Fixed L2NormFusing transformation (#4526)
* Fixed L2NormFusing transformastion * Refactor L2NormFusing transformation
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Evgeny Lazarev authored and 
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@@ -19,13 +19,19 @@ import logging as log
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import numpy as np
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from extensions.ops.normalize_l2 import NormalizeL2Op
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from mo.front.common.layout import get_features_dim
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_node_with_second_input
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from mo.graph.graph import Graph, rename_node
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from mo.graph.graph import Graph, rename_nodes
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from mo.middle.replacement import MiddleReplacementPattern
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class L2NormToNorm(MiddleReplacementPattern):
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"""
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Transformation fuses sub-graph performing l2 normalization into the NormalizeL2 operation. IE plugins do not support
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NormalizeL2 operation and there is a nGraph transformation which converts NormalizeL2 to NormalizeIE. The latter one
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allows to normalize over just channel dimension or "channel + all spatial" dimensions for 2D, 3D or 4D cases.
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"""
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enabled = True
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force_clean_up = True
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@@ -86,29 +92,33 @@ class L2NormToNorm(MiddleReplacementPattern):
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# reduction only along spatial and channel dimensions.
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input_rank = len(match['sum'].in_port(0).data.get_shape())
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if input_rank not in [2, 3, 4]:
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log.debug('IE supports L2 normalization only for 2D, 3D and 4D tensors, skip fusing transformation.')
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log.debug('IE supports L2 normalization only for 2D, 3D and 4D tensors.')
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return
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axes = match['sum'].in_port(1).data.get_value()
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axes = int64_array(axes)
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if axes.shape == ():
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axes = int64_array([axes])
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axes = int64_array([axis if axis >= 0 else axis + input_rank for axis in axes])
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axes.sort()
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if not np.array_equal(axes, int64_array(np.arange(start=1, stop=input_rank))):
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log.debug('IE doesn\'t support l2 normalization with reduction along axes {}, skip fusing transformation.'
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''.format(axes))
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transformation_applicable = False
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# check for case C + all spatial dims. Works for 2D (NC), 3D (NCH) and 4D (NCHW and NHWC)
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if len(axes) + 1 == input_rank and np.array_equal(axes, int64_array(np.arange(start=1, stop=input_rank))):
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transformation_applicable = True
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# check for pure C channel normalization
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if len(axes) == 1 and ((input_rank == 4 and get_features_dim(graph.graph['layout'], input_rank) == axes[0]) or
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(input_rank != 4 and axes[0] == 1)):
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transformation_applicable = True
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if not transformation_applicable:
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log.debug('IE doesn\'t support l2 normalization with reduction along axes {}.'.format(axes))
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return
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# rename l2_normalize node since it will be no longer output after the transformation
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output_name = match['l2_normalize'].soft_get('name', match['l2_normalize'].id)
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normalizel2_name = output_name + '/normalizel2'
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rename_node(match['l2_normalize'], normalizel2_name)
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normalize_node = create_op_node_with_second_input(graph, NormalizeL2Op, axes, {'name': output_name,
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'eps_mode': 'max', 'eps': y})
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rename_node(normalize_node, output_name)
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match['square'].in_port(0).get_source().connect(normalize_node.in_port(0))
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match['square'].in_port(0).disconnect()
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@@ -118,3 +128,4 @@ class L2NormToNorm(MiddleReplacementPattern):
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match['l2_normalize'].in_port(0).disconnect()
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match['l2_normalize'].out_port(0).get_connection().set_source(normalize_node.out_port(0))
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rename_nodes([(match['l2_normalize'], output_name + "/TBR"), (normalize_node, output_name)])
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@@ -17,6 +17,7 @@
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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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@@ -79,18 +80,31 @@ edges_after_replacement = [
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]
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@generator
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class L2NormToNormTest(unittest.TestCase):
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def test_2D(self):
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input_shape = int64_array([1, 300])
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axes = int64_array([1])
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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=axes.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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@@ -104,301 +118,43 @@ class L2NormToNormTest(unittest.TestCase):
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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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def test_2D_scalar_axis(self):
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input_shape = int64_array([1, 300])
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axes = int64_array(1)
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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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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=int64_array([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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def test_3D(self):
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input_shape = int64_array([1, 300, 300])
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axes = int64_array([1, 2])
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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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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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def test_4D(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([1, 2, 3])
