TF version: 2.11
I try to train a simple 2input classifier with TFRecords tf.data pipeline
I do not manage to convert the tf.dense Tensor with containing only a scalar to a tf.onehot vector
# get all recorddatasets abspath
training_names= [record_path+'/'+rec for rec in os.listdir(record_path) if rec.startswith('train')]
# load in tf dataset
train_dataset = tf.data.TFRecordDataset(training_names[1])
train_dataset = train_dataset.map(return_xy)
mapping function:
def return_xy(example_proto):
#parse example
sample= parse_function(example_proto)
#decode image 1
encoded_image1 = sample['image/encoded_1']
decoded_image1 = decode_image(encoded_image1)
#decode image 2
encoded_image2 = sample['image/encoded_2']
decoded_image2 = decode_image(encoded_image2)
#decode label
print(f'image/object/class/'+level: {sample['image/object/class/'+level]}')
class_label = tf.sparse.to_dense(sample['image/object/class/'+level])
print(f'type of class label :{type(class_label)}')
print(class_label)
# conversion to onehot with depth 26 :: -> how can i extract only the value or convert directly to tf.onehot??
label_onehot=tf.one_hot(class_label,26)
#resizing image
input_left=tf.image.resize(decoded_image1,[416, 416])
input_right=tf.image.resize(decoded_image2,[416, 416])
return {'input_3res1':input_left, 'input_5res2':input_right} , label_onehot
output:
image/object/class/'+level: SparseTensor(indices=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:14", shape=(None, 1), dtype=int64), values=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:31", shape=(None,), dtype=int64), dense_shape=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:48", shape=(1,), dtype=int64))
type of class label :<class 'tensorflow.python.framework.ops.Tensor'>
Tensor("SparseToDense:0", shape=(None,), dtype=int64)
However I am sure that the label is in this Tensor because when run it eagerly
raw_dataset = tf.data.TFRecordDataset([rec_file])
parsed_dataset = raw_dataset.map(parse_function) # only parsing
for sample in parsed_dataset:
class_label=tf.sparse.to_dense(sample['image/object/class/label_level3'])[0]
print(f'type of class label :{type(class_label)}')
print(f'labels from labelmap :{class_label}')
I get output:
type of class label :<class 'tensorflow.python.framework.ops.EagerTensor'>
labels from labelmap :7
If I just chose a random number for the label and pass it to tf_one_hot(randint, 26) then the model begins to train (obviously nonsensical).
So the question is how can i convert the:
Tensor("SparseToDense:0", shape=(None,), dtype=int64)
to a
Tensor("one_hot:0", shape=(26,), dtype=float32)
What I tried so far
in the call data.map(parse_xy) i tried to just call .numpy() on the tf tensors but didnt work , this only works for eager tensors.
In my understanding i cannot use eager execution because everthing in the parse_xy function gets excecuted on the whole graph: ive already tried to enable eager execution -> failed
https://www.tensorflow.org/api_docs/python/tf/config/run_functions_eagerly
Note: This flag has no effect on functions passed into tf.data transformations as arguments.
tf.data functions are never executed eagerly and are always executed as a compiled Tensorflow Graph.
ive also tried to use the tf_pyfunc but this only returns another tf.Tensor with an unknown shape
def get_onehot(tensor):
class_label=tensor[0]
return tf.one_hot(class_label,26)
and add the line in parse_xy:
label_onehot=tf.py_function(func=get_onehot, inp=[class_label], Tout=tf.int64)
but there i always get an unknown shape which a cannot just alter with .set_shape()
