218 lines
12 KiB
Python
218 lines
12 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 generator import generator, generate
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from extensions.middle.UpsampleToResample import UpsampleToResample
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from mo.front.common.partial_infer.utils import int64_array, float32_array
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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
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graph_node_attrs = {
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'placeholder': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': np.float32},
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'scales': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': None, 'shape': None},
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'scales_data': {'kind': 'data', 'value': None, 'shape': None},
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'upsample': {'type': None, 'kind': 'op', 'op': 'Upsample', 'mode': 'linear'},
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'upsample_data': {'kind': 'data', 'shape': None, 'value': None},
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'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
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}
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graph_edges = [
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('placeholder', 'placeholder_data'),
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('placeholder_data', 'upsample', {'in': 0}),
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('scales', 'scales_data'),
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('scales_data', 'upsample', {'in': 1}),
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('upsample', 'upsample_data'),
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('upsample_data', 'output'),
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]
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new_ref_graph_node_attr = {
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'placeholder': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': np.float32},
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'ss_begin': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([2]), 'shape': int64_array([1])},
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'ss_begin_data': {'kind': 'data', 'value': int64_array([2]), 'shape': int64_array([1])},
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'ss_end': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([4]), 'shape': int64_array([1])},
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'ss_end_data': {'kind': 'data', 'value': int64_array([4]), 'shape': int64_array([1])},
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'ss_stride': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([1]), 'shape': int64_array([1])},
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'ss_stride_data': {'kind': 'data', 'value': int64_array([1]), 'shape': int64_array([1])},
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'strided_slice': {'type': 'StridedSlice', 'kind': 'op', 'op': 'StridedSlice'},
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'strided_slice_data': {'kind': 'data', 'shape': None, 'value': None},
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'cast_to_float': {'kind': 'op', 'op': 'Cast', 'type': 'Convert', 'dst_type': np.float},
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'cast_to_float_d': {'kind': 'data', 'value': None, 'shape': None},
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'factor': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([5, 5]), 'shape': int64_array([2])},
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'factor_data': {'kind': 'data', 'value': int64_array([5, 5]), 'shape': int64_array([2])},
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'shapeof': {'type': 'ShapeOf', 'kind': 'op', 'op': 'ShapeOf'},
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'shapeof_data': {'kind': 'data', 'shape': None, 'value': None},
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'mul': {'type': 'Multiply', 'kind': 'op', 'op': 'Multiply'},
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'mul_data': {'kind': 'data', 'shape': None, 'value': None},
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'cast_to_int': {'kind': 'op', 'op': 'Cast', 'type': 'Convert', 'dst_type': np.int32},
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'cast_to_int_d': {'kind': 'data', 'shape': None, 'value': None},
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'axes_const': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': None, 'shape': None},
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'axes_const_data': {'kind': 'data', 'value': None, 'shape': None},
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'scales': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([5, 5]), 'shape': int64_array([2])},
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'scales_data': {'kind': 'data', 'value': None, 'shape': None},
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'interpolate': {'type': 'Interpolate', 'kind': 'op', 'op': 'Interpolate', 'axes': None},
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'interpolate_data': {'kind': 'data', 'shape': None, 'value': None},
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'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
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}
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new_ref_graph_edges = [
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('placeholder', 'placeholder_data'),
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('placeholder_data', 'shapeof', {'in': 0, 'out': 0}),
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('placeholder_data', 'interpolate', {'in': 0, 'out': 0}),
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('ss_begin', 'ss_begin_data'),
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('ss_begin_data', 'strided_slice', {'in': 1, 'out': 0}),
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('ss_end', 'ss_end_data'),
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('ss_end_data', 'strided_slice', {'in': 2, 'out': 0}),
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('ss_stride', 'ss_stride_data'),
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('ss_stride_data', 'strided_slice', {'in': 3, 'out': 0}),
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('strided_slice', 'strided_slice_data'),
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('strided_slice_data', 'cast_to_float'),
