Maxim Vafin
a6efc86a6a
* [MO] Support ONNX QuantizeLinear * Update docs * Fix cast type * Fix error messages
162 lines
7.8 KiB
Python
162 lines
7.8 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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from argparse import Namespace
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import numpy as np
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from extensions.front.onnx.quantize_linear_resolver import QuantizeLinearResolver
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from mo.utils.unittest.graph import build_graph
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nodes1_attributes = {
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'input': {'kind': 'op', 'op': 'AnyOp'},
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'quantize': {'kind': 'op', 'op': 'QuantizeLinear'},
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'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'zerop_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out': {'kind': 'op', 'op': 'AnyOp'},
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}
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nodes_ref_attributes = {
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'input': {'kind': 'op', 'op': 'AnyOp'},
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'cast': {'kind': 'op', 'op': 'Cast', 'type': 'Convert'},
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'f_quantize': {'kind': 'op', 'op': 'FakeQuantize', 'type': 'FakeQuantize'},
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'mul1': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'},
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'mul2': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'},
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'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'in_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'in_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out': {'kind': 'op', 'op': 'AnyOp'},
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}
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class TestQuantizeLinearResolver(unittest.TestCase):
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def test_quantize_uint8(self):
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graph = build_graph(nodes1_attributes,
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[('input', 'quantize'),
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('scale_param_q', 'quantize'),
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('zerop_param_q', 'quantize'),
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('quantize', 'out'),
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],
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{'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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'zerop_param_q': {'shape': np.array([]), 'value': np.uint8(128)},
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}, nodes_with_edges_only=True)
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graph.graph['cmd_params'] = Namespace(keep_shape_ops=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'f_quantize'),
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('scale_param_q', 'mul1', {'out': 0}),
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('in_low', 'mul1'),
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('mul1', 'f_quantize'),
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('scale_param_q', 'mul2', {'out': 0}),
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('in_high', 'mul2'),
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('mul2', 'f_quantize'),
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('out_low', 'f_quantize'),
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('out_high', 'f_quantize'),
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('f_quantize', 'cast'),
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('cast', 'out'),
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],
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{'in_low': {'shape': np.array([]), 'value': -128},
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'in_high': {'shape': np.array([]), 'value': 127},
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'out_low': {'shape': np.array([]), 'value': 0},
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'out_high': {'shape': np.array([]), 'value': 255},
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'cast': {'dst_type': np.uint8}
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}, nodes_with_edges_only=True)
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graph.stage = 'front'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_quantize_int8(self):
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graph = build_graph(nodes1_attributes,
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[('input', 'quantize'),
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('scale_param_q', 'quantize'),
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('zerop_param_q', 'quantize'),
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('quantize', 'out'),
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],
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{'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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'zerop_param_q': {'shape': np.array([]), 'value': np.int8(0)},
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}, nodes_with_edges_only=True)
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graph.graph['cmd_params'] = Namespace(keep_shape_ops=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'f_quantize'),
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('scale_param_q', 'mul1', {'out': 0}),
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('in_low', 'mul1'),
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('mul1', 'f_quantize'),
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('scale_param_q', 'mul2', {'out': 0}),
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('in_high', 'mul2'),
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('mul2', 'f_quantize'),
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('out_low', 'f_quantize'),
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('out_high', 'f_quantize'),
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('f_quantize', 'cast'),
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('cast', 'out'),
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],
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{'in_low': {'shape': np.array([]), 'value': -128},
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'in_high': {'shape': np.array([]), 'value': 127},
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'out_low': {'shape': np.array([]), 'value': -128},
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'out_high': {'shape': np.array([]), 'value': 127},
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'cast': {'dst_type': np.int8}
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}, nodes_with_edges_only=True)
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graph.stage = 'front'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_quantize_no_zerop(self):
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graph = build_graph(nodes1_attributes,
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[('input', 'quantize'),
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('scale_param_q', 'quantize'),
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('quantize', 'out'),
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],
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{'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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}, nodes_with_edges_only=True)
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graph.graph['cmd_params'] = Namespace(keep_shape_ops=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'f_quantize'),
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('scale_param_q', 'mul1', {'out': 0}),
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('in_low', 'mul1'),
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('mul1', 'f_quantize'),
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('scale_param_q', 'mul2', {'out': 0}),
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('in_high', 'mul2'),
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('mul2', 'f_quantize'),
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('out_low', 'f_quantize'),
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('out_high', 'f_quantize'),
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('f_quantize', 'cast'),
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('cast', 'out'),
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],
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{'in_low': {'shape': np.array([]), 'value': 0},
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'in_high': {'shape': np.array([]), 'value': 255},
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'out_low': {'shape': np.array([]), 'value': 0},
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'out_high': {'shape': np.array([]), 'value': 255},
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'cast': {'dst_type': np.uint8}
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}, nodes_with_edges_only=True)
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graph.stage = 'front'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True)
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self.assertTrue(flag, resp)
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