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| # Copyright 2023 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Tensorflow Example proto decoder for object detection. | |
| A decoder to decode string tensors containing serialized tensorflow.Example | |
| protos for object detection. | |
| """ | |
| import csv | |
| # Import libraries | |
| import tensorflow as tf, tf_keras | |
| from official.vision.dataloaders import tf_example_decoder | |
| class TfExampleDecoderLabelMap(tf_example_decoder.TfExampleDecoder): | |
| """Tensorflow Example proto decoder.""" | |
| def __init__(self, label_map, include_mask=False, regenerate_source_id=False, | |
| mask_binarize_threshold=None): | |
| super(TfExampleDecoderLabelMap, self).__init__( | |
| include_mask=include_mask, regenerate_source_id=regenerate_source_id, | |
| mask_binarize_threshold=mask_binarize_threshold) | |
| self._keys_to_features.update({ | |
| 'image/object/class/text': tf.io.VarLenFeature(tf.string), | |
| }) | |
| name_to_id = self._process_label_map(label_map) | |
| self._name_to_id_table = tf.lookup.StaticHashTable( | |
| tf.lookup.KeyValueTensorInitializer( | |
| keys=tf.constant(list(name_to_id.keys()), dtype=tf.string), | |
| values=tf.constant(list(name_to_id.values()), dtype=tf.int64)), | |
| default_value=-1) | |
| def _process_label_map(self, label_map): | |
| if label_map.endswith('.csv'): | |
| name_to_id = self._process_csv(label_map) | |
| else: | |
| raise ValueError('The label map file is in incorrect format.') | |
| return name_to_id | |
| def _process_csv(self, label_map): | |
| name_to_id = {} | |
| with tf.io.gfile.GFile(label_map, 'r') as f: | |
| reader = csv.reader(f, delimiter=',') | |
| for row in reader: | |
| if len(row) != 2: | |
| raise ValueError('Each row of the csv label map file must be in ' | |
| '`id,name` format. length = {}'.format(len(row))) | |
| id_index = int(row[0]) | |
| name = row[1] | |
| name_to_id[name] = id_index | |
| return name_to_id | |
| def _decode_classes(self, parsed_tensors): | |
| return self._name_to_id_table.lookup( | |
| parsed_tensors['image/object/class/text']) | |