A library that includes pure TF/Keras preprocessing and augmentation layers, providing support for various data types such as images, labels, bounding boxes, segmentation masks, and more.
Home Page: https://kerasaug.readthedocs.io/en/latest/
License: Apache License 2.0
Should verify with tf.debugging.enable_check_numerics()
tf.image.rgb_to_hsvFor keras==2.13, the RandomGenerator is move to keras.src.backend
Currently, RandomChoice losses data structure if batchwise=False.
Should be same implementation of Resize
RandomApply should work both batch-wise and per input. Currently, it can only work batch-wise.
Streamlit app is broken with new KerasCV
Otherwise the @tf.function might raise error for unknown dimension
Need cv2 installed
Can KerasAug uses jax as its backend?
Refs:
Pseudo code:
class BaseLayer(nn.Module):
...
@nn.compact
def __call__(self, inputs):
self.get_random_transformation_batch()
self.augment_images()
self.augment_bounding_boxes()
self.augment_segmentation_masks()
self.augment_keypoints()
return outputsI am receiving the following error when trying to apply TrivialAugmentWide or RandAugment layers with mixed precision. Any guess?
from tensorflow.keras import mixed_precision
mixed_precision.set_global_policy('mixed_float16')
def make_dataset(X,y,batch_size,autotune = tf.data.AUTOTUNE,augmentation = None,seed = seed):
def preprocess_data(images, labels, augmentation=None):
inputs = {"images": images, "labels": labels}
outputs = augmentation(inputs) if augmentation != None else inputs
return outputs["images"], outputs["labels"]
dataset = tf.data.Dataset.from_tensor_slices((X, y))
dataset = dataset.batch(batch_size).map(lambda x, y: preprocess_data(x, y, augmentation=augmentation), num_parallel_calls=autotune).prefetch(autotune)
return dataset
augmentation_layer = tfk.Sequential([
keras_aug.layers.TrivialAugmentWide(value_range=(0,1), interpolation='bilinear', name='trivial_augment'),
keras_aug.layers.RandomErase(area_factor=(0.02, 0.1), fill_mode='gaussian_noise', name='random_erase')
], name='preprocessing')
training_dataset = make_dataset(X_train, y_train, batch_size=batch_size, augmentation=augmentation_layer)
--------------------------------------------------------------------------------------------------------------------------------
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/dataset_ops.py:2268, in DatasetV2.map(self, map_func, num_parallel_calls, deterministic, name)
2264 # Loaded lazily due to a circular dependency (dataset_ops -> map_op ->
2265 # dataset_ops).
2266 # pylint: disable=g-import-not-at-top,protected-access
2267 from tensorflow.python.data.ops import map_op
-> 2268 return map_op._map_v2(
2269 self,
2270 map_func,
2271 num_parallel_calls=num_parallel_calls,
2272 deterministic=deterministic,
2273 name=name)
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/map_op.py:40, in _map_v2(input_dataset, map_func, num_parallel_calls, deterministic, name)
37 return _MapDataset(
38 input_dataset, map_func, preserve_cardinality=True, name=name)
39 else:
---> 40 return _ParallelMapDataset(
41 input_dataset,
42 map_func,
43 num_parallel_calls=num_parallel_calls,
44 deterministic=deterministic,
45 preserve_cardinality=True,
46 name=name)
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/data/ops/map_op.py:148, in _ParallelMapDataset.__init__(self, input_dataset, map_func, num_parallel_calls, deterministic, use_inter_op_parallelism, preserve_cardinality, use_legacy_function, name)
146 self._input_dataset = input_dataset
147 self._use_inter_op_parallelism = use_inter_op_parallelism
--> 148 self._map_func = structured_function.StructuredFunctionWrapper(
149 map_func,
150 self._transformation_name(),
151 dataset=input_dataset,
152 use_legacy_function=use_legacy_function)
153 if deterministic is None:
154 self._deterministic = "default"
File ~usr/local/lib/python3.11/dist-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 ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:1222, in Function.get_concrete_function(self, *args, **kwargs)
1220 def get_concrete_function(self, *args, **kwargs):
1221 # Implements GenericFunction.get_concrete_function.
