Мобильная версияКод: Выделить всё
import cv2
import tensorflow as tf
import numpy as np
CATEGORIES = ["gas", "break","stop"]
model = tf.keras.models.load_model("trained")
def prepare(filepath):
IMG_SIZE = 256 # 50 in txt-based
img_array = cv2.imread(filepath)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
arr=new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3)
return arr
x=prepare('img.jpg')
x = tf.keras.utils.normalize(x, axis=1)
x = x.astype('float32')
print(x.dtype)
prediction = model.predict([x])
print(prediction) # will be a list in a list.
print(CATEGORIES[np.argmax(prediction[0])])
float32
--------------------------------------------------------------------------- ValueError Traceback (most recent call
last) in
11 x = x.astype('float32')
12 print(x.dtype)
---> 13 prediction = model.predict([x])
14 print(prediction) # will be a list in a list.
15 print(CATEGORIES[np.argmax(prediction[0])])
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py
in predict(self, x, batch_size, verbose, steps, callbacks,
max_queue_size, workers, use_multiprocessing) 1011
max_queue_size=max_queue_size, 1012 workers=workers,
-> 1013 use_multiprocessing=use_multiprocessing) 1014 1015 def reset_metrics(self):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py
in predict(self, model, x, batch_size, verbose, steps, callbacks,
max_queue_size, workers, use_multiprocessing, **kwargs)
496 model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
497 steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498 workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
499
500
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py
in _model_iteration(self, model, mode, x, y, batch_size, verbose,
sample_weight, steps, callbacks, max_queue_size, workers,
use_multiprocessing, **kwargs)
473 mode=mode,
474 training_context=training_context,
--> 475 total_epochs=1)
476 cbks.make_logs(model, epoch_logs, result, mode)
477
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py
in run_one_epoch(model, iterator, execution_function, dataset_size,
batch_size, strategy, steps_per_epoch, num_samples, mode,
training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py
in execution_function(input_fn)
96 # numpy translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in call(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in _call(self, *args, **kwds)
613 # This is the first call of call, so we have to initialize.
614 initializers = []
--> 615 self._initialize(args, kwds, add_initializers_to=initializers)
616 finally:
617 # At this point we know that the initialization is complete (or less
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in _initialize(self, args, kwds, add_initializers_to)
495 self._concrete_stateful_fn = (
496 self._stateful_fn._get_concrete_function_internal_garbage_collected(
pylint: disable=protected-access
--> 497 *args, **kwds))
498
499 def invalid_creator_scope(*unused_args, **unused_kwds):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in _get_concrete_function_internal_garbage_collected(self, *args,
**kwargs) 2387 args, kwargs = None, None 2388 with self._lock:
-> 2389 graph_function, _, _ = self._maybe_define_function(args, kwargs) 2390 return graph_function 2391
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in _maybe_define_function(self, args, kwargs) 2701 2702
self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs) 2704 self._function_cache.primary[cache_key] =
graph_function 2705 return graph_function, args, kwargs
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in _create_graph_function(self, args, kwargs,
override_flat_arg_shapes) 2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value), 2594 self._function_attributes, 2595 # Tell the ConcreteFunction
to clean up its graph once it goes out of
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: func_outputs contains only Tensors, CompositeTensors,
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in wrapped_fn(*args, **kwds)
437 # wrapped allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().wrapped(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py
in distributed_function(input_iterator)
83 args = _prepare_feed_values(model, input_iterator, mode, strategy)
84 outputs = strategy.experimental_run_v2(
---> 85 per_replica_function, args=args)
86 # Out of PerReplica outputs reduce or pick values to return.
