def test_caffe_bn_decomposition_2(self):
graph = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'bn_op'),
('bn_mean', 'bn_op'), ('bn_var', 'bn_op'),
('bn_op', 'bn_data'), ('concat', 'concat_data'),
('bn_data', 'concat')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'bn_op': {
'epsilon': 1.2,
'op': 'BatchNormalization',
'can_be_fused': False
},
'bn_mean': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_var': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_data': {
'shape': np.array([1, 227, 227, 3])
},
'concat_data': {
'is_output': True
}
})
del graph['placeholder_1']['placeholder_1_data'][0]['in']
del graph['bn_op']['bn_data'][0]['in']
graph_ref = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'mul_1'),
('mul_1_w', 'mul_1'), ('mul_1', 'mul_1_data'),
('mul_1_data', 'add_1'), ('add_1_w', 'add_1'),
('add_1', 'add_1_data'),
('concat', 'concat_data'),
('add_1_data', 'concat')],
{
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1_w': {
'shape': np.array([3]),
'value': np.array([0.67419986, 0.55901699, 0.48795004])
},
'add_1_w': {
'shape': np.array([3]),
'value': np.array([-0.67419986, -1.11803399, -1.46385011])
},
'add_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1': {
'can_be_fused': False
},
'add_1': {
'can_be_fused': False
},
'concat_data': {
'is_output': True
}
})
graph.graph['layout'] = 'NHWC'
convert_bn_to_mul_add(graph)
graph_clean_up(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'concat_data')
self.assertTrue(flag, resp)
def _create_node(attrs: dict):
pb = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"], **attrs)
graph = build_graph({'node_0': {'pb': pb}}, [])
return Node(graph, 'node_0')
def test(self):
nodes_attributes = {
'switch_2_input': {
'shape': int64_array([1, 3]),
'type': 'Parameter',
'kind': 'op',
'op': 'Parameter'
},
'switches_input': {
'shape': int64_array([1, 3]),
'type': 'Parameter',
'kind': 'op',
'op': 'Parameter'
},
'switch_input_0': {
'kind': 'op',
'op': 'SomeOp'
},
'switch_1_input_0': {
'kind': 'op',
'op': 'SomeOp'
},
'switch': {
'kind': 'op',
'op': 'Switch'
},
'switch_1': {
'kind': 'op',
'op': 'Switch'
},
'switch_2': {
'kind': 'op',
'op': 'Switch'
},
'some_op': {
'kind': 'op',
'op': 'Max'
},
'identity': {
'kind': 'op',
'op': 'Identity'
},
'merge': {
'kind': 'op',
'op': 'Merge'
},
'select': {
'kind': 'op',
'op': 'Select'
},
'last': {
'type': None,
'value': None,
'kind': 'op',
'op': 'Result'
},
}
# check two cases when switch_2 goes to 0-th and 1-st input port of the Merge
for merge_input_port in range(2):
graph = build_graph(nodes_attributes, [
('switch_2_input', 'switch_2', {
'in': 0
}),
('switch_input_0', 'switch', {
'in': 0
}),
('switch_1_input_0', 'switch_1', {
'in': 0
}),
('switches_input', 'switch', {
'in': 1,
'out': 0
}),
('switches_input', 'switch_1', {
'in': 1,
'out': 0
}),
('switches_input', 'switch_2', {
'in': 1,
'out': 0
}),
('switch', 'some_op', {
'in': 0
}),
('switch_1', 'some_op', {
'in': 1
}),
('some_op', 'identity', {
'in': 0
}),
('switch_2', 'merge', {
'in': merge_input_port
}),
('identity', 'merge', {
'in': 1 - merge_input_port
}),
('merge', 'last', {
'in': 0
}),
],
nodes_with_edges_only=True)
graph.stage = 'front'
SwitchMergeOptimization().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes_attributes, [
('switches_input', 'select', {
'in': 0
}),
('switch_2_input', 'select', {
'in': 1
}),
('switch_input_0', 'some_op', {
'in': 0
}),
('switch_1_input_0', 'some_op', {
'in': 1
}),
('some_op', 'identity', {
'in': 0
}),
('identity', 'select', {
'in': 2
}),
('select', 'last', {
'in': 0
}),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'last',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_replace_node_several_consumers(self):
graph = build_graph(
{
'input_1': {
'type': 'Placeholder',
'value': None,
'kind': 'op'
},
'input_2': {
'type': 'Placeholder',
'value': None,
'kind': 'op'
},
'old': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_1': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_2': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_3': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
}, [
('input_1', 'old'),
('input_2', 'old'),
('old', 'output_3'),
('old', 'output_2'),
('old', 'output_1'),
])
new_node = Const(graph, {
'name': 'new'
}).create_node([Node(graph, 'input_1'),
Node(graph, 'input_2')])
replace_node(Node(graph, 'old'), new_node)
self.assertEqual(len(graph.nodes()), 6)
self.assertEqual(len(graph.edges()), 5)
self.assertListEqual(sorted(graph.out_edges('new')),
[('new', 'output_1'), ('new', 'output_2'),
('new', 'output_3')])
expected_result = [('new', 'output_1', {
'in': 0,
'out': 2,
'name': 'old'
}), ('new', 'output_2', {
'in': 0,
'out': 1,
'name': 'old'
}), ('new', 'output_3', {
'in': 0,
'out': 0,
'name': 'old'
})]
self.assertListEqual(sorted(graph.out_edges('new', data=True)),
expected_result)
def test(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('shape', {
'type': 'ShapeOf',
'kind': 'op',
'op': 'ShapeOf'
}),
**regular_op(
'random_uniform', {
'type': 'RandomUniform',
'kind': 'op',
'op': 'RandomUniform',
'name': 'dropout/RU'
}),
**regular_op('mul', {
'type': 'Mul',
'kind': 'op',
'op': 'Mul'
}),
**regular_op('add', {
'type': 'Add',
'kind': 'op',
'op': 'Add'
}),
**regular_op('add2', {
'type': 'Add',
'kind': 'op',
'op': 'Add'
}),
**regular_op('floor', {
'type': 'Floor',
'kind': 'op',
'op': 'Floor'
}),
'add_const': {
'kind': 'op',
'op': 'Const',
'value': np.array(0.0),
'data_type': np.float32
},
**result('result'),
# new nodes to be added
'broadcast_const': {
'kind': 'op',
'op': 'Const',
'value': np.array(0.5),
'data_type': np.float32
},
**regular_op('broadcast', {
'type': 'Broadcast',
'kind': 'op',
'op': 'Broadcast'
}),
}
edges = [('input', 'shape'), ('shape', 'random_uniform'),
('random_uniform', 'mul'), ('mul', 'add'),
('add_const', 'add'), ('add', 'add2'), ('add2', 'floor'),
