def test_mark_unfused_nodes_2(self):
# Placeholder->ScaleShift->Mul->Add
graph = 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'),
('add_1_data', 'mul_2'), ('mul_2_w', 'mul_2'),
('mul_2', 'mul_2_data'),
('mul_2_data', 'concat_1'),
('concat_1', 'concat_1_data'),
('placeholder_1_data', 'concat_1'),
('concat_1_data', 'op_output')], {
'placeholder_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'add_1_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_2_data': {
'shape': np.array([1, 227, 227, 3])
},
'mul_1_w': {
'shape': np.array([1]),
'value': 6
},
'add_1_w': {
'shape': np.array([1]),
'value': 6
},
'mul_2_w': {
'shape': np.array([1]),
'value': 6
},
})
graph.graph['layout'] = 'NHWC'
mark_unfused_nodes(graph, '.*')
self.assertFalse(graph.node['mul_1']['can_be_fused'],
"can_be_fused should be False")
self.assertFalse(graph.node['mul_2']['can_be_fused'],
"can_be_fused should be False")
self.assertFalse(graph.node['add_1']['can_be_fused'],
"can_be_fused should be False")
self.assertFalse(graph.node['placeholder_1']['can_be_fused'],
"can_be_fused should be False")
self.assertFalse(graph.node['concat_1']['can_be_fused'],
"can_be_fused should be False")
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())
if not argv.disable_fusing:
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)
