iliya mironov
642a62d0e2
* Add axis support * Update dequant extractor * Update qdeq ops * Refactoring quantize * Update dequantize resolver * Update dequantize op * Refactoring dequantize * Some fixes for quantize and dequantize * Update unit tests * Reafctoring quantize/dequantize axis support * Move quantize/dequantize resolvers to middle * hot fix * Fix unit tests * Fix unit tests * Update quintize resolver comment * Refactoring code according to code review * Fix according to review * Change order for transforms quantize pipline
370 lines
21 KiB
Python
370 lines
21 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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import numpy as np
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from extensions.middle.quantize_linear_resolver import QuantizeLinearResolver
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from mo.front.common.partial_infer.utils import int64_array
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph
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from generator import generator, generate
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nodes1_attributes = {
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'input': {'kind': 'op', 'op': 'AnyOp'},
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'input_data': {'kind': 'data', 'shape': None},
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'quantize': {'kind': 'op', 'op': 'QuantizeLinear', 'axis': 1},
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'quantize_data': {'kind': 'data', 'shape': None},
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'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'scale_param_q_data': {'kind': 'data', 'shape': None},
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'zerop_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'zerop_param_q_data': {'kind': 'data', 'shape': None},
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'out': {'kind': 'op', 'op': 'AnyOp'},
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'out_data': {'kind': 'data', 'shape': None},
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'result': {'kind': 'op', 'op': 'Result'},
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}
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nodes_ref_attributes = {
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'input': {'kind': 'op', 'op': 'AnyOp'},
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'input_data': {'kind': 'data', 'shape': None},
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'cast': {'kind': 'op', 'op': 'Cast', 'type': 'Convert'},
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'cast_data': {'kind': 'data', 'shape': None},
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'f_quantize': {'kind': 'op', 'op': 'FakeQuantize', 'type': 'FakeQuantize'},
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'f_quantize_data': {'kind': 'data', 'shape': None},
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'mul1': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'},
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'mul1_data': {'kind': 'data', 'shape': None},
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'mul2': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'},
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'mul2_data': {'kind': 'data', 'shape': None},
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'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'scale_param_q_data': {'kind': 'data', 'shape': None},
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'in_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'in_low_data': {'kind': 'data', 'shape': None},
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'in_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'in_high_data': {'kind': 'data', 'shape': None},
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'out_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out_low_data': {'kind': 'data', 'shape': None},
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'out_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'out_high_data': {'kind': 'data', 'shape': None},
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'out': {'kind': 'op', 'op': 'AnyOp'},
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'out_data': {'kind': 'data', 'shape': None},
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'result': {'kind': 'op', 'op': 'Result'},
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'high_reshape_const': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'high_reshape_const_data': {'kind': 'data', 'shape': None},
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'high_reshape': {'kind': 'op', 'type': 'Reshape', 'op': 'Reshape'},
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'high_reshape_data': {'kind': 'data', 'shape': None},
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'low_reshape_const': {'kind': 'op', 'type': 'Const', 'op': 'Const'},
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'low_reshape_const_data': {'kind': 'data', 'shape': None},
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'low_reshape': {'kind': 'op', 'type': 'Reshape', 'op': 'Reshape'},
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'low_reshape_data': {'kind': 'data', 'shape': None},
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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', 'input_data'),
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('input_data', 'quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'quantize'),
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('zerop_param_q', 'zerop_param_q_data'),
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('zerop_param_q_data', 'quantize'),
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('quantize', 'quantize_data'),
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('quantize_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{
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'quantize': {'axis': 2},
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'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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'scale_param_q_data': {'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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'zerop_param_q_data': {'shape': np.array([]), 'value': np.uint8(128)},
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'input_data'),
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('input_data', 'f_quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'mul1', {'out': 0}),
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('in_low', 'in_low_data'),
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('in_low_data', 'mul1'),
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('mul1', 'mul1_data'),
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('mul1_data', 'f_quantize'),
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('f_quantize', 'f_quantize_data'),
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('scale_param_q_data', 'mul2', {'out': 0}),
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('in_high', 'in_high_data'),
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('in_high_data', 'mul2'),
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('mul2', 'mul2_data'),
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('mul2_data', 'f_quantize'),
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('out_low', 'out_low_data'),
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('out_low_data', 'f_quantize'),
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('out_high', 'out_high_data'),
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('out_high_data', 'f_quantize'),
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('f_quantize_data', 'cast'),
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('cast', 'cast_data'),
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('cast_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'in_low': {'shape': np.array([]), 'value': -128},
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'in_low_data': {'shape': np.array([]), 'value': -128},
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'in_high': {'shape': np.array([]), 'value': 127},
