caffe: 数据层

在caffe中layer分为以下几类：

Data Layers
Vision Layers
Recurrent Layers
Common Layers
Normalization Layers
Activation / Neuron Layers
Utility Layers
Loss Layers

首先介绍数据层，数据层是每个模型的最底层，是模型的入口，不仅提供数据的输入，也提供数据从Blobs转换成别的格式进行保存输出。通常数据的预处理（如减去均值, 放大缩小, 裁剪和镜像等），也在这一层设置参数实现。数据来源可以来自高效的数据库（如LevelDB和LMDB），也可以直接来自于内存。如果不是很注重效率的话，数据也可来自磁盘的hdf5文件和图片格式文件。所有的数据层的都具有的公用参数：先看示例：

 layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
  }
}

下面分别介绍这些参数的含义：
1）name：表示该层的名字，一般选取有意义的名称;
2) type: 表示层类型，如果是Data，表示数据来源于LevelDB或LMDB。根据数据的来源不同，数据层的类型也不同（后面会详细阐述）。通常都是采用的LevelDB或LMDB数据，因此层类型设置为Data。
3）top或bottom: 每一层用bottom来输入数据，用top来输出数据。如果只有top没有bottom，则此层只有输出，没有输入。反之亦然。如果有多个 top或多个bottom，表示有多个blobs数据的输入和输出。
4）data 与 label: 在数据层中，至少有一个命名为data的top。如果有第二个top，若是分类问题则一般是label。根据任务的不同top的有所不同，例如：siftflow-fcn16s 的datalayer如下：

layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "sem"
  top: "geo"
  python_param {
    module: "siftflow_layers"
    layer: "SIFTFlowSegDataLayer"
    param_str: "{\'siftflow_dir\': \'../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
  }
}

5）include: 一般训练的时候和测试的时候，模型的层是不一样的。该层（layer）是属于训练阶段的层，还是属于测试阶段的层，需要用include来指定。如果没有include参数，则表示该层既在训练模型中，又在测试模型中

6）Transform_param: 数据的预处理，可以将数据变换到定义的范围内。如设置scale、mirror、crop_size、mean_file等。例如：

transform_param {
    scale: 0.00390625
    mean_file_size: "examples/cifar10/mean.binaryproto"
    # 用一个配置文件来进行均值操作
    mirror: 1  # 1表示开启镜像，0表示关闭，也可用ture和false来表示
    # 剪裁一个 227*227的图块，在训练阶段随机剪裁，在测试阶段从中间裁剪
    crop_size: 227
  }

参考caffe.proto 文件，有关transform_param 的code如下：

// Message that stores parameters used to apply transformation
// to the data layer's data
message TransformationParameter {
  // For data pre-processing, we can do simple scaling and subtracting the
  // data mean, if provided. Note that the mean subtraction is always carried
  // out before scaling.
  optional float scale = 1 [default = 1];
  // Specify if we want to randomly mirror data.
  optional bool mirror = 2 [default = false];
  // Specify if we would like to randomly crop an image.
  optional uint32 crop_size = 3 [default = 0];
  // mean_file and mean_value cannot be specified at the same time
  optional string mean_file = 4;
  // if specified can be repeated once (would subtract it from all the channels)
  // or can be repeated the same number of times as channels
  // (would subtract them from the corresponding channel)
  repeated float mean_value = 5;
  // Force the decoded image to have 3 color channels.
  optional bool force_color = 6 [default = false];
  // Force the decoded image to have 1 color channels.
  optional bool force_gray = 7 [default = false];
}

type:参数输入类型很多，所以单独拿出来做解释，下面详细介绍：
1) tpye：Data，这种最常用，例如：

layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/mnist/mnist_train_lmdb"
    batch_size: 64
    backend: LMDB
  }
}

必须设置的参数：
source: 包含数据库的目录名称，如examples/mnist/mnist_train_lmdb
batch_size: 每次处理的数据个数，如64
可选的参数：
rand_skip: 在开始的时候，路过某个数据的输入。通常对异步的SGD很有用。
backend: 选择是采用LevelDB还是LMDB, 默认是LevelDB.
参考源码：

 **message DataParameter {
  enum DB {
    LEVELDB = 0;
    LMDB = 1;
  }
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 4;
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  // DEPRECATED. Each solver accesses a different subset of the database.
  optional uint32 rand_skip = 7 [default = 0];
  optional DB backend = 8 [default = LEVELDB];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  // Force the encoded image to have 3 color channels
  optional bool force_encoded_color = 9 [default = false];
  // Prefetch queue (Increase if data feeding bandwidth varies, within the
  // limit of device memory for GPU training)
  optional uint32 prefetch = 10 [default = 4];
}**

2）type：memory_data

layer {
  top: "data"
  top: "label"
  name: "memory_data"
  type: "MemoryData"
  memory_data_param{
    batch_size: 2
    height: 100
    width: 100
    channels: 1
  }
  transform_param {
    scale: 0.0078125
    mean_file: "mean.proto"
    mirror: false
  }
}

必须设置的参数： batch_size：每一次处理的数据个数，比如2; channels：通道数; height：高度; width: 宽度;
参考源码：

message MemoryDataParameter {
  optional uint32 batch_size = 1;
  optional uint32 channels = 2;
  optional uint32 height = 3;
  optional uint32 width = 4;
}

3) type:HDF5data

layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "examples/hdf5_classification/data/train.txt"
    batch_size: 10
  }
}

必须设置的参数：source: 读取的文件名称;batch_size: 每一次处理的数据个数

// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 2;

  // Specify whether to shuffle the data.
  // If shuffle == true, the ordering of the HDF5 files is shuffled,
  // and the ordering of data within any given HDF5 file is shuffled,
  // but data between different files are not interleaved; all of a file's
  // data are output (in a random order) before moving onto another file.
  optional bool shuffle = 3 [default = false];
}

4） type:ImageData

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
  }
  image_data_param {
    source: "examples/_temp/file_list.txt"
    batch_size: 50
    new_height: 256
    new_width: 256
  }
}

必须设置的参数：
source: 一个文本文件的名字，每一行给定一个图片文件的名称和标签（label)
batch_size: 每一次处理的数据个数，即图片数
可选参数：
rand_skip: 在开始的时候，路过某个数据的输入。通常对异步的SGD很有用。
shuffle: 随机打乱顺序，默认值为false
new_height,new_width: 如果设置，则将图片进行resize
参考源码：

message ImageDataParameter {
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 4 [default = 1];
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  optional uint32 rand_skip = 7 [default = 0];
  // Whether or not ImageLayer should shuffle the list of files at every epoch.
  optional bool shuffle = 8 [default = false];
  // It will also resize images if new_height or new_width are not zero.
  optional uint32 new_height = 9 [default = 0];
  optional uint32 new_width = 10 [default = 0];
  // Specify if the images are color or gray
  optional bool is_color = 11 [default = true];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  optional string root_folder = 12 [default = ""];
}

5）type：Python 这个有点牛，可以自己定义
例如：鼎鼎大名的fcn 就采用了这个：

layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "sem"
  top: "geo"
  python_param {
    module: "siftflow_layers"
    layer: "SIFTFlowSegDataLayer"
    param_str: "{\'siftflow_dir\': \'../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
  }
}

具体可去caffe官网查看内容，先写这么多吧！！！

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