Deeplab v3 (2): 源码分析

代码： https://github.com/tensorflow/models/tree/master/research/deeplab

train.py主要函数及注释如下

main()
    # 配置GPU
    conifg = slim.deployment.model_deploy.DeploymentConfig(xxx) # Create a DeploymentConfig for multi-gpu
    # 获取slim数据集实例
    dataset = deeplab.datasets.segmentation_dataset.get_dataset(xxx) # Gets an instance of slim dataset
    # 得到数据
    samples = input_generator.get(dataset, xxx) 
    # Creates a queue to prefetch tensors from `tensors`
    inputs_queue = prefetch_queue.prefetch_queue(samples, capacity=128 * config.num_clones)
    # 
    clones = Clone(_build_deeplab(inputs_queue, xxx), scope, device)

    learning_rate = train_utils.get_model_learning_rate(xxx)
    slim.learning.train(xxx)

deeplab.datasets.segmentation_dataset.get_dataset(dataset_name, split_name, dataset_dir):
    # 将example反序列化成存储之前的格式。由tf完成
    keys_to_features
    # 将反序列化的数据组装成更高级的格式。由slim完成
    items_to_handlers   
    # 解码器，进行解码
    decoder = tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)
    return dataset.Dataset(xxx)

deeplab.utils.input_generator.get(dataset, xxx)
    # provider对象根据dataset信息读取数据
    data_provider = slim.dataset_data_provider.DatasetDataProvider(dataset, xxx)
    # 获取数据，获取到的数据是单个数据，还需要对数据进行预处理，组合数据
    image, height, width = data_provider.get([common.IMAGE, common.HEIGHT, common.WIDTH])
    original_image, image, label = input_preprocess.preprocess_image_and_label(xxx)
    return tf.train.batch(xxx)

_build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label)
    # 获取数据
    samples = inputs_queue.dequeue()
    model_options = common.ModelOptions