77 lines
2.7 KiB
Python
77 lines
2.7 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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from common.tf_layer_test_class import CommonTFLayerTest
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from common.utils.tf_utils import permute_nchw_to_nhwc
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class TestRsqrt(CommonTFLayerTest):
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def _prepare_input(self, inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = np.random.randint(1, 256, inputs_dict[input]).astype(np.float32)
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return inputs_dict
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def create_rsqrt_net(self, shape, ir_version, use_new_frontend):
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"""
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Tensorflow net IR net
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Input->Rsqrt => Input->Power
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"""
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import tensorflow as tf
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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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tf_x_shape = shape.copy()
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tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
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input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
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tf.math.rsqrt(input, name='Operation')
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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ref_net = None
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return tf_net, ref_net
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test_data_precommit = [dict(shape=[1, 3, 50, 100, 224])]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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@pytest.mark.nightly
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def test_rsqrt_precommit(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_rsqrt_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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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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test_data = [dict(shape=[1]),
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pytest.param(dict(shape=[1, 224]), marks=pytest.mark.precommit_tf_fe),
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dict(shape=[1, 3, 224]),
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dict(shape=[1, 3, 100, 224]),
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dict(shape=[1, 3, 50, 100, 224])]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_rsqrt(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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use_old_api):
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self._test(*self.create_rsqrt_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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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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