Vladimir Gavrilov
dca30b4522
* Commit.
* Added opset4 version in the class Interpolate.
* Added class ONNXResize11Op to read ONNX Resize with opset version >= 11.
* Added support for Interpolate-4 into transformations TestInterpolateReshapeWA and InterpolateConcat.
* Added support for Interpolate-4 into transformation InterpolateWithConcat.
* Deleted redundant checks from the transformation UpsampleToResample.
* Reverted last changes.
* Changed ONNX Resize extractor to support for Interpolate-4.
* Added conversion of ONNXResize11Op into Interpolate-4.
* Added support for Interpolate-4 into the transformation InterpolateSequenceToInterpolate.
* Small fix for formatting.
* Written tests for MO version of Interpolate-4 with shape_calculation_mode = sizes.
* Written tests for infer function of Interpolate-4.
* Now transformations InterpolateWithConcat, InterpolateConcat, InterpolateReshapeWA skip Interpolate-4.
* Used create_op_with_const_inputs in the transformation InterpolateSequenceToInterpolate.
* The transformation ONNXResize11ToInterpolate4 was rewritten using find_and_replace_pattern.
* Now the dictionary infers (dictionary of infer functions of Interpolate) is a class static attribute.
* Deleted unused variable.
* Restored original logic of find_and_replace_pattern method of the class InterpolateReshapeWA.
* Used create_op_with_const_inputs() in the transformation InterpolateSequenceToInterpolate for opset1 case.
* Replaced resize_name by resize.soft_get('name', resize.id).
* Small fixes.
* Added two tests for Interpolate-4 infer function.
* Fixed the transformation ONNXResize11ToInterpolateV4 for the case when ONNXResize11 operation has 3 inputs.
* Added conversion of ONNXResize11 with tf_crop_and_resize_mode to ROIPooling + ONNXResize11.
* Fixed bugs in the transformation ONNXResize11ToInterpolateV4 and in the infer function of the operation ONNXResize11.
* Small changes.
* Renamed transformation that converts ONNXResize11 into ROIPooling + ONNXResize11 and fixed BOM-file.
* Fixed tests for the transformation InterpolateSequenceToInterpolate.
* Small change.
* Now the transformation InterpolateSequenceToInterpolate preserves output layer name.
* Deleted the transformation ONNXResize11ToTFCropAndResize.
282 lines
17 KiB
Python
282 lines
17 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 generator import generator, generate
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from extensions.ops.interpolate import Interpolate
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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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graph_node_attrs_without_axes = {
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'input': {'type': 'Parameter', 'kind': 'op'},
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'input_data': {'kind': 'data', 'shape': None, 'value': None},
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'sizes': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'sizes_data': {'kind': 'data', 'shape': None, 'value': None},
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'scales': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'scales_data': {'kind': 'data', 'shape': None, 'value': None},
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'interpolate': {
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'type': 'Interpolate', 'kind': 'op', 'mode': 'nearest', 'shape_calculation_mode': 'sizes',
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'coordinate_transformation_mode': 'half_pixel', 'version': 'opset4',
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'nearest_mode': 'round_prefer_floor', 'antialias': 0,
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},
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'interpolate_data': {'kind': 'data', 'value': None, 'shape': None},
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'op_output': {'kind': 'op', 'op': 'Result'},
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}
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graph_edges_without_axes = [
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('input', 'input_data'),
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('sizes', 'sizes_data'),
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('scales', 'scales_data'),
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('input_data', 'interpolate', {'in': 0}),
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('sizes_data', 'interpolate', {'in': 1}),
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('scales_data', 'interpolate', {'in': 2}),
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('interpolate', 'interpolate_data'),
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('interpolate_data', 'op_output'),
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]
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graph_nodes_attrs = {
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'input': {'type': 'Parameter', 'kind': 'op'},
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'input_data': {'kind': 'data', 'shape': None, 'value': None},
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'sizes': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'sizes_data': {'kind': 'data', 'shape': None, 'value': None},
