retinanet onnx model has Upsample error (#3052)

* Commit.

* Fixed infer function of ONNXResize11 operation.

* Small fixes.

* Written unit tests for shape calculation for ONNXResize11.

model-optimizer/extensions/middle/ONNXResize11ToInterpolateV4.py

@@ -67,7 +67,7 @@ def replace_resize(graph: Graph, resize: Node):
                                            {1: int64_array([begin_dim]),
                                             2: int64_array([end_dim]),
                                             3: int64_array([1])},
-                                           {'name': resize_name + '/StridedSlice_',
+                                           {'name': resize_name + '/StridedSlice_sizes',
                                             'begin_mask': int64_array([1]),
                                             'end_mask': int64_array([1]),
                                             'new_axis_mask': int64_array([0]),
@@ -77,7 +77,7 @@ def replace_resize(graph: Graph, resize: Node):
                                             {1: int64_array([begin_dim]),
                                              2: int64_array([end_dim]),
                                              3: int64_array([1])},
-                                            {'name': resize_name + '/StridedSlice_',
+                                            {'name': resize_name + '/StridedSlice_scales',
                                              'begin_mask': int64_array([1]),
                                              'end_mask': int64_array([1]),
                                              'new_axis_mask': int64_array([0]),

model-optimizer/extensions/ops/ONNXResize11.py

@@ -67,6 +67,8 @@ class ONNXResize11Op(Op):
             sizes = node.in_port(3).data.get_value()
             assert sizes is not None, \
                 "Node {} with op {} has no value in input port 3".format(node.name, node.op)
-            output_shape = int64_array(sizes)
+            output_shape = input_shape.copy()
+            spatial_dimension_indices = range(2, len(input_shape))
+            output_shape[spatial_dimension_indices] = int64_array(sizes)[2:]
 
         node.out_port(0).data.set_shape(output_shape.copy())