('floor', 'result')]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
DropoutWithRandomUniformReplacer().find_and_replace_pattern(graph)
edges_ref = [('input', 'shape'), ('broadcast_const', 'broadcast'),
('shape', 'broadcast'), ('broadcast', 'mul'),
('mul', 'add'), ('add_const', 'add'), ('add', 'add2'),
('add2', 'floor'), ('floor', 'result')]
graph_ref = build_graph(nodes, edges_ref, nodes_with_edges_only=True)
# check graph structure after the transformation and output name
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(
op='Broadcast')[0]]['name'] == 'dropout/RU')
def test_2d(self):
graph = build_graph(
nodes_attrs=graph_node_attrs_when_transformation_is_not_applicable,
edges=graph_edges_when_transformation_is_not_applicable,
update_attributes={
'placeholder_data': {
'shape': int64_array([5, 8])
},
'dim': {
'value': int64_array([1])
},
'dim_data': {
'value': int64_array([1])
},
'unsqueeze_data': {
'shape': int64_array([5, 1, 8])
},
'multipliers': {
'value': int64_array([1, 10, 1])
},
'multipliers_data': {
'value': int64_array([1, 10, 1]),
'shape': int64_array([3])
},
'tile_data': {
'shape': int64_array([5, 10, 8])
},
'reshape_data': {
'shape': int64_array([50, 8])
},
'shape': {
'value': int64_array([50, 8]),
'shape': int64_array([2])
},
'shape_data': {
'value': int64_array([50, 8]),
'shape': int64_array([2])
},
'abs_data': {
'shape': int64_array([50, 8])
},
})
ref_graph = build_graph(
nodes_attrs=graph_node_attrs_when_transformation_is_not_applicable,
edges=graph_edges_when_transformation_is_not_applicable,
update_attributes={
'placeholder_data': {
'shape': int64_array([5, 8])
},
'dim': {
'value': int64_array([1])
},
'dim_data': {
'value': int64_array([1])
},
'unsqueeze_data': {
'shape': int64_array([5, 1, 8])
},
'multipliers': {
'value': int64_array([1, 10, 1])
},
'multipliers_data': {
'value': int64_array([1, 10, 1]),
'shape': int64_array([3])
},
'tile_data': {
'shape': int64_array([5, 10, 8])
},
'reshape_data': {
'shape': int64_array([50, 8])
},
'shape': {
'value': int64_array([50, 8]),
'shape': int64_array([2])
},
'shape_data': {
'value': int64_array([50, 8]),
'shape': int64_array([2])
},
'abs_data': {
'shape': int64_array([50, 8])
},
})
UnsqueezeTileReshapeBlockToInterpolate().find_and_replace_pattern(
graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_several_memory_concat(self):
graph = build_graph(
{
'in': {
'kind': 'op',
'op': None
},
'memory_2': {
'kind': 'op',
'op': 'MemoryOffset',
't': 2
},
'memory_1': {
'kind': 'op',
'op': 'MemoryOffset',
't': 1
},
'memory__3': {
'kind': 'op',
'op': 'MemoryOffset',
't': -3
},
'concat': {
'kind': 'op',
'op': 'Concat'
}
}, [('in', 'memory_2', {
'out': 0
}), ('in', 'memory_1', {
'out': 1
}), ('in', 'memory__3', {
'out': 3
}), ('memory_2', 'concat', {
'in': 0
}), ('memory_1', 'concat', {
'in': 1
}), ('in', 'concat', {
'in': 2,
'out': 2
}), ('memory__3', 'concat', {
'in': 3
})],
nodes_with_edges_only=True)
graph.stage = 'front'
ref_graph = build_graph(
{
'in': {
'kind': 'op',
'op': None
},
'memory__5': {
'kind': 'op',
'op': 'MemoryOffset',
't': -5
},
'memory__1': {
'kind': 'op',
'op': 'MemoryOffset',
't': -1
},
'memory__2': {
'kind': 'op',
'op': 'MemoryOffset',
't': -2
},
'concat': {
'kind': 'op',
'op': 'Concat'
}
}, [('in', 'memory__5', {
'out': 3
}), ('in', 'memory__1', {
'out': 1
}), ('in', 'memory__2', {
'out': 2
}), ('in', 'concat', {
'in': 0,
'out': 0
}), ('memory__2', 'concat', {
'in': 2
}), ('memory__1', 'concat', {
'in': 1
}), ('memory__5', 'concat', {
'in': 3
})],
nodes_with_edges_only=True)
MemoryOffsetAdjustment().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'concat',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_there_are_four_inputs_and_first_and_third_input_have_zero_in_their_shapes(self):
graph = build_graph(
nodes_attrs={
'const0': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([5, 0]),
'value': np.zeros((5, 0))
},
'const0_data': {'kind': 'data', 'shape': int64_array([5, 0]), 'value': None},
'placeholder': {'kind': 'op', 'type': 'Parameter', 'op': 'Parameter'},
'placeholder_data': {
'kind': 'data',
'value': None,
'shape': int64_array([5, 17]),
'data_type': None
},
'const2': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([5, 0]),
'value': np.zeros((5, 0))
},
'const2_data': {'kind': 'data', 'shape': int64_array([5, 0]), 'value': None},
'const3': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([5, 23]),
'value': np.zeros((5, 23))
},
'const3_data': {'kind': 'data', 'shape': int64_array([5, 23]), 'value': None},
'concat': {'kind': 'op', 'type': 'Concat', 'op': 'Concat', 'axis': 1},
'concat_data': {'kind': 'data', 'shape': int64_array([5, 40]), 'value': None},
'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
},
edges=[
('const0', 'const0_data'),
('placeholder', 'placeholder_data'),
('const2', 'const2_data'),
('const3', 'const3_data'),
('const0_data', 'concat', {'in': 0}),
('placeholder_data', 'concat', {'in': 1}),
('const2_data', 'concat', {'in': 2}),
('const3_data', 'concat', {'in': 3}),
('concat', 'concat_data'),
('concat_data', 'output')
]
)
ref_graph = build_graph(
nodes_attrs={
'placeholder': {'kind': 'op', 'type': 'Parameter', 'op': 'Parameter'},
'placeholder_data': {
'kind': 'data',
'value': None,
'shape': int64_array([5, 17]),
'data_type': None
},
'const3': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([5, 23]),
'value': np.zeros((5, 23))
},
'const3_data': {'kind': 'data', 'shape': int64_array([5, 23]), 'value': None},
'concat': {'kind': 'op', 'type': 'Concat', 'op': 'Concat', 'axis': 1},
'concat_data': {'kind': 'data', 'shape': int64_array([5, 40]), 'value': None},
'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
},
edges=[
('placeholder', 'placeholder_data'),
('const3', 'const3_data'),
('placeholder_data', 'concat', {'in': 0}),
('const3_data', 'concat', {'in': 1}),
('concat', 'concat_data'),
('concat_data', 'output')
]
)
