def test_two_nodes_one_bin(self):
"""Test case for two output nodes, one with 'bin' parameter, other without."""
shape = np.array([2, 3, 4])
data = np.zeros(shape)
graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes + [('next_node_2', {
'kind': 'op'
})],
edges_with_attrs=self.edges + [('data_node', 'next_node_2')],
update_nodes_attributes=[('data_node', {
'shape': shape,
'value': data
})],
update_edge_attrs={('data_node', 'next_node', 0): {
'bin': 0
}},
)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=self.nodes + self.new_nodes + [('next_node_2', {
'kind': 'op'
})],
edges_with_attrs=self.edges + self.new_edges +
[('data_node', 'next_node_2')],
update_nodes_attributes=[('data_node', {
'shape': shape,
'value': data
}), ('const_data', {
'shape': shape,
'value': data
})])
tested_pattern = CreateConstNodesReplacement()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, last_node='next_node')
self.assertTrue(flag, resp)
def test_select_infer_condition_true(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('condition', {
'value': np.array([True])
}),
('select_output', {
'shape': np.array([2, 2]),
'value': np.ones((2, 2))
})
])
# We should propagate shapes and values
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('select_output', {
'shape': np.array([2, 2]),
'value': np.ones((2, 2))
})
])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
tested_class.infer(node)
(flag, resp) = compare_graphs(graph,
graph_ref,
'select_output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_select_infer_condition_with_value(self, else_data_shape, than_data_shape, select_output_shape,
condition_value, else_value, than_value, output_value):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('condition_data', {'shape': np.array(select_output_shape),
'value': condition_value}),
('else_data', {'shape': np.array(else_data_shape),
'value': else_value}),
('than_data', {'shape': np.array(than_data_shape),
'value': than_value}),
('select_output',
{'shape': np.array(select_output_shape),
'value': None})
])
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[
('condition_data', {'shape': np.array(else_data_shape),
'value': condition_value}),
('else_data', {'shape': np.array(else_data_shape),
'value': else_value}),
('than_data', {'shape': np.array(than_data_shape),
'value': than_value}),
('select_output', {'shape': np.array(select_output_shape),
'value': output_value})])
node = Node(graph, 'select')
Select.infer(node)
if else_value is not None and than_value is not None:
(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(np.array_equal(graph.nodes['select_output']['value'], graph_ref.nodes['select_output']['value']))
def test_two_consumers(self):
"""Const data node has two consumers: Result and ReLu"""
nodes = [
('const_node', {
'type': 'Const',
'kind': 'op'
}),
('const_data', {
'kind': 'data'
}),
('result_node', {
'type': 'Result',
'kind': 'op'
}),
('relu_1', {
'type': 'ReLU',
'kind': 'op',
'op': 'ReLU'
}),
('relu_1_data', {
'kind': 'data'
}),
]
edges = [('const_node', 'const_data'), ('const_data', 'result_node'),
('const_data', 'relu_1'), ('relu_1', 'relu_1_data')]
new_nodes = [
('const_node', {
'type': 'Const',
'kind': 'op'
}),
('const_data', {
'kind': 'data'
}),
('relu_1', {
'type': 'ReLU',
'kind': 'op',
'op': 'ReLU'
}),
('relu_1_data', {
'kind': 'data'
}),
]
new_edges = [('const_node', 'const_data'), ('const_data', 'relu_1'),
('relu_1', 'relu_1_data')]
graph = build_graph_with_attrs(
nodes_with_attrs=nodes,
edges_with_attrs=edges,
)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=new_nodes,
edges_with_attrs=new_edges,
)
tested_pattern = RemoveConstToResult()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
last_node='relu_1_data')
self.assertTrue(flag, resp)
self.assertNotIn('result_node', graph.node)
def test_no_exit(self):
pattern_matcher = BackEdgesMatching()
