# pytorch-deform_conv

**Repository Path**: dwhnicholas/pytorch-deform-conv-v2

## Basic Information

- **Project Name**: pytorch-deform_conv
- **Description**: 可变卷积的pytorch版本
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-25
- **Last Updated**: 2022-10-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: cuda, PyTorch

## README

# PyTorch implementation of Deformable ConvNets v2
This repository contains code for **Deformable ConvNets v2 (Modulated Deformable Convolution)** based on [Deformable ConvNets v2: More Deformable, Better Results
](https://arxiv.org/abs/1811.11168) implemented in PyTorch. This implementation of deformable convolution based on [ChunhuanLin/deform_conv_pytorch](https://github.com/ChunhuanLin/deform_conv_pytorch), thanks to ChunhuanLin.

- **Note:**  This version code need more NVIdia ,eg:if oriented code need 2 GPUs and batchsize = 4,Now ,you can need 2 GPUs and  batchsize = 1, or you can add more gpu.

## TODO
 - [x] Initialize weight of modulated deformable convolution based on paper
 - [x] Learning rates of offset and modulation are set to different values from other layers
 - [x] Results of ScaledMNIST experiments
 - [x] Support different stride
 - [ ] Support deformable group
 - [ ] DeepLab + DCNv2
 - [ ] Results of VOC segmentation experiments

## Requirements
- Python 3.6
- PyTorch 1.0

## Usage
Replace regular convolution (following model's conv2) with modulated deformable convolution:
```python
class ConvNet(nn.Module):
  def __init__(self):
    self.relu = nn.ReLU(inplace=True)
    self.pool = nn.MaxPool2d((2, 2))
    self.avg_pool = nn.AdaptiveAvgPool2d(1)

    self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
    self.bn1 = nn.BatchNorm2d(32)
    self.conv2 = nn.DeformConv2d(32, 64, 3, padding=1, modulation=True)
    self.bn2 = nn.BatchNorm2d(64)

    self.fc = nn.Linear(64, 10)

  def forward(self, x):
    x = self.relu(self.bn1(self.conv1(x)))
    x = self.pool(x)
    x = self.relu(self.bn2(self.conv2(x)))

    x = self.avg_pool(x)
    x = x.view(x.shape[0], -1)
    x = self.fc(x)

    return x
```

## Training
### ScaledMNIST
ScaledMNIST is randomly scaled MNIST.

Use modulated deformable convolution at conv3~4:
```
python train.py --arch ScaledMNISTNet --deform True --modulation True --min-deform-layer 3
```
Use deformable convolution at conv3~4:
```
python train.py --arch ScaledMNISTNet --deform True --modulation False --min-deform-layer 3
```
Use only regular convolution:
```
python train.py --arch ScaledMNISTNet --deform False --modulation False
```

## Results
### ScaledMNIST
| Model                   |   Accuracy (%)    |   Loss   |
|:------------------------|:-----------------:|:--------:|
| w/o DCN                 |             97.22 |     0.113|
| w/  DCN @conv4          |             98.60 |     0.049|
| w/  DCN @conv3~4        |             98.95 |     0.035|
| w/  DCNv2 @conv4        |             98.45 |     0.058|
| w/  DCNv2 @conv3~4      |         **99.21** | **0.027**|