): BatchNorm2d(
        64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.13K, #flops: 1M, #acts: 0
        )
        (conv3): Conv2d(
        64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 16.38K, #flops: 51.38M, #acts: 0.8M
        )
        (bn3): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 4.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    )
    (layer2): Sequential(
    #params: 1.22M, #flops: 1.04G, #acts: 3.11M
    (0): Bottleneck(
        #params: 0.38M, #flops: 0.38G, #acts: 1.3M
        (conv1): Conv2d(
        256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 32.77K, #flops: 0.1G, #acts: 0.4M
        )
        (bn1): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 2.01M, #acts: 0
        )
        (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
        #params: 0.15M, #flops: 0.12G, #acts: 0.1M
        )
        (bn2): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.4M
        )
        (bn3): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 2.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
        #params: 0.13M, #flops: 0.1G, #acts: 0.4M
        (0): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            #params: 0.13M, #flops: 0.1G, #acts: 0.4M
        )
        (1): BatchNorm2d(
            512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
            #params: 1.02K, #flops: 2.01M, #acts: 0
        )
        )
    )
    (1): Bottleneck(
        #params: 0.28M, #flops: 0.22G, #acts: 0.6M
        (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.1M
        )
        (bn1): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.15M, #flops: 0.12G, #acts: 0.1M
        )
        (bn2): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.4M
        )
        (bn3): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 2.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
        #params: 0.28M, #flops: 0.22G, #acts: 0.6M
        (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.1M
        )
        (bn1): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.15M, #flops: 0.12G, #acts: 0.1M
        )
        (bn2): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.4M
        )
        (bn3): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 2.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
        #params: 0.28M, #flops: 0.22G, #acts: 0.6M
        (conv1): Conv2d(
        512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.1M
        )
        (bn1): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.15M, #flops: 0.12G, #acts: 0.1M
        )
        (bn2): BatchNorm2d(
        128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.26K, #flops: 0.5M, #acts: 0
        )
        (conv3): Conv2d(
        128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 65.54K, #flops: 51.38M, #acts: 0.4M
        )
        (bn3): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 2.01M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    )
    (layer3): Sequential(
    #params: 7.1M, #flops: 1.48G, #acts: 2.16M
    (0): Bottleneck(
        #params: 1.51M, #flops: 0.38G, #acts: 0.65M
        (conv1): Conv2d(
        512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.13M, #flops: 0.1G, #acts: 0.2M
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 1M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
        #params: 0.53M, #flops: 0.1G, #acts: 0.2M
        (0): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            #params: 0.52M, #flops: 0.1G, #acts: 0.2M
        )
        (1): BatchNorm2d(
            1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
            #params: 2.05K, #flops: 1M, #acts: 0
        )
        )
    )
    (1): Bottleneck(
        #params: 1.12M, #flops: 0.22G, #acts: 0.3M
        (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 50.18K
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
        #params: 1.12M, #flops: 0.22G, #acts: 0.3M
        (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 50.18K
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
        #params: 1.12M, #flops: 0.22G, #acts: 0.3M
        (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 50.18K
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
        #params: 1.12M, #flops: 0.22G, #acts: 0.3M
        (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 50.18K
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
        #params: 1.12M, #flops: 0.22G, #acts: 0.3M
        (conv1): Conv2d(
        1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 50.18K
        )
        (bn1): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 0.59M, #flops: 0.12G, #acts: 50.18K
        )
        (bn2): BatchNorm2d(
        256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 0.51K, #flops: 0.25M, #acts: 0
        )
        (conv3): Conv2d(
        256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.26M, #flops: 51.38M, #acts: 0.2M
        )
        (bn3): BatchNorm2d(
        1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 2.05K, #flops: 1M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    )
    (layer4): Sequential(
    #params: 14.96M, #flops: 0.81G, #acts: 0.63M
    (0): Bottleneck(
        #params: 6.04M, #flops: 0.37G, #acts: 0.33M
        (conv1): Conv2d(
        1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 0.52M, #flops: 0.1G, #acts: 0.1M
        )
        (bn1): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.5M, #acts: 0
        )
        (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
        #params: 2.36M, #flops: 0.12G, #acts: 25.09K
        )
        (bn2): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.13M, #acts: 0
        )
        (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 1.05M, #flops: 51.38M, #acts: 0.1M
        )
        (bn3): BatchNorm2d(
        2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 4.1K, #flops: 0.5M, #acts: 0
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
        #params: 2.1M, #flops: 0.1G, #acts: 0.1M
        (0): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            #params: 2.1M, #flops: 0.1G, #acts: 0.1M
        )
        (1): BatchNorm2d(
            2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
            #params: 4.1K, #flops: 0.5M, #acts: 0
        )
        )
    )
    (1): Bottleneck(
        #params: 4.46M, #flops: 0.22G, #acts: 0.15M
        (conv1): Conv2d(
        2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 1.05M, #flops: 51.38M, #acts: 25.09K
        )
        (bn1): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.13M, #acts: 0
        )
        (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 2.36M, #flops: 0.12G, #acts: 25.09K
        )
        (bn2): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.13M, #acts: 0
        )
        (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 1.05M, #flops: 51.38M, #acts: 0.1M
        )
        (bn3): BatchNorm2d(
        2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 4.1K, #flops: 0.5M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
        #params: 4.46M, #flops: 0.22G, #acts: 0.15M
        (conv1): Conv2d(
        2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 1.05M, #flops: 51.38M, #acts: 25.09K
        )
        (bn1): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.13M, #acts: 0
        )
        (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
        #params: 2.36M, #flops: 0.12G, #acts: 25.09K
        )
        (bn2): BatchNorm2d(
        512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 1.02K, #flops: 0.13M, #acts: 0
        )
        (conv3): Conv2d(
        512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
        #params: 1.05M, #flops: 51.38M, #acts: 0.1M
        )
        (bn3): BatchNorm2d(
        2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
        #params: 4.1K, #flops: 0.5M, #acts: 0
        )
        (relu): ReLU(inplace=True)
    )
    )
    (avgpool): AdaptiveAvgPool2d(
    output_size=(1, 1)
    #params: 0, #flops: 0.1M, #acts: 0
    )
    (fc): Linear(
    in_features=2048, out_features=1000, bias=True
    #params: 2.05M, #flops: 2.05M, #acts: 1K
    )
)
)