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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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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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def test_4D_mixed_axes(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([3, 1, 2])
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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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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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def test_4D_multiple_consumers(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([1, 2, 3])
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weights_value = np.ones(shape=int64_array([input_shape[-1]]), dtype=np.float32)
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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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('result_2', dict(kind='op', op='Result'))
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], edges + [('input_data', 'result_2')], nodes_with_edges_only=True)
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graph.stage = 'middle'
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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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('result_2', dict(kind='op', op='Result'))
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], edges_after_replacement + [('input_data', 'result_2')], 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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def test_1D_negative(self):
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input_shape = int64_array([300])
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axes = int64_array([0])
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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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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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def test_2D_negative(self):
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input_shape = int64_array([1, 300])
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axes = int64_array([0])
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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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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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def test_3D_negative(self):
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input_shape = int64_array([1, 300, 300])
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axes = int64_array([2])
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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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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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def test_4D_negative_1(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([0, 1, 2])
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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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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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def test_4D_negative_2(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([2])
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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)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
graph.stage = 'middle'
|
||||
|
||||
L2NormToNorm().find_and_replace_pattern(graph)
|
||||
|
||||
graph_ref = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
('square_data', dict(kind='data', shape=input_shape)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
def test_4D_negative_3(self):
|
||||
input_shape = int64_array([1, 300, 300, 3])
|
||||
axes = int64_array([2, 1])
|
||||
|
||||
graph = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
('square_data', dict(kind='data', shape=input_shape)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
graph.stage = 'middle'
|
||||
|
||||
L2NormToNorm().find_and_replace_pattern(graph)
|
||||
|
||||
graph_ref = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
('square_data', dict(kind='data', shape=input_shape)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
def test_4D_negative_4(self):
|
||||
input_shape = int64_array([1, 300, 300, 3])
|
||||
axes = int64_array([2, 0])
|
||||
|
||||
graph = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
('square_data', dict(kind='data', shape=input_shape)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
graph.stage = 'middle'
|
||||
|
||||
L2NormToNorm().find_and_replace_pattern(graph)
|
||||
|
||||
graph_ref = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
('square_data', dict(kind='data', shape=input_shape)),
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
def test_5D_negative(self):
|
||||
input_shape = int64_array([1, 300, 300, 300, 3])
|
||||
axes = int64_array([1, 2, 3, 4])
|
||||
|
||||
@generate(*[(int64_array([2]), int64_array([0]), 'NCHW'),
|
||||
(int64_array([2, 3]), int64_array([0]), 'NCHW'),
|
||||
(int64_array([2, 3]), int64_array([0]), 'NHWC'),
|
||||
(int64_array([2, 3]), int64_array([0, 1]), 'NCHW'),
|
||||
(int64_array([2, 3]), int64_array([0, 1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5]), int64_array([0]), 'NCHW'),
|
||||
(int64_array([2, 3, 5]), int64_array([0]), 'NHWC'),
|
||||
(int64_array([2, 3, 5]), int64_array([-1]), 'NCHW'),
|
||||
(int64_array([2, 3, 5]), int64_array([-1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5]), int64_array([0, 1]), 'NCHW'),
|
||||
(int64_array([2, 3, 5]), int64_array([0, 1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5]), int64_array([0, 2]), 'NCHW'),
|
||||
(int64_array([2, 3, 5]), int64_array([0, 2]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([2]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([2]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([3]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([1, 2]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([-2, -1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([1, 3]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([2, 3]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7, 9]), int64_array([1]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7, 9]), int64_array([-1]), 'NHWC'),
|
||||
(int64_array([2, 3, 5, 7, 9]), int64_array([1, 2, 3, 4]), 'NCHW'),
|
||||
(int64_array([2, 3, 5, 7, 9]), int64_array([-1, -2, -3, -4]), 'NHWC'),
|
||||
])
|
||||
def test_negative(self, input_shape, axes, layout):
|
||||
graph = build_graph_with_attrs(nodes + [
|
||||
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
||||
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
||||
@@ -406,6 +162,7 @@ class L2NormToNormTest(unittest.TestCase):
|
||||
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
||||
], edges, nodes_with_edges_only=True)
|
||||
graph.stage = 'middle'
|
||||
graph.graph['layout'] = layout
|
||||
|
||||
L2NormToNorm().find_and_replace_pattern(graph)
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""
|
||||
Copyright (C) 2018-2020 Intel Corporation
|
||||
Copyright (C) 2018-2021 Intel Corporation
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
@@ -16,6 +16,7 @@
|
||||
import numpy as np
|
||||
|
||||
from mo.graph.graph import Graph, Node
|
||||
from mo.graph.perm_inputs import PermuteInputs
|
||||
from mo.ops.op import Op
|
||||
|
||||
|
||||
@@ -25,15 +26,15 @@ class NormalizeL2Op(Op):
|
||||
|
||||
def __init__(self, graph: Graph, attrs: dict):
|
||||
super().__init__(graph, {
|
||||
'type': __class__.op,
|
||||
'op': __class__.op,
|
||||
'type': self.op,
|
||||
'op': self.op,
|
||||
'version': 'opset1',
|
||||
'eps': None,
|
||||
'p': None,
|
||||
'eps_mode': None,
|
||||
'in_ports_count': 2,
|
||||
'out_ports_count': 1,
|
||||
'infer': __class__.infer
|
||||
'infer': self.infer
|
||||
}, attrs)
|
||||
|
||||
def supported_attrs(self):
|
||||
@@ -58,3 +59,5 @@ class NormalizeL2Op(Op):
|
||||
node.out_port(0).data.set_value(input_value / norm_value)
|
||||
else:
|
||||
node.out_port(0).data.set_shape(input_shape)
|
||||
|
||||
PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:0', 'axis')
|
||||
Reference in new issue
Block a user