173 lines
8.6 KiB
Python
173 lines
8.6 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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from argparse import Namespace
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from generator import generate, generator
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from extensions.back.ShuffleChannelPatternOptimization import ShuffleChannelFusion, DepthToSpaceFusion
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from extensions.ops.depth_to_space import DepthToSpaceOp
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from extensions.ops.parameter import Parameter
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from extensions.ops.shufflechannel import ShuffleChannels
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from extensions.ops.transpose import Transpose
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from mo.front.common.partial_infer.utils import int64_array
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from mo.ops.reshape import Reshape
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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, result, regular_op_with_shaped_data, \
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valued_const_with_data, connect, regular_op_with_empty_data
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@generator
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class ShuffleChannelFusionTest(unittest.TestCase):
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@staticmethod
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def get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, group):
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nodes = {
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**regular_op_with_shaped_data('input', input_shape, {'type': 'Parameter', 'shape': int64_array(input_shape),
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'infer': Parameter.infer}),
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**valued_const_with_data('reshape_0_pattern', int64_array(reshape_0_pattern)),
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**regular_op_with_empty_data('reshape_0', {'type': 'Reshape', 'infer': Reshape.infer}),
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**valued_const_with_data('order', int64_array(order)),
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**regular_op_with_empty_data('transpose', {'type': 'Transpose', 'infer': Transpose.infer}),
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**valued_const_with_data('reshape_1_pattern', int64_array(reshape_1_pattern)),
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**regular_op_with_empty_data('reshape_1', {'type': 'Reshape', 'infer': Reshape.infer,
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'name': 'final_reshape'}),
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**result(),
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}
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edges = [
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*connect('input', '0:reshape_0'),
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*connect('reshape_0_pattern', '1:reshape_0'),
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*connect('reshape_0', '0:transpose'),
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*connect('order', '1:transpose'),
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*connect('transpose', '0:reshape_1'),
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*connect('reshape_1_pattern', '1:reshape_1'),
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*connect('reshape_1', 'output'),
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]
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graph = build_graph(nodes, edges, nodes_with_edges_only=True)
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for node in graph.get_op_nodes():
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node['op'] = node['type']
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graph.clean_up()
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ref_nodes = {
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**regular_op_with_shaped_data('input', input_shape, {'type': 'Parameter', 'shape': int64_array(input_shape),
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'infer': Parameter.infer}),
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**regular_op_with_empty_data('shuffle_channel', {'type': 'ShuffleChannels', 'infer': ShuffleChannels.infer,
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'name': 'final_reshape', 'group': group}),
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**result()
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}
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ref_edges = [*connect('input', 'shuffle_channel'), *connect('shuffle_channel', 'output')]
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graph_ref = build_graph(ref_nodes, ref_edges, nodes_with_edges_only=True)
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for node in graph_ref.get_op_nodes():
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node['op'] = node['type']
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graph_ref.clean_up()
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return graph, graph_ref
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@generate(*[
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([1, 512, 7, 6], [1, 2, 256, 7, 6], [0, 2, 1, 3, 4], [1, 512, 7, 6], 2),
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([2, 512, 7, 6], [2, 2, 256, 7, 6], [0, 2, 1, 3, 4], [2, 512, 7, 6], 2),
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([1, 200, 200, 200], [1, 50, 4, 200, 200], [0, 2, 1, 3, 4], [1, 200, 200, 200], 50),
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])
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def test_fusion(self, input_shape, reshape_0_pattern, order, reshape_1_pattern, group):
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graph, graph_ref = self.get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, group)
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ShuffleChannelFusion().find_and_replace_pattern(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'output')
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self.assertTrue(flag, resp)
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self.assertTrue(len(graph.get_op_nodes(name='final_reshape')) == 1 and
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graph.get_op_nodes(name='final_reshape')[0].op == 'ShuffleChannels')
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@generate(*[
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([1, 512, 7, 6], [0, 2, 256, 7, 6], [0, 2, 1, 3, 4], [1, 512, 7, 6], 2),
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([1, 512, 7, 6], [1, 2, 256, 7, 6], [0, 2, 1, 4, 3], [1, 512, 7, 6], 2),
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([1, 512, 7, 6], [1, 2, 256, 7, 6], [0, 2, 1, 3, 4], [-1, 512, 7, 6], 2),
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])
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def test_negative(self, input_shape, reshape_0_pattern, order, reshape_1_pattern, group):
