164 lines
5.6 KiB
Python
164 lines
5.6 KiB
Python
"""
|
|
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 extensions.middle.quantize_fuses import FakeQuantizeFuse
|
|
from mo.front.common.partial_infer.eltwise import eltwise_infer
|
|
from mo.utils.ir_engine.compare_graphs import compare_graphs
|
|
from mo.utils.unittest.graph import build_graph
|
|
|
|
nodes = {
|
|
'placeholder': {'kind': 'op', 'op': 'Placeholder'},
|
|
'placeholder_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224]), 'value': None},
|
|
|
|
'mi_i': {'kind': 'op', 'op': 'Const'},
|
|
'mi_i_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224]), 'value': None},
|
|
'ma_i': {'kind': 'op', 'op': 'Const'},
|
|
'ma_i_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224]), 'value': None},
|
|
'mi_o': {'kind': 'op', 'op': 'Const'},
|
|
'mi_o_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224]), 'value': None},
|
|
'ma_o': {'kind': 'op', 'op': 'Const'},
|
|
'ma_o_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224]), 'value': None},
|
|
|
|
'quantize': {'kind': 'op', 'op': 'FakeQuantize', 'keep_in_IR': True},
|
|
'quantize_d': {'kind': 'data', 'shape': None},
|
|
|
|
'mul_val': {'kind': 'op', 'op': 'Const'},
|
|
'mul_val_d': {'kind': 'data', 'shape': np.array([1]), 'value': np.array([5])},
|
|
|
|
'mul': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
|
|
'mul_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224])},
|
|
'mul_1': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
|
|
'mul_1_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224])},
|
|
'mul_2': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
|
|
'mul_2_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224])},
|
|
'mul_3': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
|
|
'mul_3_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224])},
|
|
'mul_4': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
|
|
'mul_4_d': {'kind': 'data', 'shape': np.array([1, 3, 224, 224])},
|
|
|
|
'output': {'kind': 'op', 'op': 'Result'},
|
|
}
|
|
|
|
edges = [
|
|
('placeholder', 'placeholder_d'),
|
|
('mi_i', 'mi_i_d'),
|
|
('ma_i', 'ma_i_d'),
|
|
('mi_o', 'mi_o_d'),
|
|
('ma_o', 'ma_o_d'),
|
|
('quantize', 'quantize_d'),
|
|
('mul', 'mul_d'),
|
|
('mul_val', 'mul_val_d'),
|
|
|
|
('placeholder_d', 'quantize', {'in': 0}),
|
|
('mi_i_d', 'quantize', {'in': 1}),
|
|
('ma_i_d', 'quantize', {'in': 2}),
|
|
('mi_o_d', 'quantize', {'in': 3}),
|
|
('ma_o_d', 'quantize', {'in': 4}),
|
|
|
|
('quantize_d', 'mul', {'in': 0}),
|
|
('mul_val_d', 'mul', {'in': 1}),
|
|
|
|
('mul_d', 'output'),
|
|
]
|
|
|
|
edges_ref_1 = [
|
|
('placeholder', 'placeholder_d'),
|
|
('mi_i', 'mi_i_d'),
|
|
('ma_i', 'ma_i_d'),
|
|
('mi_o', 'mi_o_d'),
|
|
('ma_o', 'ma_o_d'),
|
|
('quantize', 'quantize_d'),
|
|
('mul', 'mul_d'),
|
|
('mul_val', 'mul_val_d'),
|
|
|
|
('placeholder_d', 'mul', {'in': 0}),
|
|
('mul_val_d', 'mul', {'in': 1}),
|
|
|
|
('mul_d', 'quantize', {'in': 0}),
|
|
('mi_i_d', 'quantize', {'in': 1}),
|
|
('ma_i_d', 'quantize', {'in': 2}),
|
|
('mi_o_d', 'quantize', {'in': 3}),
|
|
('ma_o_d', 'quantize', {'in': 4}),
|
|
|
|
('quantize_d', 'output'),
|
|
]
|
|
|
|
edges_ref_5 = [
|
|
('placeholder', 'placeholder_d'),
|
|
('mi_i', 'mi_i_d'),
|
|
('ma_i', 'ma_i_d'),
|
|
('mi_o', 'mi_o_d'),
|
|
('ma_o', 'ma_o_d'),
|
|
('quantize', 'quantize_d'),
|
|
('mul', 'mul_d'),
|
|
('mul_1', 'mul_1_d'),
|
|
('mul_2', 'mul_2_d'),
|
|
('mul_3', 'mul_3_d'),
|
|
('mul_4', 'mul_4_d'),
|
|
('mul_val', 'mul_val_d'),
|
|
|
|
|
|
('placeholder_d', 'mul', {'in': 0}),
|
|
('mi_i_d', 'mul_1'),
|
|
('ma_i_d', 'mul_2'),
|
|
('mi_o_d', 'mul_3'),
|
|
('ma_o_d', 'mul_4'),
|
|
|
|
('mul_val_d', 'mul', {'in': 1, 'out': 0}),
|
|
('mul_d', 'quantize', {'in': 0}),
|
|
|
|
('mul_val_d', 'mul_1', {'in': 1, 'out': 0}),
|
|
('mul_1_d', 'quantize', {'in': 1}),
|
|
|
|
('mul_val_d', 'mul_2', {'in': 1, 'out': 0}),
|
|
('mul_2_d', 'quantize', {'in': 2}),
|
|
|
|
('mul_val_d', 'mul_3', {'in': 1, 'out': 0}),
|
|
('mul_3_d', 'quantize', {'in': 3}),
|
|
|
|
('mul_val_d', 'mul_4', {'in': 1, 'out': 0}),
|
|
('mul_4_d', 'quantize', {'in': 4}),
|
|
|
|
('quantize_d', 'output'),
|
|
]
|
|
|
|
|
|
class TestQuantizeFuses(unittest.TestCase):
|
|
def test_pool_1_port_through_quantize(self):
|
|
graph = build_graph(nodes, edges, {'mul': {'fuse_up_to_quantize_ports': [0]}}, nodes_with_edges_only=True)
|
|
graph.stage = 'middle'
|
|
FakeQuantizeFuse().find_and_replace_pattern(graph)
|
|
|
|
graph_ref = build_graph(nodes, edges_ref_1, nodes_with_edges_only=True)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|
|
|
|
def test_pool_5_ports_through_quantize(self):
|
|
graph = build_graph(nodes, edges, {'mul': {'fuse_up_to_quantize_ports': [0, 1, 2, 3, 4]}},
|
|
nodes_with_edges_only=True)
|
|
graph.stage = 'middle'
|
|
FakeQuantizeFuse().find_and_replace_pattern(graph)
|
|
|
|
graph_ref = build_graph(nodes, edges_ref_5, nodes_with_edges_only=True)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|