-> 1222 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
1223 concrete._garbage_collector.release() # pylint: disable=protected-access
1224 return concrete
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:1192, in Function._get_concrete_function_garbage_collected(self, *args, **kwargs)
1190 if self._variable_creation_config is None:
1191 initializers = []
-> 1192 self._initialize(args, kwargs, add_initializers_to=initializers)
1193 self._initialize_uninitialized_variables(initializers)
1195 if self._created_variables:
1196 # In this case we have created variables on the first call, so we run the
1197 # version which is guaranteed to never create variables.
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:694, in Function._initialize(self, args, kwds, add_initializers_to)
689 self._variable_creation_config = self._generate_scoped_tracing_options(
690 variable_capturing_scope,
691 tracing_compilation.ScopeType.VARIABLE_CREATION,
692 )
693 # Force the definition of the function for these arguments
--> 694 self._concrete_variable_creation_fn = tracing_compilation.trace_function(
695 args, kwds, self._variable_creation_config
696 )
698 def invalid_creator_scope(*unused_args, **unused_kwds):
699 """Disables variable creation."""
File ~usr/local/lib/python3.11/dist-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 )
181 _set_arg_keywords(concrete_function)
183 if not tracing_options.bind_graph_to_function:
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:284, in _maybe_define_function(args, kwargs, tracing_options)
282 else:
283 target_func_type = lookup_func_type
--> 284 concrete_function = _create_concrete_function(
285 target_func_type, lookup_func_context, func_graph, tracing_options
286 )
288 if tracing_options.function_cache is not None:
289 tracing_options.function_cache.add(
290 concrete_function, current_func_context
291 )
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:308, in _create_concrete_function(function_type, type_context, func_graph, tracing_options)
303 with func_graph.as_default():
304 placeholder_bound_args = function_type.placeholder_arguments(
305 placeholder_context
306 )
--> 308 traced_func_graph = func_graph_module.func_graph_from_py_func(
309 tracing_options.name,
310 tracing_options.python_function,
311 placeholder_bound_args.args,
312 placeholder_bound_args.kwargs,
313 None,
314 func_graph=func_graph,
315 arg_names=function_type_utils.to_arg_names(function_type),
316 create_placeholders=False,
317 )
319 transform.apply_func_graph_transforms(traced_func_graph)
321 graph_capture_container = traced_func_graph.function_captures
File ~usr/local/lib/python3.11/dist-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 ~usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:597, in Function._generate_scoped_tracing_options.<locals>.wrapped_fn(*args, **kwds)
593 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
594 # __wrapped__ allows AutoGraph to swap in a converted function. We give
595 # the function a weak reference to itself to avoid a reference cycle.
596 with OptionalXlaContext(compile_with_xla):
--> 597 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
598 return out
File ~usr/local/lib/python3.11/dist-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 ~usr/local/lib/python3.11/dist-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 ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py:693, in convert.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
691 except Exception as e: # pylint:disable=broad-except
692 if hasattr(e, 'ag_error_metadata'):
--> 693 raise e.ag_error_metadata.to_exception(e)
694 else:
695 raise
File ~usr/local/lib/python3.11/dist-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 ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py:439, in converted_call(f, args, kwargs, caller_fn_scope, options)
437 try:
438 if kwargs is not None:
--> 439 result = converted_f(*effective_args, **kwargs)
440 else:
441 result = converted_f(*effective_args)
File ~tmp/__autograph_generated_filebrqr6ub4.py:7, in outer_factory.<locals>.inner_factory.<locals>.<lambda>(x, y)
6 def inner_factory(ag__):
----> 7 tf__lam = lambda x, y: ag__.with_function_scope(lambda lscope: ag__.converted_call(preprocess_data, (x, y), dict(augmentation=augmentation), lscope), 'lscope', ag__.STD)
8 return tf__lam
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/core/function_wrappers.py:113, in with_function_scope(thunk, scope_name, options)
111 """Inline version of the FunctionScope context manager."""