CutInputHavingZeroDimFromConcat().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_when_there_are_three_inputs_and_middle_constant_has_zero_in_shape(self):
graph = build_graph(
nodes_attrs={
'const0': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([1, 2, 5]),
'value': np.zeros((1, 2, 5))
},
'const0_data': {'kind': 'data', 'shape': int64_array([1, 2, 5]), 'value': None},
'const1': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([1, 2, 0]),
'value': np.zeros((1, 2, 0))
},
'const1_data': {'kind': 'data', 'shape': int64_array([1, 2, 0]), 'value': None},
'placeholder': {'kind': 'op', 'type': 'Parameter', 'op': 'Parameter'},
'placeholder_data': {
'kind': 'data',
'value': None,
'shape': int64_array([1, 2, 17]),
'data_type': None
},
'concat': {'kind': 'op', 'type': 'Concat', 'op': 'Concat', 'axis': 2},
'concat_data': {'kind': 'data', 'shape': int64_array([1, 2, 22]), 'value': None},
'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
},
edges=[
('const0', 'const0_data'),
('const1', 'const1_data'),
('placeholder', 'placeholder_data'),
('const0_data', 'concat', {'in': 0}),
('const1_data', 'concat', {'in': 1}),
('placeholder_data', 'concat', {'in': 2}),
('concat', 'concat_data'),
('concat_data', 'output')
]
)
ref_graph = build_graph(
nodes_attrs={
'const0': {
'kind': 'op',
'type': 'Const',
'op': 'Const',
'shape': int64_array([1, 2, 5]),
'value': np.zeros((1, 2, 5))
},
'const0_data': {'kind': 'data', 'shape': int64_array([1, 2, 5]), 'value': None},
'placeholder': {'kind': 'op', 'type': 'Parameter', 'op': 'Parameter'},
'placeholder_data': {
'kind': 'data',
'value': None,
'shape': int64_array([1, 2, 17]),
'data_type': None
},
'concat': {'kind': 'op', 'type': 'Concat', 'op': 'Concat', 'axis': 2},
'concat_data': {'kind': 'data', 'shape': int64_array([1, 2, 22]), 'value': None},
'output': {'kind': 'op', 'op': 'Result', 'type': 'Result'},
},
edges=[
('const0', 'const0_data'),
('placeholder', 'placeholder_data'),
('const0_data', 'concat', {'in': 0}),
('placeholder_data', 'concat', {'in': 1}),
('concat', 'concat_data'),
('concat_data', 'output')
]
)
CutInputHavingZeroDimFromConcat().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_conversion(self, input_shape, scales, axes):
input_shape_as_array = int64_array(input_shape)
scales_as_array = float32_array(scales)
graph = build_graph(
graph_node_attrs, graph_edges, {
'placeholder_data': {
'shape': input_shape_as_array
},
'scales': {
'value': scales_as_array,
'shape': scales_as_array.shape
},
'scales_data': {
'value': scales_as_array,
'shape': scales_as_array.shape
},
'upsample_data': {
'shape':
((input_shape_as_array + 1.e-5) * scales_as_array).astype(
np.int64)
}
})
graph.graph['layout'] = 'NCHW'
ref_graph = build_graph(
new_ref_graph_node_attr, new_ref_graph_edges, {
'placeholder_data': {
'shape': int64_array(input_shape)
},
'ss_begin': {
'value': int64_array([axes[0]])
},
'ss_end': {
'value': int64_array([axes[-1] + 1])
},
'ss_begin_data': {
'value': int64_array([axes[0]])
},
'ss_end_data': {
'value': int64_array([axes[-1] + 1])
},
'factor': {
'value': scales_as_array[2:],
'shape': scales_as_array[2:].shape
},
'factor_data': {
'value': scales_as_array[2:],
'shape': scales_as_array[2:].shape
},
'axes_const': {
'value': int64_array(axes),
'shape': int64_array(axes).shape
},
'interpolate_data': {
'shape':
(input_shape_as_array * scales_as_array + 1e-5).astype(
np.int64)
},
})
UpsampleToResample().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, ref_graph, 'output')
self.assertTrue(flag, resp)
def test_1(self):
graph = build_graph(nodes_attributes,
[('placeholder', 'shuffle_channel'),
('shuffle_channel', 'result')],
nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
ref_graph = build_graph(nodes_attributes, [('placeholder', 'shape', {
'in': 0,
'out': 0
}), ('shape', 'batch_gather', {
'in': 0,
'out': 0
}), ('batch_gather_idx', 'batch_gather', {
'in': 1,
'out': 0
}), ('batch_gather_axis', 'batch_gather', {
'in': 2,
'out': 0
}), ('shape', 'channel_gather', {
'in': 0,
'out': 0
}), ('channel_gather_idx', 'channel_gather', {
'in': 1,
'out': 0
}), ('channel_gather_axis', 'channel_gather', {
'in': 2,
'out': 0
}), ('channel_gather', 'output_channels', {
'in': 0,
'out': 0
}), ('div_group', 'output_channels', {
'in': 1,
'out': 0
}), ('output_channels', 'convert', {
'in': 0,
'out': 0
}), ('batch_gather', 'concat', {
'in': 0,
'out': 0
}), ('group', 'concat', {
'in': 1,
'out': 0
}), ('convert', 'concat', {
'in': 2,
'out': 0
}), ('const', 'concat', {
'in': 3,
'out': 0
}), ('placeholder', 'reshape_split', {
'in': 0,
'out': 0
}), ('concat', 'reshape_split', {
'in': 1,
'out': 0
}), ('reshape_split', 'transpose', {
'in': 0,
'out': 0
}), ('transpose_const', 'transpose', {
'in': 1,
'out': 0
}), ('transpose', 'reshape_concat', {
'in': 0,
'out': 0
}), ('shape', 'reshape_concat', {
'in': 1,
'out': 0
}), ('reshape_concat', 'result')],
nodes_with_edges_only=True)
ShuffleChannel().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(
Node(graph, 'result').in_port(0).get_source().node.name ==
'scname')
def create_graph(cls):
cls.graph = build_graph(cls.nodes_attributes,
[('input_data_node', 'test_node'),
('test_node', 'output_data_node')],
nodes_with_edges_only=True)
def test_splice(self):
graph = build_graph(self.nodes_attributes,
[('placeholder', 'in_node'), ('in_node', 'splice'),
('splice', 'splice_data'),
('splice_data', 'out_placeholder')])
ReplaceSpliceNodePattern().find_and_replace_pattern(graph)
ref_graph = build_graph(
{
'in_placeholder': {
'kind': 'op',
'op': None
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'shape': {
'kind': 'op',
'op': 'ShapeOf'
},
'shape_data': {
'kind': 'data'
},
'crop_batch': {
'kind': 'op',
'op': 'Crop',
'offset': int64_array([0])
},
'crop_batch_data': {
'kind': 'data'
},
'crop_batch_dim': {
'kind': 'op',
'op': 'Const',
'value': int64_array([1])
},
