Evgeny Lazarev
970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
190 lines
8.3 KiB
Python
190 lines
8.3 KiB
Python
"""
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Copyright (C) 2018-2020 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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import numpy as np
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from extensions.middle.MulFakeQuantizeFuse import MulFakeQuantizeFuse
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from mo.middle.passes.eliminate_test import build_graph
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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# The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
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# dictionary with node attributes.
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nodes = {
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'x': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'x_data': {'value': None, 'shape': np.array([1, 64, 56, 56]), 'kind': 'data'},
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'mul_const': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None},
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'mul_const_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'},
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'mul': {'op': 'Mul', 'kind': 'op'},
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'mul_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'},
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'mi_i': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None},
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'mi_i_data': {'value': np.array([-10]), 'shape': np.array([]), 'kind': 'data'},
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'ma_i': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None},
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'ma_i_data': {'value': np.array([10]), 'shape': np.array([]), 'kind': 'data'},
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'mi_o': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None},
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'mi_o_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'},
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'ma_o': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None},
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'ma_o_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'},
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'quantize': {'type': 'FakeQuantize', 'kind': 'op', 'op': 'FakeQuantize', 'levels': 2},
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'quantize_data': {'value': None, 'shape': np.array([1, 64, 56, 56]), 'kind': 'data'},
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'output': {'op': 'Result', 'kind': 'op'},
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}
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edges = [
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('x', 'x_data'),
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('mul_const', 'mul_const_data'),
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('mul', 'mul_data'),
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('mi_i', 'mi_i_data'),
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('ma_i', 'ma_i_data'),
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('mi_o', 'mi_o_data'),
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('ma_o', 'ma_o_data'),
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('quantize', 'quantize_data'),
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('quantize_data', 'output'),
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('x_data', 'mul', {'in': 0}),
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('mul_const_data', 'mul', {'in': 1}),
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('mul_data', 'quantize', {'in': 0}),
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('mi_i_data', 'quantize', {'in': 1}),
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('ma_i_data', 'quantize', {'in': 2}),
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('mi_o_data', 'quantize', {'in': 3}),
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('ma_o_data', 'quantize', {'in': 4}),
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]
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edges_ref = [
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('x', 'x_data'),
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('mul_const', 'mul_const_data'),
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('mul', 'mul_data'),
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('mi_i', 'mi_i_data'),
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('ma_i', 'ma_i_data'),
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('mi_o', 'mi_o_data'),
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('ma_o', 'ma_o_data'),
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('quantize', 'quantize_data'),
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('quantize_data', 'output'),
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('x_data', 'quantize', {'in': 0}),
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('mi_i_data', 'quantize', {'in': 1}),
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('ma_i_data', 'quantize', {'in': 2}),
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('mi_o_data', 'quantize', {'in': 3}),
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('ma_o_data', 'quantize', {'in': 4}),
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('x_data', 'mul', {'in': 0}),
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('mul_const_data', 'mul', {'in': 1}),
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]
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class MulQuantizeFuseTest(unittest.TestCase):
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def test_1(self):
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graph = build_graph(nodes, edges, {
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'mul': {'can_be_fused': True},
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'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (3, 1, 1))},
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))},
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'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 3, 1, 1))},
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}, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph_ref = build_graph(nodes, edges_ref, {
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (3, 1, 1))},
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'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))},
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'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 3, 1, 1))},
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'mi_i_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([-10]), (3, 1, 1))},
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'ma_i_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([10]), (3, 1, 1))},
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}, nodes_with_edges_only=True)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_2(self):
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graph = build_graph(nodes, edges, {
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'mul': {'can_be_fused': True},
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'mul_const_data': {'shape': np.array([1]), 'value': np.array([-1])},
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mi_o_data': {'shape': np.array([1]), 'value': np.array([0])},
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'ma_o_data': {'shape': np.array([1]), 'value': np.array([1])},
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}, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph_ref = build_graph(nodes, edges_ref, {
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mul_const_data': {'shape': np.array([1]), 'value': np.array([-1])},
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'mi_o_data': {'shape': np.array([1]), 'value': np.array([1])},
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'ma_o_data': {'shape': np.array([1]), 'value': np.array([0])},
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'mi_i_data': {'shape': np.array([1]), 'value': np.array([10])},
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'ma_i_data': {'shape': np.array([1]), 'value': np.array([-10])},
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}, nodes_with_edges_only=True)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_3(self):
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graph = build_graph(nodes, edges, {
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'mul': {'can_be_fused': True},
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'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.array([[[-1]], [[1]], [[-1]]])},
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))},
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'ma_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 1, 1, 1))},
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}, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph_ref = build_graph(nodes, edges_ref, {
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'quantize_data': {'shape': np.array([2, 3, 4, 4])},
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'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.array([[[-1]], [[1]], [[-1]]])},
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'mi_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[1]], [[0]], [[1]]])},
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'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[0]], [[1]], [[0]]])},
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'mi_i_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[10]], [[-10]], [[10]]])},
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'ma_i_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[-10]], [[10]], [[-10]]])},
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}, nodes_with_edges_only=True)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def negative_test_1(self):
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graph = build_graph(nodes, edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph_ref = build_graph(nodes, edges, nodes_with_edges_only=True)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def negative_test_2(self):
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graph = build_graph(nodes, edges, {'mul': {'can_be_fused': False}}, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph_ref = build_graph(nodes, edges, {'mul': {'can_be_fused': False}}, nodes_with_edges_only=True)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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