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'scales': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'scales_data': {'kind': 'data', 'shape': None, 'value': None},
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'axes': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'axes_data': {'kind': 'data', 'shape': None, 'value': None},
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'interpolate': {
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'type': 'Interpolate', 'kind': 'op', 'mode': 'nearest', 'shape_calculation_mode': 'sizes',
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'coordinate_transformation_mode': 'half_pixel', 'version': 'opset4',
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'nearest_mode': 'round_prefer_floor', 'antialias': 0,
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},
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'interpolate_data': {'kind': 'data', 'value': None, 'shape': None},
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'op_output': {'kind': 'op', 'op': 'Result'},
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}
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graph_edges = [
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('input', 'input_data'),
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('sizes', 'sizes_data'),
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('scales', 'scales_data'),
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('axes', 'axes_data'),
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('input_data', 'interpolate', {'in': 0}),
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('sizes_data', 'interpolate', {'in': 1}),
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('scales_data', 'interpolate', {'in': 2}),
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('axes_data', 'interpolate', {'in': 3}),
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('interpolate', 'interpolate_data'),
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('interpolate_data', 'op_output'),
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]
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@generator
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class TestInterpolateOp(unittest.TestCase):
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@generate(*[([0], [0], [1, 3, 100, 200], [1, 3, 350, 150], [350, 150], [3.5, 150 / 200], [2, 3]),
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([0, 3, 10, 10], [0], [16, 7, 190, 400], [8, 10, 390, 600],
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[8, 390, 600], [0.5, 390 / 200, 600 / 410], [0, 2, 3]),
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([10, 5, 0, 10], [0, 4, 16, 18], [4, 33, 1024, 8000], [56, 42, 520, 8028],
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[56, 520], [4.0, 0.5], [0, 2]),
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([0], [0], [1, 16, 85, 470, 690], [20, 16, 40, 470, 1380],
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[20, 40, 1380], [20.0, 40.0 / 85.0, 1380.0 / 690.0], [0, 2, 4]),
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([4, 3, 11, 22, 5], [1, 3, 4, 8, 5], [1, 16, 85, 470, 690], [60, 22, 430, 500, 345],
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[60, 430, 345], [10.0, 4.3, 345.0 / 700.0], [0, 2, 4]),
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([0], [0], [5, 77, 444, 88, 6050], [100, 308, 4440, 44, 6050],
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[100, 308, 4440, 44], [20.0, 4.0, 10.0, 0.5], [0, 1, 2, 3]),
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([0], [0], [1, 100, 200], [1, 350, 150], [350, 150], [3.5, 150 / 200], [1, 2]),
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([0, 3, 10], [0], [16, 7, 190], [8, 10, 390], [8, 390], [0.5, 390 / 200], [0, 2]),
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([10, 0, 10], [0, 16, 18], [4, 1024, 8000], [56, 520, 8028], [56, 520], [4.0, 0.5], [0, 1]),
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([0], [0], [1, 690], [20, 1380], [20, 1380], [20.0, 1380.0 / 690.0], [0, 1]),
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([4, 3, 11, 22, 5, 0], [1, 3, 4, 8, 5, 0], [1, 16, 85, 470, 690, 349], [60, 22, 430, 500, 345, 349],
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[60, 430, 345], [10.0, 4.3, 345.0 / 700.0], [0, 2, 4])
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])
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def test_interpolate4_using_sizes(self, pads_begin, pads_end, input_shape, output_shape, sizes, scales, axes):
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graph = build_graph(nodes_attrs=graph_nodes_attrs,
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edges=graph_edges,
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update_attributes={
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'input_data': {'shape': input_shape},
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'sizes': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'sizes_data': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'scales': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'scales_data': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'axes': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
