wehub-resource-sync
94 lines
2.2 KiB
Python
94 lines
2.2 KiB
Python
import sys
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from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException
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def _is_module_imported(module_name: str) -> bool:
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return module_name in sys.modules
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def _try_get_item(x):
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try:
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return x.item()
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except Exception as e:
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raise MlflowException(
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f"Failed to convert metric value to float: {e}",
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error_code=INVALID_PARAMETER_VALUE,
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)
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def _converter_requires(module_name: str):
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"""Wrapper function that checks if specified `module_name` is already imported before
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invoking wrapped function.
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"""
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def decorator(func):
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def wrapper(x):
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if not _is_module_imported(module_name):
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return x
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return func(x)
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return wrapper
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return decorator
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def convert_metric_value_to_float_if_possible(x) -> float:
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if x is None or type(x) == float:
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return x
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converter_fns_to_try = [
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convert_metric_value_to_float_if_ndarray,
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convert_metric_value_to_float_if_tensorflow_tensor,
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convert_metric_value_to_float_if_torch_tensor,
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]
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for converter_fn in converter_fns_to_try:
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possible_float = converter_fn(x)
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if type(possible_float) == float:
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return possible_float
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try:
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return float(x)
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except ValueError:
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return x # let backend handle conversion if possible
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@_converter_requires("numpy")
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def convert_metric_value_to_float_if_ndarray(x):
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import numpy as np
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if isinstance(x, np.ndarray):
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return float(_try_get_item(x))
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return x
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@_converter_requires("torch")
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def convert_metric_value_to_float_if_torch_tensor(x):
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import torch
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if isinstance(x, torch.Tensor):
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extracted_tensor_val = x.detach().cpu()
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return float(_try_get_item(extracted_tensor_val))
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return x
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@_converter_requires("tensorflow")
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def convert_metric_value_to_float_if_tensorflow_tensor(x):
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import tensorflow as tf
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if isinstance(x, tf.Tensor):
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try:
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return float(x)
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except Exception as e:
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raise MlflowException(
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f"Failed to convert metric value to float: {e!r}",
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error_code=INVALID_PARAMETER_VALUE,
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)
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return x
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