# senet.pytorch

**Repository Path**: stoppable911222/senet.pytorch

## Basic Information

- **Project Name**: senet.pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2019-08-13
- **Last Updated**: 2021-05-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 IMAGENET_ROOT` runs SE-ResNet50 with ImageNet(2012) dataset.
    + You need to prepare dataset by yourself
    + 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

* Python>=3.6
* PyTorch>=1.0
* torchvision>=0.3


### For training

To run `cifar.py` or `imagenet.py`, you need

* `pip install git+https://github.com/moskomule/homura`

## 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,)
 ```

## Result

### SE-ResNet20/Cifar10

```
python cifar.py [--baseline]
```

|                  | ResNet20       | SE-ResNet20 (reduction 4 or 8)    |
|:-------------    | :------------- | :------------- |
|max. test accuracy|  92%           | 93%            |

### SE-ResNet50/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"))
```

## References

[paper](https://arxiv.org/pdf/1709.01507.pdf)

[authors' Caffe implementation](https://github.com/hujie-frank/SENet)