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'axes_data': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
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'interpolate': {'pads_begin': int64_array(pads_begin),
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'pads_end': int64_array(pads_end)}
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})
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node = Node(graph, 'interpolate')
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tested_class = Interpolate(graph=graph, attrs=node.attrs())
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tested_class.infer(node)
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msg = "Interpolate-4 infer failed for case: sizes={}, scales={}, pads_begin={}, pads_end={}, axes={}," \
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" expected_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['interpolate_data']['shape'], int64_array(output_shape)),
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msg.format(sizes, scales, pads_begin, pads_end, axes, output_shape,
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graph.node['interpolate_data']['shape']))
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@generate(*[([0], [0], [1, 3, 100, 200], [1, 3, 350, 150], [350, 150], [3.5, 150 / 200], [2, 3]),
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([0, 3, 10, 10], [0], [16, 7, 190, 400], [8, 10, 390, 600],
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[8, 390, 600], [0.5, 390 / 200, 600 / 410], [0, 2, 3]),
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([10, 5, 0, 10], [0, 4, 16, 18], [4, 33, 1024, 8000], [56, 42, 520, 8028],
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[56, 520], [4.0, 0.5], [0, 2]),
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([0], [0], [1, 16, 85, 470, 690], [20, 16, 40, 470, 1380],
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[20, 40, 1380], [20.0, 40.0 / 85.0, 1380.0 / 690.0], [0, 2, 4]),
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([4, 3, 11, 22, 5], [1, 3, 4, 8, 5], [1, 16, 85, 470, 690], [60, 22, 430, 500, 345],
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[60, 430, 345], [10.0, 4.3, 345.0 / 700.0], [0, 2, 4]),
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([0], [0], [5, 77, 444, 88, 6050], [100, 308, 4440, 44, 6050],
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[100, 308, 4440, 44], [20.0, 4.0, 10.0, 0.5], [0, 1, 2, 3]),
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([0], [0], [1, 100, 200], [1, 350, 150], [350, 150], [3.5, 150 / 200], [1, 2]),
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([0, 3, 10], [0], [16, 7, 190], [8, 10, 390], [8, 390], [0.5, 390 / 200], [0, 2]),
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([10, 0, 10], [0, 16, 18], [4, 1024, 8000], [56, 520, 8028], [56, 520], [4.0, 0.5], [0, 1]),
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([0], [0], [1, 690], [20, 1380], [20, 1380], [20.0, 1380.0 / 690.0], [0, 1]),
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([4, 3, 11, 22, 5, 0], [1, 3, 4, 8, 5, 0], [1, 16, 85, 470, 690, 349], [60, 22, 430, 500, 345, 349],
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[60, 430, 345], [10.0, 4.3, 345.0 / 700.0], [0, 2, 4]),
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([4, 3, 11, 22, 5, 0, 0], [1, 3, 4, 8, 5, 0, 0], [1, 16, 85, 470, 690, 349, 3],
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[60, 22, 430, 500, 345, 349, 1],
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[60, 430, 345, 1], [10.0, 4.3, 345.0 / 700.0, 1 / 3], [0, 2, 4, 6]),
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([4, 3, 11, 22, 5, 0, 0], [1, 3, 4, 8, 5, 0, 0], [1, 16, 85, 470, 690, 349, 3],
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[60, 22, 430, 500, 345, 349, 1],
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[60, 430, 345, 1], [10.0, 4.3, 345.0 / 700.0, 0.3333333], [0, 2, 4, 6]),
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])
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def test_interpolate4_using_scales(self, pads_begin, pads_end, input_shape, output_shape, sizes, scales, axes):
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graph = build_graph(nodes_attrs=graph_nodes_attrs,
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edges=graph_edges,
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update_attributes={
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'input_data': {'shape': input_shape},
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'sizes': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'sizes_data': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'scales': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'scales_data': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'axes': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
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'axes_data': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
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'interpolate': {'pads_begin': int64_array(pads_begin),
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'pads_end': int64_array(pads_end),
