54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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class TestShape(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'input' in inputs_info
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input_shape = inputs_info['input']
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inputs_data = {}
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inputs_data['input'] = np.random.randint(-10, 10, input_shape).astype(self.input_type)
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return inputs_data
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def create_shape_net(self, input_shape, input_type, out_type):
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self.input_type = input_type
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types_map = {
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np.float32: tf.float32,
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np.int32: tf.int32
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}
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assert input_type in types_map, "Incorrect test case"
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tf_type = types_map[input_type]
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tf.compat.v1.reset_default_graph()
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# Create the graph and model
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with tf.compat.v1.Session() as sess:
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input = tf.compat.v1.placeholder(tf_type, input_shape, 'input')
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if out_type is not None:
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tf.raw_ops.Shape(input=input, out_type=out_type)
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else:
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tf.raw_ops.Shape(input=input)
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[2, 3], input_type=np.float32, out_type=tf.int32),
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dict(input_shape=[3, 4, 5], input_type=np.int32, out_type=tf.int64),
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dict(input_shape=[1], input_type=np.int32, out_type=None),
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_shape_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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self._test(*self.create_shape_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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