112 with FunctionScope('lambda_', scope_name, options) as scope:
--> 113 return thunk(scope)
File ~tmp/__autograph_generated_filebrqr6ub4.py:7, in outer_factory.<locals>.inner_factory.<locals>.<lambda>(lscope)
6 def inner_factory(ag__):
----> 7 tf__lam = lambda x, y: ag__.with_function_scope(lambda lscope: ag__.converted_call(preprocess_data, (x, y), dict(augmentation=augmentation), lscope), 'lscope', ag__.STD)
8 return tf__lam
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py:439, in converted_call(f, args, kwargs, caller_fn_scope, options)
437 try:
438 if kwargs is not None:
--> 439 result = converted_f(*effective_args, **kwargs)
440 else:
441 result = converted_f(*effective_args)
File ~tmp/__autograph_generated_filelvb17_ff.py:11, in outer_factory.<locals>.inner_factory.<locals>.tf__preprocess_data(images, labels, augmentation)
9 retval_ = ag__.UndefinedReturnValue()
10 inputs = {'images': ag__.ld(images), 'labels': ag__.ld(labels)}
---> 11 outputs = ag__.if_exp(ag__.ld(augmentation) != None, lambda: ag__.converted_call(ag__.ld(augmentation), (ag__.ld(inputs),), None, fscope), lambda: ag__.ld(inputs), 'augmentation != None')
12 try:
13 do_return = True
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/operators/conditional_expressions.py:27, in if_exp(cond, if_true, if_false, expr_repr)
25 return _tf_if_exp(cond, if_true, if_false, expr_repr)
26 else:
---> 27 return _py_if_exp(cond, if_true, if_false)
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/operators/conditional_expressions.py:52, in _py_if_exp(cond, if_true, if_false)
51 def _py_if_exp(cond, if_true, if_false):
---> 52 return if_true() if cond else if_false()
File ~tmp/__autograph_generated_filelvb17_ff.py:11, in outer_factory.<locals>.inner_factory.<locals>.tf__preprocess_data.<locals>.<lambda>()
9 retval_ = ag__.UndefinedReturnValue()
10 inputs = {'images': ag__.ld(images), 'labels': ag__.ld(labels)}
---> 11 outputs = ag__.if_exp(ag__.ld(augmentation) != None, lambda: ag__.converted_call(ag__.ld(augmentation), (ag__.ld(inputs),), None, fscope), lambda: ag__.ld(inputs), 'augmentation != None')
12 try:
13 do_return = True
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py:331, in converted_call(f, args, kwargs, caller_fn_scope, options)
329 if conversion.is_in_allowlist_cache(f, options):
330 logging.log(2, 'Allowlisted %s: from cache', f)
--> 331 return _call_unconverted(f, args, kwargs, options, False)
333 if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED:
334 logging.log(2, 'Allowlisted: %s: AutoGraph is disabled in context', f)
File ~usr/local/lib/python3.11/dist-packages/tensorflow/python/autograph/impl/api.py:460, in _call_unconverted(f, args, kwargs, options, update_cache)
458 if kwargs is not None:
459 return f(*args, **kwargs)
--> 460 return f(*args)
File ~usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~tmp/__autograph_generated_file12c62saa.py:33, in outer_factory.<locals>.inner_factory.<locals>.tf__call(self, inputs)
31 nonlocal do_return, retval_
32 raise ag__.converted_call(ag__.ld(ValueError), (f'Image augmentation layers are expecting inputs to be rank 3 (HWC) or 4D (NHWC) tensors. Got shape: {ag__.ld(images).shape}',), None, fscope)
---> 33 ag__.if_stmt(ag__.or_(lambda: ag__.ld(images).shape.rank == 3, lambda: ag__.ld(images).shape.rank == 4), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
34 return fscope.ret(retval_, do_return)
