Flops counting tool for neural networks in pytorch framework

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

Supported layers:

Conv1d/2d/3d (including grouping)
ConvTranspose1d/2d/3d (including grouping)
BatchNorm1d/2d/3d, GroupNorm, InstanceNorm1d/2d/3d, LayerNorm
Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU, GELU)
Linear
Upsample
Poolings (AvgPool1d/2d/3d, MaxPool1d/2d/3d and adaptive ones)

Experimental support:

RNN, LSTM, GRU (NLH layout is assumed)
RNNCell, LSTMCell, GRUCell
torch.nn.MultiheadAttention
torchvision.ops.DeformConv2d
visual transformers from timm

Requirements: Pytorch >= 1.1, torchvision >= 0.3

Thanks to @warmspringwinds for the initial version of script.

Usage tips

This tool doesn't take into account some of the torch.nn.functional.* and tensor.* operations. Therefore unsupported operations are not contributing to the final complexity estimation. See ptflops/pytorch_ops.py:FUNCTIONAL_MAPPING,TENSOR_OPS_MAPPING to check supported ops.
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 a keyword arguments.
verbose parameter allows to get information about modules that don't contribute to the final numbers.
ignore_modules option forces ptflops to ignore the listed modules. This can be useful for research purposes. For instance, one can drop all convolutions from the counting process specifying ignore_modules=[torch.nn.Conv2d].

Install the latest version

From PyPI:

pip install ptflops

From this repository:

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()
  macs, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True,
                                           print_per_layer_stat=True, verbose=True)
  print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
  print('{:<30}  {:<8}'.format('Number of parameters: ', params))

Citation

If ptflops was useful for your paper or tech report, please cite me:

@online{ptflops,
  author = {Vladislav Sovrasov},
  title = {ptflops: a flops counting tool for neural networks in pytorch framework},
  year = 2018-2023,
  url = {https://github.com/sovrasov/flops-counter.pytorch},
}

Benchmark

torchvision

Model	Input Resolution	Params(M)	MACs(G)
alexnet	224x224	61.10	0.72
convnext_base	224x224	88.59	15.43
densenet121	224x224	7.98	2.90
efficientnet_b0	224x224	5.29	0.41
efficientnet_v2_m	224x224	54.14	5.43
googlenet	224x224	13.00	1.51
inception_v3	224x224	27.16	2.86
maxvit_t	224x224	30.92	5.48
mnasnet1_0	224x224	4.38	0.33
mobilenet_v2	224x224	3.50	0.32
mobilenet_v3_large	224x224	5.48	0.23
regnet_y_1_6gf	224x224	11.20	1.65
resnet18	224x224	11.69	1.83
resnet50	224x224	25.56	4.13
resnext50_32x4d	224x224	25.03	4.29
shufflenet_v2_x1_0	224x224	2.28	0.15
squeezenet1_0	224x224	1.25	0.84
vgg16	224x224	138.36	15.52
vit_b_16	224x224	86.57	17.60
wide_resnet50_2	224x224	68.88	11.45