import sys

 sys.path.append('/projects/caffe-ssd/python')

 import caffe
4 net = caffe.NetSpec()

net.data ,net.label = caffe.layers.ImageData(
    name="InputData"

    source="train.txt",

    batch_size=32,

    new_width=48,

    new_height=48,

    ntop=2,

    is_color=True,

    shuffle=True,

    root_folder='/',

    transform_param=dict(crop_size=40,mirror=True))

print str(net.to_proto())

输出：

layer {

  name: "InputData"

  type: "ImageData"

  top: "data"

  top: "label"

  transform_param {

    mirror: true

    crop_size: 40

  }

  image_data_param {

    source: "train.txt"

    batch_size: 32

    shuffle: true

    new_height: 48

    new_width: 48

    is_color: true

    root_folder: "/"

  }

}

net.data, net.label = caffe.layers.Data(

    name="InputData",

    source="train_lmdb",

    backend = caffe.params.Data.LMDB,

    batch_size=32,

    ntop=2,
    include=dict(phase=caffe.TRAIN)

    transform_param=dict(

        crop_size=227,

        mean_value=[104, 117, 123],

        mirror=True

    )

)

输出：

layer {

  name: "InputData"

  type: "Data"

  top: "data"

  top: "label"

  transform_param {

    mirror: true

    crop_size: 227

    mean_value: 104

    mean_value: 117

    mean_value: 123

  }

  data_param {

    source: "train_lmdb"

    batch_size: 32

    backend: LMDB

  }

}

net.data, net.label = caffe.layers.HDF5Data(

            name="InputData",

            source='./training_data_paths.txt',

            batch_size=64,

            include=dict(phase=caffe.TRAIN),

            ntop=2

            )

输出：

layer {

  name: "InputData"

  type: "HDF5Data"

  top: "data"

  top: "label"

  include {

    phase: TRAIN

  }

  hdf5_data_param {

    source: "./training_data_paths.txt"

    batch_size: 64

  }

}

另有：

image = L.HDF5Data(

    hdf5_data_param={ 'source': './training_data_paths.txt', 'batch_size': 64 },

     include={'phase': caffe.TRAIN }

)