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'in_high_data': {'shape': np.array([]), 'value': 127},
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'out_low': {'shape': np.array([]), 'value': 0},
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'out_low_data': {'shape': np.array([]), 'value': 0},
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'out_high': {'shape': np.array([]), 'value': 255},
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'out_high_data': {'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 = 'middle'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', 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', 'input_data'),
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('input_data', 'quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'quantize'),
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('zerop_param_q', 'zerop_param_q_data'),
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('zerop_param_q_data', 'quantize'),
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('quantize', 'quantize_data'),
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('quantize_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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'scale_param_q_data': {'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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'zerop_param_q_data': {'shape': np.array([]), 'value': np.int8(0)},
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'input_data'),
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('input_data', 'f_quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'mul1', {'out': 0}),
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('in_low', 'in_low_data'),
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('in_low_data', 'mul1'),
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('mul1', 'mul1_data'),
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('mul1_data', 'f_quantize'),
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('f_quantize', 'f_quantize_data'),
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('scale_param_q_data', 'mul2', {'out': 0}),
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('in_high', 'in_high_data'),
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('in_high_data', 'mul2'),
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('mul2', 'mul2_data'),
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('mul2_data', 'f_quantize'),
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('out_low', 'out_low_data'),
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('out_low_data', 'f_quantize'),
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('out_high', 'out_high_data'),
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('out_high_data', 'f_quantize'),
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('f_quantize_data', 'cast'),
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('cast', 'cast_data'),
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('cast_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'in_low': {'shape': np.array([]), 'value': -128},
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'in_low_data': {'shape': np.array([]), 'value': -128},
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'in_high': {'shape': np.array([]), 'value': 127},
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'in_high_data': {'shape': np.array([]), 'value': 127},
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'out_low': {'shape': np.array([]), 'value': -128},
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'out_low_data': {'shape': np.array([]), 'value': -128},
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'out_high': {'shape': np.array([]), 'value': 127},
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'out_high_data': {'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 = 'middle'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', 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', 'input_data'),
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('input_data', 'quantize'),
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('quantize', 'quantize_data'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'quantize'),
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('quantize', 'quantize_data'),
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('quantize_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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'scale_param_q_data': {'shape': np.array([]), 'value': np.float32(1.0 / 255)},
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'input_data'),
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('input_data', 'f_quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'mul1', {'out': 0}),
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('in_low', 'in_low_data'),
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('in_low_data', 'mul1'),
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('mul1', 'mul1_data'),
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('mul1_data', 'f_quantize'),
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('f_quantize', 'f_quantize_data'),
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('scale_param_q_data', 'mul2', {'out': 0}),
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('in_high', 'in_high_data'),
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('in_high_data', 'mul2'),
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('mul2', 'mul2_data'),
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('mul2_data', 'f_quantize'),
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('out_low', 'out_low_data'),
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('out_low_data', 'f_quantize'),
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('out_high', 'out_high_data'),
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('out_high_data', 'f_quantize'),
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('f_quantize_data', 'cast'),
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('cast', 'cast_data'),
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('cast_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'in_low': {'shape': np.array([]), 'value': 0},
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'in_low_data': {'shape': np.array([]), 'value': 0},
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'in_high': {'shape': np.array([]), 'value': 255},
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'in_high_data': {'shape': np.array([]), 'value': 255},
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'out_low': {'shape': np.array([]), 'value': 0},
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'out_low_data': {'shape': np.array([]), 'value': 0},
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'out_high': {'shape': np.array([]), 'value': 255},
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'out_high_data': {'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 = 'middle'
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QuantizeLinearResolver().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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@generator
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class TestQuantizeWithAxis(unittest.TestCase):
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@generate(*[(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.uint8), int64_array([1, 1, 4, 1]),
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np.array([-2., -3., -4., -5.]), np.array([253., 252., 251., 250.]),
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0, 255, 2),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.uint8), int64_array([1, 3, 1, 1]),
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np.array([-2., -3., -4., -5.]), np.array([253., 252., 251., 250.]),
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0, 255, 1),
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(int64_array([2, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.uint8), int64_array([2, 1, 1, 1]),
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np.array([-2., -3., -4., -5.]), np.array([253., 252., 251., 250.]),
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0, 255, 0),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.uint8), int64_array([1, 1, 1, 4]),
