b6098fed90
* [TF FE] Provide single tensor names for inputs and outputs in SavedModel Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> * Fix build issue * Xfail some cases due to internal problems in TF * Xfail other layer test * Extend documentation for function to adjust tensor names * Use old path of tf2 layer testing for legacy frontend --------- Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
75 lines
3.0 KiB
Python
75 lines
3.0 KiB
Python
# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import os
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from common.layer_test_class import CommonLayerTest
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from common.utils.tflite_utils import get_tflite_results, save_tf2_saved_model_to_tflite
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def save_to_tf2_savedmodel(tf2_model, path_to_saved_tf2_model):
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import tensorflow as tf
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assert int(tf.__version__.split('.')[0]) >= 2, "TensorFlow 2 must be used for this suite validation"
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tf.keras.models.save_model(tf2_model, path_to_saved_tf2_model, save_format='tf')
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assert os.path.isdir(path_to_saved_tf2_model), "the model haven't been saved " \
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"here: {}".format(path_to_saved_tf2_model)
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return path_to_saved_tf2_model
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class CommonTF2LayerTest(CommonLayerTest):
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input_model_key = "saved_model_dir"
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def produce_model_path(self, framework_model, save_path):
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if not getattr(self, 'tflite', False):
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return save_to_tf2_savedmodel(framework_model, save_path)
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else:
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self.input_model_key = 'input_model'
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tf2_saved_model = save_to_tf2_savedmodel(framework_model, save_path)
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return save_tf2_saved_model_to_tflite(tf2_saved_model)
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def get_framework_results(self, inputs_dict, model_path):
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if not getattr(self, 'tflite', False):
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return self.get_tf2_keras_results(inputs_dict, model_path)
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else:
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# get results from tflite
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return get_tflite_results(self.use_new_frontend, self.use_old_api, inputs_dict, model_path)
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def get_tf2_keras_results(self, inputs_dict, model_path):
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import tensorflow as tf
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result = dict()
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if self.use_new_frontend:
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imported = tf.saved_model.load(model_path)
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f = imported.signatures["serving_default"]
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result = f(**inputs_dict)
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else:
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# load a model
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loaded_model = tf.keras.models.load_model(model_path, custom_objects=None)
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# prepare input
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input_for_model = []
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# order inputs based on input names in tests
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# since TF2 Keras model accepts a list of tensors for prediction
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for input_name in sorted(inputs_dict):
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input_value = inputs_dict[input_name]
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input_for_model.append(input_value)
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if len(input_for_model) == 1:
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input_for_model = input_for_model[0]
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# infer by original framework and complete a dictionary with reference results
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tf_res_list = loaded_model(input_for_model)
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if tf.is_tensor(tf_res_list):
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tf_res_list = [tf_res_list]
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else:
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# in this case tf_res_list is a list of the single tuple of outputs
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tf_res_list = tf_res_list[0]
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for ind, tf_res in enumerate(tf_res_list):
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if ind == 0:
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output = "Identity"
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else:
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output = "Identity_{}".format(ind)
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tf_res = tf_res.numpy()
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result[output] = tf_res
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return result
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