'crop_batch_dim_data': {
'kind': 'data'
},
'second_dim': {
'kind': 'op',
'op': 'Const',
'value': int64_array([143])
},
'second_dim_data': {
'kind': 'data'
},
'gather_shape': {
'kind': 'op',
'op': 'Concat'
},
'gather_shape_data': {
'kind': 'data'
},
'fill_value': {
'kind': 'op',
'op': 'Const',
'value': int64_array([0])
},
'fill_value_data': {
'kind': 'data'
},
'broadcast': {
'kind': 'op',
'op': 'Broadcast'
},
'broadcast_data': {
'kind': 'data'
},
'memory_in': {
'kind': 'op',
'op': 'ReadValue'
},
'memory_in_data': {
'kind': 'data'
},
'crop_mem': {
'kind': 'op',
'op': 'Crop',
'offset': 13,
'dim': 130
},
'crop_mem_data': {
'kind': 'data'
},
'concat': {
'kind': 'op',
'op': 'Concat'
},
'concat_data': {
'kind': 'data',
'shape': [1, 143]
},
'memory_out': {
'kind': 'op',
'op': 'Assign'
},
'memory_out_data': {
'kind': 'data'
},
'result': {
'kind': 'op',
'op': 'Result'
},
'out_placeholder': {
'kind': 'op',
'op': 'placeholder'
},
}, [
('in_placeholder', 'in_node'),
('in_node', 'shape'),
('shape', 'shape_data'),
('shape_data', 'crop_batch'),
('crop_batch', 'crop_batch_data'),
('crop_batch_dim', 'crop_batch_dim_data'),
('crop_batch_dim_data', 'crop_batch', {
'in': 1
}),
('second_dim', 'second_dim_data'),
('second_dim_data', 'gather_shape', {
'in': 1
}),
('crop_batch_data', 'gather_shape', {
'in': 0
}),
('gather_shape', 'gather_shape_data'),
('fill_value', 'fill_value_data'),
('fill_value_data', 'broadcast', {
'in': 0
}),
('gather_shape_data', 'broadcast', {
'in': 1
}),
('broadcast', 'broadcast_data'),
('broadcast_data', 'memory_in'),
('memory_in', 'memory_in_data'),
('memory_in_data', 'crop_mem'),
('crop_mem', 'crop_mem_data'),
('crop_mem_data', 'concat', {
'in': 0
}),
('in_node', 'concat', {
'in': 1
}),
('concat', 'concat_data'),
('concat_data', 'memory_out'),
('memory_out', 'memory_out_data'),
('memory_out_data', 'result'),
('concat_data', 'out_placeholder'),
])
(flag, resp) = compare_graphs(graph, ref_graph, 'out_placeholder')
self.assertTrue(flag, resp)
def test_splice_with_constdim(self):
graph = build_graph(self.nodes_attributes,
[('placeholder', 'in_node'), ('in_node', 'splice'),
('splice', 'splice_data'),
('splice_data', 'out_placeholder')])
Node(graph, 'splice')['const_dim'] = 10
Node(graph, 'splice_data')['shape'] = [1, 43]
ReplaceSpliceNodePattern().find_and_replace_pattern(graph)
ref_graph = build_graph(
{
'in_placeholder': {
'kind': 'op',
'op': None
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'split': {
'kind': 'op',
'op': 'Split'
},
'split_data_0': {
'kind': 'data'
},
'split_data_1': {
'kind': 'data'
},
'shape': {
'kind': 'op',
'op': 'ShapeOf'
},
'shape_data': {
'kind': 'data'
},
'crop_batch': {
'kind': 'op',
'op': 'Crop',
'offset': int64_array([0])
},
'crop_batch_data': {
'kind': 'data'
},
'crop_batch_dim': {
'kind': 'op',
'op': 'Const',
'value': int64_array([1])
},
'crop_batch_dim_data': {
'kind': 'data'
},
'second_dim': {
'kind': 'op',
'op': 'Const',
'value': int64_array([33])
},
'second_dim_data': {
'kind': 'data'
},
'gather_shape': {
'kind': 'op',
'op': 'Concat'
},
'gather_shape_data': {
'kind': 'data'
},
'fill_value': {
'kind': 'op',
'op': 'Const',
'value': int64_array([0])
},
'fill_value_data': {
'kind': 'data'
},
'broadcast': {
'kind': 'op',
'op': 'Broadcast'
},
'broadcast_data': {
'kind': 'data'
},
'memory_in': {
'kind': 'op',
'op': 'ReadValue'
},
'memory_in_data': {
'kind': 'data'
},
'crop_mem': {
'kind': 'op',
'op': 'Crop',
'offset': 3,
'dim': 30
},
'crop_mem_data': {
'kind': 'data'
},
'concat': {
'kind': 'op',
'op': 'Concat'
},
'concat_data': {
'kind': 'data'
},
'memory_out': {
'kind': 'op',
'op': 'Assign'
},
'memory_out_data': {
'kind': 'data'
},
'result': {
'kind': 'op',
'op': 'Result'
},
'shape_2': {
'kind': 'op',
'op': 'ShapeOf'
},
'shape_2_data': {
'kind': 'data'
},
'crop_batch_2': {
'kind': 'op',
'op': 'Crop',
'offset': int64_array([0])
},
'crop_batch_2_data': {
'kind': 'data'
},
'crop_batch_dim_2': {
'kind': 'op',
'op': 'Const',
'value': int64_array([1])
},
'crop_batch_dim_2_data': {
'kind': 'data'
},
'second_dim_2': {
'kind': 'op',
'op': 'Const',
'value': int64_array([33])
},
'second_dim_2_data': {
'kind': 'data'
},
'gather_shape_2': {
'kind': 'op',
'op': 'Concat'
},
'gather_shape_2_data': {
'kind': 'data'
},
'fill_value_2': {
'kind': 'op',
'op': 'Const',
'value': int64_array([0])
},
'fill_value_2_data': {
'kind': 'data'
},
'broadcast_2': {
'kind': 'op',
'op': 'Broadcast'
},
'broadcast_2_data': {
'kind': 'data'
},
'memory_in_constdims': {
'kind': 'op',
'op': 'ReadValue'
},
'memory_in_constdims_data': {
'kind': 'data'
},
'crop_mem_constdims': {
'kind': 'op',
'op': 'Crop',
'offset': 10,
'dim': 100
},
'crop_mem_constdims_data': {
'kind': 'data'
},
'concat_constdims': {
'kind': 'op',
'op': 'Concat'
},
'concat_constdims_data': {
'kind': 'data'
},
'memory_out_constdims': {
'kind': 'op',
'op': 'Assign'
},
'memory_out_constdims_data': {
'kind': 'data'
},
'result_constdims': {
'kind': 'op',
'op': 'Result'
},
'crop_first_constdims': {
'kind': 'op',
'op': 'Crop',
'offset': 0,
'dim': 10
},
'crop_first_constdims_data': {
'kind': 'data'
},
'concat_all': {
'kind': 'op',
'op': 'Concat'
},
'concat_all_data': {
'kind': 'data',
'shape': [1, 43]
},
'out_placeholder': {
'kind': 'op',
'op': 'placeholder'
},
'axis_const': {
'kind': 'op'
},
'axis_const_data': {
'value': None,
'shape': None,
'kind': 'data'
},
'split_dim_const': {
'kind': 'op'
},
'split_dim_const_data': {
'value': None,
'shape': None,
'kind': 'data'
},
}, [
('in_placeholder', 'in_node'),
('in_node', 'split', {
'in': 0
}),
('split', 'split_data_0', {
'out': 0
}),
('split', 'split_data_1', {
'out': 1
}),
('split_data_0', 'shape'),
('shape', 'shape_data'),
('shape_data', 'crop_batch'),
('crop_batch', 'crop_batch_data'),
('crop_batch_dim', 'crop_batch_dim_data'),
('crop_batch_dim_data', 'crop_batch', {
'in': 1
}),
('second_dim', 'second_dim_data'),
('second_dim_data', 'gather_shape', {