model-optimizer/extensions/ops/ONNXResize11_test.py

@@ -0,0 +1,140 @@
+"""
+ Copyright (C) 2018-2020 Intel Corporation
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+"""
+
+import unittest
+
+import numpy as np
+from generator import generator, generate
+
+from extensions.ops.ONNXResize11 import ONNXResize11Op
+from mo.front.common.partial_infer.utils import int64_array
+from mo.graph.graph import Node
+from mo.utils.unittest.graph import build_graph
+
+
+graph_node_attrs_sizes = {
+    'input': {'type': 'Parameter', 'kind': 'op'},
+    'input_data': {'kind': 'data', 'shape': None, 'value': None},
+    'roi': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
+    'roi_data': {'kind': 'data', 'shape': None, 'value': None},
+    'scales': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
+    'scales_data': {'kind': 'data', 'shape': None, 'value': None},
+    'sizes': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
+    'sizes_data': {'kind': 'data', 'shape': None, 'value': None},
+    'onnx_resize11': {
+        'op': 'ONNXResize11', 'kind': 'op', 'mode': 'nearest', 'nearest_mode': 'round_prefer_floor',
+        'coordinate_transformation_mode': 'half_pixel', 'cube_coeff': -0.75
+    },
+    'onnx_resize11_data': {'kind': 'data', 'value': None, 'shape': None},
+    'op_output': {'kind': 'op', 'op': 'Result'},
+}
+
+graph_edges_sizes = [
+    ('input', 'input_data'),
+    ('roi', 'roi_data'),
+    ('sizes', 'sizes_data'),
+    ('scales', 'scales_data'),
+    ('input_data', 'onnx_resize11', {'in': 0}),
+    ('roi_data', 'onnx_resize11', {'in': 1}),
+    ('scales_data', 'onnx_resize11', {'in': 2}),
+    ('sizes_data', 'onnx_resize11', {'in': 3}),
+    ('onnx_resize11', 'onnx_resize11_data'),
+    ('onnx_resize11_data', 'op_output'),
+]
+
+
+graph_node_attrs_scales = {
+    'input': {'type': 'Parameter', 'kind': 'op'},
+    'input_data': {'kind': 'data', 'shape': None, 'value': None},
+    'roi': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
+    'roi_data': {'kind': 'data', 'shape': None, 'value': None},
+    'scales': {'type': 'Const', 'kind': 'op', 'shape': None, 'value': None},
+    'scales_data': {'kind': 'data', 'shape': None, 'value': None},
+    'onnx_resize11': {
+        'op': 'ONNXResize11', 'kind': 'op', 'mode': 'nearest', 'nearest_mode': 'round_prefer_floor',
+        'coordinate_transformation_mode': 'half_pixel', 'cube_coeff': -0.75
+    },
+    'onnx_resize11_data': {'kind': 'data', 'value': None, 'shape': None},
+    'op_output': {'kind': 'op', 'op': 'Result'},
+}
+
+graph_edges_scales = [
+    ('input', 'input_data'),
+    ('roi', 'roi_data'),
+    ('scales', 'scales_data'),
+    ('input_data', 'onnx_resize11', {'in': 0}),
+    ('roi_data', 'onnx_resize11', {'in': 1}),
+    ('scales_data', 'onnx_resize11', {'in': 2}),
+    ('onnx_resize11', 'onnx_resize11_data'),
+    ('onnx_resize11_data', 'op_output'),
+]
+
+
+@generator
+class TestONNXResize11Op(unittest.TestCase):
+    @generate(*[([1, 260, 100, 150], [1, 260, 200, 350], [1, 260, 200, 350], [1.0, 1.0, 1.0, 1.0]),
+                ([1, 260, 100, 150], [1, 260, 200, 350], [1, 1, 200, 350], [1.0, 1.0, 1.0, 1.0]),
+                ([5, 14, 300, 40], [5, 14, 140, 280], [1, 1, 140, 280], [1.0, 1.0, 1.0, 1.0]),
+                ([5, 14, 300, 40], [5, 14, 140, 280], [5, 14, 140, 280], [1.0, 1.0, 1.0, 1.0]),
+                ([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]),
+                ([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]),
+                ([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]),
+                ([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])])
+    def test_onnx_resize11_using_sizes(self, input_shape, output_shape, sizes, scales):
+        np_scales = np.array(scales)
+        np_sizes = int64_array(sizes)
+        graph = build_graph(nodes_attrs=graph_node_attrs_sizes,
+                            edges=graph_edges_sizes,
+                            update_attributes={
+                                'input_data': {'shape': int64_array(input_shape)},
+                                'scales': {'shape': int64_array(np_scales.shape), 'value': np_scales},
+                                'scales_data': {'shape': int64_array(np_scales.shape), 'value': np_scales},
+                                'sizes': {'shape': int64_array(np_sizes.shape), 'value': np_sizes},
+                                'sizes_data': {'shape': int64_array(np_sizes.shape), 'value': np_sizes},
+                            })
+        node = Node(graph, 'onnx_resize11')
+        ONNXResize11Op.onnx_resize_infer(node)
+
+        msg = "ONNXResize11 infer failed for case: sizes={}, scales={}, expected_shape={}, actual_shape={}"
+
+        self.assertTrue(np.array_equal(graph.node['onnx_resize11_data']['shape'], int64_array(output_shape)),
+                        msg.format(sizes, scales, output_shape, graph.node['onnx_resize11_data']['shape']))
+
+    @generate(*[([1, 260, 100, 150], [1, 260, 200, 350], [1.0, 1.0, 2.0, 350 / 150]),
+                ([1, 3, 100, 200], [1, 3, 350, 150], [1.0, 1.0, 3.5, 150 / 200]),
+                ([5, 14, 300, 40], [5, 14, 140, 280], [1.0, 1.0, 140 / 300, 7.0]),
+                ([5, 14, 300, 40], [5, 14, 140, 560], [1.0, 1.0, 140 / 300, 14.0]),
+                ([1, 3, 260, 100, 150], [1, 3, 780, 200, 350], [1.0, 1.0, 3.0, 2.0, 350 / 150]),
+                ([1, 3, 450, 100, 150], [1, 3, 260, 200, 350], [1.0, 1.0, 260 / 450, 2.0, 350 / 150]),
+                ([5, 14, 1000, 300, 40], [5, 14, 500, 140, 280], [1.0, 1.0, 0.5, 140 / 300, 7.0]),
+                ([4, 3, 180, 1340], [4, 3, 60, 804], [1.0, 1.0, 0.33333334, 0.6]),
+                ([4, 3, 500, 180, 1340], [4, 3, 750, 60, 804], [1.0, 1.0, 1.5, 0.33333334, 0.6])])
+    def test_onnx_resize_using_scales(self, input_shape, output_shape, scales):
+        np_scales = np.array(scales)
+        graph = build_graph(nodes_attrs=graph_node_attrs_scales,
+                            edges=graph_edges_scales,
+                            update_attributes={
+                                'input_data': {'shape': int64_array(input_shape)},
+                                'scales': {'shape': int64_array(np_scales.shape), 'value': np_scales},
+                                'scales_data': {'shape': int64_array(np_scales.shape), 'value': np_scales},
+                            })
+        node = Node(graph, 'onnx_resize11')
+        ONNXResize11Op.onnx_resize_infer(node)
+
+        msg = "ONNXResize11 infer failed for case: scales={}, expected_shape={}, actual_shape={}"
+
+        self.assertTrue(np.array_equal(graph.node['onnx_resize11_data']['shape'], int64_array(output_shape)),
+                        msg.format(scales, output_shape, graph.node['onnx_resize11_data']['shape']))