Michal_Lukac:

but it fails during training:


If you mean that is failing with fit etc. is that your are then running this loop in graph mode and you are in the same case as:

  

      github.com/tensorflow/tensorflow
  

  
    
  
	  
  

  
    
      autograph fails inside keras model train_step including a for loop over a tensor 4
    

    
      
        opened 
        
          
        
        Aug 7, 2020
      
      


      
        
          
          klaimans
        
      
    

    
        TF 2.3
        comp:autograph
        comp:keras
        stat:awaiting tensorflower
        type:bug
    
  


  
    <em>Please make sure that this is a bug. As per our
[GitHub Policy](https://git…hub.com/tensorflow/tensorflow/blob/master/ISSUES.md),
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:bug_template</em>

**System information**
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linus Ubuntu 18.04
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: no
- TensorFlow installed from (source or binary): binary (docker image latest-gpu-py3)
- TensorFlow version (use command below): 2.3
- Python version: Python 3.6.9
- Bazel version (if compiling from source):
- GCC/Compiler version (if compiling from source):
- CUDA/cuDNN version:
- GPU model and memory: V100

**Describe the current behavior**

When writing a python "for" loop inside a tf.keras.Model.train_step I get the following error:

OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.

The same function works correctly when outside of a keras model but still decorated with tf.function. 

**Describe the expected behavior**

autograph should support iterating over a tensor also inside a keras model

**Standalone code to reproduce the issue**

```
import tensorflow as tf
import numpy as np

t = tf.Variable(0)

@tf.function()
def foo():
    for n in tf.range(tf.constant(10)):
        t.assign_add(n)
    return t

nt = foo()
nt #  <tf.Tensor: shape=(), dtype=int32, numpy=45>

class mymodel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.t = tf.Variable(0)
    def train_step(self, data):
        for n in tf.range(tf.constant(10)):
            t.assign_add(n)
        return {"loss": t}

mm = mymodel()
mm.compile()
mm.fit(np.random.random((5)), steps_per_epoch=1) # this doesn't work see trace below
```

**Other info / logs** 

OperatorNotAllowedInGraphErrorTraceback (most recent call last)
<ipython-input-18-c68155fbb474> in <module>
----> 1 mm.fit(np.random.random((5)), steps_per_epoch=1)

~usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    821       # This is the first call of __call__, so we have to initialize.
    822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
    824     finally:
    825       # At this point we know that the initialization is complete (or less

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    695     self._concrete_stateful_fn = (
    696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 697             *args, **kwds))
    698 
    699     def invalid_creator_scope(*unused_args, **unused_kwds):

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2853       args, kwargs = None, None
   2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2856     return graph_function
   2857 

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3073             arg_names=arg_names,
   3074             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075             capture_by_value=self._capture_by_value),
   3076         self._function_attributes,
   3077         function_spec=self.function_spec,

~usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

~usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

OperatorNotAllowedInGraphError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    <ipython-input-12-62f0dcb0797d>:6 train_step
        for n in tf.range(tf.constant(10)):
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:503 __iter__
        self._disallow_iteration()
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:496 _disallow_iteration
        self._disallow_when_autograph_enabled("iterating over `tf.Tensor`")
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:474 _disallow_when_autograph_enabled
        " indicate you are trying to use an unsupported feature.".format(task))

    OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
  

  

  
    
    
  

  


Probably if you will pass run_eagerly=True it will run fine but it will slower in eager mode with these nested loops.

  
      

      TensorFlow
  

  
    

tf.keras.Model  |  TensorFlow Core v2.5.0

  Model groups layers into an object with training and inference features.


  

  
    
    
  

  


Did you find a similar loop in the official Solo implementation at:

  
      

      github.com
  

  
    

WXinlong/SOLO


  SOLO and SOLOv2 for instance segmentation, ECCV 2020 &amp; NeurIPS 2020.