958e425775
This operation is used for Wide and Deep Model Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from extensions.ops.bucketize import Bucketize
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph
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nodes_attributes = {'input_tensor': {'shape': None, 'value': None, 'kind': 'data'},
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'input_buckets': {'shape': None, 'value': None, 'kind': 'data'},
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'bucketize_node': {'op': 'Bucketize', 'kind': 'op', 'with_right_bound': False, 'output_type': np.int32},
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'output': {'shape': None, 'value': None, 'kind': 'data'}}
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# graph 1
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edges1 = [('input_tensor', 'bucketize_node', {'in': 0}),
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('input_buckets', 'bucketize_node', {'in': 1}),
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('bucketize_node', 'output', {'out': 0})]
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inputs1 = {'input_tensor': {'shape': int64_array([4]), 'value': np.array([0.2, 6.4, 3.0, 1.6])},
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'input_buckets': {'shape': int64_array([5]), 'value': np.array([0.0, 1.0, 2.5, 4.0, 10.0])}}
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inputs2 = {'input_tensor': {'shape': int64_array([4]), 'value': np.array([0.2, 6.4, 3.0, 1.6])},
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'input_buckets': {'shape': int64_array([5]), 'value': np.array([])}}
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inputs3 = {'input_tensor': {'shape': int64_array([10, 40]), 'value': None},
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'input_buckets': {'shape': int64_array([5]), 'value': None}}
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class TestBucketize(unittest.TestCase):
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def test_infer1(self):
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graph = build_graph(nodes_attributes, edges1, inputs1)
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bucketize_node = Node(graph, 'bucketize_node')
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Bucketize.infer(bucketize_node)
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# prepare reference results
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ref_output_value = np.array([1, 4, 3, 2], dtype=np.int32)
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# get the result
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(ref_output_value, res_output_value),
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'values do not match expected: {} and given: {}'.format(ref_output_value, res_output_value))
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def test_infer2(self):
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graph = build_graph(nodes_attributes, edges1, inputs2)
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bucketize_node = Node(graph, 'bucketize_node')
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Bucketize.infer(bucketize_node)
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# prepare reference results
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ref_output_value = np.array([0, 0, 0, 0], dtype=np.int32)
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# get the result
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(ref_output_value, res_output_value),
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'values do not match expected: {} and given: {}'.format(ref_output_value, res_output_value))
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def test_partial_infer1(self):
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graph = build_graph(nodes_attributes, edges1, inputs3)
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bucketize_node = Node(graph, 'bucketize_node')
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Bucketize.infer(bucketize_node)
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# prepare reference results
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ref_output_shape = np.array([10, 40], dtype=np.int32)
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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