pattern = pattern_matcher.pattern()
graph = build_graph_with_attrs(nodes_with_attrs=pattern['nodes'], edges_with_attrs=pattern['edges'], update_edge_attrs=None,
new_nodes_with_attrs=[('from_body_data', {'kind':'data'})],
new_edges_with_attrs=[('from_body_data', 'NextIteration')])
pattern_matcher.find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(nodes_with_attrs=[('condition', {'kind': 'op', 'op':'TensorIteratorCondition'}),
('condition_data', {'kind': 'data'}),
('back_edge', {'kind': 'op', 'op': 'TensorIteratorBackEdge'}),
('enter_data', {'kind': 'data'}),
('from_body_data', {'kind': 'data'}),
('Identity_1_data', {'kind': 'data'}),],
edges_with_attrs=[('condition', 'condition_data'),
('enter_data', 'back_edge', {'in': 0}),
('condition_data', 'back_edge', {'in': 2}), # {in:2}
('from_body_data', 'back_edge', {'in': 1}),
('back_edge', 'Identity_1_data')],
update_edge_attrs=None,
new_nodes_with_attrs=[],
new_edges_with_attrs=[],
)
(flag, resp) = compare_graphs(graph, graph_ref, 'Identity_1_data', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_select_infer_condition_shapes_broadcast(self, else_data_shape, than_data_shape, select_output_shape):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else_data', {'shape': np.array(else_data_shape),
'value': np.zeros(else_data_shape, dtype=np.float)}),
('than_data', {'shape': np.array(than_data_shape),
'value': np.zeros(than_data_shape, dtype=np.float)}),
('select_output', {'shape': np.array(select_output_shape),
'value': np.zeros(select_output_shape, dtype=np.float)})
])
# We should propagate shapes and values
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[
('else_data', {'shape': np.array(else_data_shape),
'value': np.zeros(else_data_shape, dtype=np.float)}),
('than_data', {'shape': np.array(than_data_shape),
'value': np.zeros(than_data_shape, dtype=np.float)}),
('select_output', {'shape': np.array(select_output_shape), 'value': np.zeros(select_output_shape)})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
tested_class.infer(node)
(flag, resp) = compare_graphs(graph, graph_ref, 'select_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_in_port_with_data(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges)
new_input_shape = np.array([1, 2, 3, 4])
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges[1:],
new_nodes_with_attrs=[
('input_node', {
'kind': 'op',
'op': 'Parameter',
'shape': new_input_shape
}),
('input_data', {
'kind': 'data'
})
],
new_edges_with_attrs=[
('input_node', 'input_data', {
'in': 0,
'out': 0
}),
('input_data', 'op_node', {
'in': 1,
'out': 0
})
])
add_input_op(graph, 'op_node', 1, data=True, shape=new_input_shape)
graph.remove_edge('future_input', 'op_node')
(flag, resp) = compare_graphs(graph, graph_ref, last_node='op_node')
self.assertTrue(flag, resp)
def test_4D_multiple_consumers(self):
input_shape = int64_array([1, 300, 300, 3])
axes = int64_array([1, 2, 3])
weights_value = np.ones(shape=int64_array([input_shape[-1]]), dtype=np.float32)
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)),
('result_2', dict(kind='op', op='Result'))
], edges + [('input_data', 'result_2')], 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)),
('weights_node_data', dict(kind='data', value=axes.sort())),
('result_2', dict(kind='op', op='Result'))
], edges_after_replacement + [('input_data', 'result_2')], nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
self.assertTrue(flag, resp)
def test_out_port_no_data(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes_out,
edges_with_attrs=self.edges_out)
new_input_shape = np.array([1, 2, 3, 4])
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes_out,
edges_with_attrs=self.edges_out[1:],
new_nodes_with_attrs=[
('input_node', {
'kind': 'op',
'op': 'Parameter',
'shape': new_input_shape
})
],
new_edges_with_attrs=[
('input_node', 'future_input', {
