retinanet onnx model has Upsample error (#3052)
* Commit. * Fixed infer function of ONNXResize11 operation. * Small fixes. * Written unit tests for shape calculation for ONNXResize11.
This commit is contained in:
Vladimir Gavrilov
GitHub
parent
20df6eada6
commit
e479b011f5
@ -67,7 +67,7 @@ def replace_resize(graph: Graph, resize: Node):
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{1: int64_array([begin_dim]),
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2: int64_array([end_dim]),
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3: int64_array([1])},
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{'name': resize_name + '/StridedSlice_',
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{'name': resize_name + '/StridedSlice_sizes',
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'begin_mask': int64_array([1]),
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'end_mask': int64_array([1]),
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'new_axis_mask': int64_array([0]),
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@ -77,7 +77,7 @@ def replace_resize(graph: Graph, resize: Node):
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{1: int64_array([begin_dim]),
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2: int64_array([end_dim]),
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3: int64_array([1])},
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{'name': resize_name + '/StridedSlice_',
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{'name': resize_name + '/StridedSlice_scales',
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'begin_mask': int64_array([1]),
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'end_mask': int64_array([1]),
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'new_axis_mask': int64_array([0]),
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@ -67,6 +67,8 @@ class ONNXResize11Op(Op):
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sizes = node.in_port(3).data.get_value()
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assert sizes is not None, \
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"Node {} with op {} has no value in input port 3".format(node.name, node.op)
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output_shape = int64_array(sizes)
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output_shape = input_shape.copy()
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spatial_dimension_indices = range(2, len(input_shape))
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output_shape[spatial_dimension_indices] = int64_array(sizes)[2:]
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node.out_port(0).data.set_shape(output_shape.copy())
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node.out_port(0).data.set_shape(output_shape.copy())
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140
model-optimizer/extensions/ops/ONNXResize11_test.py
Normal file
140
model-optimizer/extensions/ops/ONNXResize11_test.py
Normal file
@ -0,0 +1,140 @@
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"""
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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 generator import generator, generate
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from extensions.ops.ONNXResize11 import ONNXResize11Op
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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_sizes = {
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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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'roi': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'roi_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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'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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'onnx_resize11': {
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'op': 'ONNXResize11', 'kind': 'op', 'mode': 'nearest', 'nearest_mode': 'round_prefer_floor',
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'coordinate_transformation_mode': 'half_pixel', 'cube_coeff': -0.75
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},
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'onnx_resize11_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_sizes = [
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('input', 'input_data'),
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('roi', 'roi_data'),
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('sizes', 'sizes_data'),
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('scales', 'scales_data'),
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('input_data', 'onnx_resize11', {'in': 0}),
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('roi_data', 'onnx_resize11', {'in': 1}),
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('scales_data', 'onnx_resize11', {'in': 2}),
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('sizes_data', 'onnx_resize11', {'in': 3}),
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('onnx_resize11', 'onnx_resize11_data'),
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('onnx_resize11_data', 'op_output'),
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]
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graph_node_attrs_scales = {
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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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'roi': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
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'roi_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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'onnx_resize11': {
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'op': 'ONNXResize11', 'kind': 'op', 'mode': 'nearest', 'nearest_mode': 'round_prefer_floor',
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'coordinate_transformation_mode': 'half_pixel', 'cube_coeff': -0.75
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},
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'onnx_resize11_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_scales = [
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('input', 'input_data'),
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('roi', 'roi_data'),