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'shape_calculation_mode': 'scales'}
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})
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node = Node(graph, 'interpolate')
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tested_class = Interpolate(graph=graph, attrs=node.attrs())
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tested_class.infer(node)
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msg = "Interpolate-4 infer failed for case: sizes={}, scales={}, pads_begin={}, pads_end={}, axes={}," \
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" expected_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['interpolate_data']['shape'], int64_array(output_shape)),
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msg.format(sizes, scales, pads_begin, pads_end, axes, output_shape,
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graph.node['interpolate_data']['shape']))
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@generate(*[([0], [0], [1, 3, 100, 200], [1, 3, 350, 150], [1, 3, 350, 150], [1.0, 1.0, 3.5, 150 / 200]),
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([0, 3, 10, 10], [0], [16, 7, 190, 400], [8, 10, 390, 600],
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[8, 10, 390, 600], [0.5, 1.0, 390 / 200, 600 / 410]),
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([10, 5, 0, 10], [0, 4, 16, 18], [4, 33, 1024, 8000], [56, 42, 520, 8028],
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[56, 42, 520, 8028], [4.0, 1.0, 0.5, 1.0]),
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([0], [0], [1, 16, 85, 470, 690], [20, 16, 40, 470, 1380],
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[20, 16, 40, 470, 1380], [20.0, 1.0, 40.0 / 85.0, 1.0, 1380.0 / 690.0]),
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([4, 3, 11, 22, 5], [1, 3, 4, 8, 5], [1, 16, 85, 470, 690], [60, 22, 430, 500, 345],
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[60, 22, 430, 500, 345], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0]),
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([0], [0], [5, 77, 444, 88, 6050], [100, 308, 4440, 44, 6050],
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[100, 308, 4440, 44, 6050], [20.0, 4.0, 10.0, 0.5, 1.0]),
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([0], [0], [1, 100, 200], [1, 350, 150], [1, 350, 150], [1.0, 3.5, 150 / 200]),
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([0, 3, 10], [0], [16, 7, 190], [8, 10, 390], [8, 10, 390], [0.5, 1.0, 390 / 200]),
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([10, 0, 10], [0, 16, 18], [4, 1024, 8000], [56, 520, 8028], [56, 520, 8028], [4.0, 0.5, 1.0]),
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([0], [0], [1, 690], [20, 1380], [20, 1380], [20.0, 1380.0 / 690.0]),
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([4, 3, 11, 22, 5, 0], [1, 3, 4, 8, 5, 0], [1, 16, 85, 470, 690, 349], [60, 22, 430, 500, 345, 349],
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[60, 22, 430, 500, 345, 349], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0, 1.0]),
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([4, 3, 11, 22, 5, 0, 0], [1, 3, 4, 8, 5, 0, 0], [1, 16, 85, 470, 690, 349, 3],
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[60, 22, 430, 500, 345, 349, 1],
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[60, 22, 430, 500, 345, 349, 1], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0, 1.0, 1 / 3]),
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])
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def test_interpolate4_using_sizes_without_axes(self, pads_begin, pads_end, input_shape, output_shape, sizes,
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scales):
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graph = build_graph(nodes_attrs=graph_node_attrs_without_axes,
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edges=graph_edges_without_axes,
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update_attributes={
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'input_data': {'shape': input_shape},
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'sizes': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'sizes_data': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'scales': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'scales_data': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'interpolate': {'pads_begin': int64_array(pads_begin),
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'pads_end': int64_array(pads_end),
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'shape_calculation_mode': 'sizes'}
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})
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node = Node(graph, 'interpolate')
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tested_class = Interpolate(graph=graph, attrs=node.attrs())
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tested_class.infer(node)
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msg = "Interpolate-4 infer failed for case: sizes={}, scales={}, pads_begin={}, pads_end={}," \
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" expected_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['interpolate_data']['shape'], int64_array(output_shape)),
