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
Usage
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
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 xTraining
- 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, 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
To Do Lists
- Support Onnx Conversion