def replace_pattern(self, graph: Graph, match: dict):
scale = graph.graph['cmd_params'].scale
if scale is None or scale == 1:
return
assert (len(match['placeholder'].out_nodes()))
AddMeanScaleValues.apply_scale(graph, match['placeholder'],
{'scale': np.array([scale])})
def test_mean_values_without_data_name(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', 'result'),
], {'parameter': {
'name': 'None'
}})
mean_values = parse_tuple_pairs('(1,2,3)')
scale_values = parse_tuple_pairs('')
mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
argv = Namespace(mean_scale_values=mean_scale)
graph = build_graph(nodes, [*connect('parameter', 'result')],
{'parameter': {
'name': 'None'
}},
nodes_with_edges_only=True,
cli=argv)
self.set_graph_attrs(graph, ['None'])
self.set_graph_attrs(graph_ref, ['None'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, ['None'])
def test_debug_info_absence(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', '0:mul_scale'),
*connect('scale', '1:mul_scale'),
*connect('mul_scale', 'result'),
])
argv = Namespace(mean_scale_values=[[
np.array([1., 2., 3.]),
np.array([1., 2., 3.])
]])
graph = build_graph(nodes, [*connect('parameter', 'result')],
nodes_with_edges_only=True,
cli=argv)
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, [])
def test_mean_values_with_colon_in_node_name_and_port(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', 'result'),
])
argv = Namespace(mean_scale_values={
'0:param:0': {
'scale': np.array([1.]),
'mean': np.array([1., 2., 3.])
}
})
graph = build_graph(nodes, [*connect('parameter', 'result')], {
'parameter': {
'name': 'param:0',
'id': 'param:0/placeholder_0',
'initial_node_name': 'param:0'
}
},
nodes_with_edges_only=True,
cli=argv)
self.set_graph_attrs(graph, ['parameter'])
self.set_graph_attrs(graph_ref, ['parameter'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mean_values_optimized_and_scale_values_explicit(self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:mul_scale'),
*connect('scale', '1:mul_scale'),
*connect('mul_scale', 'result'),
])
argv = Namespace(
mean_scale_values={
'parameter': {
'scale': np.array([1., 2., 3.]),
'mean': np.array([0., 0., 0.])
}
})
graph = build_graph(nodes, [*connect('parameter', 'result')],
nodes_with_edges_only=True,
cli=argv)
self.set_graph_attrs(graph, ['parameter'])
self.set_graph_attrs(graph_ref, ['parameter'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, ['parameter'])
def test_add_mean_scale_values3(self):
graph = build_graph(nodes_attributes, [('pl_1', 'pl_1_data')], {
'pl_1_data': {
'shape': np.array([1, 3, 38, 38]),
'infer': None
},
'pl_1': {
'shape': np.array([1, 3, 38, 38])
},
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
argv = Namespace(mean_scale_values=[[
np.array([1., 2., 3.]),
np.array([1., 2., 3.])
]])
graph.graph['cmd_params'] = argv
AddMeanScaleValues().find_and_replace_pattern(graph)
mul_op_cnt = 0
add_op_cnt = 0
for node in graph.nodes():
node = Node(graph, node)
if node.has_valid('op') and node.op == 'Mul':
mul_op_cnt += 1
if node.has_valid('op') and node.op == 'Add':
add_op_cnt += 1
self.assertEqual(add_op_cnt, 1, "Found more than one Add op in graph")
self.assertEqual(mul_op_cnt, 1, "Found more than one Nul op in graph")
def test_add_mean_scale_values_without_data_name(self):
graph = build_graph(nodes_attributes, [('node_1', 'node_2'),
('node_2', 'op_output')],
{
'node_2': {
'shape': None,
'data_type': None
},
'node_1': {
'shape': np.array([1, 3, 227, 227]),
'op': 'Parameter',
'name': 'data',
'data_type': None
}
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
mean_values = parse_tuple_pairs('(124,117,104)')
scale_values = parse_tuple_pairs('')
# input = None
mean_scale = get_mean_scale_dictionary(mean_values, scale_values, None)
argv = Namespace(mean_scale_values=mean_scale)
graph.graph['cmd_params'] = argv
self.assertEqual(len(graph), 3)
AddMeanScaleValues().find_and_replace_pattern(graph)
self.assertEqual(len(graph), 6)
def test_mean_values_explicit_and_scale_values_explicit_on_cutted_graph(self):
"""
Test case when user cutted start of the network and specified mean/scale value to the new input node 'node_3'.