'in': 0,
'out': 0
})
])
add_input_op(graph,
'op_node',
1,
data=False,
shape=new_input_shape,
is_out_port=True)
graph.remove_edge('op_node', 'future_input')
(flag, resp) = compare_graphs(graph,
graph_ref,
last_node='another_node')
self.assertTrue(flag, resp)
(flag, resp) = compare_graphs(graph,
graph_ref,
last_node='future_input')
self.assertTrue(flag, resp)
def test_merge_infer_simple_case_one_executable(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges)
# We should propagate value of the first input since only this input is executable
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('merge_output', {
'shape': np.array([2, 2]),
'value': np.ones((2, 2))
}),
('merge', {
'is_not_fully_inferred':
False
})
])
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
(flag, resp) = compare_graphs(graph,
graph_ref,
'merge_output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_force_precision_parameter(self):
precision = 'FP16'
shape = np.array([2, 3, 4])
data = np.zeros(shape)
graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('data_node', {'shape': shape, 'value': data, 'force_precision': precision})]
)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=self.nodes + self.new_nodes,
edges_with_attrs=self.edges + self.new_edges,
update_nodes_attributes=[('data_node', {'shape': shape, 'value': data}),
('const_data', {'shape': shape, 'value': data, 'force_precision': precision}),
('const', {'force_precision': precision})]
)
tested_pattern = CreateConstNodesReplacement()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, last_node='next_node')
self.assertTrue(flag, resp)
#check that force precision was added to data and Const nodes
force_precision_const_node = graph.nodes['data_node_const']['force_precision']
force_precision_new_data = graph.nodes['data_node_copy_']['force_precision']
self.assertEqual(force_precision_const_node, precision)
self.assertEqual(force_precision_new_data, precision)
def test_switch_infer_no_condition(self):
nodes = [('tensor', {
'value': None,
'kind': 'data',
'executable': True,
'shape': np.array([1, 2, 1])
}), ('pred_id', {
'value': None,
'kind': 'data',
'executable': True
}), ('switch', {
'type': 'Switch',
'kind': 'op',
'op': 'Switch'
}),
('switch_data_0', {
'value': None,
'kind': 'data',
'executable': True
}),
('switch_data_1', {
'value': None,
'kind': 'data',
'executable': True
})]
edges = [('tensor', 'switch', {
'in': 0
}), ('pred_id', 'switch', {
'in': 1
}), ('switch', 'switch_data_0', {
'out': 0
}), ('switch', 'switch_data_1', {
'out': 1
})]
graph = build_graph_with_attrs(nodes_with_attrs=nodes,
edges_with_attrs=edges)
# We should propagate only shapes
graph_ref = build_graph_with_attrs(nodes_with_attrs=nodes,
edges_with_attrs=edges,
update_nodes_attributes=[
('switch_data_0', {
'shape': np.array([1, 2, 1])
}),
('switch_data_1', {
'shape': np.array([1, 2, 1])
})
])
tested_class = Switch(graph=graph, attrs={})
node = Node(graph, 'switch')
tested_class.infer(node)
(flag, resp) = compare_graphs(graph,
graph_ref,
'switch_data_0',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_positive_matmul_infer(self, A_shape, B_shape, C_shape,
transpose_a, transpose_b):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('A_d', {
'shape': int64_array(A_shape)
}),
('B_d', {
'shape': int64_array(B_shape)
}),
('mat_mul', {
'transpose_a': transpose_a,
'transpose_b': transpose_b
}),
])
node = Node(graph, 'mat_mul')
MatMul.infer(node)
msg = "MatMul infer failed for case: A_shape={}, B_shape={}, transpose_a={}, transpose_b={}" \
"expexted_shape={}, actual_shape={}"
self.assertTrue(
np.array_equal(graph.node['mat_mul_d']['shape'],
int64_array(C_shape)),
msg.format(A_shape, B_shape, transpose_a, transpose_b, C_shape,
graph.node['mat_mul_d']['shape']))
def test_two_nodes_with_bin(self):
"""Test case for data node with 2 consumers with bin edge attr.
