HighResolutionNet with Depthwise Separable Convolution for Face Alignment

{
  "title": "HighResolutionNet with Depthwise Separable Convolution for Face Alignment",
  "description": "This code implements the HighResolutionNet architecture for face alignment, modified to utilize depthwise separable convolutions for improved efficiency. The code includes a custom implementation of SeparableConv2d to facilitate this modification.",
  "keywords": "face alignment, HighResolutionNet, depthwise separable convolution, PyTorch, efficient model",
  "content": "```python
class SeparableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
        super(SeparableConv2d, self).__init__()
        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias)
        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=bias)

    def forward(self, x):
        out = self.depthwise(x)
        out = self.pointwise(out)
        return out

# Replace conv3x3 function with SeparableConv2d
def conv3x3(in_planes, out_planes, stride=1):
    ""3x3 convolution with padding""
    return SeparableConv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)

# ... rest of the code remains the same ...
```"
}