def test6_not_constant(self):
# ,--------------->consumer3 ,->consumer3
# data---(new_shape1)-->consumer1 => data----->consumer1
# `-(new_shape1)-->consumer2 `-->consumer2
#
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_1_data', 'eltwise_2'),
('placeholder_1_data', 'eltwise_3'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_1_data', 'concat'),
('eltwise_2_data', 'concat'),
('eltwise_3_data', 'concat'),
], {
'placeholder_1_data': {
'shape': int64_array([1, 3])
},
'eltwise_1_data': {
'shape': int64_array([1, 3])
},
'eltwise_2_data': {
'shape': int64_array([1, 3])
},
'eltwise_3_data': {
'shape': int64_array([1, 3])
},
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_1_data', 'eltwise_2'),
('placeholder_1_data', 'eltwise_3'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_1_data', 'concat'),
('eltwise_2_data', 'concat'),
('eltwise_3_data', 'concat'),
], {'placeholder_1_data': {
'shape': int64_array([1, 3])
}},
nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'concat',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test7_axis1_not_constant(self):
#
# data1(1,3,64,64)----. data(1,3,64,64)-------.
# data2(3,64,1)-------->Eltwise-->data(1,3,64,64)=> data(3,64,1)->Unsqueeze(0)->data(1,3,64,1)-->Eltwise->...
# data3(3,1)------' data(3,1)->Unsqueeze(2, 0)->data(1,3,1,1)-'
#
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('placeholder_3_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([3, 64, 1])
},
'placeholder_3_data': {
'shape': np.array([3, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_1': {
'axis': 1
}
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('placeholder_3_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_1', 'reshape_1_data'),
('reshape_2', 'reshape_2_data'),
('reshape_1_data', 'eltwise_1'),
('reshape_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data')], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([3, 64, 1])
},
'placeholder_3_data': {
'shape': np.array([3, 1])
},
'reshape_1_const': {
'value': int64_array([0]),
'shape': int64_array([1])
},
'reshape_1_const_data': {
'value': int64_array([0]),
'shape': int64_array([1])
},
'reshape_1_data': {
'shape': np.array([1, 3, 64, 1])
},
'reshape_2_const': {
'value': int64_array([2, 0]),
'shape': int64_array([2])
},
'reshape_2_const_data': {
'value': int64_array([2, 0]),
'shape': int64_array([2])
},
'reshape_2_data': {
'shape': np.array([1, 3, 1, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'eltwise_1',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_mega_hardcore(self):
# ORIGINAL GRAPH
#
# data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(64,1)-----,-'--------------------------------'------------------------------'
# \/ /
# data3(64,1)----`-->Eltwise3->data(64,1)----------'
#
# REFERENCE GRAPH AFTER TRANSFORMATION
#
# data1(1,3,64,64)---------------------,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
# /\ /\ /\
# data2(64,1)-,- Reshape1(1,1,64,64)--'--------------------------------o-------------------------------'
# | |
# | Reshape(1,1,64,1)
# \/ |
# data3(64,1)----------->Eltwise3->data(64,1)--------------------------'
#
graph = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_2_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('placeholder_2_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
graph_ref = build_graph(nodes_attributes, [
('placeholder_1', 'placeholder_1_data'),
('placeholder_2', 'placeholder_2_data'),
('placeholder_3', 'placeholder_3_data'),
('placeholder_1_data', 'eltwise_1'),
('placeholder_2_data', 'reshape_1'),
('reshape_1_const', 'reshape_1_const_data'),
('reshape_1_const_data', 'reshape_1'),
('reshape_1', 'reshape_1_data'),
('reshape_1_data', 'eltwise_1'),
('eltwise_1', 'eltwise_1_data'),
('eltwise_1_data', 'eltwise_2'),
('placeholder_2_data', 'eltwise_3'),
('placeholder_3_data', 'eltwise_3'),
('eltwise_3', 'eltwise_3_data'),
('eltwise_3_data', 'reshape_2'),
('reshape_2_const', 'reshape_2_const_data'),
('reshape_2_const_data', 'reshape_2'),
('reshape_2', 'reshape_2_data'),
('reshape_2_data', 'eltwise_2'),
('eltwise_2', 'eltwise_2_data'),
('eltwise_2_data', 'eltwise_4'),
('reshape_1_data', 'eltwise_4'),
('eltwise_4', 'eltwise_4_data'),
], {
'placeholder_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'placeholder_2_data': {
'shape': np.array([64, 1]),
'value': np.ones([64, 1])
},
'placeholder_3_data': {
'shape': np.array([64, 1])
},
'reshape_1_const': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_1_const_data': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_1_data': {
'shape': np.array([1, 1, 64, 1])
},
'reshape_2_const': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_2_const_data': {
'value': int64_array([0, 1]),
'shape': int64_array([2])
},
'reshape_2_data': {
'shape': np.array([1, 1, 64, 1])
},
'eltwise_1_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_2_data': {
'shape': np.array([1, 3, 64, 64])
},
'eltwise_3_data': {
'shape': np.array([64, 1])
},
'eltwise_4_data': {
'shape': np.array([1, 3, 64, 64])
}
},
nodes_with_edges_only=True)
normalize_eltwise_inputs(graph)
(flag, resp) = compare_graphs(graph,
graph_ref,
'eltwise_4',
check_op_attrs=True)
self.assertTrue(flag, resp)
def find_and_replace_pattern(self, graph: Graph):
fw = graph.graph['fw']
argv = graph.graph['cmd_params']
layout = graph.graph['layout']
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
for_graph_and_each_sub_graph_recursively(graph, lambda graph: mark_unfused_nodes(graph, argv.finegrain_fusing))
# Converting FusedBatchNorm layer to Mul->Add->Mul->Add sequence
# IE doesn't support batchNormInference with 4 inputs, so we have to split it to two ScaleShift
for_graph_and_each_sub_graph_recursively(graph, convert_batch_norm)
if fw == 'caffe':
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(graph, Div().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(graph, Sub().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if not argv.disable_fusing:
if fw != 'caffe':
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
# Fusing the sequences of Mul/Add operations
for_graph_and_each_sub_graph_recursively(graph, fuse_mul_add_sequence)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
normalize_eltwise_inputs(graph)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
# Fusing linear operation to Convolution
for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if not argv.disable_gfusing:
for_graph_and_each_sub_graph_recursively(graph, grouped_convolutions_fusing)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if not argv.disable_fusing:
for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
for_graph_and_each_sub_graph_recursively(graph, normalize_eltwise_inputs)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
MarkNodesToFuseUpToFakeQuantize().find_and_replace_pattern(graph)
FakeQuantizeFuse().find_and_replace_pattern(graph)
AddFakeQuantizeFuse().find_and_replace_pattern(graph)
MulFakeQuantizeFuse().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if layout != 'NHWC' and not argv.disable_resnet_optimization:
stride_optimization(graph)