Nothing should happened."""
shape = np.array([2, 3, 4])
data = np.zeros(shape)
graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes + [('next_node_2', {
'kind': 'op'
})],
edges_with_attrs=self.edges + [('data_node', 'next_node_2')],
update_nodes_attributes=[('data_node', {
'shape': shape,
'value': data
})],
update_edge_attrs={
('data_node', 'next_node', 0): {
'bin': 0
},
('data_node', 'next_node_2', 0): {
'bin': 0
}
},
)
tested_pattern = CreateConstNodesReplacement()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph, last_node='next_node')
self.assertTrue(flag, resp)
def test_only_consumer(self):
"""Result node is only consumer of Const data node"""
nodes = [
('const_node', {'type': 'Const', 'kind': 'op'}),
('const_data', {'kind': 'data', 'value': np.array(5)}),
('result_node', {'type': 'Result', 'kind': 'op'}),
('placeholder_1', {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}),
('placeholder_1_data', {'kind': 'data'}),
('relu_1', {'type': 'ReLU', 'kind': 'op', 'op': 'ReLU'}),
('relu_1_data', {'kind': 'data'}),
]
edges = [
('const_node', 'const_data'),
('const_data', 'result_node'),
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'relu_1'),
('relu_1', 'relu_1_data')
]
new_nodes=[
('placeholder_1', {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}),
('placeholder_1_data', {'kind': 'data'}),
('relu_1', {'type': 'ReLU', 'kind': 'op', 'op': 'ReLU'}),
('relu_1_data', {'kind': 'data'}),
]
new_edges=[
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'relu_1'),
('relu_1', 'relu_1_data')
]
graph = build_graph_with_attrs(
nodes_with_attrs=nodes,
edges_with_attrs=edges,
)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=new_nodes,
edges_with_attrs=new_edges,
)
tested_pattern = RemoveConstToResult()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, last_node='relu_1_data')
self.assertTrue(flag, resp)
self.assertNotIn('const_node', graph.node)
self.assertNotIn('const_data', graph.node)
self.assertNotIn('result_node', graph.node)
def test_single_consumer(self):
graph = build_graph_with_attrs(nodes,
edges,
nodes_with_edges_only=True)
graph.stage = 'middle'
L2NormToNorm().find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(nodes,
edges_after_replacement,
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(
type='Normalize')[0]]['name'] == 'l2_norm_name')
self.assertTrue(flag, resp)
def test_not_dynamic(self):
pattern_matcher = LoopConditionMatcher()
pattern = pattern_matcher.pattern()
graph = build_graph_with_attrs(nodes_with_attrs=pattern['nodes'], edges_with_attrs=pattern['edges'],
new_nodes_with_attrs=[('maximum', {'kind': 'op', 'op': 'Maximum'}),
('maximum_data', {'kind': 'data'}),
('TensorIteratorInput', {'kind': 'op', 'op': 'TensorIteratorInput'})],
new_edges_with_attrs=[('maximum', 'maximum_data'),
('Identity_1_data', 'TensorIteratorInput')],
update_nodes_attributes=[('init_1_data', {'value': np.array([0])}),
('init_2_data', {'value': np.array([0])}),
('add_1_y_data', {'value': np.array(1)}),
('add_2_y_data', {'value': np.array(1)}),
('loop_cond_data', {'value': None}),
('Identity_2_data', {'value': None}, ),
('Enter_1_less_data', {'value': None},),
('Enter_2_less_data', {'value': None},),
])
pattern_matcher.find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=[('TensorIteratorCondition', {'kind': 'op', 'op': 'TensorIteratorCondition'}),
('loop_cond_data', {'kind': 'data'}),
('identity_data', {'kind': 'data'}),
('StridedSlice', {'kind': 'op', 'op':'StridedSlice'}),
('StridedSlice_data', {'kind': 'data'}),
('Maximum', {'kind': 'op', 'op': 'Maximum'}),
('Maximum_data', {'kind': 'data'}),
('minimum_data', {'kind': 'data'}),
