in_data.py 文件源码

def read_data(file_queue):
    """
    Data is saved in binary files.
    Each row has:
        1st byte      -> label
        2nd-last byte -> 3D Volume [height, width, depth, channels]
    Args:
        file_queue      -> a queue of file names saved as strings
    Rtns:
        An object with:
            height      -> volume height
            width       -> volume width
            depth       -> volume depth
            nChan       -> number of channels
            key         -> scalar tensor with file name and record number
            label       -> 1D int32 tensor with the associated label
            img3_uint8  -> 4D uint8 tensor with image data
    """

    class record_data(object):
        pass
    img3_obj = record_data()

    # Dimensions of data
    label_bytes     = 1
    img3_obj.height = CFG['height']
    img3_obj.width  = CFG['width']
    img3_obj.depth  = CFG['depth']
    img3_obj.nChan  = CFG['nChan']

    # Size in memory
    img3_bytes   = img3_obj.height*img3_obj.width*img3_obj.depth*img3_obj.nChan
    record_bytes = label_bytes + img3_bytes

    # Read a record
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    img3_obj.key,value = reader.read(file_queue)

    # Convert from a string to a vector of uint8 that is record_bytes long
    record_bytes = tf.decode_raw(value, tf.uint8)

    # First byte represent the label, which we convert from uint8 -> int32
    img3_obj.label = tf.cast(tf.slice(record_bytes,[0],[label_bytes]),tf.int32)

    # Remaining bytes after the label represent the image, which we reshape from
    # [depth * height * width] to [depth,height, width]
    depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes],
                [img3_bytes]),[img3_obj.depth,img3_obj.height,img3_obj.width,
                                                                img3_obj.nChan])
    img3_obj.img3_uint8 = tf.transpose(depth_major,[2,1,0,3])
    return img3_obj