🎃🎃输出总计算量

print("Model Flops:{}".format(analysis_results['flops_str']))

📌输出

Model Flops:4.145G

🎉🎉输出总参数量?

print("Model Parameters:{}".format(analysis_results['params_str']))

📌输出

Model Parameters:25.557M

💪??????????????💪???????其他函数包计算参数量和计算量

🌲1.thop

第一步：安装模块

pip install thop

第二步：计算

# -- coding: utf-8 --
import torch
import torchvision
from thop import profile

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

dummy_input = torch.randn(1, 3, 224, 224)
flops, params = profile(model, (dummy_input,))
print('flops: ', flops, 'params: ', params)
print('flops: %.2f M, params: %.2f M' % (flops / 1000000.0, params / 1000000.0))

或者：

from torchvision.models import resnet18
from thop import profile
model = resnet18()
input = torch.randn(1, 3, 224, 224) #模型输入的形状,batch_size=1
flops, params = profile(model, inputs=(input, ))
print(flops/1e9,params/1e6) #flops单位G，para单位M

📌结果

==> Building model..
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
flops:  714206912.0 params:  61100840.0
flops: 714.21 M, params: 61.10 M

注意：profile(net, (inputs,))的 (inputs,)中必须加上逗号，否者会报错

🌲2.ptflops

第一步：安装模块

pip install ptflops

第二步：计算

import torchvision
from ptflops import get_model_complexity_info

model = torchvision.models.alexnet(pretrained=False)
flops, params = get_model_complexity_info(model, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
print('flops: ', flops, 'params: ', params)