File ~tmp/__autograph_generated_file12c62saa.py:25, in outer_factory.<locals>.inner_factory.<locals>.tf__call.<locals>.if_body()
23 try:
24 do_return = True
---> 25 retval_ = ag__.converted_call(ag__.ld(self)._format_output, (ag__.converted_call(ag__.ld(self)._batch_augment, (ag__.ld(inputs),), None, fscope), ag__.ld(metadata)), None, fscope)
26 except:
27 do_return = False
File ~tmp/__autograph_generated_filep1xqu7wy.py:35, in outer_factory.<locals>.inner_factory.<locals>.tf___batch_augment(self, inputs)
33 ag__.if_stmt(ag__.ld(bounding_boxes) is not None, if_body, else_body, get_state, set_state, ('inputs[BOUNDING_BOXES]', 'ori_bbox_info'), 2)
34 inputs_for_trivial_augment_single_input = {'inputs': ag__.ld(inputs), 'transformations': ag__.ld(transformations)}
---> 35 result = ag__.converted_call(ag__.ld(tf).map_fn, (ag__.ld(self).trivial_augment_single_input, ag__.ld(inputs_for_trivial_augment_single_input)), dict(fn_output_signature=ag__.converted_call(ag__.ld(augmentation_utils).compute_signature, (ag__.ld(inputs), ag__.ld(self).compute_dtype), None, fscope)), fscope)
36 bounding_boxes = ag__.converted_call(ag__.ld(result).get, (ag__.ld(BOUNDING_BOXES), None), None, fscope)
38 def get_state_2():
File ~tmp/__autograph_generated_filev6ti3m33.py:26, in outer_factory.<locals>.inner_factory.<locals>.tf__trivial_augment_single_input(self, inputs)
24 idx = ag__.Undefined('idx')
25 ag__.for_stmt(ag__.converted_call(ag__.ld(enumerate), (ag__.ld(self).aug_layers,), None, fscope), None, loop_body, get_state, set_state, (), {'iterate_names': '(idx, layer)'})
---> 26 result = ag__.converted_call(ag__.ld(tf).switch_case, (ag__.ld(random_indice),), dict(branch_fns=ag__.ld(branch_fns)), fscope)
28 def get_state_1():
29 return (ag__.ldu(lambda: result[BOUNDING_BOXES], 'result[BOUNDING_BOXES]'),)
File ~tmp/__autograph_generated_file12c62saa.py:33, in outer_factory.<locals>.inner_factory.<locals>.tf__call(self, inputs)
31 nonlocal do_return, retval_
32 raise ag__.converted_call(ag__.ld(ValueError), (f'Image augmentation layers are expecting inputs to be rank 3 (HWC) or 4D (NHWC) tensors. Got shape: {ag__.ld(images).shape}',), None, fscope)
---> 33 ag__.if_stmt(ag__.or_(lambda: ag__.ld(images).shape.rank == 3, lambda: ag__.ld(images).shape.rank == 4), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
34 return fscope.ret(retval_, do_return)
File ~tmp/__autograph_generated_file12c62saa.py:25, in outer_factory.<locals>.inner_factory.<locals>.tf__call.<locals>.if_body()
23 try:
24 do_return = True
---> 25 retval_ = ag__.converted_call(ag__.ld(self)._format_output, (ag__.converted_call(ag__.ld(self)._batch_augment, (ag__.ld(inputs),), None, fscope), ag__.ld(metadata)), None, fscope)
26 except:
27 do_return = False
File ~tmp/__autograph_generated_filevkq0dz7z.py:36, in outer_factory.<locals>.inner_factory.<locals>.tf___batch_augment(self, inputs)
34 images = ag__.converted_call(ag__.ld(self).augment_images, (ag__.ld(images),), dict(transformations=ag__.ld(transformations), bounding_boxes=ag__.ld(bounding_boxes), labels=ag__.ld(labels)), fscope)
35 inputs_for_raggeds = ag__.Undefined('inputs_for_raggeds')
---> 36 ag__.if_stmt(ag__.and_(lambda: ag__.converted_call(ag__.ld(isinstance), (ag__.ld(images), ag__.ld(tf).RaggedTensor), None, fscope), lambda: ag__.not_(ag__.ld(self).force_no_unwrap_ragged_image_call)), if_body, else_body, get_state, set_state, ('images',), 1)
38 def get_state_1():
39 return (images,)
File ~tmp/__autograph_generated_filevkq0dz7z.py:34, in outer_factory.<locals>.inner_factory.<locals>.tf___batch_augment.<locals>.else_body()