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np.array([-2., -3., -4., -5.]), np.array([253., 252., 251., 250.]),
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0, 255, -1),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.uint8), int64_array([1, 1, 4, 1]),
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np.array([-2., -3., -4., -5.]), np.array([253., 252., 251., 250.]),
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0, 255, -2),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.int8), int64_array([1, 1, 4, 1]),
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np.array([-130., -131., -132., -133.]), np.array([125., 124., 123., 122.]),
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-128.0, 127.0, 2),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.int8), int64_array([1, 3, 1, 1]),
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np.array([-130., -131., -132., -133.]), np.array([125., 124., 123., 122.]),
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-128.0, 127.0, 1),
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(int64_array([2, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.int8), int64_array([2, 1, 1, 1]),
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np.array([-130., -131., -132., -133.]), np.array([125., 124., 123., 122.]),
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-128.0, 127.0, 0),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.int8), int64_array([1, 1, 1, 4]),
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np.array([-130., -131., -132., -133.]), np.array([125., 124., 123., 122.]),
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-128.0, 127.0, -1),
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(int64_array([1, 3, 4, 4]), np.array([2, 3, 4, 5], dtype=np.float32),
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np.array([2, 3, 4, 5], dtype=np.int8), int64_array([1, 1, 4, 1]),
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np.array([-130., -131., -132., -133.]), np.array([125., 124., 123., 122.]),
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-128.0, 127.0, -2),
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])
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def test_quantize_with_axis(self, input_shape, scale_param_value, zero_param_value,
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target_shape, in_low, in_high, out_low, out_high, axis):
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graph = build_graph(nodes1_attributes,
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[('input', 'input_data'),
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('input_data', 'quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'quantize'),
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('zerop_param_q', 'zerop_param_q_data'),
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('zerop_param_q_data', 'quantize'),
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('quantize', 'quantize_data'),
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('quantize_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{
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'quantize': {'axis': axis},
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'input': {'shape': input_shape},
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'input_data': {'shape': input_shape},
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'scale_param_q': {'shape': scale_param_value.shape, 'value': scale_param_value},
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'scale_param_q_data': {'shape': scale_param_value.shape, 'value': scale_param_value},
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'zerop_param_q': {'shape': zero_param_value.shape, 'value': zero_param_value},
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'zerop_param_q_data': {'shape': zero_param_value.shape, 'value': zero_param_value},
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}, nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_ref_attributes,
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[('input', 'input_data'),
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('input_data', 'f_quantize'),
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('scale_param_q', 'scale_param_q_data'),
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('scale_param_q_data', 'mul1', {'out': 0}),
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('in_low', 'in_low_data'),
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('in_low_data', 'mul1'),
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('mul1', 'mul1_data'),
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('mul1_data', 'high_reshape'),
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('high_reshape_const', 'high_reshape_const_data'),
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('high_reshape_const_data', 'high_reshape'),
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('high_reshape', 'high_reshape_data'),
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('high_reshape_data', 'f_quantize'),
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('f_quantize', 'f_quantize_data'),
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('scale_param_q_data', 'mul2', {'out': 0}),
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('in_high', 'in_high_data'),
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('in_high_data', 'mul2'),
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('mul2', 'mul2_data'),
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('mul2_data', 'low_reshape'),
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('low_reshape', 'low_reshape_data'),
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('low_reshape_data', 'f_quantize'),
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('low_reshape_const', 'low_reshape_const_data'),
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('low_reshape_const_data', 'low_reshape'),
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('out_low', 'out_low_data'),
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('out_low_data', 'f_quantize'),
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('out_high', 'out_high_data'),
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('out_high_data', 'f_quantize'),
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('f_quantize_data', 'cast'),
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('cast', 'cast_data'),
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('cast_data', 'out'),
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('out', 'out_data'),
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('out_data', 'result'),
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],
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{'in_low': {'shape': in_low.shape, 'value': in_low},
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'in_low_data': {'shape': in_low.shape, 'value': in_low},
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'in_high': {'shape': in_high.shape, 'value': in_high},
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'in_high_data': {'shape': in_high.shape, 'value': in_high},
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'out_low': {'shape': np.array([]), 'value': out_low},
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'out_low_data': {'shape': np.array([]), 'value': out_low},
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'out_high': {'shape': np.array([]), 'value': out_high},
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'out_high_data': {'shape': np.array([]), 'value': out_high},
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'cast': {'dst_type': zero_param_value.dtype},
|
|
'low_reshape_const_data': {'shape': target_shape.shape, 'value': target_shape},
|
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'high_reshape_const_data': {'shape': target_shape.shape, 'value': target_shape},
|
|
}, nodes_with_edges_only=True)
|
|
|
|
graph.stage = 'middle'
|
|
QuantizeLinearResolver().find_and_replace_pattern(graph)
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|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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|
self.assertTrue(flag, resp)
|