48 lines
2.0 KiB
Python
48 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 TestBucketize(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'input' in inputs_info, "Test error: inputs_info must contain `input`"
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input_shape = inputs_info['input']
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input_type = self.input_type
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inputs_data = {}
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input_data = np.random.randint(-20, 20, input_shape).astype(input_type)
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inputs_data['input'] = input_data
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return inputs_data
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def create_bucketize_net(self, input_shape, input_type, boundaries_size):
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self.input_type = input_type
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tf.compat.v1.reset_default_graph()
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with tf.compat.v1.Session() as sess:
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input = tf.compat.v1.placeholder(input_type, input_shape, 'input')
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# generate boundaries list
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# use wider range for boundaries than input data in order to cover all bucket indices cases
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boundaries = np.sort(np.unique(np.random.randint(-200, 200, [boundaries_size]).astype(np.float32))).tolist()
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tf.raw_ops.Bucketize(input=input, boundaries=boundaries)
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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=[5], input_type=np.int32, boundaries_size=1),
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dict(input_shape=[3, 4], input_type=np.float32, boundaries_size=0),
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dict(input_shape=[2, 3, 4], input_type=np.float32, boundaries_size=300),
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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_bucketize_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_bucketize_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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