How can I run a PyWavelets-based transform entirely on the GPU in my PyTorch classifier?

Below is an explanation and some guidance on how you might move your wavelet transform from CPU to GPU:

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Why Your Current Implementation Is CPU Bound

Your current implementation uses PyWavelets which works entirely on NumPy arrays. In the process:

1. You convert tensors from the GPU to CPU (via .cpu().numpy())
2. Run the transform on CPU (via PyWavelets)
3. Convert back to tensors and move them to the original device

Each of these steps introduces significant CPU overhead and data transfer latency, especially in a training loop.

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Options to Improve Performance

1. Reimplement the Wavelet Transform Using Native Torch Operations

To run the transform on the GPU, you can reimplement the Haar wavelet transform using operations that are available in PyTorch. The Haar wavelet transform is essentially a set of fixed (low-pass and high-pass) filters applied via convolutions. For example, you might create these filters as constant tensors and apply them with torch.nn.functional.conv2d. An outline might look like:

PYTHONCopied
import torch
import torch.nn.functional as F
import torch.nn as nn

class HaarWaveletLayer(nn.Module):
    def __init__(self):
        super(HaarWaveletLayer, self).__init__()
        # Define Haar filters (for 2D transform)
        ll = torch.tensor([[0.5, 0.5],
                           [0.5, 0.5]], dtype=torch.float32)
        lh = torch.tensor([[0.5, 0.5],
                           [-0.5, -0.5]], dtype=torch.float32)
        hl = torch.tensor([[0.5, -0.5],
                           [0.5, -0.5]], dtype=torch.float32)
        hh = torch.tensor([[0.5, -0.5],
                           [-0.5, 0.5]], dtype=torch.float32)
        # Stack filters together so they can be applied in one convolution
        filters = torch.stack([ll, lh, hl, hh], dim=0).unsqueeze(1)  # shape: (4,1,2,2)
        # Register filters as buffers so that they move with the module device
        self.register_buffer("filters", filters)

    def forward(self, x):
        # x should be in shape (N, C, H, W). Process each channel separately.
        outputs = []
        for c in range(x.shape[1]):
            channel = x[:, c:c+1, :, :]
            # Apply convolution with stride 2 to get downsampled coefficients
            coeffs = F.conv2d(channel, self.filters, stride=2)
            outputs.append(coeffs)
        # Concatenate along channel dimension: each original channel gives 4 channels
        return torch.cat(outputs, dim=1)

In this example, the Haar wavelet transform is implemented as a convolution with pre-defined filters. All operations here run on the same device as the input (and can be easily moved to GPU) without converting to NumPy.

2. Explore Third-Party Libraries That Support GPU Wavelet Transforms

There are libraries like Kymatio which implement wavelet scattering transforms (and sometimes other wavelet transforms) in PyTorch that support GPU acceleration. Check if these libraries offer the transforms you need or can be adapted for your use.

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Next Steps

• Prototype the convolution-based approach: Seeing good performance on GPU without the CPU-GPU transfers can greatly speed up your training.
• Ensure Device Consistency: When using the native torch implementation, ensure that input and filters are on the same device (e.g., if using Intel GPU support, use .to("xpu") as needed).

By moving entirely to PyTorch operations, your wavelet transform will run completely on the GPU, alleviating the CPU bottleneck you are experiencing.

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This approach aligns with the need to avoid CPU-bound operations in your training loop and makes full use of GPU acceleration.