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.
- 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.
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
.
import torch.hub
hub_model = torch.hub.load(
'moskomule/senet.pytorch',
'se_resnet20',
num_classes=10)
Also, a pretrained SE-ResNet50 model is available.
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% (**) |
-
(**): When using
imagenet.py
with the--distributed
setting on 8 GPUs. The weight is available.
# !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"))