Calculate the FLOPs and Parameters of Model¶
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
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