📌结果

AlexNet(
  61.101 M, 100.000% Params, 0.716 GMac, 100.000% MACs, 
  (features): Sequential(
    2.47 M, 4.042% Params, 0.657 GMac, 91.804% MACs, 
    (0): Conv2d(0.023 M, 0.038% Params, 0.07 GMac, 9.848% MACs, 3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.027% MACs, inplace=True)
    (2): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.027% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(0.307 M, 0.503% Params, 0.224 GMac, 31.316% MACs, 64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.020% MACs, inplace=True)
    (5): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.020% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(0.664 M, 1.087% Params, 0.112 GMac, 15.681% MACs, 192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.009% MACs, inplace=True)
    (8): Conv2d(0.885 M, 1.448% Params, 0.15 GMac, 20.902% MACs, 384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, inplace=True)
    (10): Conv2d(0.59 M, 0.966% Params, 0.1 GMac, 13.936% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, inplace=True)
    (12): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, output_size=(6, 6))
  (classifier): Sequential(
    58.631 M, 95.958% Params, 0.059 GMac, 8.195% MACs, 
    (0): Dropout(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, p=0.5, inplace=False)
    (1): Linear(37.753 M, 61.788% Params, 0.038 GMac, 5.276% MACs, in_features=9216, out_features=4096, bias=True)
    (2): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, inplace=True)
    (3): Dropout(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, p=0.5, inplace=False)
    (4): Linear(16.781 M, 27.465% Params, 0.017 GMac, 2.345% MACs, in_features=4096, out_features=4096, bias=True)
    (5): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, inplace=True)
    (6): Linear(4.097 M, 6.705% Params, 0.004 GMac, 0.573% MACs, in_features=4096, out_features=1000, bias=True)
  )
)
flops:  0.72 GMac params:  61.1 M

🌲3.torchsummary

第一步：安装模块

pip install torch

第二步：计算

import torch
import torchvision
from pytorch_model_summary import summary

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

dummy_input = torch.randn(1, 3, 224, 224)
print(summary(model, dummy_input, show_input=False, show_hierarchical=False))

?注意：新版本的torch导入summary包时，需要用：

from torchsummary import summary

📌结果

==> Building model..
-----------------------------------------------------------------------------
           Layer (type)         Output Shape         Param #     Tr. Param #
=============================================================================
               Conv2d-1      [1, 64, 55, 55]          23,296          23,296
                 ReLU-2      [1, 64, 55, 55]               0               0
            MaxPool2d-3      [1, 64, 27, 27]               0               0
               Conv2d-4     [1, 192, 27, 27]         307,392         307,392
                 ReLU-5     [1, 192, 27, 27]               0               0
            MaxPool2d-6     [1, 192, 13, 13]               0               0
               Conv2d-7     [1, 384, 13, 13]         663,936         663,936
                 ReLU-8     [1, 384, 13, 13]               0               0
               Conv2d-9     [1, 256, 13, 13]         884,992         884,992
                ReLU-10     [1, 256, 13, 13]               0               0
              Conv2d-11     [1, 256, 13, 13]         590,080         590,080
                ReLU-12     [1, 256, 13, 13]               0               0
           MaxPool2d-13       [1, 256, 6, 6]               0               0
   AdaptiveAvgPool2d-14       [1, 256, 6, 6]               0               0
             Dropout-15            [1, 9216]               0               0
              Linear-16            [1, 4096]      37,752,832      37,752,832
                ReLU-17            [1, 4096]               0               0
             Dropout-18            [1, 4096]               0               0
              Linear-19            [1, 4096]      16,781,312      16,781,312
                ReLU-20            [1, 4096]               0               0
              Linear-21            [1, 1000]       4,097,000       4,097,000
=============================================================================
Total params: 61,100,840
Trainable params: 61,100,840
Non-trainable params: 0
-----------------------------------------------------------------------------

🌲4.torchsummary计算参数总量和可训练参数总量

import torch
import torchvision
from pytorch_model_summary import summary


# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

pytorch_total_params = sum(p.numel() for p in model.parameters())
trainable_pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

print('Total - ', pytorch_total_params)
print('Trainable - ', trainable_pytorch_total_params)

📌结果

==> Building model..
Total -  61100840
Trainable -  61100840

整理不易，欢迎一键三连！！！

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文章来源:https://blog.csdn.net/qq_38308388/article/details/135099281
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