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('scales', 'scales_data'),
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('input_data', 'onnx_resize11', {'in': 0}),
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('roi_data', 'onnx_resize11', {'in': 1}),
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('scales_data', 'onnx_resize11', {'in': 2}),
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('onnx_resize11', 'onnx_resize11_data'),
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('onnx_resize11_data', 'op_output'),
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]
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@generator
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class TestONNXResize11Op(unittest.TestCase):
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@generate(*[([1, 260, 100, 150], [1, 260, 200, 350], [1, 260, 200, 350], [1.0, 1.0, 1.0, 1.0]),
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([1, 260, 100, 150], [1, 260, 200, 350], [1, 1, 200, 350], [1.0, 1.0, 1.0, 1.0]),
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([5, 14, 300, 40], [5, 14, 140, 280], [1, 1, 140, 280], [1.0, 1.0, 1.0, 1.0]),
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([5, 14, 300, 40], [5, 14, 140, 280], [5, 14, 140, 280], [1.0, 1.0, 1.0, 1.0]),
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([1, 3, 260, 100, 150], [1, 3, 780, 200, 350], [1, 3, 780, 200, 350], [1.0, 1.0, 1.0, 1.0, 1.0]),
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([1, 3, 450, 100, 150], [1, 3, 260, 200, 350], [1, 3, 260, 200, 350], [1.0, 1.0, 1.0, 1.0, 1.0]),
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([5, 14, 1000, 300, 40], [5, 14, 500, 140, 280], [1, 1, 500, 140, 280], [1.0, 1.0, 1.0, 1.0, 1.0]),
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([5, 14, 1000, 300, 40], [5, 14, 500, 140, 280], [5, 14, 500, 140, 280], [1.0, 1.0, 1.0, 1.0, 1.0])])
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def test_onnx_resize11_using_sizes(self, input_shape, output_shape, sizes, scales):
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np_scales = np.array(scales)
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np_sizes = int64_array(sizes)
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graph = build_graph(nodes_attrs=graph_node_attrs_sizes,
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edges=graph_edges_sizes,
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update_attributes={
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'input_data': {'shape': int64_array(input_shape)},
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'scales': {'shape': int64_array(np_scales.shape), 'value': np_scales},
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'scales_data': {'shape': int64_array(np_scales.shape), 'value': np_scales},
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'sizes': {'shape': int64_array(np_sizes.shape), 'value': np_sizes},
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'sizes_data': {'shape': int64_array(np_sizes.shape), 'value': np_sizes},
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})
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node = Node(graph, 'onnx_resize11')
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ONNXResize11Op.onnx_resize_infer(node)
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msg = "ONNXResize11 infer failed for case: sizes={}, scales={}, expected_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['onnx_resize11_data']['shape'], int64_array(output_shape)),
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msg.format(sizes, scales, output_shape, graph.node['onnx_resize11_data']['shape']))
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@generate(*[([1, 260, 100, 150], [1, 260, 200, 350], [1.0, 1.0, 2.0, 350 / 150]),
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([1, 3, 100, 200], [1, 3, 350, 150], [1.0, 1.0, 3.5, 150 / 200]),
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([5, 14, 300, 40], [5, 14, 140, 280], [1.0, 1.0, 140 / 300, 7.0]),
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([5, 14, 300, 40], [5, 14, 140, 560], [1.0, 1.0, 140 / 300, 14.0]),
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([1, 3, 260, 100, 150], [1, 3, 780, 200, 350], [1.0, 1.0, 3.0, 2.0, 350 / 150]),
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([1, 3, 450, 100, 150], [1, 3, 260, 200, 350], [1.0, 1.0, 260 / 450, 2.0, 350 / 150]),
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([5, 14, 1000, 300, 40], [5, 14, 500, 140, 280], [1.0, 1.0, 0.5, 140 / 300, 7.0]),
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([4, 3, 180, 1340], [4, 3, 60, 804], [1.0, 1.0, 0.33333334, 0.6]),
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([4, 3, 500, 180, 1340], [4, 3, 750, 60, 804], [1.0, 1.0, 1.5, 0.33333334, 0.6])])
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def test_onnx_resize_using_scales(self, input_shape, output_shape, scales):
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np_scales = np.array(scales)
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graph = build_graph(nodes_attrs=graph_node_attrs_scales,
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edges=graph_edges_scales,
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update_attributes={
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'input_data': {'shape': int64_array(input_shape)},
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'scales': {'shape': int64_array(np_scales.shape), 'value': np_scales},
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'scales_data': {'shape': int64_array(np_scales.shape), 'value': np_scales},
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})
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node = Node(graph, 'onnx_resize11')
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ONNXResize11Op.onnx_resize_infer(node)
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msg = "ONNXResize11 infer failed for case: scales={}, expected_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['onnx_resize11_data']['shape'], int64_array(output_shape)),
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msg.format(scales, output_shape, graph.node['onnx_resize11_data']['shape']))
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