tensorflow – 使用tfslim解码tfrecord

2019年7月28日 246次阅读

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个浮点标签.
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,这一切都很好.但是当我使用bellow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.

INFO:tensorflow:Error reported to Coordinator: , assertion failed: [Unable to decode bytes as JPEG, PNG, GIF, or BMP]
[[Node: case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert = Assert[T=[DT_STRING], summarize=3, _device=”/job:localhost/replica:0/task:0/cpu:0″](case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/is_bmp, case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert/data_0)]]
INFO:tensorflow:Caught OutOfRangeError. Stopping Training.
INFO:sensorflow:Finished training! Saving model to disk.

要使用Densenet解决我的问题,我应该先修复此错误.
有人可以帮我解决这个问题.此代码适用于数据集,如花,MNIST和CIFAR10,可在https://github.com/pudae/tensorflow-densenet/tree/master/datasets获得,但不适用于我的数据.

最佳答案 感谢pudae,
问题解决了.我需要使用：

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

而不是这个用于加载数据.现在完美无缺.

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