139 lines
6.4 KiB
Python
139 lines
6.4 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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import numpy as np
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from extensions.back.MarkNodesWithShapeValues import MarkNodesWithShapeValues
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from mo.front.common.partial_infer.utils import int64_array, float32_array
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from mo.graph.graph import Node
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph, result, regular_op_with_empty_data, shaped_const_with_data, connect, \
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regular_op, regular_op_with_shaped_data
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class TestMarkDataTypeInShapeOfSubgraphs(unittest.TestCase):
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def test_run_with_shape_subgraph_input(self):
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inp_shape = (1, 3, 1000, 1000)
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dst_type = np.float32
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nodes = {
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**shaped_const_with_data('input', int64_array(inp_shape)),
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**regular_op_with_empty_data('shape', {'type': 'ShapeOf'}),
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**regular_op_with_empty_data('cast_to_float', {'type': 'Cast', 'dst_type': dst_type}),
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**regular_op('mul_const', {'op': 'Const'}),
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**{'mul_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.])}},
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**regular_op_with_empty_data('mul', {'type': 'Mul'}),
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**regular_op_with_empty_data('cast_to_int', {'type': 'Cast', 'dst_type': np.int64}),
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**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
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**result('res'),
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}
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nodes_ref = {
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**shaped_const_with_data('input', int64_array(inp_shape)),
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**regular_op_with_empty_data('shape', {'type': 'ShapeOf'}),
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**regular_op_with_empty_data('cast_to_float', {'type': 'Cast', 'dst_type': dst_type,
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'returns_shape_value': True}),
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**regular_op_with_empty_data('mul', {'type': 'Mul', 'returns_shape_value': True}),
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**regular_op('mul_const', {'op': 'Const', 'returns_shape_value': True}),
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**{'mul_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.]),
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'correct_data_type': True}},
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**regular_op_with_empty_data('cast_to_int', {'type': 'Cast', 'dst_type': np.int64,
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'returns_shape_value': True}),
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**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
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**result('res'),
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}
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edges = [
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*connect('input', '0:interpolate'),
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*connect('input', '0:shape', skip_data=True),
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*connect('shape', '0:cast_to_float'),
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*connect('cast_to_float', '0:mul'),
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*connect('mul_const', '1:mul'),
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*connect('mul', '0:cast_to_int'),
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*connect('cast_to_int', '1:interpolate'),
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*connect('interpolate', 'res'),
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]
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graph = build_graph(nodes, edges)
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interp_node = Node(graph, 'interpolate')
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interp_node.add_input_port(2)
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MarkNodesWithShapeValues().find_and_replace_pattern(graph)
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graph_ref = build_graph(nodes_ref, edges)
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(flag, resp) = compare_graphs(graph, graph_ref, 'res', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_run_with_const_input(self):
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inp_shape = (1, 3, 1000, 1000)
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nodes = {
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**shaped_const_with_data('input', int64_array(inp_shape)),
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**regular_op('sizes_const', {'op': 'Const'}),
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**{'sizes_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.])}},
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**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
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**result('res'),
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}
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nodes_ref = {
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**shaped_const_with_data('input', int64_array(inp_shape)),
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**regular_op('sizes_const', {'op': 'Const', 'returns_shape_value': True}),
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**{'sizes_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.])}},
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**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
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**result('res'),
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}
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edges = [
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*connect('input', '0:interpolate'),
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*connect('sizes_const', '1:interpolate'),
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*connect('interpolate', 'res'),
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]
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graph = build_graph(nodes, edges)
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interp_node = Node(graph, 'interpolate')
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interp_node.add_input_port(2)
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MarkNodesWithShapeValues().find_and_replace_pattern(graph)
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graph_ref = build_graph(nodes_ref, edges)
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(flag, resp) = compare_graphs(graph, graph_ref, 'res', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_run_with_solitary_shapeof_in_shape_value_subgraph(self):
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# in this case MarkNodesWithShapeValues must leave graph unchanged
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# so reference nodes are exactly the same
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inp_shape_1 = int64_array((1, 3, 100, 100))
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inp_shape_2 = int64_array((1, 3, 100, 50)) # inp_2 and const will be concatenated to (1, 3, 200, 50)
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const_shape = int64_array((1, 3, 100, 50))
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nodes = {
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**regular_op_with_shaped_data('input_1', inp_shape_1, {'op': 'Parameter', 'type': 'Parameter'}),
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**regular_op_with_shaped_data('input_2', inp_shape_2, {'op': 'Parameter', 'type': 'Parameter',
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'returns_shape_value': False}),
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**shaped_const_with_data('const', const_shape),
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**regular_op_with_empty_data('concat', {'op': 'Concat', 'type': 'Concat', 'axis': 2,
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'returns_shape_value': False}),
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**regular_op_with_empty_data('shapeof', {'op': 'ShapeOf', 'type': 'ShapeOf'}),
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**regular_op_with_empty_data('reshape', {'op': 'Reshape', 'type': 'Reshape'}),
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**result('res'),
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}
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edges = [
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*connect('input_1', '0:reshape'),
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*connect('input_2', '0:concat'),
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*connect('const', '1:concat'),
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*connect('concat', 'shapeof'),
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*connect('shapeof', '1:reshape'),
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*connect('reshape', 'res'),
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]
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graph = build_graph(nodes, edges)
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MarkNodesWithShapeValues().find_and_replace_pattern(graph)
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graph_ref = build_graph(nodes, edges)
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(flag, resp) = compare_graphs(graph, graph_ref, 'res', check_op_attrs=True)
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self.assertTrue(flag, "'returns_shape_value' should be False or unset for ShapeOf input nodes" + ': ' + str(resp))
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