[MO] update ROIAlign op to opset9 (#11623)
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Bartek Szmelczynski authored and 
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@@ -3,7 +3,7 @@
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from openvino.tools.mo.ops.roialign import ROIAlign
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from openvino.tools.mo.front.extractor import FrontExtractorOp
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from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
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from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr, get_onnx_opset_version
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class ROIAlignExtractor(FrontExtractorOp):
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@@ -17,8 +17,16 @@ class ROIAlignExtractor(FrontExtractorOp):
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output_width = onnx_attr(node, 'output_width', 'i', default=1)
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sampling_ratio = onnx_attr(node, 'sampling_ratio', 'i', default=0)
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spatial_scale = onnx_attr(node, 'spatial_scale', 'f', default=1.0)
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ROIAlign.update_node_stat(node, {'pooled_h': output_height, 'pooled_w': output_width,
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'sampling_ratio': sampling_ratio, 'spatial_scale': spatial_scale,
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'mode': mode})
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onnx_opset_version = get_onnx_opset_version(node)
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if onnx_opset_version >= 16:
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aligned_mode = onnx_attr(node, 'coordinate_transformation_mode', 's', default=b'half_pixel').decode()
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if aligned_mode == "output_half_pixel":
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aligned_mode = "asymmetric"
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ROIAlign.update_node_stat(node, {'pooled_h': output_height, 'pooled_w': output_width,
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'sampling_ratio': sampling_ratio, 'spatial_scale': spatial_scale,
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'mode': mode, 'aligned_mode': aligned_mode})
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else:
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ROIAlign.update_node_stat(node, {'pooled_h': output_height, 'pooled_w': output_width,
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'sampling_ratio': sampling_ratio, 'spatial_scale': spatial_scale,
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'mode': mode})
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return cls.enabled
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@@ -18,33 +18,45 @@ class ROIAlign(Op):
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assert 'pooled_w' in attrs, '`pooled_w` attribute is not set for ROIAlign during creation'
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assert 'sampling_ratio' in attrs, '`sampling_ratio` attribute is not set for ROIAlign during creation'
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assert 'spatial_scale' in attrs, '`spatial_scale` attribute is not set for ROIAlign during creation'
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super().__init__(graph, {
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'op': self.op,
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'type': self.op,
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'version': 'opset3',
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'version': 'opset9',
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'infer': self.infer,
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'reverse_infer': self.reverse_infer,
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'in_ports_count': 3,
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'out_ports_count': 1,
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'aligned_mode': 'asymmetric',
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}, attrs)
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def backend_attrs(self):
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return [
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('mode', lambda node: str(node.mode)),
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('pooled_h', lambda node: str(int(node.pooled_h))),
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('pooled_w', lambda node: str(int(node.pooled_w))),
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('sampling_ratio', lambda node: str(int(node.sampling_ratio))),
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('spatial_scale', lambda node: str(float(node.spatial_scale))),
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]
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version = self.get_opset()
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if version == 'opset3':
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return [
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('mode', lambda node: str(node.mode)),
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('pooled_h', lambda node: str(int(node.pooled_h))),
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('pooled_w', lambda node: str(int(node.pooled_w))),
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('sampling_ratio', lambda node: str(int(node.sampling_ratio))),
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('spatial_scale', lambda node: str(float(node.spatial_scale))),
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]
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elif version == 'opset9':
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return [
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('mode', lambda node: str(node.mode)),
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('pooled_h', lambda node: str(int(node.pooled_h))),
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('pooled_w', lambda node: str(int(node.pooled_w))),
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('sampling_ratio', lambda node: str(int(node.sampling_ratio))),
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('spatial_scale', lambda node: str(float(node.spatial_scale))),
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('aligned_mode', lambda node: str(node.aligned_mode))
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]
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@staticmethod
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def infer(node):
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layout = node.graph.graph['layout']
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node_name = node.soft_get('name', node.id)
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assert len([port for port in node.in_ports().values() if not port.disconnected()]) == 3, \
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'The node "{}" must 3 inputs'.format(node_name)
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@@ -53,7 +65,9 @@ class ROIAlign(Op):
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assert node.has_valid('mode'), '"mode" attribute is not set for node "{}"'.format(node_name)
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assert node.mode in ['avg', 'max'], \
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'"mode" attribute range of values is ["avg", "max"], got {} for node "{}"'.format(node.mode, node_name)
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if node.get_opset() == 'opset9':
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assert node.aligned_mode in ['asymmetric', 'half_pixel_for_nn', 'half_pixel'], \
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'"aligned_mode" attribute range of values is ["asymmetric", "half_pixel_for_nn", "half_pixel"]'
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input_shape = node.in_port(0).data.get_shape()
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rois_shape = node.in_port(1).data.get_shape()
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indices_shape = node.in_port(2).data.get_shape()
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@@ -63,7 +77,6 @@ class ROIAlign(Op):
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'to number of ROIs for node "{}"'.format(node_name)
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assert compatible_dims(rois_shape[1], 4), 'The size of ROI element must be 4 for node "{}"'.format(node_name)
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assert len(input_shape) == 4, 'The rank of port 0 input tensor of node "{}" must be 4.'.format(node_name)
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node.out_port(0).data.set_shape(
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shape_for_layout(layout,
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batch=rois_shape[0],
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@@ -0,0 +1,140 @@
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# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array, float32_array
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import numpy as np
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from openvino.tools.mo.graph.graph import Node
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from openvino.tools.mo.ops.roialign import ROIAlign
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from unit_tests.utils.graph import build_graph
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class TestROIAlignOps(unittest.TestCase):
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node_attrs = {
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# input 1