32 def else_body():
33 nonlocal images
---> 34 images = ag__.converted_call(ag__.ld(self).augment_images, (ag__.ld(images),), dict(transformations=ag__.ld(transformations), bounding_boxes=ag__.ld(bounding_boxes), labels=ag__.ld(labels)), fscope)
File ~tmp/__autograph_generated_filegmle6ti9.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__augment_images(self, images, transformations, **kwargs)
13 scales = 255.0 / (ag__.ld(highs) - ag__.ld(lows))
14 eq_idxs = ag__.converted_call(ag__.ld(tf).math.is_inf, (ag__.ld(scales),), None, fscope)
---> 15 lows = ag__.converted_call(ag__.ld(tf).where, (ag__.ld(eq_idxs), 0.0, ag__.ld(lows)), None, fscope)
16 scales = ag__.converted_call(ag__.ld(tf).where, (ag__.ld(eq_idxs), 1.0, ag__.ld(scales)), None, fscope)
17 images = ag__.converted_call(ag__.ld(tf).clip_by_value, ((ag__.ld(images) - ag__.ld(lows)) * ag__.ld(scales), 0, 255), None, fscope)
TypeError: in user code:
File "/tmp/ipykernel_11/1206971647.py", line 15, in None *
lambda x, y: preprocess_data(x, y, augmentation=augmentation)
File "/tmp/ipykernel_11/1206971647.py", line 11, in preprocess_data *
outputs = augmentation(inputs) if augmentation != None else inputs
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler **
raise e.with_traceback(filtered_tb) from None
File "/tmp/__autograph_generated_file12c62saa.py", line 33, in tf__call
ag__.if_stmt(ag__.or_(lambda: ag__.ld(images).shape.rank == 3, lambda: ag__.ld(images).shape.rank == 4), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
File "/tmp/__autograph_generated_file12c62saa.py", line 28, in if_body
raise
File "/tmp/__autograph_generated_filep1xqu7wy.py", line 35, in tf___batch_augment
result = ag__.converted_call(ag__.ld(tf).map_fn, (ag__.ld(self).trivial_augment_single_input, ag__.ld(inputs_for_trivial_augment_single_input)), dict(fn_output_signature=ag__.converted_call(ag__.ld(augmentation_utils).compute_signature, (ag__.ld(inputs), ag__.ld(self).compute_dtype), None, fscope)), fscope)
File "/tmp/__autograph_generated_filev6ti3m33.py", line 26, in tf__trivial_augment_single_input
result = ag__.converted_call(ag__.ld(tf).switch_case, (ag__.ld(random_indice),), dict(branch_fns=ag__.ld(branch_fns)), fscope)
File "/tmp/__autograph_generated_file12c62saa.py", line 33, in tf__call
ag__.if_stmt(ag__.or_(lambda: ag__.ld(images).shape.rank == 3, lambda: ag__.ld(images).shape.rank == 4), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
File "/tmp/__autograph_generated_file12c62saa.py", line 28, in if_body
raise
File "/tmp/__autograph_generated_filevkq0dz7z.py", line 36, in tf___batch_augment
ag__.if_stmt(ag__.and_(lambda: ag__.converted_call(ag__.ld(isinstance), (ag__.ld(images), ag__.ld(tf).RaggedTensor), None, fscope), lambda: ag__.not_(ag__.ld(self).force_no_unwrap_ragged_image_call)), if_body, else_body, get_state, set_state, ('images',), 1)
File "/tmp/__autograph_generated_filevkq0dz7z.py", line 34, in else_body
images = ag__.converted_call(ag__.ld(self).augment_images, (ag__.ld(images),), dict(transformations=ag__.ld(transformations), bounding_boxes=ag__.ld(bounding_boxes), labels=ag__.ld(labels)), fscope)
File "/tmp/__autograph_generated_filegmle6ti9.py", line 15, in tf__augment_images
lows = ag__.converted_call(ag__.ld(tf).where, (ag__.ld(eq_idxs), 0.0, ag__.ld(lows)), None, fscope)
TypeError: Exception encountered when calling layer 'trivial_augment' (type TrivialAugmentWide).