('TensorIteratorInput', {'kind': 'op', 'op': 'TensorIteratorInput'})
],
edges_with_attrs=[('Maximum', 'Maximum_data'),
('StridedSlice', 'StridedSlice_data'),
('StridedSlice_data', 'TensorIteratorCondition', {'in':0}),
('minimum_data', 'TensorIteratorCondition', {'in':1}),
('TensorIteratorCondition', 'loop_cond_data'),
('TensorIteratorCondition', 'identity_data'),
('identity_data', 'TensorIteratorInput'),
],
update_edge_attrs=None,
new_nodes_with_attrs=[],
new_edges_with_attrs=[],
)
(flag, resp) = compare_graphs(graph, graph_ref, 'loop_cond_data', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_from4D_to3D(self):
input_shape = np.array([1, 2, 3, 4])
new_shape = np.array([3, 4, 2])
nhwc_shape = np.array([1, 3, 4, 2])
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[
('input_data', {
'shape': input_shape
}), ('reshape', {
'dim': new_shape
}),
('reshape_data', {
'shape': new_shape
})
])
graph.graph['layout'] = 'NHWC'
# add permute attrs to reshape
reshape = Node(graph, 'reshape')
PermuteAttrs.create_permute_attrs(reshape, attrs=[('dim', 'output:0')])
tested_pattern = PermuteForReshape()
tested_pattern.find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=self.nodes + self.permute_nodes,
edges_with_attrs=self.edges[1:] + self.permute_edges,
update_nodes_attributes=[('input_data', {
'shape': input_shape
}), ('reshape', {
'dim': new_shape
}), ('reshape_data', {
'shape': new_shape
}), ('permute_data', {
'shape': nhwc_shape
})])
# check graphs equality
(flag, resp) = compare_graphs(graph,
graph_ref,
last_node='reshape_data')
self.assertTrue(flag, resp)
# check righ order in new permutation node
permute_order = graph.node['reshape/Permute_']['order']
self.assertTrue(np.all(
permute_order == np.array([0, 2, 3, 1]))) # from NCHW to NHWC
def test_select_infer_assert_shapes(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else_data', {'shape': np.array([3, 3]), 'value':np.zeros((3, 3))})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "Input shape do not broadcast"):
tested_class.infer(node)
def test_merge_infer_complex_case(self):
"""
Case as in cycles when in first visit only one input are inferred and in the second -- both.
"""
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('first', {'is_partial_inferred': False,
'value': None}),
('second', {'executable': True})])
# In first visit we should propagate only shapes
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('second', {'executable': True}),
('first', {'is_partial_inferred': False,
'value': None}),
('merge_output', {'shape': np.array([2, 2]),
'value': None}),
('merge', {'is_not_fully_inferred': True})])
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
(flag, resp) = compare_graphs(graph, graph_ref, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)
# Imitate that inputs nodes now is inferred
graph.node['first']['is_partial_inferred'] = True
# Run infer second time
tested_class = Merge(graph=graph, attrs={})
node = Node(graph, 'merge')
tested_class.merge_infer(node)
graph_ref = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('second', {'executable': True}),
('first', {'is_partial_inferred': True,
'value': None}),
('merge_output', {'shape': np.array([2, 2]),
'value': None}),
('merge', {'is_not_fully_inferred': False})])
(flag, resp) = compare_graphs(graph, graph_ref, 'merge_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_1(self):
"""
Acyclic case => graph.graph['is_cyclic'] should be False.
"""
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges)
tested_pass = AddIsCyclicAttribute()
tested_pass.find_and_replace_pattern(graph)
assert graph.graph['is_cyclic'] is False
def test_one_node(self):
"""We should add Const node and data node."""