'in': 1
}),
('crop_batch_data', 'gather_shape', {
'in': 0
}),
('gather_shape', 'gather_shape_data'),
('fill_value', 'fill_value_data'),
('fill_value_data', 'broadcast', {
'in': 0
}),
('gather_shape_data', 'broadcast', {
'in': 1
}),
('broadcast', 'broadcast_data'),
('broadcast_data', 'memory_in'),
('memory_in', 'memory_in_data'),
('memory_in_data', 'crop_mem'),
('crop_mem', 'crop_mem_data'),
('crop_mem_data', 'concat', {
'in': 0
}),
('split_data_0', 'concat', {
'in': 1
}),
('concat', 'concat_data'),
('concat_data', 'memory_out'),
('memory_out', 'memory_out_data'),
('memory_out_data', 'result'),
('split_data_1', 'shape_2'),
('shape_2', 'shape_2_data'),
('shape_2_data', 'crop_batch_2'),
('crop_batch_2', 'crop_batch_2_data'),
('crop_batch_dim_2', 'crop_batch_dim_2_data'),
('crop_batch_dim_2_data', 'crop_batch_2', {
'in': 1
}),
('second_dim_2', 'second_dim_2_data'),
('second_dim_2_data', 'gather_shape_2', {
'in': 1
}),
('crop_batch_2_data', 'gather_shape_2', {
'in': 0
}),
('gather_shape_2', 'gather_shape_2_data'),
('fill_value_2', 'fill_value_2_data'),
('fill_value_2_data', 'broadcast_2', {
'in': 0
}),
('gather_shape_2_data', 'broadcast_2', {
'in': 1
}),
('broadcast_2', 'broadcast_2_data'),
('broadcast_2_data', 'memory_in_constdims'),
('memory_in_constdims', 'memory_in_constdims_data'),
('memory_in_constdims_data', 'crop_mem_constdims'),
('crop_mem_constdims', 'crop_mem_constdims_data'),
('crop_mem_constdims_data', 'concat_constdims', {
'in': 0
}),
('split_data_1', 'concat_constdims', {
'in': 1
}),
('concat_constdims', 'concat_constdims_data'),
('concat_constdims_data', 'memory_out_constdims'),
('memory_out_constdims', 'memory_out_constdims_data'),
('memory_out_constdims_data', 'result_constdims'),
('concat_constdims_data', 'crop_first_constdims'),
('crop_first_constdims', 'crop_first_constdims_data'),
('crop_first_constdims_data', 'concat_all', {
'in': 1
}),
('concat_data', 'concat_all', {
'in': 0
}),
('concat_all', 'concat_all_data'),
('concat_all_data', 'out_placeholder'),
('axis_const', 'axis_const_data'),
('split_dim_const', 'split_dim_const_data'),
('axis_const_data', 'split', {
'in': 1
}),
('split_dim_const_data', 'split', {
'in': 2
}),
])
(flag, resp) = compare_graphs(graph, ref_graph, 'out_placeholder')
self.assertTrue(flag, resp)
def test_two_inputs_two_shapes_positive_1(self):
shape_1 = [1, 2, 3, 4]
shape_2 = [4, 3, 2, 1]
inputs = {
'node_1': [{
'shape': shape_1
}],
'node_4': [{
'shape': shape_2
}]
}
nodes = {
'input_1': {
'type': 'Identity',
'kind': 'op',
'op': 'Placeholder'
},
'input_2': {
'type': 'Identity',
'kind': 'op',
'op': 'Placeholder'
},
'node_1': {
'type': 'Identity',
'kind': 'op',
'op': 'NotPlaceholder'
},
'node_2': {
'type': 'Identity',
'kind': 'op',
'op': 'NotPlaceholder'
},
'node_3': {
'type': 'Identity',
'kind': 'op',
'op': 'NotPlaceholder'
},
'node_4': {
'type': 'Identity',
'kind': 'op',
'op': 'NotPlaceholder'
},
'output': {
'kind': 'op',
'op': 'OpOutput'
}
}
edges = [('input_1', 'node_1'), ('node_1', 'node_2'),
('node_3', 'output'), ('input_2', 'node_4'),
('node_4', 'output')]
graph = build_graph(nodes, edges)
add_input_ops(graph=graph,
user_defined_inputs=inputs,
before_infer=True)
new_input_1 = list(graph.in_edges('node_1'))[0][0]
new_input_2 = list(graph.in_edges('node_4'))[0][0]
self.assertFalse(graph.node['input_1']['is_input'])
self.assertTrue(graph.node[new_input_1]['is_input'])
self.assertTrue(graph.node[new_input_2]['is_input'])
self.assertTrue((new_input_1, 'node_1') in graph.edges())
self.assertTrue((new_input_2, 'node_4') in graph.edges())
self.assertListEqual(shape_1, graph.node[new_input_1]['shape'])
self.assertListEqual(shape_2, graph.node[new_input_2]['shape'])
def test_scaleshift2_axis1_to_mul(self):
graph = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_1_data', 'scaleshift_1'),
('placeholder_2_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'placeholder_2_data': {
'shape': np.array([227])
},
'scaleshift_1': {
'axis': 1
},
'scaleshift_1_data': {}
})
graph_ref = build_graph(
nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_2_data', 'placeholder_2/Reshape_'),
('placeholder_2/Reshape_const',
'placeholder_2/Reshape_const_data'),
('placeholder_2/Reshape_const_data', 'placeholder_2/Reshape_'),
('placeholder_2/Reshape_', 'placeholder_2/Reshape_data'),
('placeholder_1_data', 'mul_1'),
('placeholder_2/Reshape_data', 'mul_1'),
('mul_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'placeholder_2_data': {
'shape': np.array([227])
},
'placeholder_2/Reshape_const': {
'value': np.array([1, 227, 1, 1]),
'shape': [4]
},
'placeholder_2/Reshape_const_data': {
'value': np.array([1, 227, 1, 1]),
'shape': [4]
},
'placeholder_2/Reshape_data': {
'shape': np.array([1, 227, 1, 1])
},
'mul_1': {
'can_be_fused': True
},
'scaleshift_1_data': {}
})
graph.graph['layout'] = 'NHWC'
convert_scale_shift_to_mul_add(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1')
self.assertTrue(flag, resp)
def test_add_input_with_output_port_after_infer(self):
shape = np.array([1, 2, 3, 4])
inputs = {'conv_1': [{'shape': shape, 'out': 0}]}
nodes = {
'old_input': {
'type': 'Identity',
'kind': 'op',
'op': 'Placeholder'
},
'inp_data': {
'kind': 'data',
'shape': shape + 1
},
'conv_1': {
'type': 'Convolution',
'kind': 'op',
'op': 'NotPlaceholder'
},
'conv_data': {
'kind': 'data',
'shape': shape
},
'relu_1': {
'type': 'ReLU',
'kind': 'op',
'op': 'NotPlaceholder'
},
}
edges = [
('old_input', 'inp_data'),
('inp_data', 'conv_1'),
('conv_1', 'conv_data'),
('conv_data', 'relu_1'),
]
graph = build_graph(nodes, edges)
add_input_ops(graph=graph,
user_defined_inputs=inputs,
before_infer=False)
graph_ref = build_graph(
nodes_attrs={
'new_input': {
'kind': 'op',
'op': 'Placeholder',
'shape': shape
},
**nodes
},
edges=[
('old_input', 'inp_data'),
('inp_data', 'conv_1'),
('new_input', 'conv_data'),
('conv_data', 'relu_1'),
],
)
