ptflops 0.7.3

Description:

ptflops 0.7.3

Flops counting tool for neural networks in pytorch framework


This tool is designed to compute the theoretical amount of multiply-add operations
in neural networks. It can also compute the number of parameters and
print per-layer computational cost of a given network.
ptflops has two backends, pytorch and aten. pytorch backend is a legacy one, it considers nn.Modules only. However,
it's still useful, since it provides a better par-layer analytics for CNNs. In all other cases it's recommended to use
aten backend, which considers aten operations, and therefore it covers more model architectures (including transformers).
aten backend
Operations considered:

aten.mm, aten.matmul, aten.addmm, aten.bmm
aten.convolution

Usage tips

Use verbose=True to see the operations which were not considered during complexity computation.
This backend prints per-module statistics only for modules directly nested into the root nn.Module.
Deeper modules at the second level of nesting are not shown in the per-layer statistics.

pytorch backend
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

Usage tips

This backend 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].

Requirements: Pytorch >= 1.1, torchvision >= 0.3
Thanks to @warmspringwinds and Horace He for the initial version of the script.
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, backend='pytorch'
print_per_layer_stat=True, verbose=True)
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))

macs, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, backend='aten'
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 = 2018-2024,
url = {https://github.com/sovrasov/flops-counter.pytorch},
}

Benchmark
torchvision



Model
Input Resolution
Params(M)
MACs(G) (pytorch)
MACs(G) (aten)




alexnet
224x224
61.10
0.72
0.71


convnext_base
224x224
88.59
15.43
15.38


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
5.75
5.71


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
1.81


resnet50
224x224
25.56
4.13
4.09


resnext50_32x4d
224x224
25.03
4.29



shufflenet_v2_x1_0
224x224
2.28
0.15



squeezenet1_0
224x224
1.25
0.84
0.82


vgg16
224x224
138.36
15.52
15.48


vit_b_16
224x224
86.57
17.61 (wrong)
16.86


wide_resnet50_2
224x224
68.88
11.45




timm
Model | Input Resolution | Params(M) | MACs(G)