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Flops counter for convolutional networks in pytorch framework
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ptflops Add support of 3d convs and poolings May 15, 2019
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README.md Add support of 3d convs and poolings May 15, 2019
sample.py Add support of 3d convs and poolings May 15, 2019
setup.py v0.2 May 15, 2019

README.md

Flops counter for convolutional networks in pytorch framework

Pypi version

This script is designed to compute the theoretical amount of multiply-add operations in convolutional neural networks. It also can compute the number of parameters and print per-layer computational cost of a given network.

Supported layers:

  • Conv2d/3d (including grouping)
  • ConvTranspose2d (including grouping)
  • BatchNorm2d/3d
  • Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU)
  • Linear
  • Upsample
  • Poolings (AvgPool2d/3d, MaxPool2d/3d and adaptive ones)

Requirements: Pytorch 0.4.1 or 1.0, torchvision 0.2.1

Thanks to @warmspringwinds for the initial version of script.

Usage tips

  • This script doesn't take into account torch.nn.functional.* operations. For an instance, if one have a semantic segmentation model and use torch.nn.functional.interpolate to upscale features, these operations won't contribute to overall amount of flops. To avoid that one can use torch.nn.Upsample instead of torch.nn.functional.interpolate.
  • ptflops launches a given model on a random tensor and estimates amount of computations during inference. Complicated models can have several inputs, some of them could be optional. To construct non-trivial input one can use the input_constructor argument of the get_model_complexity_info. input_constructor is a function that takes the input spatial resolution as a tuple and returns a dict with named input arguments of the model. Next this dict would be passed to the model as keyworded arguments.

Install the latest version

pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git

Example

import torchvision.models as models
import torch
from ptflops import get_model_complexity_info

with torch.cuda.device(0):
  net = models.densenet161()
  flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
  print('Flops:  ' + flops)
  print('Params: ' + params)

Benchmark

torchvision

Model Input Resolution Params(M) MACs(G) Top-1 error Top-5 error
alexnet 224x224 61.1 0.72 43.45 20.91
vgg11 224x224 132.86 7.63 30.98 11.37
vgg13 224x224 133.05 11.34 30.07 10.75
vgg16 224x224 138.36 15.5 28.41 9.62
vgg19 224x224 143.67 19.67 27.62 9.12
vgg11_bn 224x224 132.87 7.64 29.62 10.19
vgg13_bn 224x224 133.05 11.36 28.45 9.63
vgg16_bn 224x224 138.37 15.53 26.63 8.50
vgg19_bn 224x224 143.68 19.7 25.76 8.15
resnet18 224x224 11.69 1.82 30.24 10.92
resnet34 224x224 21.8 3.68 26.70 8.58
resnet50 224x224 25.56 4.12 23.85 7.13
resnet101 224x224 44.55 7.85 22.63 6.44
resnet152 224x224 60.19 11.58 21.69 5.94
squeezenet1_0 224x224 1.25 0.83 41.90 19.58
squeezenet1_1 224x224 1.24 0.36 41.81 19.38
densenet121 224x224 7.98 2.88 25.35 7.83
densenet169 224x224 14.15 3.42 24.00 7.00
densenet201 224x224 20.01 4.37 22.80 6.43
densenet161 224x224 28.68 7.82 22.35 6.20
inception_v3 224x224 27.16 2.85 22.55 6.44
  • Top-1 error - ImageNet single-crop top-1 error (224x224)
  • Top-5 error - ImageNet single-crop top-5 error (224x224)

Cadene/pretrained-models.pytorch

Model Input Resolution Params(M) MACs(G) Acc@1 Acc@5
alexnet 224x224 61.1 0.72 56.432 79.194
bninception 224x224 11.3 2.05 73.524 91.562
cafferesnet101 224x224 44.55 7.62 76.2 92.766
densenet121 224x224 7.98 2.88 74.646 92.136
densenet161 224x224 28.68 7.82 77.56 93.798
densenet169 224x224 14.15 3.42 76.026 92.992
densenet201 224x224 20.01 4.37 77.152 93.548
dpn107 224x224 86.92 18.42 79.746 94.684
dpn131 224x224 79.25 16.13 79.432 94.574
dpn68 224x224 12.61 2.36 75.868 92.774
dpn68b 224x224 12.61 2.36 77.034 93.59
dpn92 224x224 37.67 6.56 79.4 94.62
dpn98 224x224 61.57 11.76 79.224 94.488
fbresnet152 224x224 60.27 11.6 77.386 93.594
inceptionresnetv2 299x299 55.84 13.22 80.17 95.234
inceptionv3 299x299 27.16 5.73 77.294 93.454
inceptionv4 299x299 42.68 12.31 80.062 94.926
nasnetalarge 331x331 88.75 24.04 82.566 96.086
nasnetamobile 224x224 5.29 0.59 74.08 91.74
pnasnet5large 331x331 86.06 25.21 82.736 95.992
polynet 331x331 95.37 34.9 81.002 95.624
resnet101 224x224 44.55 7.85 77.438 93.672
resnet152 224x224 60.19 11.58 78.428 94.11
resnet18 224x224 11.69 1.82 70.142 89.274
resnet34 224x224 21.8 3.68 73.554 91.456
resnet50 224x224 25.56 4.12 76.002 92.98
resnext101_32x4d 224x224 44.18 8.03 78.188 93.886
resnext101_64x4d 224x224 83.46 15.55 78.956 94.252
se_resnet101 224x224 49.33 7.63 78.396 94.258
se_resnet152 224x224 66.82 11.37 78.658 94.374
se_resnet50 224x224 28.09 3.9 77.636 93.752
se_resnext101_32x4d 224x224 48.96 8.05 80.236 95.028
se_resnext50_32x4d 224x224 27.56 4.28 79.076 94.434
senet154 224x224 115.09 20.82 81.304 95.498
squeezenet1_0 224x224 1.25 0.83 58.108 80.428
squeezenet1_1 224x224 1.24 0.36 58.25 80.8
vgg11 224x224 132.86 7.63 68.97 88.746
vgg11_bn 224x224 132.87 7.64 70.452 89.818
vgg13 224x224 133.05 11.34 69.662 89.264
vgg13_bn 224x224 133.05 11.36 71.508 90.494
vgg16 224x224 138.36 15.5 71.636 90.354
vgg16_bn 224x224 138.37 15.53 73.518 91.608
vgg19 224x224 143.67 19.67 72.08 90.822
vgg19_bn 224x224 143.68 19.7 74.266 92.066
xception 299x299 22.86 8.42 78.888 94.292
  • Acc@1 - ImageNet single-crop top-1 accuracy on validation images of the same size used during the training process.
  • Acc@5 - ImageNet single-crop top-5 accuracy on validation images of the same size used during the training process.
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