Ubuntu 24.04
Python 3.12.3
keras 3.7.0
keras-core 0.1.7
keras-cv 0.9.0
Following this example the data set visualizes/displays the bounding boxes properly. However the augmenter fails. train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)
https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/yolov8.ipynb
augmenter = keras.Sequential(
layers=[
keras_cv.layers.AutoContrast((0, 255)),
]
)
train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.shuffle(BATCH_SIZE * 4)
train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[14], line 4
2 train_ds = train_ds.shuffle(BATCH_SIZE * 4)
3 train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
----> 4 train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/dataset_ops.py:2341, in DatasetV2.map(self, map_func, num_parallel_calls, deterministic, synchronous, use_unbounded_threadpool, name)
2336 # Loaded lazily due to a circular dependency (dataset_ops -> map_op ->
2337 # dataset_ops).
2338 # pylint: disable=g-import-not-at-top,protected-access
2339 from tensorflow.python.data.ops import map_op
-> 2341 return map_op._map_v2(
2342 self,
2343 map_func,
2344 num_parallel_calls=num_parallel_calls,
2345 deterministic=deterministic,
2346 synchronous=synchronous,
2347 use_unbounded_threadpool=use_unbounded_threadpool,
2348 name=name,
2349 )
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/map_op.py:57, in _map_v2(input_dataset, map_func, num_parallel_calls, deterministic, synchronous, use_unbounded_threadpool, name)
51 if synchronous:
52 raise ValueError(
53 "`synchronous` is not supported with `num_parallel_calls`, but"
54 " `num_parallel_calls` was set to ",
55 num_parallel_calls,
56 )
---> 57 return _ParallelMapDataset(
58 input_dataset,
59 map_func,
60 num_parallel_calls=num_parallel_calls,
61 deterministic=deterministic,
62 preserve_cardinality=True,
63 use_unbounded_threadpool=use_unbounded_threadpool,
64 name=name)
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/map_op.py:202, in _ParallelMapDataset.__init__(self, input_dataset, map_func, num_parallel_calls, deterministic, use_inter_op_parallelism, preserve_cardinality, use_legacy_function, use_unbounded_threadpool, name)
200 self._input_dataset = input_dataset
201 self._use_inter_op_parallelism = use_inter_op_parallelism
--> 202 self._map_func = structured_function.StructuredFunctionWrapper(
203 map_func,
204 self._transformation_name(),
205 dataset=input_dataset,
206 use_legacy_function=use_legacy_function)
207 if deterministic is None:
208 self._deterministic = "default"
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/structured_function.py:265, in StructuredFunctionWrapper.__init__(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)
258 warnings.warn(
259 "Even though the `tf.config.experimental_run_functions_eagerly` "
260 "option is set, this option does not apply to tf.data functions. "
261 "To force eager execution of tf.data functions, please use "
262 "`tf.data.experimental.enable_debug_mode()`.")
263 fn_factory = trace_tf_function(defun_kwargs)
--> 265 self._function = fn_factory()
266 # There is no graph to add in eager mode.
267 add_to_graph &= not context.executing_eagerly()
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:1251, in Function.get_concrete_function(self, *args, **kwargs)
1249 def get_concrete_function(self, *args, **kwargs):
1250 # Implements PolymorphicFunction.get_concrete_function.
-> 1251 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
1252 concrete._garbage_collector.release() # pylint: disable=protected-access
1253 return concrete
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:1221, in Function._get_concrete_function_garbage_collected(self, *args, **kwargs)
1219 if self._variable_creation_config is None:
1220 initializers = []
-> 1221 self._initialize(args, kwargs, add_initializers_to=initializers)
1222 self._initialize_uninitialized_variables(initializers)
1224 if self._created_variables:
1225 # In this case we have created variables on the first call, so we run the
1226 # version which is guaranteed to never create variables.
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:696, in Function._initialize(self, args, kwds, add_initializers_to)
691 self._variable_creation_config = self._generate_scoped_tracing_options(
692 variable_capturing_scope,
693 tracing_compilation.ScopeType.VARIABLE_CREATION,
694 )
695 # Force the definition of the function for these arguments
--> 696 self._concrete_variable_creation_fn = tracing_compilation.trace_function(
697 args, kwds, self._variable_creation_config
698 )
700 def invalid_creator_scope(*unused_args, **unused_kwds):
701 """Disables variable creation."""