"""
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', 'result'),
*connect('parameter_2', '0:mul_scale'),
*connect('scale', '1:mul_scale'),
*connect('mul_scale', 'op'),
*connect('op', 'result_2'),
])
argv = Namespace(
mean_scale_values={'parameter': {'mean': np.array([1, 2, 3])}, 'op': {'scale': np.array([1, 2, 3])}})
graph = build_graph(
nodes, [*connect('parameter', 'result'), *connect('parameter_2', 'op'), *connect('op', 'result_2')],
{'parameter_2': {'initial_node_name': 'op'}}, nodes_with_edges_only=True, cli=argv)
self.set_graph_attrs(graph, ['parameter', 'parameter_2'])
self.set_graph_attrs(graph_ref, ['parameter', 'parameter_2'])
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(flag, resp)
(flag, resp) = compare_graphs(graph, graph_ref, 'result_2', check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs(graph, graph_ref, ['parameter', 'parameter_2'])
def test_add_mean_scale_values_cut_graph(self):
"""
Test case when user cutted start of the network and specified mean/scale value to the new input node 'node_3'.
"""
graph = build_graph(nodes_attributes, [
('pl_1', 'pl_1_data'),
('pl_2', 'pl_2_data'),
('pl_2_data', 'node_3'),
('node_3', 'node_3_data'),
('pl_1_data', 'node_1'),
('node_3_data', 'node_1'),
], {
'pl_1_data': {
'shape': np.array([1, 3, 38, 38]),
'infer': None
},
'pl_2_data': {
'shape': np.array([1, 3, 38, 38]),
'infer': None
},
'pl_2': {
'initial_node_name': 'node_3',
'shape': np.array([1, 3, 38, 38])
},
'pl_1': {
'shape': np.array([1, 3, 38, 38])
},
},
nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
argv = Namespace(
mean_scale_values={
'pl_1': {
'mean': np.array([1, 2, 3])
},
'node_3': {
'scale': np.array([1, 2, 3])
}
})
graph.graph['cmd_params'] = argv
AddMeanScaleValues().find_and_replace_pattern(graph)
mul_op_cnt = 0
add_op_cnt = 0
for node in graph.nodes():
node = Node(graph, node)
if node.has_valid('op') and node.op == 'Mul':
mul_op_cnt += 1
if node.has_valid('op') and node.op == 'Add':
add_op_cnt += 1
self.assertEqual(add_op_cnt, 1, "There should be exactly one Add op")
self.assertEqual(mul_op_cnt, 1, "There should be exactly one Mul op")
self.assertEqual(
Node(graph, 'pl_2').out_node().out_node().op, 'Mul',
"The Mul op should be added after pl_2")
self.assertEqual(
Node(graph, 'pl_1').out_node().out_node().op, 'Add',
"The Add op should be added after pl_1")
def test_mean_values_explicit_and_scale_values_explicit_with_shape_of(
self):
graph_ref = build_graph(nodes, [
*connect('parameter', '0:add_mean'),
*connect('mean', '1:add_mean'),
*connect('add_mean', '0:mul_scale'),
*connect('scale', '1:mul_scale'),
*connect('mul_scale', 'result'),
*connect_data('parameter', 'shape_of'),
*connect('shape_of', 'result_2'),
],
nodes_with_edges_only=True)
argv = Namespace(
mean_scale_values={
'parameter': {
'mean': np.array([1, 2, 3]),
'scale': np.array([1, 2, 3])
}
})
graph = build_graph(nodes, [
*connect('parameter', 'result'),
*connect_data('parameter', 'shape_of'),
*connect('shape_of', 'result_2'),
],
nodes_with_edges_only=True,
cli=argv)
graph.graph['layout'] = 'NCHW'
AddMeanScaleValues().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result_2',
check_op_attrs=True)
self.assertTrue(flag, resp)
