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@ -105,21 +105,21 @@ class TFRecordToMR:
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source (str): the TFRecord file to be transformed.
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destination (str): the MindRecord file path to tranform into.
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feature_dict (dict): a dictionary than states the feature type, i.e.
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feature_dict = {"xxxx": tf.io.FixedLenFeature([], tf.string),
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feature_dict = {"xxxx": tf.io.FixedLenFeature([], tf.string), \
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"yyyy": tf.io.FixedLenFeature([], tf.int64)}
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****** follow case which uses VarLenFeature not support ******
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feature_dict = {"context": {"xxxx": tf.io.FixedLenFeature([], tf.string),
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"yyyy": tf.io.VarLenFeature(tf.int64)},
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**Follow case which uses VarLenFeature not support**
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feature_dict = {"context": {"xxxx": tf.io.FixedLenFeature([], tf.string), \
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"yyyy": tf.io.VarLenFeature(tf.int64)}, \
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"sequence": {"zzzz": tf.io.FixedLenSequenceFeature([], tf.float32)}}
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bytes_fields (list): the bytes fields which are in feature_dict.
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Rasies:
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ValueError, when:
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1) parameter TFRecord is not string.
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2) parameter MindRecord is not string.
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3) feature_dict is not FixedLenFeature.
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4) parameter bytes_field is not list(str) or not in feature_dict
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Exception, when tensorflow module not found or version is not correct.
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ValueError: the following condition will cause ValueError, 1) parameter TFRecord is not string, 2) parameter
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MindRecord is not string, 3) feature_dict is not FixedLenFeature, 4) parameter bytes_field is not list(str)
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or not in feature_dict.
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Exception: when tensorflow module not found or version is not correct.
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"""
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def __init__(self, source, destination, feature_dict, bytes_fields=None):
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if not tf:
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