INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, assertion failed: [Unable to decode bytes as JPEG, PNG, GIF, or BMP]

[ghost]

Hi all,
I use Python 2.7.13 and Tensorflow 1.3.0 on CPU.

I want to use DensNet( https://github.com/pudae/tensorflow-densenet ) for regression problem. My data contains 60000 jpeg images with 37 float labels for each image.
I saved my data into tfrecords files by:

`
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!')` 

---

And I decode it by:

`
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)`

---

It's all fine up to this point. But when I use the bellow commands for decoding TFRecord files:

`
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)`

---

I get the following error.

INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, 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.

---

To use Densenet for my problem, I should fix this error first.
Could anybody please help me out of this problem. This code works perfectly for the datasets like flowers, MNIST and CIFAR10 available at https://github.com/pudae/tensorflow-densenet/tree/master/datasets but does not work for my data.