commit | a5bd834c59b2821f3d1911217a281f5960a519de | [log] [tgz] |
---|---|---|
author | patosgui <126727212+patosgui@users.noreply.github.com> | Tue Jun 04 23:43:25 2024 +0200 |
committer | GitHub <noreply@github.com> | Tue Jun 04 21:43:25 2024 +0000 |
tree | ee6c23c49e93b427cd9a128ce8e4a3974c0c39c5 | |
parent | bb7fc1f2c341aff5695f852934fa7f5760e8e159 [diff] |
Fix conversion of pathlib.Path to str (#17573) This fixes the error shown in [0] that I see when trying to import a big model. [0] ``` Traceback (most recent call last): File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/code/iree/build/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py", line 144, in <module> _cli_main() File "/code/iree/build/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py", line 140, in _cli_main sys.exit(main(parse_arguments())) File "/code/iree/build/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py", line 44, in main model_proto = load_onnx_model(args) File "/code/iree/build/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py", line 107, in load_onnx_model onnx.load_external_data_for_model(inferred_model, data_dir) File "/code/iree/.venv/lib/python3.10/site-packages/onnx/external_data_helper.py", line 65, in load_external_data_for_model load_external_data_for_tensor(tensor, base_dir) File "/code/iree/.venv/lib/python3.10/site-packages/onnx/external_data_helper.py", line 43, in load_external_data_for_tensor external_data_file_path = c_checker._resolve_external_data_location( # type: ignore[attr-defined] TypeError: _resolve_external_data_location(): incompatible function arguments. The following argument types are supported: 1. (arg0: str, arg1: str, arg2: str) -> str ```
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.