Table of Contents
Shortcuts

Calculate the FLOPs and Parameters of Model

  1. Define a Model

    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

  2. Calculate the FLOPs and Parameters

    from mmengine.analysis import get_model_complexity_info

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

  • Show in table form

    print(analysis_results['out_table'])
    Click to expand 
    +------------------------+----------------------+------------+--------------+
| 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           |
+------------------------+----------------------+------------+--------------+

  • Show in model structure

    print(analysis_results['out_arch'])
    Click to expand 
    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
    )
)
)

  • Show FLOPs as a string

    print('Model Flops:{}'.format(analysis_results['flops_str']))
# Model Flops:4.145G

  • Show Parameters as a string

    print('Model Parameters:{}'.format(analysis_results['params_str']))
# Model Parameters:25.557M

For the definition of FLOPs and Parameters of model and more usage, please refer to Model Complexity Analysis

Read the Docs v: latest
Versions
latest
stable
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.1
v0.9.0
v0.8.5
v0.8.4
v0.8.3
v0.8.2
v0.8.1
v0.8.0
v0.7.4
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.0
v0.5.0
v0.4.0
v0.3.0
v0.2.0
Downloads
epub
On Read the Docs
Project Home
Builds
Free document hosting provided by Read the Docs.