使用tf.data实现tensorflow输入管道时出错

Question

我在使用新的tf.data tensorflow类实现输入管道时遇到问题。

具体来说，当我将卷积操作包含在预处理中时 - 我使用map方法添加到管道中 - 我收到以下错误

tensorflow.python.framework.errors_impl.UnimplementedError: Generic conv implementation only supports NHWC tensor format for now.
 [[{{node conv_debug}} = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](conv_debug-0-TransposeNHWCToNCHW-LayoutOptimizer, ArithmeticOptimizer/FoldMultiplyIntoConv_scaled_conv_debug_Const)]]

当我从管道中排除卷积时，一切都按预期工作。

我附上了重现问题所需的最小代码。

测试了3种配置：

• Tensorflow 1.12.0，CUDA 10.0，CUDnn 7.4.1，得到了错误。
• Tensorflow 1.11.0，CUDA 9.0，CUDnn 7.3.1，得到了错误。
• Tensorflow 1.8.0，CUDA 8.0，CUDnn 6.0，它的工作原理。

我做错了还是与CUDA / CUDnn相关的问题？

谢谢！

import numpy as np
import tensorflow as tf

image_height, image_width = 100, 200

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def serialize_to_record(record_name, label, image):
        """Create a data record and store it"""
        writer = tf.python_io.TFRecordWriter(record_name)
        image_raw = image.tostring() 
        label_raw = label
        sample = tf.train.Example(features=tf.train.Features(feature={
            'image_raw': _bytes_feature(image_raw),
            'label_raw': _bytes_feature(label_raw)}))        
        writer.write(sample.SerializeToString())
        writer.close()
        return


def _dataset_parser(record):
    """Read and deserialize a tensorflow record"""
    parsed = tf.parse_single_example(record,
        features={'image_raw': tf.FixedLenFeature([], tf.string),
                  'label_raw': tf.FixedLenFeature([], tf.string)})
    image_ = tf.decode_raw(parsed['image_raw'], tf.uint8)
    image_.set_shape(image_height * image_width * 3)
    image_ = tf.reshape(image_, (image_height, image_width, 3))
    image = tf.cast(image_, tf.float32) / 255.0
    label = parsed['label_raw']

    return {'image': image, 'label': label}


def _dataset_preprocessor(datum):
    """dummy preprocessor consisting of a convolution with a random kernel"""
    image = datum['image']
    kernel = np.random.rand(5, 5, 3, 3)
    kernel_tf = tf.constant(kernel, dtype=tf.float32)
    image = tf.expand_dims(image, axis=0)
    image = tf.nn.conv2d(image, kernel_tf, [1, 1, 1, 1], padding='SAME', name='conv_debug')
    image = tf.squeeze(image, axis=0)
    datum['image'] = image
    return datum


def _dataset_operator(record):
    """define a sequence of operation to run on the dataset"""
    datum = _dataset_parser(record)
    datum = _dataset_preprocessor(datum)
    return datum


def _dataset_operator_noconv(record):
    """define a sequence of operation to run on the dataset"""
    datum = _dataset_parser(record)
    return datum


if __name__ == '__main__':

    # create a random tensor
    image = (255.0 * np.random.rand(image_height, image_width, 3)).astype(np.uint8)
    record_path = 'example.tfrecord'

    # store a tf record to disk
    serialize_to_record(record_path, label='example', image=image)

    # build a dummy dataset of copies of the generated image
    N = 32
    dataset_filenames = [record_path for n in range(N)]
    dataset = tf.data.TFRecordDataset(dataset_filenames)

    # add parser and preprocessor to the pipeline
    include_convolution_to_pipeline = True
    if include_convolution_to_pipeline:
        dataset = dataset.map(_dataset_operator)
    else:
        dataset = dataset.map(_dataset_operator_noconv)

    # complete pipeline for iteratively visiting the dataset in batches of 8 samples
    dataset = dataset.shuffle(buffer_size=100)
    dataset = dataset.batch(8)
    dataset = dataset.repeat()
    iterator = dataset.make_initializable_iterator()
    next_data = iterator.get_next()

    # init session and go for the first batch
    sess = tf.Session()
    sess.run(iterator.initializer)
    next_data_ = sess.run(next_data)

    print('***')

Answer 1

作为错误消息状态，卷积操作需要NCHW数据格式。无论您想要什么样的数据格式，它仍然需要batch_size作为维度之一。但是您正在尝试在批处理之前应用地图功能。它通常不是标准订单，但如果您需要卷积，则需要批量应用地图功能。

dataset = dataset.map(_dataset_operator)
dataset = dataset.shuffle(buffer_size=100)
dataset = dataset.batch(8)
dataset = dataset.map(_dataset_operator)
dataset = dataset.repeat()