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https://github.com/moskomule/senet.pytorch
PyTorch implementation of SENet
https://github.com/moskomule/senet.pytorch
pytorch senet
Last synced: 7 days ago
JSON representation
PyTorch implementation of SENet
- Host: GitHub
- URL: https://github.com/moskomule/senet.pytorch
- Owner: moskomule
- License: mit
- Created: 2017-09-12T12:26:27.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-03-02T04:09:12.000Z (almost 4 years ago)
- Last Synced: 2024-11-30T08:40:56.287Z (14 days ago)
- Topics: pytorch, senet
- Language: Python
- Size: 59.6 KB
- Stars: 2,292
- Watchers: 16
- Forks: 441
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-pytorch : https://github.com/moskomule/senet.pytorch
- awesome-image-classification - unofficial-pytorch : https://github.com/moskomule/senet.pytorch
README
# SENet.pytorch
An implementation of SENet, proposed in **Squeeze-and-Excitation Networks** by Jie Hu, Li Shen and Gang Sun, who are the winners of ILSVRC 2017 classification competition.
Now SE-ResNet (18, 34, 50, 101, 152/20, 32) and SE-Inception-v3 are implemented.
* `python cifar.py` runs SE-ResNet20 with Cifar10 dataset.
* `python imagenet.py` and `python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} imagenet.py` run SE-ResNet50 with ImageNet(2012) dataset,
+ You need to prepare dataset by yourself in `~/.torch/data` or set an enviroment variable `IMAGENET_ROOT=${PATH_TO_YOUR_IMAGENET}`
+ First download files and then follow the [instruction](https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset).
+ The number of workers and some hyper parameters are fixed so check and change them if you need.
+ This script uses all GPUs available. To specify GPUs, use `CUDA_VISIBLE_DEVICES` variable. (e.g. `CUDA_VISIBLE_DEVICES=1,2` to use GPU 1 and 2)
For SE-Inception-v3, the input size is required to be 299x299 [as the original Inception](https://github.com/tensorflow/models/tree/master/inception).
## Pre-requirements
The codebase is tested on the following setting.
* Python>=3.8
* PyTorch>=1.6.0
* torchvision>=0.7
### For training
To run `cifar.py` or `imagenet.py`, you need
* `pip install git+https://github.com/moskomule/[email protected]`
## hub
You can use some SE-ResNet (`se_resnet{20, 56, 50, 101}`) via `torch.hub`.
```python
import torch.hub
hub_model = torch.hub.load(
'moskomule/senet.pytorch',
'se_resnet20',
num_classes=10)
```
Also, a pretrained SE-ResNet50 model is available.
```python
import torch.hub
hub_model = torch.hub.load(
'moskomule/senet.pytorch',
'se_resnet50',
pretrained=True,)
```
## Results
### SE-ResNet20/Cifar10
```
python cifar.py [--baseline]
```
Note that the CIFAR-10 dataset expected to be under `~/.torch/data`.
| | ResNet20 | SE-ResNet20 (reduction 4 or 8) |
|:------------- | :------------- | :------------- |
|max. test accuracy| 92% | 93% |
### SE-ResNet50/ImageNet
```
python [-m torch.distributed.launch --nproc_per_node=${NUM_GPUS}] imagenet.py
```
The option [-m ...] is for distributed training. Note that the Imagenet dataset is expected to be under `~/.torch/data` or specified as `IMAGENET_ROOT=${PATH_TO_IMAGENET}`.
*The initial learning rate and mini-batch size are different from the original version because of my computational resource* .
| | ResNet | SE-ResNet |
|:------------- | :------------- | :------------- |
|max. test accuracy(top1)| 76.15 %(*) | 77.06% (**) |
+ (*): [ResNet-50 in torchvision](https://pytorch.org/docs/stable/torchvision/models.html)
+ (**): When using `imagenet.py` with the `--distributed` setting on 8 GPUs. The weight is [available](https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl).
```python
# !wget https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl
senet = se_resnet50(num_classes=1000)
senet.load_state_dict(torch.load("seresnet50-60a8950a85b2b.pkl"))
```
## Contribution
I cannot maintain this repository actively, but any contributions are welcome. Feel free to send PRs and issues.
## References
[paper](https://arxiv.org/pdf/1709.01507.pdf)
[authors' Caffe implementation](https://github.com/hujie-frank/SENet)