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msg.format(sizes, scales, pads_begin, pads_end, output_shape,
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graph.node['interpolate_data']['shape']))
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@generate(*[([0], [0], [1, 3, 100, 200], [1, 3, 350, 150], [1, 3, 350, 150], [1.0, 1.0, 3.5, 150 / 200]),
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([0, 3, 10, 10], [0], [16, 7, 190, 400], [8, 10, 390, 600],
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[8, 10, 390, 600], [0.5, 1.0, 390 / 200, 600 / 410]),
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([10, 5, 0, 10], [0, 4, 16, 18], [4, 33, 1024, 8000], [56, 42, 520, 8028],
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[56, 42, 520, 8028], [4.0, 1.0, 0.5, 1.0]),
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([0], [0], [1, 16, 85, 470, 690], [20, 16, 40, 470, 1380],
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[20, 16, 40, 470, 1380], [20.0, 1.0, 40.0 / 85.0, 1.0, 1380.0 / 690.0]),
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([4, 3, 11, 22, 5], [1, 3, 4, 8, 5], [1, 16, 85, 470, 690], [60, 22, 430, 500, 345],
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[60, 22, 430, 500, 345], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0]),
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([0], [0], [5, 77, 444, 88, 6050], [100, 308, 4440, 44, 6050],
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[100, 308, 4440, 44, 6050], [20.0, 4.0, 10.0, 0.5, 1.0]),
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([0], [0], [1, 100, 200], [1, 350, 150], [1, 350, 150], [1.0, 3.5, 150 / 200]),
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([0, 3, 10], [0], [16, 7, 190], [8, 10, 390], [8, 10, 390], [0.5, 1.0, 390 / 200]),
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([10, 0, 10], [0, 16, 18], [4, 1024, 8000], [56, 520, 8028], [56, 520, 8028], [4.0, 0.5, 1.0]),
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([0], [0], [1, 690], [20, 1380], [20, 1380], [20.0, 1380.0 / 690.0]),
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([4, 3, 11, 22, 5, 0], [1, 3, 4, 8, 5, 0], [1, 16, 85, 470, 690, 349], [60, 22, 430, 500, 345, 349],
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[60, 22, 430, 500, 345, 349], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0, 1.0]),
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([4, 3, 11, 22, 5, 0, 0], [1, 3, 4, 8, 5, 0, 0], [1, 16, 85, 470, 690, 349, 3],
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[60, 22, 430, 500, 345, 349, 1],
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[60, 22, 430, 500, 345, 349, 1], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0, 1.0, 1 / 3]),
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([4, 3, 11, 22, 5, 0, 0], [1, 3, 4, 8, 5, 0, 0], [1, 16, 85, 470, 690, 349, 3],
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[60, 22, 430, 500, 345, 349, 1],
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[60, 22, 430, 500, 345, 349, 1], [10.0, 1.0, 4.3, 1.0, 345.0 / 700.0, 1.0, 0.3333333]),
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])
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def test_interpolate4_using_scales_without_axes(self, pads_begin, pads_end, input_shape, output_shape, sizes,
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scales):
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graph = build_graph(nodes_attrs=graph_node_attrs_without_axes,
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edges=graph_edges_without_axes,
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update_attributes={
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'input_data': {'shape': input_shape},
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'sizes': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'sizes_data': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
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'scales': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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|
'scales_data': {'shape': np.array(scales).shape, 'value': np.array(scales)},
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'interpolate': {'pads_begin': int64_array(pads_begin),
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|
'pads_end': int64_array(pads_end),
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|
'shape_calculation_mode': 'scales'}
|
|
})
|
|
|
|
node = Node(graph, 'interpolate')
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|
tested_class = Interpolate(graph=graph, attrs=node.attrs())
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|
tested_class.infer(node)
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|
|
|
msg = "Interpolate-4 infer failed for case: sizes={}, scales={}, pads_begin={}, pads_end={}," \
|
|
" expected_shape={}, actual_shape={}"
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|
|
|
self.assertTrue(np.array_equal(graph.node['interpolate_data']['shape'], int64_array(output_shape)),
|
|
msg.format(sizes, scales, pads_begin, pads_end, output_shape,
|
|
graph.node['interpolate_data']['shape']))
|