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('cast_to_float', 'cast_to_float_d'),
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('shapeof', 'shapeof_data'),
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('shapeof_data', 'strided_slice', {'in': 0, 'out': 0}),
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('factor', 'factor_data'),
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('cast_to_float_d', 'mul', {'in': 0, 'out': 0}),
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('factor_data', 'mul', {'in': 1, 'out': 0}),
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('mul', 'mul_data'),
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('mul_data', 'cast_to_int'),
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('cast_to_int', 'cast_to_int_d'),
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('cast_to_int_d', 'interpolate', {'in': 1, 'out': 0}),
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('axes_const', 'axes_const_data'),
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('axes_const_data', 'interpolate', {'in': 3, 'out': 0}),
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('scales', 'scales_data'),
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('scales_data', 'interpolate', {'in': 2, 'out': 0}),
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('interpolate', 'interpolate_data'),
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('interpolate_data', 'output')
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]
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ref_graph_node_attrs = {
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'placeholder': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': np.float32},
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'factor': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([5, 5]), 'shape': int64_array([2])},
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'factor_data': {'kind': 'data', 'value': None, 'shape': None},
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'shapeof': {'type': 'ShapeOf', 'kind': 'op', 'op': 'ShapeOf'},
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'shapeof_data': {'kind': 'data', 'shape': None, 'value': None},
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'strided_slice': {'type': 'StridedSlice', 'kind': 'op', 'op': 'StridedSlice'},
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'strided_slice_data': {'kind': 'data', 'shape': None, 'value': None},
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'ss_begin': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([2]), 'shape': int64_array([1])},
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'ss_begin_data': {'kind': 'data', 'value': None, 'shape': None},
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'ss_end': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([4]), 'shape': int64_array([1])},
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'ss_end_data': {'kind': 'data', 'value': None, 'shape': None},
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'ss_stride': {'kind': 'op', 'op': 'Const', 'type': 'Const', 'value': int64_array([1]), 'shape': int64_array([1])},
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'ss_stride_data': {'kind': 'data', 'value': None, 'shape': None},
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'cast_to_float': {'kind': 'op', 'op': 'Cast', 'type': 'Convert', 'dst_type': np.float},
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'cast_to_float_d': {'kind': 'data', 'value': None, 'shape': None},
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'mul': {'type': 'Multiply', 'kind': 'op', 'op': 'Multiply'},
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'mul_data': {'kind': 'data', 'shape': None, 'value': None},
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'cast_to_int': {'kind': 'op', 'op': 'Cast', 'type': 'Convert', 'dst_type': np.int32},
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'cast_to_int_d': {'kind': 'data', 'shape': None, 'value': None},
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'interpolate': {'type': 'Interpolate', 'kind': 'op', 'op': 'Interpolate', 'axes': None},
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'interpolate_data': {'kind': 'data', 'shape': None, 'value': None},
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'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
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}
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ref_graph_edges = [
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('placeholder', 'placeholder_data'),
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('placeholder_data', 'interpolate', {'in': 0, 'out': 0}),
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('placeholder_data', 'shapeof', {'in': 0, 'out': 0}),
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('shapeof', 'shapeof_data'),
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('interpolate', 'interpolate_data'),
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('factor', 'factor_data'),
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('shapeof_data', 'strided_slice', {'in': 0, 'out': 0}),
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('ss_begin', 'ss_begin_data'),
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('ss_begin_data', 'strided_slice', {'in': 1, 'out': 0}),
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('ss_end', 'ss_end_data'),
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('ss_end_data', 'strided_slice', {'in': 2, 'out': 0}),
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('ss_stride', 'ss_stride_data'),
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('ss_stride_data', 'strided_slice', {'in': 3, 'out': 0}),
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('strided_slice', 'strided_slice_data'),
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('strided_slice_data', 'cast_to_float'),
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('cast_to_float', 'cast_to_float_d'),
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('cast_to_float_d', 'mul', {'in': 0, 'out': 0}),
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('factor_data', 'mul', {'in': 1, 'out': 0}),
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('mul', 'mul_data'),
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('mul_data', 'cast_to_int'),
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('cast_to_int', 'cast_to_int_d'),
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('cast_to_int_d', 'interpolate', {'in': 1, 'out': 0}),
