7b65ba365e
SparseToDense used in Wide and Deep model is expressed through ScatterND operation. ScatterND is more functional than SparseToDense. Hence, it was decided to replace SparseToDense with ScatterND. ScatterND is more useful for other models. Remove SparseToDense from the previous opset Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
167 lines
7.7 KiB
Python
167 lines
7.7 KiB
Python
"""
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Copyright (C) 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.scatternd import ScatterNDUpdate
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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': {'shape': None, 'value': None, 'kind': 'data'},
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'indices': {'shape': None, 'value': None, 'kind': 'data'},
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'updates': {'shape': None, 'value': None, 'kind': 'data'},
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'scatternd_node': {'op': 'ScatterNDUpdate', 'kind': 'op'},
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'output': {'shape': None, 'value': None, 'kind': 'data'}}
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# graph 1
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edges = [('input', 'scatternd_node', {'in': 0}),
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('indices', 'scatternd_node', {'in': 1}),
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('updates', 'scatternd_node', {'in': 2}),
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('scatternd_node', 'output', {'out': 0})]
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# test data for partial infer
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inputs1 = {'input': {'shape': int64_array([10, 40]), 'value': None},
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'indices': {'shape': int64_array([3, 2]), 'value': None},
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'updates': {'shape': int64_array([3]), 'value': None}}
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inputs2 = {'input': {'shape': int64_array([20, 30]), 'value': None},
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'indices': {'shape': int64_array([2]), 'value': None},
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'updates': {'shape': int64_array([]), 'value': None}}
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inputs3 = {'input': {'shape': int64_array([20, 30, 5]), 'value': None},
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'indices': {'shape': int64_array([2]), 'value': None},
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'updates': {'shape': int64_array([5]), 'value': None}}
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inputs4 = {'input': {'shape': int64_array([10, 40, 50]), 'value': None},
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'indices': {'shape': int64_array([7, 3, 2]), 'value': None},
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'updates': {'shape': int64_array([7, 3, 50]), 'value': None}}
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# test data for constant folding
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inputs5 = {'input': {'shape': int64_array([8]), 'value': int64_array([1, 2, 3, 4, 5, 6, 7, 8])},
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'indices': {'shape': int64_array([4, 1]), 'value': int64_array([[4], [3], [1], [7]])},
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'updates': {'shape': int64_array([4]), 'value': int64_array([9, 10, 11, 12])}}
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output5 = int64_array([1, 11, 3, 10, 9, 6, 7, 12])
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inputs6 = {'input': {'shape': int64_array([4, 4, 4]), 'value': int64_array([[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]])},
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'indices': {'shape': int64_array([2, 1]), 'value': int64_array([[0], [2]])},
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'updates': {'shape': int64_array([2, 4, 4]), 'value': int64_array([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
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[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]])}}
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output6 = int64_array([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
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[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]])
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inputs7 = {'input': {'shape': int64_array([8]), 'value': int64_array([1, 2, 3, 4, 5, 6, 7, 8])},
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'indices': {'shape': int64_array([1]), 'value': int64_array([4])},
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'updates': {'shape': int64_array([]), 'value': 9}}
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output7 = int64_array([1, 2, 3, 4, 9, 6, 7, 8])
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class TestScatterNDUpdate(unittest.TestCase):
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def test_partial_infer1(self):
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graph = build_graph(nodes_attributes, edges, inputs1)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_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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def test_partial_infer2(self):
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graph = build_graph(nodes_attributes, edges, inputs2)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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# prepare reference results
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ref_output_shape = np.array([20, 30], 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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def test_partial_infer3(self):
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graph = build_graph(nodes_attributes, edges, inputs3)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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# prepare reference results
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ref_output_shape = np.array([20, 30, 5], 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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def test_partial_infer4(self):
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graph = build_graph(nodes_attributes, edges, inputs4)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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# prepare reference results
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ref_output_shape = np.array([10, 40, 50], 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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def test_infer5(self):
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graph = build_graph(nodes_attributes, edges, inputs5)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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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(output5, res_output_value),
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'values do not match expected: {} and given: {}'.format(output5, res_output_value))
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def test_infer6(self):
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graph = build_graph(nodes_attributes, edges, inputs6)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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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(output6, res_output_value),
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'values do not match expected: {} and given: {}'.format(output5, res_output_value))
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def test_infer7_scalar(self):
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graph = build_graph(nodes_attributes, edges, inputs7)
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scatternd_node = Node(graph, 'scatternd_node')
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ScatterNDUpdate.infer(scatternd_node)
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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(output7, res_output_value),
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'values do not match expected: {} and given: {}'.format(output5, res_output_value))
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