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.
96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
# /// script
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# requires-python = ">=3.11,<3.12"
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# dependencies = ["torch==2.13.0", "onnx==1.19.0", "numpy==1.26.4", "einops==0.8.2", "gdown==6.2.0"]
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# [tool.uv.sources]
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# torch = { index = "pytorch" }
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# [[tool.uv.index]]
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# name = "pytorch"
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# url = "https://download.pytorch.org/whl/cpu"
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# explicit = true
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# ///
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"""Export the official HAT Sharper checkpoint with FP16 weight storage."""
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import argparse
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import hashlib
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import json
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from pathlib import Path
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from urllib.request import urlopen
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import gdown
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import onnx
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import torch
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from onnx_weights import compress_weights
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REVISION = "1638a9a822581657811867bf670717f8371fc3e5"
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CHECKPOINT_SHA256 = "5800b67136006eb8cab3b4ed7c8d73b6a195bb18e6cc709b674f9aa069c00271"
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SOURCE_URL = f"https://raw.githubusercontent.com/XPixelGroup/HAT/{REVISION}"
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def load_model(checkpoint, cache):
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architecture = cache / "hat_arch.py"
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if not architecture.exists():
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architecture.write_bytes(urlopen(SOURCE_URL + "/hat/archs/hat_arch.py", timeout=60).read())
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if hashlib.sha256(architecture.read_bytes()).hexdigest() != "81d8cecf491975246c9ebb20480898c656f59de020f38b05daa68741e426117f":
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raise RuntimeError("HAT architecture SHA-256 mismatch")
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# Export only: remove BasicSR's training registry and use the equivalent
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# PyTorch initialization utilities. The official forward remains unchanged.
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source = architecture.read_text(encoding="utf-8")
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source = source.replace("from basicsr.utils.registry import ARCH_REGISTRY", "")
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source = source.replace("@ARCH_REGISTRY.register()", "")
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source = source.replace(
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"from basicsr.archs.arch_util import to_2tuple, trunc_normal_",
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"from torch.nn.modules.utils import _pair as to_2tuple\nfrom torch.nn.init import trunc_normal_",
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)
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namespace = {"__name__": "hat_arch"}
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exec(compile(source, str(architecture), "exec"), namespace)
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model = namespace["HAT"](
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upscale=4, in_chans=3, img_size=64, window_size=16,
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compress_ratio=3, squeeze_factor=30, conv_scale=0.01,
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overlap_ratio=0.5, img_range=1., depths=[6] * 6,
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embed_dim=180, num_heads=[6] * 6, mlp_ratio=2,
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upsampler="pixelshuffle", resi_connection="1conv",
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).eval()
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with checkpoint.open("rb") as stream:
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if hashlib.file_digest(stream, "sha256").hexdigest() != CHECKPOINT_SHA256:
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raise RuntimeError("HAT Sharper checkpoint SHA-256 mismatch")
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model.load_state_dict(torch.load(checkpoint, map_location="cpu", weights_only=True)["params_ema"], strict=True)
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return model
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--destination", type=Path, required=True)
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parser.add_argument("--checkpoint", type=Path)
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args = parser.parse_args()
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cache = Path(__file__).resolve().parents[2] / ".model-cache" / "hat-sharper"
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cache.mkdir(parents=True, exist_ok=True)
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checkpoint = args.checkpoint or cache / "Real_HAT_GAN_sharper.pth"
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if not checkpoint.exists():
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gdown.download(id="1EioFq5-mKmv1uqta_Byd9cgXp9SU3zjj", output=str(checkpoint))
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torch.set_num_threads(8)
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torch.manual_seed(0)
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model = load_model(checkpoint, cache)
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args.destination.mkdir(parents=True, exist_ok=True)
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output = args.destination / "model.onnx"
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with torch.inference_mode():
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torch.onnx.export(model, torch.zeros(1, 3, 256, 256), str(output),
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opset_version=17, input_names=["input"], output_names=["output"],
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do_constant_folding=False, dynamo=False)
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graph = onnx.load(output)
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compress_weights(graph)
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onnx.checker.check_model(graph)
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onnx.save(graph, output)
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(args.destination / "LICENSE.txt").write_bytes(urlopen(SOURCE_URL + "/LICENSE", timeout=60).read())
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if sum(path.stat().st_size for path in args.destination.iterdir()) >= 60_000_000:
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raise RuntimeError("HAT asset exceeds 60 MB")
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with output.open("rb") as stream:
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digest = hashlib.file_digest(stream, "sha256").hexdigest()
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print(json.dumps({"bytes": output.stat().st_size, "sha256": digest,
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"revision": REVISION, "checkpoint_sha256": CHECKPOINT_SHA256}), flush=True)
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if __name__ == "__main__":
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main()
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