# Check that new input is added right (with right ports !)
(flag, resp) = compare_graphs(graph, graph_ref, last_node='relu_1')
self.assertTrue(flag, resp)
# Check that other graph is not damaged
(flag, resp) = compare_graphs(graph, graph_ref, last_node='conv_1')
self.assertTrue(flag, resp)
# Checks for new input and edges
self.assertTrue('conv_1/placeholder_out_port_0' in graph.nodes())
new_input = 'conv_1/placeholder_out_port_0'
self.assertTrue(graph.node[new_input]['is_input'])
self.assertTrue((new_input, 'conv_data') in graph.edges())
self.assertTrue(('conv_1', 'conv_data') not in graph.edges())
def test_scaleshift_can_be_fused(self):
graph = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('const_scaleshift_1_w', 'scaleshift_1_w'),
('const_scaleshift_1_b', 'scaleshift_1_b'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'scaleshift_1_w': {
'shape': np.array([3]),
'value': np.array([1, 1, 1])
},
'scaleshift_1_b': {
'shape': np.array([3]),
'value': np.array([0, 0, 0])
},
'scaleshift_1': {
'can_be_fused': False
},
'scaleshift_1_data': {
'shape': np.array([1, 227, 227, 3])
}
})
graph_ref = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('const_scaleshift_1_w', 'scaleshift_1_w'),
('const_scaleshift_1_b', 'scaleshift_1_b'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'const_scaleshift_1_w': {
'shape': np.array([3]),
'value': np.array([1, 1, 1])
},
'scaleshift_1_w': {
'shape': np.array([3]),
'value': np.array([1, 1, 1])
},
'const_scaleshift_1_b': {
'shape': np.array([3]),
'value': np.array([0, 0, 0])
},
'scaleshift_1_b': {
'shape': np.array([3]),
'value': np.array([0, 0, 0])
},
'scaleshift_1': {
'can_be_fused': False
},
'scaleshift_1_data': {
'shape': np.array([1, 227, 227, 3])
}
})
convert_scale_shift_to_mul_add(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)
def test_replacer(self):
graph = build_graph(
nodes_attrs=nodes_attributes,
edges=[
('slice_like', 'model_reshape0', {'in': 0}),
('model_reshape0_const', 'model_reshape0', {'in': 1}),
('model_reshape0', 'model_reshape1', {'in': 0}),
('model_reshape1_const', 'model_reshape1', {'in': 1}),
('model_reshape1', 'model_reshape2', {'in': 0}),
('model_reshape2_const', 'model_reshape2', {'in': 1}),
('model_reshape2', 'reshape0', {'in': 0}),
('reshape0_const', 'reshape0', {'in': 1}),
('reshape0', 'concat'),
('concat', 'detection_output', {'in': 2})
],
nodes_with_edges_only=True
)
ref_graph = build_graph(
nodes_attrs=nodes_attributes,
edges=[
('slice_like', 'model_reshape0', {'in': 0}),
('model_reshape0_const', 'model_reshape0', {'in': 1}),
('model_reshape0', 'model_reshape1', {'in': 0}),
('model_reshape1_const', 'model_reshape1', {'in': 1}),
('model_reshape1', 'model_reshape2', {'in': 0}),
('model_reshape2_const', 'model_reshape2', {'in': 1}),
('model_reshape2', 'reshape0', {'in': 0}),
('reshape0_const', 'reshape0', {'in': 1}),
('reshape0', 'concat'),
('concat', 'reshape1', {'in': 0}),
('reshape1_const', 'reshape1', {'in': 1}),
('reshape1', 'split', {'in': 0}),
('split_const', 'split', {'in': 1}),
('split', 'reshape2', {'out': 0, 'in': 0}),
('reshape2_const', 'reshape2', {'in': 1}),
('reshape2', 'value', {'in': 0}),
('value_const', 'value', {'in': 1}),
('value', 'xmin', {'out': 0, 'in': 0}),
('value', 'ymin', {'out': 1, 'in': 0}),
('value', 'xmax', {'out': 0, 'in': 1}),
('value', 'ymax', {'out': 1, 'in': 1}),
('value', 'div_1', {'out': 2, 'in': 0}),
('value', 'div_2', {'out': 3, 'in': 0}),
('div_1_const', 'div_1', {'in': 1}),
('div_2_const', 'div_2', {'in': 1}),
('div_1', 'xmin', {'in': 1, 'out': 0}),
('div_1', 'xmax', {'in': 0, 'out': 0}),
('div_2', 'ymin', {'in': 1, 'out': 0}),
('div_2', 'ymax', {'in': 0, 'out': 0}),
('xmin', 'concat_value', {'in': 0}),
('ymin', 'concat_value', {'in': 1}),
('xmax', 'concat_value', {'in': 2}),
('ymax', 'concat_value', {'in': 3}),
('concat_value', 'reshape3', {'in': 0}),
('reshape3_const', 'reshape3', {'in': 1}),
('reshape3', 'end_concat', {'in': 0}),
('split', 'end_concat', {'in': 1}),
('end_concat', 'detection_output', {'in': 2})
],
update_attributes={
'concat': {'axis': 1}
},
nodes_with_edges_only=True
)
graph.stage = 'front'
graph.graph['cmd_params'].data_type = 'FP32'
SsdAnchorsReplacer().find_and_replace_pattern(graph)
flag, resp = compare_graphs(graph, ref_graph, 'detection_output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_bn_decomposition_2(self):
graph = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'bn_op'),
('const_bn_const', 'bn_const'),
('const_bn_beta', 'bn_beta'),
('const_bn_mean', 'bn_mean'),
('const_bn_var', 'bn_var'),
('bn_const', 'bn_op'), ('bn_beta', 'bn_op'),
('bn_mean', 'bn_op'), ('bn_var', 'bn_op'),
('bn_op', 'bn_data'), ('concat', 'concat_data'),
('bn_data', 'concat'),
('concat_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'bn_op': {
'eps': 1.2,
'can_be_fused': False
},
'bn_const': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_beta': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_mean': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_var': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'bn_data': {
'shape': np.array([1, 227, 227, 3])
},
'concat_data': {}
})
graph_ref = build_graph(
nodes_attributes, [('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'mul_1'),
('const_mul_1_w', 'mul_1_w'),
('mul_1_w', 'mul_1'), ('mul_1', 'mul_1_data'),
('mul_1_data', 'add_1'),
('const_add_1_w', 'add_1_w'),
('add_1_w', 'add_1'), ('add_1', 'add_1_data'),
('add_1_data', 'mul_2'),
('const_mul_2_w', 'mul_2_w'),
('mul_2_w', 'mul_2'), ('mul_2', 'mul_2_data'),
('mul_2_data', 'add_2'),
('const_add_2_w', 'add_2_w'),
('add_2_w', 'add_2'), ('add_2', 'add_2_data'),
('concat', 'concat_data'),
('add_2_data', 'concat'),
('concat_data', 'op_output')],
{
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'const_mul_1_w': {
'shape': np.array([3]),
'value': np.array([0.67419986, 0.55901699, 0.48795004])
},
'mul_1_w': {
'shape': np.array([3]),
'value': np.array([0.67419986, 0.55901699, 0.48795004])
},
'const_mul_2_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'mul_2_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'const_add_1_w': {
