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SRMnet

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PyTorch implementation of "SRM : A Style-based Recalibration Module for Convolutional Neural Networks"

// repository documentation

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# SRM Network PyTorch
An implementation of SRM block, proposed in "SRM : A Style-based Recalibration Module for Convolutional Neural Networks".

## Requirements
- Python >= 3.6
- PyTorch >= 1.1
- torchvision
- back > 0.0.3

back is PyTorch [backbone](https://github.com/EvgenyKashin/backbone) for training loop.
## Implementation notes
<img src="imgs/srm.png">

For implementing channel-wise fully connected (CFC) layer I used
Conv1d layer which is equal to CFC with next parameters:
```python
Conv1d(channels, channels, kernel_size=2, groups=channels)
``` 
It turns out the use of depthwise 1d convolution. 
## Training
```bash
# Cifar10
python cifar10_train.py --model_name srmnet

# ImageNet
python imagenet_train.py --model_name srmnet

# Logs
tensorboard --logdir=logs --host=0.0.0.0 --port=8080

```

## Training parameters
### Cifar
```python
batch_size = 128
epochs_count = 100
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9,
                      weight_decay=1e-4)
scheduler = MultiStepLR(optimizer, [70, 80], 0.1)
```
### ImageNet
```python
batch_size = 64
epochs_count = 100
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9,
                      weight_decay=1e-4)
scheduler = StepLR(optimizer, 30, 0.1)
```
## Results
### Cifar10
|           |ResNet32|Se-ResNet32|SRM-ResNet32|
|:----------|:-------|:----------|:-----------|
|accuracy   |92.1%   |92.5%      |92.9%       |
|weights    |466,906 |470,266(+0.72%)|469,146(+0.48%)|

Dark blue - ResNet

Blue - Se-ResNet

Green - SRM-ResNet

[Weights](weights) for best models.

### ImageNet
|           |ResNet50|Se-ResNet50|SRM-ResNe50|
|:----------|:-------|:----------|:-----------|
|accuracy(top1)   |%   |%      |%       |
|weights    |25,557,032 |28,071,976(+9.84%)|25,617,448(+0.23%)|

SRMnet

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