AttributeError: 'Tensor' object has no attribute 'to_sparse'

Question

I was trying out this tutorial using this Plant Leaves dataset (with over 35k images consisting.JPG, .PNG as well as.JPEG files) with tensorflow version 1.14
And I followed similar steps except; skipping "Load using keras.preprocessing" part. I directly jumped over to "Load using tf.data" part. But when I ran the snippet it threw me this error:

File "D:\Softwares\Anaconda\lib\site-packages\tensorflow\python\ops\ragged\ragged_string_ops.py", line 640, in strings_split_v1
    return ragged_result.to_sparse()

AttributeError: 'Tensor' object has no attribute 'to_sparse'

Complete error:
My code snippet is:

dir_root=pathlib.Path("D:/Projects/IIIT/LeafID/Dataset/PlantVillage")
list_ds=tf.data.Dataset.list_files(str(dir_root/"*/*"))

def getLabel(fpath):
    parts = tf.strings.split(fpath, os.path.sep)
    return parts[-2] == clnames

def decodeimg(img):
    img=tf.image.decode_jpeg(img,channels=3)
    img=tf.image.convert_image_dtype(img,tf.float32)
    return tf.image.resize(img,[64,64])

def process_path(fpath):
    label=getLabel(fpath)
    img=tf.io.read_file(fpath)
    img=decodeimg(img)
    return img, label

label_ds=list_ds.map(process_path,num_parallel_calls=AUTOTUNE)

which is almost similar to the code here , except the variables. I couldn't understand what's the problem here? Is there something wrong with the process of images getting converted to tensor? Because when I open ragged_string_ops.py, it shows me something like this:

if result_type == "SparseTensor":
      return ragged_result.to_sparse()

TIA

Answer 1

I ran into a similar issue working through that tutorial, and found this issue suggesting it's a bug with strings.split in certain tensorflow versions (I saw this issue in tf 1.14, and the OP in the github issue saw it in 1.15): https://github.com/tensorflow/tensorflow/issues/33623

From the linked issue comments, it looks like two possible solutions are (1) adding brackets around your string, eg c = tf.strings.split(['a b']) or (2) adding resultType='RaggedTensor', eg tf.strings.split('a b',result_type='RaggedTensor') to return a tensor (although this looks like it's buggy behavior, and may be corrected in later versions of tf).

Hope this helps.