'shape': np.array([3]),
'value': np.array([-0.67419986, -1.11803399, -1.46385011])
},
'add_1_w': {
'shape': np.array([3]),
'value': np.array([-0.67419986, -1.11803399, -1.46385011])
},
'const_add_2_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'add_2_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'add_2_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1': {
'can_be_fused': False
},
'mul_2': {
'can_be_fused': False
},
'add_1': {
'can_be_fused': False
},
'add_2': {
'can_be_fused': False
},
'concat_data': {}
})
graph.graph['layout'] = 'NHWC'
convert_batch_norm(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'concat_data')
self.assertTrue(flag, resp)
def test_remove_noop_nodes_check_out_port(self):
graph = build_graph(
{
'input': {
'type': 'Placeholder',
'value': None,
'kind': 'op'
},
'noop': {
'type': 'NoOp',
'value': None,
'kind': 'op'
},
'output_1': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_2': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_3': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
}, [('input', 'noop'), ('noop', 'output_1', {
'in': 4,
'out': 1
}), ('noop', 'output_2', {
'in': 2,
'out': 1
}), ('noop', 'output_3', {
'in': 10,
'out': 1
})])
ref_graph = build_graph(
{
'input': {
'type': 'Placeholder',
'value': None,
'kind': 'op'
},
'output_1': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_2': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
'output_3': {
'type': 'Identity',
'value': None,
'kind': 'op'
},
}, [('input', 'output_1', {
'in': 4,
'out': 0
}), ('input', 'output_2', {
'in': 2,
'out': 0
}), ('input', 'output_3', {
'in': 10,
'out': 0
})],
nodes_with_edges_only=True)
erase_node(Node(graph, 'noop'))
compare_graphs(graph, ref_graph, 'output_1')
def test_positive(self):
graph = build_graph(nodes_attributes,
[('placeholder', 'placeholder_data'),
('placeholder_data', 'transpose', {
'in': 0
}), ('transpose_const', 'transpose_const_data'),
('transpose_const_data', 'transpose', {
'in': 1
}), ('transpose', 'transpose_data'),
('transpose_data', 'reduceMean', {
'in': 0
}), ('reduceMeanConst', 'reduceMeanConst_data'),
('reduceMeanConst_data', 'reduceMean', {
'in': 1
}), ('reduceMean', 'reduceMean_data'),
('reduceMean_data', 'convolution')], {
'transpose_const': {
'value': int64_array([0, 2, 3, 1])
},
'transpose_const_data': {
'value': int64_array([0, 2, 3, 1])
},
'reduceMeanConst': {
'value': int64_array([1, 2])
},
'reduceMeanConst_data': {
'value': int64_array([1, 2])
}
},
nodes_with_edges_only=True)
ref_graph = build_graph(nodes_attributes,
[('placeholder', 'placeholder_data'),
('placeholder_data', 'reduceMean', {
'in': 0
}),
('transpose_const', 'transpose_const_data'),
('transpose_const_data', 'gather', {
'in': 0
}),
('reduceMeanConst', 'reduceMeanConst_data'),
('reduceMeanConst_data', 'gather', {
'in': 1
}), ('gather_const', 'gather_const_data'),
('gather_const_data', 'gather', {
'in': 2
}), ('gather', 'gather_data'),
('gather_data', 'reduceMean', {
'in': 1
}), ('reduceMean', 'reduceMean_data'),
('reduceMean_data', 'convolution')], {
'transpose_const_data': {
'value': int64_array([0, 2, 3, 1])
},
'reduceMeanConst_data': {
'value': int64_array([1, 2])
},
},
nodes_with_edges_only=True)
TransposeReduce().find_and_replace_pattern(graph)
flag, resp = compare_graphs(graph,
ref_graph,
'convolution',
check_op_attrs=True)
self.assertTrue(flag, resp)
def setUp(self):
self.graph = build_graph(nodes, edges)
def test_remove_duplication_neibor(self):
graph = build_graph(
{
'input': {
'kind': 'op',
'op': 'Parameter'
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'splice_1': {
'kind': 'op',
'op': 'Splice',
'context': range(-5, 1)
},
'splice_data_1': {
'kind': 'data',
'shape': [1, 78],
'value': None
},
'placeholder_1': {
'kind': 'op',
'op': None
},
'splice_2': {
'kind': 'op',
'op': 'Splice',
'context': range(0, 2)
},
'splice_data_2': {
'kind': 'data',
'shape': [1, 26],
'value': None
},
'placeholder_2': {
'kind': 'op',
'op': None
},
}, [
('input', 'in_node'),
('in_node', 'splice_1'),
('splice_1', 'splice_data_1'),
('splice_data_1', 'placeholder_1'),
('in_node', 'splice_2'),
('splice_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'),
],
nodes_with_edges_only=True)
MergeNeighborSplicePattern().find_and_replace_pattern(graph)
ref_graph = build_graph(
{
'input': {
'kind': 'op',
'op': 'Parameter'
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'splice_1': {
'kind': 'op',
'op': 'Splice',
'context': range(-5, 2)
},
'splice_data_1': {
'kind': 'data',
'shape': [1, 91],
'value': None
},
'crop_1': {
'kind': 'op',
'op': 'Crop',
'offset': 0,
'dim': 78,
'axis': -1
},
'crop_1_data': {
'kind': 'data',
'shape': [1, 78]
},
'placeholder_1': {
'kind': 'op'
},
'crop_2': {
'kind': 'op',
'op': 'Crop',
'offset': 65,
'dim': 26,
'axis': -1
},
'splice_data_2': {
'kind': 'data',
'shape': [1, 26],
'value': None
},
'placeholder_2': {
'kind': 'op'
},
}, [
('input', 'in_node'),
('in_node', 'splice_1'),
('splice_1', 'splice_data_1'),
('splice_data_1', 'crop_1'),
('crop_1', 'crop_1_data'),
('crop_1_data', 'placeholder_1'),
('splice_data_1', 'crop_2'),
('crop_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, ref_graph, 'placeholder_2')
self.assertTrue(flag, resp)
def test_infer_invalid4(self):
graph = build_graph(nodes_attributes, edges1, inputs4_inv)
ctcgreedydecoder_node = Node(graph, 'ctcgreedydecoder_node')
self.assertRaises(AssertionError, CTCGreedyDecoderSeqLenOp.infer,
ctcgreedydecoder_node)
def test_remove_duplication(self):
graph = build_graph(
{
'input': {
'kind': 'op',
'op': 'Parameter'
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'splice_1': {
'kind': 'op',
'op': 'Splice',
'context': range(-5, 6)
},
'splice_data_1': {
'kind': 'data',
'shape': [1, 143]
},
'placeholder_1': {
'kind': 'op',
'op': None
},
'splice_2': {
'kind': 'op',
'op': 'Splice',
'context': range(-1, 2)
},
'splice_data_2': {
'kind': 'data',
'shape': [1, 39]
},
'placeholder_2': {
'kind': 'op',
'op': None
},
}, [
('input', 'in_node'),
('in_node', 'splice_1'),
('splice_1', 'splice_data_1'),
('splice_data_1', 'placeholder_1'),
('in_node', 'splice_2'),