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"1_input": {"kind": "op", "type": "Parameter", "value": None},
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"input_data": {"shape": None, "kind": "data", "value": None},
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#input 2
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"2_rois": {"kind": "op", "type": "Parameter","value": None},
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"rois_data": {"shape": None,"kind": "data", "value": None},
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# input 3
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"3_indices": {"kind": "op","type": "Parameter"},
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"indices_data": {"shape": None, "kind": "data", "value": None},
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# ROIAlign
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"node": {
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"kind": "op",
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"type": "ROIAlign",
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"pooled_h": None,
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"pooled_w": None,
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"mode": None,
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"sampling_ratio": 2,
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"spatial_scale": 16,
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"aligned_mode": None,
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},
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"node_data": {"shape": None, "kind": "data", "value": None},
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# output
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"result": {"kind": "op","type": "Result"},
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}
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def test_roialignv1(self):
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graph = build_graph(
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self.node_attrs,
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[
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("1_input", "input_data"),
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("input_data", "node", {"in": 0}),
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("2_rois", "rois_data"),
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("rois_data", "node", {"in": 1}),
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("3_indices", "indices_data"),
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("indices_data", "node", {"in": 2}),
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("node", "node_data"),
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("node_data", "result"),
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],
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{
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'input_data': {'shape': int64_array([1, 256, 200, 272])},
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'rois_data': {'shape': int64_array([1000, 4])},
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'indices_data': {'shape': int64_array([1000])},
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'node': {'mode': 'max', 'pooled_h': 7, 'pooled_w': 7, 'aligned_mode': 'asymmetric', 'version': 'opset9'},
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}
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)
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graph.graph["layout"] = "NCHW"
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node = Node(graph, "node")
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ROIAlign.infer(node)
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self.assertListEqual(list([1000, 256, 7, 7]), graph.node['node_data']['shape'].data.tolist())
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def test_roialignv2(self):
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graph = build_graph(
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self.node_attrs,
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[
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("1_input", "input_data"),
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("input_data", "node", {"in": 0}),
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("2_rois", "rois_data"),
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("rois_data", "node", {"in": 1}),
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("3_indices", "indices_data"),
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("indices_data", "node", {"in": 2}),
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("node", "node_data"),
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("node_data", "result"),
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],
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{
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'input_data': {'shape': int64_array([7, 256, 200, 200])},
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'rois_data': {'shape': int64_array([300, 4])},
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'indices_data': {'shape': int64_array([300])},
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'node': {'mode': 'max', 'pooled_h': 5, 'pooled_w': 6, 'aligned_mode': 'half_pixel_for_nn', 'version':'opset9'},
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}
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)
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graph.graph["layout"] = "NCHW"
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node = Node(graph, "node")
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ROIAlign.infer(node)
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self.assertListEqual(list([300, 256, 5, 6]), graph.node['node_data']['shape'].data.tolist())
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def test_roialignv3(self):
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graph = build_graph(
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self.node_attrs,
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[
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("1_input", "input_data"),
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("input_data", "node", {"in": 0}),
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("2_rois", "rois_data"),
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("rois_data", "node", {"in": 1}),
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("3_indices", "indices_data"),
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("indices_data", "node", {"in": 2}),
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("node", "node_data"),
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("node_data", "result"),
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],
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{
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'input_data': {'shape': int64_array([2, 3, 5, 5])},
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'rois_data': {'shape': int64_array([7, 4])},
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'indices_data': {'shape': int64_array([7])},
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'node': {'mode': 'max', 'pooled_h': 2, 'pooled_w': 2, 'aligned_mode': 'half_pixel', 'version': 'opset9'},
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}
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)
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graph.graph["layout"] = "NCHW"
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node = Node(graph, "node")
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ROIAlign.infer(node)
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self.assertListEqual(list([7, 3, 2, 2]), graph.node['node_data']['shape'].data.tolist())
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def test_roialign_wrong_aligned_mode(self):
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graph = build_graph(
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self.node_attrs,
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[
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("1_input", "input_data"),
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("input_data", "node", {"in": 0}),
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("2_rois", "rois_data"),
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("rois_data", "node", {"in": 1}),
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("3_indices", "indices_data"),
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("indices_data", "node", {"in": 2}),
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("node", "node_data"),
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("node_data", "result"),
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],
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{
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'input_data': {'shape': int64_array([2, 3, 5, 5])},
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'rois_data': {'shape': int64_array([7, 4])},
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'indices_data': {'shape': int64_array([7])},
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'node': {'mode': 'max', 'pooled_h': 2, 'pooled_w': 2, 'aligned_mode': 'full_pixel', 'version': 'opset9'},
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}
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)
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graph.graph["layout"] = "NCHW"
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node = Node(graph, "node")
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self.assertRaises(AssertionError, ROIAlign.infer, node)
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