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('interpolate_data', 'output'),
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]
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@generator
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class UpsampleToResampleTest(unittest.TestCase):
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@generate(*[([2, 10, 20, 30], [1, 1, 5, 5], [2, 3]),
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([2, 20, 30, 40], [1, 1, 3, 3], [2, 3]),
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([2, 10, 20, 30], [1, 1, 6, 5], [2, 3]),
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([2, 20, 30, 40], [1, 1, 3, 4], [2, 3]),
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([2, 3, 20, 30, 40], [1, 1, 3, 3, 3], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 3, 4, 3], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 4, 3, 3], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 3, 3, 4], [2, 3, 4]),
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([2, 10, 20, 30], [1, 1, 5.5, 5.7], [2, 3]),
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([2, 20, 30, 40], [1, 1, 3.3, 3.1], [2, 3]),
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([2, 10, 20, 30], [1, 1, 6.18, 5.34], [2, 3]),
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([2, 20, 30, 40], [1, 1, 3.79, 4.16], [2, 3]),
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([2, 3, 20, 30, 40], [1, 1, 3.12, 3.87, 3.92], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 3.74, 4.873, 3.287], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 4.8, 3.6, 3.11], [2, 3, 4]),
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([2, 3, 20, 30, 40], [1, 1, 3.33, 3.73, 4.765], [2, 3, 4]),
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])
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def test_conversion(self, input_shape, scales, axes):
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input_shape_as_array = int64_array(input_shape)
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scales_as_array = float32_array(scales)
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graph = build_graph(graph_node_attrs,
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graph_edges,
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{
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'placeholder_data': {'shape': input_shape_as_array},
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'scales': {'value': scales_as_array, 'shape': scales_as_array.shape},
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'scales_data': {'value': scales_as_array, 'shape': scales_as_array.shape},
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'upsample_data':
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{'shape': ((input_shape_as_array + 1.e-5) * scales_as_array).astype(np.int64)}
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})
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graph.graph['layout'] = 'NCHW'
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ref_graph = build_graph(new_ref_graph_node_attr,
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new_ref_graph_edges,
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{
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'placeholder_data': {'shape': int64_array(input_shape)},
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'ss_begin': {'value': int64_array([axes[0]])},
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'ss_end': {'value': int64_array([axes[-1] + 1])},
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'ss_begin_data': {'value': int64_array([axes[0]])},
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'ss_end_data': {'value': int64_array([axes[-1] + 1])},
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'factor': {'value': scales_as_array[2:],
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'shape': scales_as_array[2:].shape},
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'factor_data': {'value': scales_as_array[2:],
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'shape': scales_as_array[2:].shape},
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'axes_const': {'value': int64_array(axes), 'shape': int64_array(axes).shape},
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'interpolate_data': {
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'shape': (input_shape_as_array * scales_as_array + 1e-5).astype(np.int64)},
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})
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UpsampleToResample().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, ref_graph, 'output')
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self.assertTrue(flag, resp)
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@generate(*[([2, 10, 20, 30], [1, 2, 5, 5],),
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([2, 3, 20, 30, 40], [1, 2, 3, 3, 3],),
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])
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def test_pattern_does_not_satisfy(self, input_shape, scales):
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graph = build_graph(graph_node_attrs, graph_edges,
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{'placeholder_data': {'shape': int64_array(input_shape)},
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'scales': {'value': int64_array(scales), 'shape': int64_array(scales).shape},
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'scales_data': {'value': int64_array(scales), 'shape': int64_array(scales).shape},
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'upsample_data': {'shape': int64_array(input_shape) * int64_array(scales)}})
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graph.graph['layout'] = 'NCHW'
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ref_graph = build_graph(graph_node_attrs, graph_edges,
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{'placeholder_data': {'shape': int64_array(input_shape)},
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'scales': {'value': int64_array(scales), 'shape': int64_array(scales).shape},
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'scales_data': {'value': int64_array(scales), 'shape': int64_array(scales).shape},
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'upsample_data': {'shape': int64_array(input_shape) * int64_array(scales)}})
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UpsampleToResample().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, ref_graph, 'output')
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self.assertTrue(flag, resp)
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