https://github.com/developer0hye/pytorch-deformable-convolution-v2
Don't feel pain to use Deformable Convolution
https://github.com/developer0hye/pytorch-deformable-convolution-v2
cnn convolutional-neural-networks dcn dcnv1 dcnv2 deep-learning deformable-convolution deformable-convolutional-networks object-detection pytorch pytorch-deformable-convolution pytorch-deformable-convolution-v2 segmentation
Last synced: 6 months ago
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Don't feel pain to use Deformable Convolution
- Host: GitHub
- URL: https://github.com/developer0hye/pytorch-deformable-convolution-v2
- Owner: developer0hye
- License: mit
- Created: 2021-04-03T09:04:25.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2023-06-07T10:50:02.000Z (almost 3 years ago)
- Last Synced: 2025-09-05T03:46:38.037Z (7 months ago)
- Topics: cnn, convolutional-neural-networks, dcn, dcnv1, dcnv2, deep-learning, deformable-convolution, deformable-convolutional-networks, object-detection, pytorch, pytorch-deformable-convolution, pytorch-deformable-convolution-v2, segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 5.37 MB
- Stars: 344
- Watchers: 2
- Forks: 34
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.CFF
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README
# PyTorch-Deformable-Convolution-v2
Don't feel pain to use Deformable Convolution v2(DCNv2)
If you are curious about how to visualize offset(red point), refer to [offset_visualization.py](./offset_visualization.py)
# Usage
```python
from dcn import DeformableConv2d
class Model(nn.Module):
...
self.conv = DeformableConv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1)
...
```
# Experiment
You can simply reproduce the results of my experiment on Google Colab.
Refer to experiment.ipynb!
## Task
**Scaled-MNIST** Handwritten Digit Classification
## Model
Simple CNN Model including 5 conv layers
```python
class MNISTClassifier(nn.Module):
def __init__(self,
deformable=False):
super(MNISTClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, bias=True)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=True)
self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=True)
conv = nn.Conv2d if deformable==False else DeformableConv2d
self.conv4 = conv(32, 32, kernel_size=3, stride=1, padding=1, bias=True)
self.conv5 = conv(32, 32, kernel_size=3, stride=1, padding=1, bias=True)
self.pool = nn.MaxPool2d(2)
self.gap = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(32, 10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = self.pool(x) # [14, 14]
x = torch.relu(self.conv2(x))
x = self.pool(x) # [7, 7]
x = torch.relu(self.conv3(x))
x = torch.relu(self.conv4(x))
x = torch.relu(self.conv5(x))
x = self.gap(x)
x = x.flatten(start_dim=1)
x = self.fc(x)
return x
```
## Training
- Optimizer: Adam
- Learning Rate: 1e-3
- Learning Rate Scheduler: StepLR(step_size=1, gamma=0.7)
- Batch Size: 64
- Epochs: 14
- Augmentation: **NONE**
## Test
In the [paper](https://arxiv.org/abs/1811.11168), authors mentioned that the network's ability to model geometric transformation with DCNv2 is considerably enhanced.
I verified it with scale augmentation.
All images in the test set of MNIST dataset are augmented by scale augmentation(x0.5, x0.6, ..., x1.4, x1.5).
### Results
|Model|Top-1 Accuracy(%)|
|---|---|
|w/o DCNv2|90.03%|
|**w/ DCNv2**|**92.90%**|
## References
[mxnet implementation](https://github.com/apache/incubator-mxnet/blob/5722f8b38af58c5a296e46ca695bfaf7cff85040/python/mxnet/gluon/nn/conv_layers.py#L1447-L1631
)
## To Do Lists
- [ ] Support Onnx Conversion