nan
e8129fe495
- Added validation documentation for AI model tiers. - Introduced `background.py` for background removal logic, including inference functions for alpha and foreground extraction. - Created `onnx_birefnet.py` to handle BiRefNet model sessions and alpha inference. - Developed tests for background models, ensuring proper dispatch and session reuse. - Added tests for BiRefNet ONNX model to validate alpha output and error handling for invalid values. - Implemented model export scripts for BiRefNet and HAT Sharper, ensuring proper weight compression and licensing. - Created `deform_sampling.py` for deformable convolution operations used in BiRefNet. - Added utility functions for compressing ONNX weights without altering graph arithmetic. - Introduced a reference plane node for depth field adjustments in geometry processing.
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""Export official PyTorch super-resolution models to ONNX."""
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import numpy as np
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OPSET_VERSION = 17
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def _require_build_dependencies():
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try:
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import onnx
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import onnxruntime
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import torch
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except ImportError as error:
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raise RuntimeError(
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"Model build dependencies are missing. Run with: "
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"uv sync"
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) from error
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return onnx, onnxruntime, torch
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def _load_weights(torch, checkpoint_path, key):
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checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
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return checkpoint[key]
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def _validate_export(onnx, onnxruntime, torch, model, sample, destination):
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onnx.checker.check_model(onnx.load(destination))
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expected = model(sample).detach().numpy()
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session = onnxruntime.InferenceSession(
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str(destination),
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providers=["CPUExecutionProvider"],
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)
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actual = session.run(None, {"input": sample.numpy()})[0]
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np.testing.assert_allclose(actual, expected, rtol=1e-4, atol=1e-5)
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def _export(torch, model, sample, destination, dynamic_axes=None):
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model.eval()
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torch.onnx.export(
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model,
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sample,
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destination,
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export_params=True,
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opset_version=OPSET_VERSION,
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do_constant_folding=True,
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input_names=["input"],
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output_names=["output"],
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dynamic_axes=dynamic_axes,
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dynamo=False,
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)
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def _restore_realesrgan_scope_names(onnx, destination):
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model = onnx.load(destination)
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for node in model.graph.node:
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node.name = node.name.replace("/body.", "/body/body.")
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for values in (node.input, node.output):
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for index, value in enumerate(values):
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values[index] = value.replace("/body.", "/body/body.")
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onnx.save(model, destination)
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def export_realesrgan_wdn_x4(checkpoint_path, destination):
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onnx, onnxruntime, torch = _require_build_dependencies()
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nn = torch.nn
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class SRVGGNetCompact(nn.Module):
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def __init__(self):
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super().__init__()
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self.upscale = 4
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self.body = nn.ModuleList([nn.Conv2d(3, 64, 3, 1, 1), nn.PReLU(64)])
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for _ in range(32):
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self.body.extend([nn.Conv2d(64, 64, 3, 1, 1), nn.PReLU(64)])
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self.body.append(nn.Conv2d(64, 3 * self.upscale**2, 3, 1, 1))
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self.upsampler = nn.PixelShuffle(self.upscale)
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def forward(self, value):
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result = value
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for layer in self.body:
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result = layer(result)
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result = self.upsampler(result)
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return result + torch.nn.functional.interpolate(
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value,
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scale_factor=self.upscale,
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mode="nearest",
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)
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model = SRVGGNetCompact()
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model.load_state_dict(_load_weights(torch, checkpoint_path, "params"), strict=True)
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sample = torch.rand(1, 3, 16, 16)
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dynamic_axes = {
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"input": {2: "height", 3: "width"},
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"output": {2: "height4", 3: "width4"},
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}
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_export(torch, model, sample, destination, dynamic_axes)
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_restore_realesrgan_scope_names(onnx, destination)
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_validate_export(onnx, onnxruntime, torch, model, sample, destination)
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def export_model(exporter, checkpoint_path, destination):
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exporters = {
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"realesrgan_wdn_x4": export_realesrgan_wdn_x4,
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}
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try:
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export = exporters[exporter]
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except KeyError as error:
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raise ValueError(f"Unknown model exporter: {exporter}") from error
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export(checkpoint_path, destination)
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