87 all_outputs = dist_utils.unwrap_output_dict(
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py
in experimental_run_v2(self, fn, args, kwargs)
761 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
762 convert_by_default=False)
--> 763 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
764
765 def reduce(self, reduce_op, value, axis):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py
in call_for_each_replica(self, fn, args, kwargs) 1817 kwargs
= {} 1818 with self._container_strategy().scope():
-> 1819 return self._call_for_each_replica(fn, args, kwargs) 1820 1821 def _call_for_each_replica(self, fn, args, kwargs):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py
in _call_for_each_replica(self, fn, args, kwargs) 2162
self._container_strategy(), 2163
replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2164 return fn(*args, **kwargs) 2165 2166 def _reduce_to(self, reduce_op, value, destinations):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\autograph\impl\api.py
in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py
in _predict_on_batch(failed resolving arguments)
210 del y, sample_weights
211 # Note that the x and batch_index is already per-replica value.
--> 212 result = predict_on_batch(model, x)
213 if batch_index is None:
214 return result
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py
in predict_on_batch(model, x, standalone)
554
555 with backend.eager_learning_phase_scope(0):
--> 556 return predict_on_batch_fn(inputs) # pylint: disable=not-callable
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py
in call(self, inputs, *args, **kwargs)
776 outputs = base_layer_utils.mark_as_return(outputs, acd)
777 else:
--> 778 outputs = call_fn(cast_inputs, *args, **kwargs)
779
780 except errors.OperatorNotAllowedInGraphError as e:
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py
in call(self, inputs, training, mask)
279 kwargs['training'] = training
280
--> 281 outputs = layer(inputs, **kwargs)
282
283 # outputs will be the inputs to the next layer.
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py
in call(self, inputs, *args, **kwargs)
776 outputs = base_layer_utils.mark_as_return(outputs, acd)
777 else:
--> 778 outputs = call_fn(cast_inputs, *args, **kwargs)
779
780 except errors.OperatorNotAllowedInGraphError as e:
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\saving\saved_model\utils.py
in return_outputs_and_add_losses(*args, **kwargs)
57 inputs = args[inputs_arg_index]
58 args = args[inputs_arg_index + 1:]
---> 59 outputs, losses = fn(inputs, *args, **kwargs)
60 layer.add_loss(losses, inputs)
61 return outputs
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in call(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in _call(self, *args, **kwds)
604 # In this case we have not created variables on the first call. So we can
605 # run the first trace but we should fail if variables are created.
--> 606 results = self._stateful_fn(*args, **kwds)
607 if self._created_variables:
608 raise ValueError("Creating variables on a non-first call to a function"
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in call(self, *args, **kwargs) 2360 """Calls a graph
function specialized to the inputs.""" 2361 with self._lock:
-> 2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs) 2363 return
graph_function._filtered_call(args, kwargs) # pylint:
disable=protected-access 2364
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in _maybe_define_function(self, args, kwargs) 2701 2702
self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs) 2704 self._function_cache.primary[cache_key] =
graph_function 2705 return graph_function, args, kwargs
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py
in _create_graph_function(self, args, kwargs,
override_flat_arg_shapes) 2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value), 2594 self._function_attributes, 2595 # Tell the ConcreteFunction
to clean up its graph once it goes out of
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: func_outputs contains only Tensors, CompositeTensors,
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py
in wrapped_fn(*args, **kwds)
437 # wrapped allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().wrapped(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\saved_model\function_deserialization.py
in restored_function_body(*args, **kwargs)
260 .format(_pretty_format_positional(args), kwargs,
261 len(saved_function.concrete_functions),
--> 262 "\n\n".join(signature_descriptions)))
263
264 concrete_function_objects = []
ValueError: Could not find matching function to call loaded from the
SavedModel. Got: Positional arguments (1 total):
* Tensor("inputs:0", shape=(None, 256, 256, 3), dtype=uint8) Keyword arguments: {}
Expected these arguments to match one of the following 1 option(s):
Option 1: Positional arguments (1 total):
* TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name='inputs') Keyword arguments: {}
Подробнее здесь: https://stackoverflow.com/questions/615 ... type-uint8
