tensorflow deeplab3+ build_voc2012_data 代码阅读

约定不等于承诺〃 2021-11-29 12:04 280阅读 0赞

官方的代码，因为目前市面上有三个版本tf官方，keras，pytorch，但是keras在训练finetune达到原始的识别率有问题，pytorch也有问题，没法复现，因为目前backbone是xception。

其他两个我就不给连接了，github一搜就有。

主要看官方的：[https://github.com/tensorflow/models/tree/master/research/deeplab][https_github.com_tensorflow_models_tree_master_research_deeplab]

官方提供了bash的命令语句，就是.sh后缀的文件。所以其实是一键化处理。

解读代码从sh开始，首先是将数据转成TFrecord，build\_voc2012\_data.py

# Copyright 2018 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.
    # ==============================================================================
    
    """Converts PASCAL VOC 2012 data to TFRecord file format with Example protos.
    
    PASCAL VOC 2012 dataset is expected to have the following directory structure:
    
      + pascal_voc_seg
        - build_data.py
        - build_voc2012_data.py (current working directory).
        + VOCdevkit
          + VOC2012
            + JPEGImages
            + SegmentationClass
            + ImageSets
              + Segmentation
        + tfrecord
    
    Image folder:
      ./VOCdevkit/VOC2012/JPEGImages
    
    Semantic segmentation annotations:
      ./VOCdevkit/VOC2012/SegmentationClass
    
    list folder:
      ./VOCdevkit/VOC2012/ImageSets/Segmentation
    
    This script converts data into sharded data files and save at tfrecord folder.
    
    The Example proto contains the following fields:
    
      image/encoded: encoded image content.
      image/filename: image filename.
      image/format: image file format.
      image/height: image height.
      image/width: image width.
      image/channels: image channels.
      image/segmentation/class/encoded: encoded semantic segmentation content.
      image/segmentation/class/format: semantic segmentation file format.
    """
    import math
    import os.path
    import sys
    import build_data
    import tensorflow as tf
    
    FLAGS = tf.app.flags.FLAGS
    
    tf.app.flags.DEFINE_string('image_folder',
                               './VOCdevkit/VOC2012/JPEGImages',
                               'Folder containing images.')
    
    tf.app.flags.DEFINE_string(
        'semantic_segmentation_folder',
        './VOCdevkit/VOC2012/SegmentationClassRaw',
        'Folder containing semantic segmentation annotations.')
    
    tf.app.flags.DEFINE_string(
        'list_folder',
        './VOCdevkit/VOC2012/ImageSets/Segmentation',
        'Folder containing lists for training and validation')
    
    tf.app.flags.DEFINE_string(
        'output_dir',
        './tfrecord',
        'Path to save converted SSTable of TensorFlow examples.')
    
    
    _NUM_SHARDS = 4
    
    
    def _convert_dataset(dataset_split):
      """Converts the specified dataset split to TFRecord format.
    
      Args:
        dataset_split: The dataset split (e.g., train, test).
    
      Raises:
        RuntimeError: If loaded image and label have different shape.
      """
      dataset = os.path.basename(dataset_split)[:-4]
      sys.stdout.write('Processing ' + dataset)
      filenames = [x.strip('\n') for x in open(dataset_split, 'r')]
      num_images = len(filenames)
      num_per_shard = int(math.ceil(num_images / float(_NUM_SHARDS)))
    
      image_reader = build_data.ImageReader('jpeg', channels=3)
      label_reader = build_data.ImageReader('png', channels=1)
    
      for shard_id in range(_NUM_SHARDS):
        output_filename = os.path.join(
            FLAGS.output_dir,
            '%s-%05d-of-%05d.tfrecord' % (dataset, shard_id, _NUM_SHARDS))
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_idx = shard_id * num_per_shard
          end_idx = min((shard_id + 1) * num_per_shard, num_images)
          for i in range(start_idx, end_idx):
            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                i + 1, len(filenames), shard_id))
            sys.stdout.flush()
            # Read the image.
            image_filename = os.path.join(
                FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
            image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
            height, width = image_reader.read_image_dims(image_data)
            # Read the semantic segmentation annotation.
            seg_filename = os.path.join(
                FLAGS.semantic_segmentation_folder,
                filenames[i] + '.' + FLAGS.label_format)
            seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
            seg_height, seg_width = label_reader.read_image_dims(seg_data)
            if height != seg_height or width != seg_width:
              raise RuntimeError('Shape mismatched between image and label.')
            # Convert to tf example.
            example = build_data.image_seg_to_tfexample(
                image_data, filenames[i], height, width, seg_data)
            tfrecord_writer.write(example.SerializeToString())
        sys.stdout.write('\n')
        sys.stdout.flush()
    
    
    def main(unused_argv):
      dataset_splits = tf.gfile.Glob(os.path.join(FLAGS.list_folder, '*.txt'))
      for dataset_split in dataset_splits:
        _convert_dataset(dataset_split)
    
    
    if __name__ == '__main__':
      tf.app.run()

可以看下转换之后：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNDk4NTM_size_16_color_FFFFFF_t_70][]

分别分成了四个碎片。根据的是voc官方提供的split list。

这个代码仅仅对voc数据的val,train 以及test数据进行了数据格式转换，并且存储。TFrecord格式。

仿写是比较容易的。

[https_github.com_tensorflow_models_tree_master_research_deeplab]: https://github.com/tensorflow/models/tree/master/research/deeplab
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNDk4NTM_size_16_color_FFFFFF_t_70]: /images/20211129/5c1cd9a37c124b2b975d862d55de4af8.png