Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Lyken17/pytorch-OpCounter
Count the MACs / FLOPs of your PyTorch model.
https://github.com/Lyken17/pytorch-OpCounter
Last synced: 2 months ago
JSON representation
Count the MACs / FLOPs of your PyTorch model.
- Host: GitHub
- URL: https://github.com/Lyken17/pytorch-OpCounter
- Owner: Lyken17
- License: mit
- Created: 2018-01-26T06:20:22.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-06T01:16:34.000Z (4 months ago)
- Last Synced: 2024-03-16T09:06:34.854Z (3 months ago)
- Language: Python
- Homepage:
- Size: 173 KB
- Stars: 4,613
- Watchers: 30
- Forks: 507
- Open Issues: 77
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- Awesome-pytorch-list - pytorch-OpCounter
- awesome-AutoML-and-Lightweight-Models - Lyken17/pytorch-OpCounter
- awesome-stars - Lyken17/pytorch-OpCounter - Count the MACs / FLOPs of your PyTorch model. (Python)
- awesome-stars - Lyken17/pytorch-OpCounter - Count the MACs / FLOPs of your PyTorch model. (Python)
- Awesome-pytorch-list-CNVersion - pytorch-OpCounter
- awesome-stars - Lyken17/pytorch-OpCounter - Count the MACs / FLOPs of your PyTorch model. (Python)
- awesome-stars - pytorch-OpCounter
README
# THOP: PyTorch-OpCounter
## How to install
`pip install thop` (now continously intergrated on [Github actions](https://github.com/features/actions))
OR
`pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git`
## How to use
* Basic usage
```python
from torchvision.models import resnet50
from thop import profile
model = resnet50()
input = torch.randn(1, 3, 224, 224)
macs, params = profile(model, inputs=(input, ))
```
* Define the rule for 3rd party module.
```python
class YourModule(nn.Module):
# your definition
def count_your_model(model, x, y):
# your rule here
input = torch.randn(1, 3, 224, 224)
macs, params = profile(model, inputs=(input, ),
custom_ops={YourModule: count_your_model})
```
* Improve the output readability
Call `thop.clever_format` to give a better format of the output.
```python
from thop import clever_format
macs, params = clever_format([macs, params], "%.3f")
```
## Results of Recent Models
The implementation are adapted from `torchvision`. Following results can be obtained using [benchmark/evaluate_famous_models.py](benchmark/evaluate_famous_models.py).
Model | Params(M) | MACs(G)
---|---|---
alexnet | 61.10 | 0.77
vgg11 | 132.86 | 7.74
vgg11_bn | 132.87 | 7.77
vgg13 | 133.05 | 11.44
vgg13_bn | 133.05 | 11.49
vgg16 | 138.36 | 15.61
vgg16_bn | 138.37 | 15.66
vgg19 | 143.67 | 19.77
vgg19_bn | 143.68 | 19.83
resnet18 | 11.69 | 1.82
resnet34 | 21.80 | 3.68
resnet50 | 25.56 | 4.14
resnet101 | 44.55 | 7.87
resnet152 | 60.19 | 11.61
wide_resnet101_2 | 126.89 | 22.84
wide_resnet50_2 | 68.88 | 11.46
Model | Params(M) | MACs(G)
---|---|---
resnext50_32x4d | 25.03 | 4.29
resnext101_32x8d | 88.79 | 16.54
densenet121 | 7.98 | 2.90
densenet161 | 28.68 | 7.85
densenet169 | 14.15 | 3.44
densenet201 | 20.01 | 4.39
squeezenet1_0 | 1.25 | 0.82
squeezenet1_1 | 1.24 | 0.35
mnasnet0_5 | 2.22 | 0.14
mnasnet0_75 | 3.17 | 0.24
mnasnet1_0 | 4.38 | 0.34
mnasnet1_3 | 6.28 | 0.53
mobilenet_v2 | 3.50 | 0.33
shufflenet_v2_x0_5 | 1.37 | 0.05
shufflenet_v2_x1_0 | 2.28 | 0.15
shufflenet_v2_x1_5 | 3.50 | 0.31
shufflenet_v2_x2_0 | 7.39 | 0.60
inception_v3 | 27.16 | 5.75