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https://github.com/sovrasov/flops-counter.pytorch
Flops counter for convolutional networks in pytorch framework
https://github.com/sovrasov/flops-counter.pytorch
deep-neural-networks deeplearning flops-counter pytorch pytorch-cnn pytorch-utils transformer transformer-models
Last synced: about 2 months ago
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Flops counter for convolutional networks in pytorch framework
- Host: GitHub
- URL: https://github.com/sovrasov/flops-counter.pytorch
- Owner: sovrasov
- License: mit
- Created: 2018-08-17T09:54:59.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-16T16:37:07.000Z (2 months ago)
- Last Synced: 2024-07-28T11:33:02.864Z (about 2 months ago)
- Topics: deep-neural-networks, deeplearning, flops-counter, pytorch, pytorch-cnn, pytorch-utils, transformer, transformer-models
- Language: Python
- Homepage:
- Size: 163 KB
- Stars: 2,751
- Watchers: 16
- Forks: 307
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Flops counting tool for neural networks in pytorch framework
[](https://pypi.org/project/ptflops/)
[](https://travis-ci.com/sovrasov/flops-counter.pytorch)
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).
The default backend is `aten`. Please, don't use `pytorch` backend for transformer architectures.
## `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.
- `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.ops.aten.convolution, torch.ops.aten._convolution]`.
## `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](https://github.com/huggingface/pytorch-image-models)
### 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.
Sometimes considering functional style conflicts with hooks for `nn.Module` (for instance, custom ones). In that case, counting with these ops can be disabled by
passing `backend_specific_config={"count_functional" : False}`.
- `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 >= 2.0. Use `pip install ptflops==0.7.2.2` to work with torch 1.x.
## Install the latest version
From PyPI:
```bash
pip install ptflops
```
From this repository:
```bash
pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git
```
## Example
```python
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},
}
```
## Credits
Thanks to @warmspringwinds and Horace He for the initial version of the script.
## Benchmark
### [torchvision](https://pytorch.org/vision/0.16/models.html)
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](https://github.com/huggingface/pytorch-image-models)
Model | Input Resolution | Params(M) | MACs(G)