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graph, _ = self.get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, group)
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graph_ref = graph.copy()
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ShuffleChannelFusion().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output')
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self.assertTrue(flag, resp)
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@generator
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class DepthToSpaceFusionTest(unittest.TestCase):
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@staticmethod
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def get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, block_size):
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nodes = {
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**regular_op_with_shaped_data('input', input_shape, {'type': 'Parameter', 'shape': int64_array(input_shape),
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'infer': Parameter.infer}),
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**valued_const_with_data('reshape_0_pattern', int64_array(reshape_0_pattern)),
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**regular_op_with_empty_data('reshape_0', {'type': 'Reshape', 'infer': Reshape.infer}),
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**valued_const_with_data('order', int64_array(order)),
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**regular_op_with_empty_data('transpose', {'type': 'Transpose', 'infer': Transpose.infer}),
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**valued_const_with_data('reshape_1_pattern', int64_array(reshape_1_pattern)),
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**regular_op_with_empty_data('reshape_1', {'type': 'Reshape', 'infer': Reshape.infer,
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'name': 'final_reshape'}),
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**result(),
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}
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edges = [
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*connect('input', '0:reshape_0'),
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*connect('reshape_0_pattern', '1:reshape_0'),
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*connect('reshape_0', '0:transpose'),
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*connect('order', '1:transpose'),
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*connect('transpose', '0:reshape_1'),
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*connect('reshape_1_pattern', '1:reshape_1'),
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*connect('reshape_1', 'output'),
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]
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graph = build_graph(nodes, edges, nodes_with_edges_only=True, cli=Namespace())
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for node in graph.get_op_nodes():
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node['op'] = node['type']
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graph.clean_up()
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ref_nodes = {
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**regular_op_with_shaped_data('input', input_shape, {'type': 'Parameter', 'shape': int64_array(input_shape),
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'infer': Parameter.infer}),
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**regular_op_with_empty_data('depth_to_space', {'type': 'DepthToSpace', 'infer': DepthToSpaceOp.infer,
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'name': 'final_reshape', 'block_size': block_size}),
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**result()
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}
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ref_edges = [*connect('input', 'depth_to_space'), *connect('depth_to_space', 'output')]
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graph_ref = build_graph(ref_nodes, ref_edges, nodes_with_edges_only=True)
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for node in graph_ref.get_op_nodes():
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node['op'] = node['type']
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graph_ref.clean_up()
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graph.graph['layout'] = 'NCHW'
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graph_ref.graph['layout'] = 'NCHW'
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return graph, graph_ref
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@generate(*[
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([1, 512, 7, 6], [1, 2, 2, 128, 7, 6], [0, 1, 4, 2, 5, 3], [1, 128, 14, 12], 2),
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([2, 512, 7, 6], [2, 2, 2, 128, 7, 6], [0, 1, 4, 2, 5, 3], [2, 128, 14, 12], 2),
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([1, 200, 200, 200], [1, 2, 2, 50, 200, 200], [0, 1, 4, 2, 5, 3], [1, 50, 400, 400], 2),
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])
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def test_fusion(self, input_shape, reshape_0_pattern, order, reshape_1_pattern, block_size):
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graph, graph_ref = self.get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, block_size)
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DepthToSpaceFusion().find_and_replace_pattern(graph)
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graph.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'output')
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self.assertTrue(flag, resp)
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self.assertTrue(len(graph.get_op_nodes(name='final_reshape')) == 1 and
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graph.get_op_nodes(name='final_reshape')[0].op == 'DepthToSpace')
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@generate(*[
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([1, 512, 7, 6], [0, 2, 2, 128, 7, 6], [0, 1, 4, 2, 5, 3], [1, 128, 14, 12], 2),
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([2, 512, 7, 6], [2, 2, 2, 128, 7, 6], [0, 1, 4, 2, 5, 3], [-1, 128, 14, 12], 2),
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([1, 200, 200, 200], [1, 2, 2, 50, 200, 200], [0, 1, 4, 2, 3, 5], [1, 50, 400, 400], 2),
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])
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def test_negative(self, input_shape, reshape_0_pattern, order, reshape_1_pattern, group):
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graph, _ = self.get_graphs(input_shape, reshape_0_pattern, order, reshape_1_pattern, group)
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graph_ref = graph.copy()
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DepthToSpaceFusion().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output')
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self.assertTrue(flag, resp)
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