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:178, in trace_function(args, kwargs, tracing_options)
175 args = tracing_options.input_signature
176 kwargs = {}
--> 178 concrete_function = _maybe_define_function(
179 args, kwargs, tracing_options
180 )
182 if not tracing_options.bind_graph_to_function:
183 concrete_function._garbage_collector.release() # pylint: disable=protected-access
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:283, in _maybe_define_function(args, kwargs, tracing_options)
281 else:
282 target_func_type = lookup_func_type
--> 283 concrete_function = _create_concrete_function(
284 target_func_type, lookup_func_context, func_graph, tracing_options
285 )
287 if tracing_options.function_cache is not None:
288 tracing_options.function_cache.add(
289 concrete_function, current_func_context
290 )
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:310, in _create_concrete_function(function_type, type_context, func_graph, tracing_options)
303 placeholder_bound_args = function_type.placeholder_arguments(
304 placeholder_context
305 )
307 disable_acd = tracing_options.attributes and tracing_options.attributes.get(
308 attributes_lib.DISABLE_ACD, False
309 )
--> 310 traced_func_graph = func_graph_module.func_graph_from_py_func(
311 tracing_options.name,
312 tracing_options.python_function,
313 placeholder_bound_args.args,
314 placeholder_bound_args.kwargs,
315 None,
316 func_graph=func_graph,
317 add_control_dependencies=not disable_acd,
318 arg_names=function_type_utils.to_arg_names(function_type),
319 create_placeholders=False,
320 )
322 transform.apply_func_graph_transforms(traced_func_graph)
324 graph_capture_container = traced_func_graph.function_captures
File ~/test/lib/python3.12/site-packages/tensorflow/python/framework/func_graph.py:1059, in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, create_placeholders)
1056 return x
1058 _, original_func = tf_decorator.unwrap(python_func)
-> 1059 func_outputs = python_func(*func_args, **func_kwargs)
1061 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
1062 # TensorArrays and `None`s.
1063 func_outputs = variable_utils.convert_variables_to_tensors(func_outputs)
File ~/test/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:599, in Function._generate_scoped_tracing_options.<locals>.wrapped_fn(*args, **kwds)
595 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
596 # __wrapped__ allows AutoGraph to swap in a converted function. We give
597 # the function a weak reference to itself to avoid a reference cycle.
598 with OptionalXlaContext(compile_with_xla):
--> 599 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
600 return out
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/structured_function.py:231, in StructuredFunctionWrapper.__init__.<locals>.trace_tf_function.<locals>.wrapped_fn(*args)
230 def wrapped_fn(*args): # pylint: disable=missing-docstring
--> 231 ret = wrapper_helper(*args)
232 ret = structure.to_tensor_list(self._output_structure, ret)
233 return [ops.convert_to_tensor(t) for t in ret]
File ~/test/lib/python3.12/site-packages/tensorflow/python/data/ops/structured_function.py:161, in StructuredFunctionWrapper.__init__.<locals>.wrapper_helper(*args)
159 if not _should_unpack(nested_args):
160 nested_args = (nested_args,)
--> 161 ret = autograph.tf_convert(self._func, ag_ctx)(*nested_args)
162 ret = variable_utils.convert_variables_to_tensors(ret)
163 if _should_pack(ret):
File ~/test/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py:690, in convert.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
688 try:
689 with conversion_ctx:
--> 690 return converted_call(f, args, kwargs, options=options)
691 except Exception as e: # pylint:disable=broad-except
692 if hasattr(e, 'ag_error_metadata'):
File ~/test/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py:377, in converted_call(f, args, kwargs, caller_fn_scope, options)
374 return _call_unconverted(f, args, kwargs, options)
376 if not options.user_requested and conversion.is_allowlisted(f):
--> 377 return _call_unconverted(f, args, kwargs, options)
379 # internal_convert_user_code is for example turned off when issuing a dynamic
380 # call conversion from generated code while in nonrecursive mode. In that
381 # case we evidently don't want to recurse, but we still have to convert
382 # things like builtins.
383 if not options.internal_convert_user_code:
File ~/test/lib/python3.12/site-packages/tensorflow/python/autograph/impl/api.py:459, in _call_unconverted(f, args, kwargs, options, update_cache)
456 return f.__self__.call(args, kwargs)
458 if kwargs is not None:
--> 459 return f(*args, **kwargs)
460 return f(*args)
File ~/test/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
119 filtered_tb = _process_traceback_frames(e.__traceback__)
120 # To get the full stack trace, call:
121 # `keras.config.disable_traceback_filtering()`
--> 122 raise e.with_traceback(filtered_tb) from None
123 finally:
124 del filtered_tb
File ~/test/lib/python3.12/site-packages/optree/ops.py:747, in tree_map(func, tree, is_leaf, none_is_leaf, namespace, *rests)
745 leaves, treespec = _C.flatten(tree, is_leaf, none_is_leaf, namespace)
746 flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests]
--> 747 return treespec.unflatten(map(func, *flat_args))
ValueError: as_list() is not defined on an unknown TensorShape.
Comment From: fchollet
It looks like your dataset is yielding tensors that have no shape (i.e. not even a rank), and that isn't supported by augmentation layers. Fix: make sure your dataset yields tensors with at least a defined rank and a define channel dimension. (e.g. (None, None, None, 3) would be ok).
Note that you can use set_shape to achieve this, if you know the shape of your tensors but TF doesn't.
Comment From: apiszcz
I am attempting to create them as the example shows, using a list of lists for each element.
Comment From: dhantule
Hi @apiszcz,
Could you provide the reproducible code in a colab gist ? Thanks!
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