165 lines
8.8 KiB
Python
165 lines
8.8 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 generator import generator, generate
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from extensions.middle.L2NormFusing import L2NormToNorm
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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_with_attrs
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# A list with nodes attributes used to build various graphs.
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nodes = [
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('l2_normalize_mul', dict(kind='op', op='Mul', name='l2_norm_name')),
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('l2_normalize_mul_data', dict(kind='data')),
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('maximum', dict(kind='op', op='Maximum')),
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('maximum_data', dict(kind='data')),
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('maximum_y_const', dict(kind='op', op='Const', value=np.array(12.e-13, dtype=np.float32))),
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('maximum_y_data', dict(kind='data', value=np.array(12.e-13, dtype=np.float32))),
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('rsqrt_pow', dict(kind='data', value=-0.5)),
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('rsqrt', dict(kind='op', op='Pow')),
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('rsqrt_data', dict(kind='data')),
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('square_pow', dict(kind='op', op='Const', value=2.)),
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('square_pow_data', dict(kind='data', value=2.)),
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('square', dict(kind='op', op='Pow')),
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('sum', dict(kind='op', op='ReduceSum')),
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('sum_data', dict(kind='data')),
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('sum_axes', dict(kind='op', op='Const')),
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# nodes added after replacement
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('normalize_node', dict(kind='op', op='NormalizeL2')),
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('weights_node', dict(kind='op', op='Const')),
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('result', dict(kind='op', op='Result'))
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]
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edges = [
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('input', 'input_data', {'out': 0}),
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('input_data', 'square', {'in': 0}),
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('square_pow', 'square_pow_data', {'out': 0}),
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('square_pow_data', 'square', {'in': 1}),
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('square', 'square_data'),
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('square_data', 'sum'),
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('sum_axes', 'sum_axes_data'),
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('sum_axes_data', 'sum'),
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('sum', 'sum_data'),
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('maximum_y_const', 'maximum_y_data'),
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('maximum_y_data', 'maximum'),
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('sum_data', 'maximum'),
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('maximum', 'maximum_data'),
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('maximum_data', 'rsqrt', {'in': 0}),
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('rsqrt_pow', 'rsqrt', {'in': 1}),
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('rsqrt', 'rsqrt_data'),
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('rsqrt_data', 'l2_normalize_mul'),
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('input_data', 'l2_normalize_mul'),
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('l2_normalize_mul', 'l2_normalize_mul_data'),
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('l2_normalize_mul_data', 'result'),
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]
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edges_after_replacement = [
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('input', 'input_data', {'out': 0}),
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('input_data', 'normalize_node'),
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('weights_node', 'weights_node_data'),
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('weights_node_data', 'normalize_node'),
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('normalize_node', 'l2_normalize_mul_data'),
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('l2_normalize_mul_data', 'result'),
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]
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@generator
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class L2NormToNormTest(unittest.TestCase):
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@generate(*[(int64_array([2, 3]), int64_array([1]), 'NCHW'), # NC layout, normalize C dimension
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(int64_array([2, 3]), int64_array([1]), 'NHWC'), # NC layout, normalize C dimension
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(int64_array([2, 3, 5]), int64_array([1]), 'NCHW'), # NCH layout, normalize C dimension
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(int64_array([2, 3, 5]), int64_array([1]), 'NHWC'), # NCH layout, normalize C dimension
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(int64_array([2, 3, 5]), int64_array([-1, -2]), 'NHWC'), # NCH layout, normalize CH dimensions
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(int64_array([2, 3, 5]), int64_array([-1, -2]), 'NCHW'), # NCH layout, normalize CH dimensions
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(int64_array([2, 3, 5]), int64_array([1, 2]), 'NCHW'), # NCH layout, normalize CH dimensions
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(int64_array([2, 3, 5]), int64_array([1, 2]), 'NHWC'), # NCH layout, normalize CH dimensions
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(int64_array([2, 3, 5, 7]), int64_array([1]), 'NCHW'), # NCHW layout, normalize C dimension
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(int64_array([2, 3, 5, 7]), int64_array([-1]), 'NHWC'), # NHWC layout, normalize C dimension
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(int64_array([2, 3, 5, 7]), int64_array([3]), 'NHWC'), # NCHW layout, normalize C dimension
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(int64_array([2, 3, 5, 7]), int64_array([-1, 1, 2]), 'NCHW'), # NCHW layout, normalize CHW dimensions
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(int64_array([2, 3, 5, 7]), int64_array([-3, -2, -1]), 'NHWC'), # NCHW layout, normalize HWC dimensions
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])
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def test_positive(self, input_shape, axes, layout):
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph.graph['layout'] = layout
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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@generate(*[(int64_array([2]), int64_array([0]), 'NCHW'),
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(int64_array([2, 3]), int64_array([0]), 'NCHW'),
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(int64_array([2, 3]), int64_array([0]), 'NHWC'),
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(int64_array([2, 3]), int64_array([0, 1]), 'NCHW'),
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(int64_array([2, 3]), int64_array([0, 1]), 'NHWC'),
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(int64_array([2, 3, 5]), int64_array([0]), 'NCHW'),
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(int64_array([2, 3, 5]), int64_array([0]), 'NHWC'),
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(int64_array([2, 3, 5]), int64_array([-1]), 'NCHW'),
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(int64_array([2, 3, 5]), int64_array([-1]), 'NHWC'),
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(int64_array([2, 3, 5]), int64_array([0, 1]), 'NCHW'),
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(int64_array([2, 3, 5]), int64_array([0, 1]), 'NHWC'),
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(int64_array([2, 3, 5]), int64_array([0, 2]), 'NCHW'),
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(int64_array([2, 3, 5]), int64_array([0, 2]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([0]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([0]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([2]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([2]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([3]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([1]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([1, 2]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([-2, -1]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([1, 3]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([2, 3]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NHWC'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NCHW'),
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(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NHWC'),
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(int64_array([2, 3, 5, 7, 9]), int64_array([1]), 'NCHW'),
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(int64_array([2, 3, 5, 7, 9]), int64_array([-1]), 'NHWC'),
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(int64_array([2, 3, 5, 7, 9]), int64_array([1, 2, 3, 4]), 'NCHW'),
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(int64_array([2, 3, 5, 7, 9]), int64_array([-1, -2, -3, -4]), 'NHWC'),
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])
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def test_negative(self, input_shape, axes, layout):
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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graph.graph['layout'] = layout
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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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