in user code:
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/base/vectorized_base_random_layer.py", line 613, in call *
if images.shape.rank == 3 or images.shape.rank == 4:
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/augmentation/auto/trivial_augment_wide.py", line 281, in _batch_augment *
result = tf.map_fn(
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/augmentation/auto/trivial_augment_wide.py", line 316, in trivial_augment_single_input *
result = tf.switch_case(random_indice, branch_fns=branch_fns)
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/tmp/__autograph_generated_file12c62saa.py", line 33, in tf__call
ag__.if_stmt(ag__.or_(lambda: ag__.ld(images).shape.rank == 3, lambda: ag__.ld(images).shape.rank == 4), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2)
File "/tmp/__autograph_generated_file12c62saa.py", line 28, in if_body
raise
File "/tmp/__autograph_generated_filevkq0dz7z.py", line 36, in tf___batch_augment
ag__.if_stmt(ag__.and_(lambda: ag__.converted_call(ag__.ld(isinstance), (ag__.ld(images), ag__.ld(tf).RaggedTensor), None, fscope), lambda: ag__.not_(ag__.ld(self).force_no_unwrap_ragged_image_call)), if_body, else_body, get_state, set_state, ('images',), 1)
File "/tmp/__autograph_generated_filevkq0dz7z.py", line 34, in else_body
images = ag__.converted_call(ag__.ld(self).augment_images, (ag__.ld(images),), dict(transformations=ag__.ld(transformations), bounding_boxes=ag__.ld(bounding_boxes), labels=ag__.ld(labels)), fscope)
File "/tmp/__autograph_generated_filegmle6ti9.py", line 15, in tf__augment_images
lows = ag__.converted_call(ag__.ld(tf).where, (ag__.ld(eq_idxs), 0.0, ag__.ld(lows)), None, fscope)
TypeError: Exception encountered when calling layer 'trivial_augment' (type AutoContrast).
in user code:
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/base/vectorized_base_random_layer.py", line 614, in call *
return self._format_output(self._batch_augment(inputs), metadata)
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/base/vectorized_base_random_layer.py", line 416, in _batch_augment *
images = self.augment_images(
File "/usr/local/lib/python3.11/dist-packages/keras_aug/layers/preprocessing/intensity/auto_contrast.py", line 50, in augment_images *
lows = tf.where(eq_idxs, 0.0, lows)
TypeError: Input 'e' of 'SelectV2' Op has type float16 that does not match type float32 of argument 't'.
Call arguments received by layer 'trivial_augment' (type AutoContrast):
• inputs={'images': 'tf.Tensor(shape=(1, 32, 32, 3), dtype=float16)', 'labels': 'tf.Tensor(shape=(1, 10), dtype=float16)'}
Call arguments received by layer 'trivial_augment' (type TrivialAugmentWide):
• inputs={'images': 'tf.Tensor(shape=(None, 32, 32, 3), dtype=float16)', 'labels': 'tf.Tensor(shape=(None, 10), dtype=float16)'}
Might take a long time to complete...
Need to bump version to v0.5.7 to support tensorflow 2.13 and keras 2.13
Candidates:
Tests should be run in the batch mode
Once resolved, the layers with bounding_boxes inputs should be able to run on XLA
Sometimes we want the same zoom factor applies to height and width.
Due to the bug in KerasCV==0.5.0
ModuleNotFoundError: No module named 'keras.engine'
The current focus of this repository is on 2D augmentations, such as geometry, intensity, mixing, and regularization. Version 0.1.0 will be released once it can reproduce all augmentations in YOLOv8 and existing KerasCV augmentations.
crop_to_aspect_ratio, pad_to_aspect_ratio, yolov8: LetterBox)KerasAug will not support RandomMedianBlur due to the limitations of the implementation of tfa.image.median_filter2d, which uses tf.image.extract_patches and cannot support dynamic filter size.
KerasAug cannot support RandomCopyPaste augmentation until the data structure for instance segmentation is clarified.
Should use opencv-python-headless instead of opencv-python
This issue keeping track of a list of unit tests:
React
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