('splice_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'),
],
nodes_with_edges_only=True)
RemoveMemoryDuplicationPattern().find_and_replace_pattern(graph)
ref_graph = build_graph(
{
'input': {
'kind': 'op',
'op': 'Parameter'
},
'in_node': {
'kind': 'data',
'shape': [1, 13]
},
'splice_1': {
'kind': 'op',
'op': 'Splice',
'context': range(-5, 6)
},
'splice_data_1': {
'kind': 'data',
'shape': [1, 143]
},
'placeholder_1': {
'kind': 'op'
},
'crop_2': {
'kind': 'op',
'op': 'Crop',
'offset': 52,
'dim': 39,
'axis': -1
},
'splice_data_2': {
'kind': 'data',
'shape': [1, 39]
},
'placeholder_2': {
'kind': 'op'
},
}, [
('input', 'in_node'),
('in_node', 'splice_1'),
('splice_1', 'splice_data_1'),
('splice_data_1', 'placeholder_1'),
('splice_data_1', 'crop_2'),
('crop_2', 'splice_data_2'),
('splice_data_2', 'placeholder_2'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, ref_graph, 'placeholder_2')
self.assertTrue(flag, resp)
def test_conv_infer_3D_convolution(self):
graph = build_graph(nodes_attributes,
[
('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{
'conv_output': {
'shape': None
},
'conv_input': {
'shape': int64_array([1, 3, 16, 224, 224])
},
'conv_weights': {
'shape': int64_array([3, 64, 1, 7, 7]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']
},
'conv_node': {
'type': 'Convolution',
'bias_term': None,
'stride': None,
'dilation': None,
'batch_dims': int64_array([0]),
'channel_dims': int64_array([1]),
'output_spatial_shape': None,
'input_feature_channel': 0,
'output_feature_channel': 1,
'group': 1,
'output_shape': None,
'layout': 'NCHW'
}
})
conv_node = Node(graph, 'conv_node')
conv_output = Node(graph, 'conv_output')
Convolution.infer(conv_node)
# Check bias_term attribute
self.assertTrue(conv_node.has_valid('bias_term'))
self.assertTrue(not conv_node.bias_term)
# Check kernel_spatial_idx attr detection
self.assertTrue(conv_node.has_valid('kernel_spatial_idx'))
self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.kernel_spatial_idx))
# Check spatial_dims attr detection
self.assertTrue(conv_node.has_valid('spatial_dims'))
self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.spatial_dims))
# Check kernel_spatial attr detection
self.assertTrue(conv_node.has_valid('kernel_spatial'))
self.assertTrue(np.array_equal(int64_array([1, 7, 7]), conv_node.kernel_spatial))
# Check output attribute
self.assertTrue(conv_node.has_valid('output'))
self.assertEqual(64, conv_node.output)
# Check dilation value. Should be set to default
self.assertTrue(conv_node.has_valid('dilation'))
self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.dilation))
# Check stride value. Should be set to default
self.assertTrue(conv_node.has_valid('stride'))
self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.stride))
# Check pad value. Should be set to default
self.assertTrue(conv_node.has_valid('pad'))
self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]), conv_node.pad))
# Check pad_spatial_shape
self.assertTrue(conv_node.has_valid('pad_spatial_shape'))
self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0]]), conv_node.pad_spatial_shape))
# Check resulting output shape
self.assertTrue(np.array_equal(int64_array([1, 64, 16, 218, 218]), conv_output.shape))
def test_one_input_no_shape(self):
shape = None
inputs = {'conv_1': [{'shape': shape}]}
nodes = {
'old_input': {
'type': 'Placeholder',
'kind': 'op',
'op': 'Placeholder'
},
'old_input_data': {
'kind': 'data',
'value': None,
'shape': np.array([-1, 224, 224, 3])
},
'conv_1': {
'type': 'Convolution',
'kind': 'op',
'op': 'NotPlaceholder'
},
'conv_1_data': {
'kind': 'data',
'value': True,
'shape': np.array([-1, 224, 224, 3])
},
'relu_1': {
'type': 'ReLU',
'kind': 'op',
'op': 'NotPlaceholder'
},
'relu_1_data': {
'kind': 'data',
'value': None,
'shape': np.array([-1, 112, 112, 64])
},
'output': {
'type': 'SoftMax',
'kind': 'op',
'op': 'NotPlaceholder'
},
'output_data': {
'name': 'output_data',
'kind': 'data',
'shape': np.array([-1, 112, 112, 64])
},
'op_output': {
'kind': 'op',
'op': 'OpOutput'
}
}
edges = [('old_input', 'old_input_data'), ('old_input_data', 'conv_1'),
('conv_1', 'conv_1_data'), ('conv_1_data', 'relu_1'),
('relu_1', 'relu_1_data'), ('relu_1_data', 'output'),
('output', 'output_data'), ('output_data', 'op_output')]
graph = build_graph(nodes, edges)
add_input_ops(graph=graph,
user_defined_inputs=inputs,
before_infer=False)
new_input = list(graph.in_edges(list(
graph.in_edges('conv_1'))[0][0]))[0][0]
new_input_data = list(graph.in_edges('conv_1'))[0][0]
self.assertFalse(graph.node['old_input']['is_input'])
self.assertTrue(graph.node[new_input]['is_input'])
self.assertTrue((new_input_data, 'conv_1') in graph.edges())
self.assertTrue(('old_input_data', 'conv_1') not in graph.edges())
self.assertIsNotNone(graph.node[new_input_data]['shape'])
def test_infer(self, input_value, exp_value, axis=-1):
graph = build_graph(generate_nodes(int64_array(input_value), axis), edges)
onehot_node = Node(graph, 'one_hot')
OneHot.infer(onehot_node)
res_value = graph.node['one_hot_d']['value']
self.assertTrue(np.array_equal(exp_value, int64_array(res_value)))
def test_scaleshift_to_mul_add(self):
graph = build_graph(
nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'scaleshift_1_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'scaleshift_1_b': {
'shape': np.array([3]),
'value': np.array([3, 2, 1])
},
'scaleshift_1_data': {
'is_output': True
}
})
graph_ref = build_graph(
nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'mul_1'),
('mul_1_w', 'mul_1'),
('mul_1', 'mul_1_data'),
('mul_1_data', 'add_1'),
('add_1_w', 'add_1'),
('add_1', 'scaleshift_1_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1_w': {
'shape': np.array([3]),
'value': np.array([1, 2, 3])
},
'add_1_w': {
'shape': np.array([3]),
'value': np.array([3, 2, 1])
},
'mul_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'add_1': {
'can_be_fused': True
},
'mul_1': {
'can_be_fused': True
},
'scaleshift_1_data': {
'is_output': True
}
})
graph.graph['layout'] = 'NHWC'
convert_scale_shift_to_mul_add(graph)
graph_clean_up(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)
