使用 tfslim 解码 tfrecord

我在 CPU 上使用 Python 2.7.13 和 Tensorflow 1.3.0。

我想使用 DensNet( https://github.com/pudae/tensorflow-densenet ) 来解决回归问题。我的数据包含 60000 个 jpeg 图像，每个图像有 37 个浮动标签。 我通过以下方式将数据保存到 tfrecords 文件中：

def Read_Labels(label_path):
labels_csv = pd.read_csv(label_path)
labels = np.array(labels_csv)
return labels[:,1:]

`

def load_image(addr):
# read an image and resize to (224, 224)
img = cv2.imread(addr)
img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.astype(np.float32)
return img

def Shuffle_images_with_labels(shuffle_data, photo_filenames, labels):
if shuffle_data:
    c = list(zip(photo_filenames, labels))
    shuffle(c)
    addrs, labels = zip(*c)
    return addrs, labels

def image_to_tfexample_mine(image_data, image_format, height, width, label):
return tf.train.Example(features=tf.train.Features(feature={
  'image/encoded': bytes_feature(image_data),    
  'image/format': bytes_feature(image_format),
  'image/class/label': _float_feature(label),
  'image/height': int64_feature(height),
  'image/width': int64_feature(width),
}))

def _convert_dataset(split_name, filenames, labels, dataset_dir):
assert split_name in ['train', 'validation']

num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))

with tf.Graph().as_default():

    for shard_id in range(_NUM_SHARDS):
      output_filename = _get_dataset_filename(dataset_path, split_name, shard_id)

      with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
          start_ndx = shard_id * num_per_shard
          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
          for i in range(start_ndx, end_ndx):
              sys.stdout.write('\r>> Converting image %d/%d shard %d' % (
                      i+1, len(filenames), shard_id))
              sys.stdout.flush()

              img = load_image(filenames[i])
              image_data = tf.compat.as_bytes(img.tostring())

              label = labels[i]

              example = image_to_tfexample_mine(image_data, image_format, height, width, label)

              # Serialize to string and write on the file
              tfrecord_writer.write(example.SerializeToString())

sys.stdout.write('\n')
sys.stdout.flush()

def run(dataset_dir):

labels = Read_Labels(dataset_dir + '/training_labels.csv')

photo_filenames = _get_filenames_and_classes(dataset_dir + '/images_training')

shuffle_data = True 

photo_filenames, labels = Shuffle_images_with_labels(
        shuffle_data,photo_filenames, labels)

training_filenames = photo_filenames[_NUM_VALIDATION:]
training_labels = labels[_NUM_VALIDATION:]

validation_filenames = photo_filenames[:_NUM_VALIDATION]
validation_labels = labels[:_NUM_VALIDATION]

_convert_dataset('train',
                 training_filenames, training_labels, dataset_path)
_convert_dataset('validation',
                 validation_filenames, validation_labels, dataset_path)

print('\nFinished converting the Flowers dataset!')

我通过以下方式解码它：

with tf.Session() as sess:

feature = {
  'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
  'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
  'image/class/label': tf.FixedLenFeature(
      [37,], tf.float32, default_value=tf.zeros([37,], dtype=tf.float32)),
   }

filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)

reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)

features = tf.parse_single_example(serialized_example, features=feature)

image = tf.decode_raw(features['image/encoded'], tf.float32)
print(image.get_shape())

label = tf.cast(features['image/class/label'], tf.float32)

image = tf.reshape(image, [224, 224, 3])

images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10)

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)

coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)

for batch_index in range(6):
    img, lbl = sess.run([images, labels])
    img = img.astype(np.uint8)
    print(img.shape)
    for j in range(6):
        plt.subplot(2, 3, j+1)
        plt.imshow(img[j, ...])
    plt.show()

coord.request_stop()

coord.join(threads)

到目前为止一切都很好。但是当我使用以下命令解码 TFRecord 文件时：

 reader = tf.TFRecordReader

keys_to_features = {
      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
      'image/class/label': tf.FixedLenFeature(
      [37,], tf.float32, default_value=tf.zeros([37,], dtype=tf.float32)),
      }

items_to_handlers = {
      'image': slim.tfexample_decoder.Image('image/encoded'),
      'label': slim.tfexample_decoder.Tensor('image/class/label'),
   }

decoder = slim.tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

我收到以下错误。

信息：tensorflow：向协调器报告错误：，断言失败：[无法将字节解码为 JPEG、PNG、GIF 或 BMP] [[节点：case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert = Assert[T=[DT_STRING]，summary=3，_device="/job:localhost/replica:0/task:0/cpu: 0"](案例/If_0/decode_image/cond_jpeg/cond_png/cond_gif/is_bmp,案例/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert/data_0)]] 信息：tensorflow：捕获 OutOfRangeError。停止训练。 信息：sensorflow：培训完成！将模型保存到磁盘。

---

要使用 Densenet 解决我的问题，我应该首先修复此错误。 有人可以帮我解决这个问题吗？此代码非常适合 https://github.com/pudae/tensorflow-densenet/tree/master/datasets 上提供的花、MNIST 和 CIFAR10 等数据集，但不适用于我的数据。

感谢普达，问题已经解决了。我需要使用：

 image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()

而不是加载数据。现在效果很好。

 img = load_image(filenames[i])
 image_data = tf.compat.as_bytes(img.tostring())