wehub-resource-sync
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
from typing import Dict, Optional, Union
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import numpy as np
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import tensorflow as tf
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from ray.air.util.data_batch_conversion import _unwrap_ndarray_object_type_if_needed
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def convert_ndarray_to_tf_tensor(
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ndarray: np.ndarray,
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dtype: Optional[tf.dtypes.DType] = None,
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type_spec: Optional[tf.TypeSpec] = None,
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) -> tf.Tensor:
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"""Convert a NumPy ndarray to a TensorFlow Tensor.
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Args:
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ndarray: A NumPy ndarray that we wish to convert to a TensorFlow Tensor.
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dtype: A TensorFlow dtype for the created tensor; if None, the dtype will be
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inferred from the NumPy ndarray data.
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type_spec: A type spec that specifies the shape and dtype of the returned
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tensor. If you specify ``dtype``, the dtype stored in the type spec is
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ignored.
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Returns:
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A TensorFlow Tensor.
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"""
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if dtype is None and type_spec is not None:
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dtype = type_spec.dtype
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is_ragged = isinstance(type_spec, tf.RaggedTensorSpec)
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ndarray = _unwrap_ndarray_object_type_if_needed(ndarray)
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if is_ragged:
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return tf.ragged.constant(ndarray, dtype=dtype)
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else:
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return tf.convert_to_tensor(ndarray, dtype=dtype)
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def convert_ndarray_batch_to_tf_tensor_batch(
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ndarrays: Union[np.ndarray, Dict[str, np.ndarray]],
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dtypes: Optional[Union[tf.dtypes.DType, Dict[str, tf.dtypes.DType]]] = None,
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) -> Union[tf.Tensor, Dict[str, tf.Tensor]]:
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"""Convert a NumPy ndarray batch to a TensorFlow Tensor batch.
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Args:
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ndarrays: A (dict of) NumPy ndarray(s) that we wish to convert to a TensorFlow
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Tensor.
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dtypes: A (dict of) TensorFlow dtype(s) for the created tensor; if None, the
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dtype will be inferred from the NumPy ndarray data.
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Returns:
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A (dict of) TensorFlow Tensor(s).
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"""
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if isinstance(ndarrays, np.ndarray):
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# Single-tensor case.
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if isinstance(dtypes, dict):
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if len(dtypes) != 1:
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raise ValueError(
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"When constructing a single-tensor batch, only a single dtype "
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f"should be given, instead got: {dtypes}"
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)
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dtypes = next(iter(dtypes.values()))
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batch = convert_ndarray_to_tf_tensor(ndarrays, dtypes)
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else:
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# Multi-tensor case.
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batch = {
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col_name: convert_ndarray_to_tf_tensor(
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col_ndarray,
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dtype=dtypes[col_name] if isinstance(dtypes, dict) else dtypes,
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)
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for col_name, col_ndarray in ndarrays.items()
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}
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return batch
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