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https://github.com/immich-app/immich.git
synced 2025-01-07 00:50:23 -05:00
prevent multidimensional bias
This commit is contained in:
mertalev
parent
b39cca1b43
commit
b5acb71b05
1 changed files with 41 additions and 35 deletions
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@ -14,6 +14,8 @@ import numpy as np
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import onnxsim
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from shutil import rmtree
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# hack: changed Mul op in onnx2tf to skip broadcast if graph_node.o().op == 'Sigmoid'
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# i can explain
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# armnn only supports up to 4d tranposes, but the model has a 5d transpose due to a redundant unsqueeze
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# this function folds the unsqueeze+transpose+squeeze into a single 4d transpose
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@ -262,7 +264,7 @@ class ExportBase:
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rmtree(tflite_model_dir, ignore_errors=True)
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finally:
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raise e
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print(f"Finished exporting {self.name} ({self.task}) with fp32 precision")
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print(f"Finished exporting {self.model_name} ({self.task}) with fp32 precision")
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fp16_args = args.copy()
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fp16_args.extend(["-m", tflite_fp16, "-p", armnn_fp16])
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@ -276,7 +278,7 @@ class ExportBase:
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rmtree(tflite_model_dir, ignore_errors=True)
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finally:
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raise e
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print(f"Finished exporting {self.name} ({self.task}) with fp16 precision")
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print(f"Finished exporting {self.model_name} ({self.task}) with fp16 precision")
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return armnn_fp32, armnn_fp16
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@ -311,43 +313,29 @@ class MClipTextual(ExportBase):
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def main() -> None:
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if platform.machine() not in ("x86_64", "AMD64"):
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raise RuntimeError(f"Can only run on x86_64 / AMD64, not {platform.machine()}")
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upload_to_hf = "HF_AUTH_TOKEN" in os.environ
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if upload_to_hf:
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login(token=os.environ["HF_AUTH_TOKEN"])
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hf_token = os.environ.get("HF_AUTH_TOKEN")
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if hf_token:
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login(token=hf_token)
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os.environ["LD_LIBRARY_PATH"] = "armnn"
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failed: list[Callable[[], ExportBase]] = [
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lambda: OpenClipVisual("ViT-H-14-378-quickgelu", (1, 3, 378, 378), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
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lambda: OpenClipVisual("ViT-H-14-quickgelu", (1, 3, 224, 224), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
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lambda: OpenClipTextual("nllb-clip-base-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
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lambda: OpenClipTextual("nllb-clip-large-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b_e16"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b_e16"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b-s34b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b-s34b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-B-16-plus-240", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-B-16-plus-240", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e31"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e32"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b82k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion2b-s32b-b82k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-H-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-H-14", (1, 77), pretrained="laion2b-s32b-b79k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-g-14", (1, 3, 224, 224), pretrained="laion2b-s12b-b42k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipTextual("ViT-g-14", (1, 77), pretrained="laion2b-s12b-b42k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-16Plus", (1, 3, 240, 240)), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("XLM-Roberta-Large-ViT-H-14", (1, 3, 224, 224), pretrained="frozen_laion5b_s13b_b90k"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("nllb-clip-base-siglip", (1, 3, 384, 384), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("nllb-clip-large-siglip", (1, 3, 384, 384), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::erf
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e31"),
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e31"),
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e32"),
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e32"),
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lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b82k"),
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lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion2b-s32b-b82k"),
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lambda: OpenClipVisual("ViT-H-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b79k"),
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lambda: OpenClipTextual("ViT-H-14", (1, 77), pretrained="laion2b-s32b-b79k"),
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lambda: OpenClipVisual("ViT-g-14", (1, 3, 224, 224), pretrained="laion2b-s12b-b42k"),
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lambda: OpenClipTextual("ViT-g-14", (1, 77), pretrained="laion2b-s12b-b42k"),
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lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-16Plus", (1, 3, 240, 240)),
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lambda: OpenClipVisual("XLM-Roberta-Large-ViT-H-14", (1, 3, 224, 224), pretrained="frozen_laion5b_s13b_b90k"),
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lambda: OpenClipVisual("nllb-clip-base-siglip", (1, 3, 384, 384), pretrained="v1"),
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lambda: OpenClipVisual("nllb-clip-large-siglip", (1, 3, 384, 384), pretrained="v1"),
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lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
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lambda: OpenClipTextual("RN50", (1, 77), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
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lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="cc12m"), # BatchNorm operation with mean/var output is not implemented
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@ -359,6 +347,20 @@ def main() -> None:
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]
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succeeded: list[Callable[[], ExportBase]] = [
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# lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b_e16"),
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# lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b_e16"),
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# lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e31"),
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# lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e31"),
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# lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion400m_e32"),
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# lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion400m_e32"),
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# lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b-s34b-b79k"),
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# lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b-s34b-b79k"),
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# lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e31"),
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# lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e31"),
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# lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="laion400m_e32"),
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# lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="laion400m_e32"),
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# lambda: OpenClipVisual("ViT-B-16-plus-240", (1, 3, 240, 240), pretrained="laion400m_e31"),
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# lambda: OpenClipTextual("ViT-B-16-plus-240", (1, 77), pretrained="laion400m_e31"),
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# lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="openai"),
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# lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="openai"),
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lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="openai"),
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@ -392,10 +394,14 @@ def main() -> None:
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armnn_fp32, armnn_fp16 = model.to_armnn(output_dir)
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relative_fp32 = os.path.relpath(armnn_fp32, start=model_dir)
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relative_fp16 = os.path.relpath(armnn_fp16, start=model_dir)
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if upload_to_hf and os.path.isfile(armnn_fp32):
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if hf_token and os.path.isfile(armnn_fp32):
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print(f"Uploading {model.model_name} ({model.task}) ARM NN model with fp32 precision")
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upload_file(path_or_fileobj=armnn_fp32, path_in_repo=relative_fp32, repo_id=model.repo_name)
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if upload_to_hf and os.path.isfile(armnn_fp16):
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print(f"Finished uploading {model.model_name} ({model.task}) ARM NN model with fp32 precision")
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if hf_token and os.path.isfile(armnn_fp16):
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print(f"Uploading {model.model_name} ({model.task}) ARM NN model with fp16 precision")
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upload_file(path_or_fileobj=armnn_fp16, path_in_repo=relative_fp16, repo_id=model.repo_name)
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print(f"Finished uploading {model.model_name} ({model.task}) ARM NN model with fp16 precision")
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except Exception as exc:
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print(f"Failed to export {model.model_name} ({model.task}): {exc}")
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raise exc
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