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.
48 lines
2.3 KiB
Python
48 lines
2.3 KiB
Python
"""Express BiRefNet's single-group deformable convolution with GridSample."""
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import torch
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from torch.nn import functional as F
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def deform_conv(input, offset, weight, bias=None, stride=(1, 1), padding=0, mask=None):
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batch, channels, height, width = map(int, input.shape)
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output_height, output_width = map(int, offset.shape[-2:])
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kernel_height, kernel_width = map(int, weight.shape[-2:])
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count = kernel_height * kernel_width
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y, x = torch.meshgrid(
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torch.arange(output_height, dtype=input.dtype, device=input.device),
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torch.arange(output_width, dtype=input.dtype, device=input.device), indexing="ij",
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)
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ky, kx = torch.meshgrid(
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torch.arange(kernel_height, dtype=input.dtype, device=input.device),
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torch.arange(kernel_width, dtype=input.dtype, device=input.device), indexing="ij",
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)
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offsets = offset.reshape(batch, count, 2, output_height, output_width)
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sy = y[None, None] * stride[0] - padding + ky.reshape(1, count, 1, 1) + offsets[:, :, 0]
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sx = x[None, None] * stride[1] - padding + kx.reshape(1, count, 1, 1) + offsets[:, :, 1]
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coordinates = torch.stack((sx * (2.0 / (width - 1)) - 1, sy * (2.0 / (height - 1)) - 1), -1)
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grid = coordinates.permute(0, 2, 3, 1, 4).reshape(batch, output_height, output_width * count, 2)
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sampled = F.grid_sample(input, grid, mode="bilinear", padding_mode="zeros", align_corners=True)
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sampled = sampled.reshape(batch, channels, output_height, output_width, count).permute(0, 1, 4, 2, 3)
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sampled = (sampled * mask[:, None]).reshape(batch, channels * count, output_height, output_width)
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return F.conv2d(sampled, weight.reshape(int(weight.shape[0]), channels * count, 1, 1), bias)
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def validate_deform():
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from torchvision.ops import deform_conv2d
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torch.manual_seed(12)
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for kernel in (1, 3, 7):
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image = torch.randn(1, 4, 17, 19)
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offset = torch.randn(1, kernel * kernel * 2, 17, 19) * 3
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mask = torch.rand(1, kernel * kernel, 17, 19) * 2
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weights = torch.randn(5, 4, kernel, kernel)
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bias = torch.randn(5)
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reference = deform_conv2d(image, offset, weights, bias, padding=kernel // 2, mask=mask)
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actual = deform_conv(image, offset, weights, bias, padding=kernel // 2, mask=mask)
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torch.testing.assert_close(actual, reference, atol=1e-4, rtol=1e-4)
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if __name__ == "__main__":
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validate_deform()
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