shape = np.array([2, 3, 4])
data = np.zeros(shape)
graph = build_graph_with_attrs(
nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
update_nodes_attributes=[('data_node', {'shape': shape, 'value': data})]
)
graph_ref = build_graph_with_attrs(
nodes_with_attrs=self.nodes + self.new_nodes,
edges_with_attrs=self.edges + self.new_edges,
update_nodes_attributes=[('data_node', {'shape': shape, 'value': data}),
('const_data', {'shape': shape, 'value': data})]
)
tested_pattern = CreateConstNodesReplacement()
tested_pattern.find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, last_node='next_node')
self.assertTrue(flag, resp)
def test_select_infer_assert_shapes(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('else', {'shape': np.array([3,3]), 'value':np.zeros((3,3))})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "TensorFlow \'Select\' operation has 3 inputs: \'condition\',"
" \'then\' and \'else\' tensors.\'then\' and \'else\' tensors"
" must have the same shape by TensorFlow reference"):
tested_class.infer(node)
def test_select_infer_assert_condition_bool(self):
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
update_nodes_attributes=[('condition', {'value': np.array([3])})])
tested_class = Select(graph=graph, attrs={})
node = Node(graph, 'select')
with self.assertRaisesRegex(AssertionError, "TensorFlow \'Select\' operation has 3 inputs: \'condition\',"
" \'then\' and \'else\' tensors. Value of \'condition\' tensor"
" must be boolen by TensorFlow reference"):
tested_class.infer(node)
def test(self):
pattern_matcher = MVNUnrolled()
pattern = pattern_matcher.pattern()
graph = build_graph_with_attrs(nodes_with_attrs=pattern['nodes'], edges_with_attrs=pattern['edges'], update_edge_attrs=None,
new_nodes_with_attrs=[('reduction_indicies', {'kind': 'data'}),
('conv2d', {'kind': 'op'}),
('variance_reduction', {'kind': 'data'}),
('pow2', {'kind': 'data'}),
('eps', {'kind': 'data'}),
('next_op', {'kind': 'op'})],
new_edges_with_attrs=[('reduction_indicies', 'mean', {'in': 1}),
('conv2d', 'mean',{'in': 0, 'out': 1}),
('variance_reduction', 'variance', {'in': 1}),
('pow2', 'pow', {'in': 1}),
('eps', 'add'), ('truediv', 'next_op')])
graph.graph['layout'] = 'NHWC'
pattern_matcher.find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(nodes_with_attrs=pattern['nodes'][:-1],
edges_with_attrs=pattern['edges'][:-2], update_edge_attrs=None,
new_nodes_with_attrs=[('reduction_indicies', {'kind':'data'}),
('conv2d', {'kind':'op'}),
('variance_reduction', {'kind':'data'}),
('pow2', {'kind': 'data'}),
('eps', {'kind': 'data'}),
('mvn', {'kind': 'op', 'op': 'MVN'}),
('next_op', {'kind': 'op'})],
new_edges_with_attrs=[('reduction_indicies', 'mean', {'in':1}),
('conv2d', 'mean', {'in': 0}),
('variance_reduction', 'variance',{'in': 1}),
('pow2', 'pow', {'in': 1}),
('eps', 'add'),
('conv2d', 'mvn',{'in': 0}),
('reduction_indicies', 'mvn', {'in': 1}),
('variance_reduction', 'mvn',{'in': 2}),
('pow2', 'mvn', {'in': 3}),
('eps', 'mvn',{'in': 4}),
('mvn', 'next_op')])
(flag, resp) = compare_graphs(graph, graph_ref, 'next_op', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_multiple_consumers(self):
graph = build_graph_with_attrs(
nodes + [('result_2', dict(kind='op', op='Result'))],
edges + [('input_data', 'result_2')],
nodes_with_edges_only=True)
graph.stage = 'middle'
L2NormToNorm().find_and_replace_pattern(graph)
graph_ref = build_graph_with_attrs(
nodes + [('result_2', dict(kind='op', op='Result'))],
edges_after_replacement + [('input_data', 'result_2')],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(
type='Normalize')[0]]['name'] == 'l2_norm_name')
self.assertTrue(flag, resp)
def test_2(self):
"""
Cyclic case => graph.graph['is_cyclic'] should be True.
:return:
"""
graph = build_graph_with_attrs(nodes_with_attrs=self.nodes,
edges_with_attrs=self.edges,
new_edges_with_attrs=[('node_2', 'node_1')])
tested_pass = AddIsCyclicAttribute()
tested_pass.find_and_replace_pattern(graph)
assert graph.graph['is_cyclic'] is True
def test_positive(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)),
('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'
graph.graph['layout'] = layout
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)),
('weights_node_data', dict(kind='data', value=axes.sort())),
],
edges_after_replacement,
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'result',
check_op_attrs=True)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(
type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
self.assertTrue(flag, resp)
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)),
('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'
graph.graph['layout'] = layout
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)
