def wrapper(*args, **kwargs): """Calls method within call context.""" layer = call_collection.layer training = None inputs = call_collection.get_input_arg_value(args, kwargs) # pylint: disable=protected-access if (args or kwargs) and call_collection.training_arg_was_passed( args, kwargs): training = call_collection.get_training_arg_value(args, kwargs) # pylint: enable=protected-access original_losses = _reset_layer_losses(layer) with base_layer_utils.call_context().enter( layer, inputs=inputs, build_graph=False, training=training, saving=True): with base_layer_utils.autocast_context_manager(layer._compute_dtype): # pylint: disable=protected-access ret = method(*args, **kwargs) _restore_layer_losses(original_losses) return ret
def inference(self, inputs, *args, **kwargs): call_context = base_layer_utils.call_context() input_list = nest.flatten(inputs) # We will attempt to build a TF graph if & only if all inputs are symbolic. # This is always the case in graph mode. It can also be the case in eager # mode when all inputs can be traced back to `keras.Input()` (when building # models using the functional API). build_graph = tf_utils.are_all_symbolic_tensors(input_list) # Accept NumPy and scalar inputs by converting to Tensors. if any(isinstance(x, (np.ndarray, float, int)) for x in input_list): def _convert_non_tensor(x): # Don't call `ops.convert_to_tensor` on all `inputs` because # `SparseTensors` can't be converted to `Tensor`. if isinstance(x, (np.ndarray, float, int)): return ops.convert_to_tensor(x) return x inputs = nest.map_structure(_convert_non_tensor, inputs) input_list = nest.flatten(inputs) # Handle `mask` propagation from previous layer to current layer. Masks can # be propagated explicitly via the `mask` argument, or implicitly via # setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed # explicitly take priority. mask_arg_passed_by_framework = False input_masks = self._collect_input_masks(inputs, args, kwargs) if (self._expects_mask_arg and input_masks is not None and not self._call_arg_was_passed('mask', args, kwargs)): mask_arg_passed_by_framework = True kwargs['mask'] = input_masks # If `training` argument was not explicitly passed, propagate `training` # value from this layer's calling layer. training_arg_passed_by_framework = False # Priority 1: `training` was explicitly passed. if self._call_arg_was_passed('training', args, kwargs): training_value = self._get_call_arg_value('training', args, kwargs) if not self._expects_training_arg: kwargs.pop('training') else: training_value = None # Priority 2: `training` was passed to a parent layer. if call_context.training is not None: training_value = call_context.training # Priority 3a: `learning_phase()` has been set. elif backend.global_learning_phase_is_set(): training_value = backend.learning_phase() # Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph. elif build_graph: with backend.get_graph().as_default(): if base_layer_utils.is_in_keras_graph(): training_value = backend.learning_phase() if self._expects_training_arg and training_value is not None: # Force the training_value to be bool type which matches to the contract # for layer/model call args. if tensor_util.is_tensor(training_value): training_value = math_ops.cast(training_value, dtypes.bool) else: training_value = bool(training_value) kwargs['training'] = training_value training_arg_passed_by_framework = True # Only create Keras history if at least one tensor originates from a # `keras.Input`. Otherwise this Layer may be being used outside the Keras # framework. if build_graph and base_layer_utils.needs_keras_history(inputs): base_layer_utils.create_keras_history(inputs) # Clear eager losses on top level model call. # We are clearing the losses only on the top level model call and not on # every layer/model call because layer/model may be reused. if (base_layer_utils.is_in_eager_or_tf_function() and not call_context.in_call): self._clear_losses() with call_context.enter(self, inputs, build_graph, training_value): # Check input assumptions set after layer building, e.g. input shape. if build_graph: # Symbolic execution on symbolic tensors. We will attempt to build # the corresponding TF subgraph inside `backend.get_graph()` # TODO(reedwm): We should assert input compatibility after the inputs # are casted, not before. input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list) and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison raise ValueError('Layer %s does not support RaggedTensors as input. ' 'Inputs received: %s. You can try converting your ' 'input to an uniform tensor.' % (self.name, inputs)) graph = backend.get_graph() with graph.as_default(), backend.name_scope(self._name_scope()): # Build layer if applicable (if the `build` method has been # overridden). self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) # Wrapping `call` function in autograph to allow for dynamic control # flow and control dependencies in call. We are limiting this to # subclassed layers as autograph is strictly needed only for # subclassed layers and models. # tf_convert will respect the value of autograph setting in the # enclosing tf.function, if any. if (base_layer_utils.is_subclassed(self) and not base_layer_utils.from_saved_model(self)): call_fn = autograph.tf_convert( self._inference, ag_ctx.control_status_ctx()) else: call_fn = self._inference if not self.dynamic: try: with base_layer_utils.autocast_context_manager( self._compute_dtype): # Add auto_control_deps in V2 when they are not already added by # a `tf.function`. if (ops.executing_eagerly_outside_functions() and not base_layer_utils.is_in_eager_or_tf_function()): with auto_control_deps.AutomaticControlDependencies() as acd: outputs = call_fn(cast_inputs, *args, **kwargs) # Wrap Tensors in `outputs` in `tf.identity` to avoid # circular dependencies. outputs = base_layer_utils.mark_as_return(outputs, acd) else: outputs = call_fn(cast_inputs, *args, **kwargs) except errors.OperatorNotAllowedInGraphError as e: raise TypeError('You are attempting to use Python control ' 'flow in a layer that was not declared to be ' 'dynamic. Pass `dynamic=True` to the class ' 'constructor.\nEncountered error:\n"""\n' + str(e) + '\n"""') else: # We will use static shape inference to return symbolic tensors # matching the specifications of the layer outputs. # Since `self.dynamic` is True, we will never attempt to # run the underlying TF graph (which is disconnected). # TODO(fchollet): consider py_func as an alternative, which # would enable us to run the underlying graph if needed. outputs = self._symbolic_call(inputs) if outputs is None: raise ValueError('A layer\'s `call` method should return a ' 'Tensor or a list of Tensors, not None ' '(layer: ' + self.name + ').') if base_layer_utils.have_all_keras_metadata(inputs): if training_arg_passed_by_framework: kwargs.pop('training') if mask_arg_passed_by_framework: kwargs.pop('mask') inputs, outputs = self._set_connectivity_metadata_( inputs, outputs, args, kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) if hasattr(self, '_set_inputs') and not self.inputs: # Subclassed network: explicitly set metadata normally set by # a call to self._set_inputs(). # TODO(b/120997007): This should be done in Eager as well, but # causes garbage collection issues because of the placeholders # created on the default Keras graph. self._set_inputs(inputs, outputs) else: # Eager execution on data tensors. with backend.name_scope(self._name_scope()): self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) with base_layer_utils.autocast_context_manager( self._compute_dtype): outputs = self._inference(cast_inputs, *args, **kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) return outputs