def inference_resize_shape(g):
for node in g.node:
if node.op_type != 'Resize':
continue
output_value = helper.find_value_by_name(g, node.output[0])
output_value = helper.find_output_by_name(
g, node.output[0]) if output_value is None else output_value
if output_value is not None:
continue
# currently, only support 4 input
if len(node.input) == 4: # input: X, roi, scales, sizes
shape_node = helper.find_node_by_output_name(g, node.input[3])
if shape_node.op_type != 'Constant':
continue
_, shape_value = helper.constant_to_list(shape_node)
output_value = onnx.helper.make_tensor_value_info(
node.output[0], onnx.TensorProto.FLOAT,
[int(v) for v in shape_value])
g.value_info.extend([output_value])
return True
return False
def replace_Squeeze_with_Reshape(g):
"""
Replace Squeeze nodes with Reshape node.
:param g: the input graph
"""
node_to_remove = []
for node in g.node:
# Find Squeeze node
if node.op_type != 'Squeeze':
continue
# Get the shape and Construct the shape
output_value = helper.find_value_by_name(g, node.output[0])
if output_value is None:
output_value = helper.find_output_by_name(g, node.output[0])
if output_value is None:
raise RuntimeError("Cannot get shape for Squeeze")
shape = [
dim.dim_value for dim in output_value.type.tensor_type.shape.dim
]
const_node = helper.list_to_constant(node.name + "_shape",
[len(shape)], shape)
# Construct the Reshape layer with same input, output and name.
new_node = onnx.helper.make_node("Reshape",
[node.input[0], node.name + "_shape"],
node.output,
name=node.name)
# Append constructed nodes and append old node to remove_list
g.node.extend([const_node, new_node])
node_to_remove.append(node)
# Remove old nodes
for node in node_to_remove:
g.node.remove(node)
# Topological sort
topological_sort(g)
def add_nop_bn_after(g, value_names):
"""Add do-nothing BatchNormalization nodes after the given value info. It will\\
take the given names as the inputs of the new node and replace the inputs\\
of the following nodes.
:param g: the graph\\
:param value_names: a list of string which are the names of value_info.
"""
for value_name in value_names:
# Find the value first
value = helper.find_value_by_name(g, value_name)
if value is None:
value = helper.find_input_by_name(g, value_name)
if value is None:
value = helper.find_output_by_name(g, value_name)
if value is None:
print("Cannot find an value_info named {}".format(value_name))
continue
# Get the channel number from value info
shape = helper.get_shape_from_value_info(value)
channel = shape[1]
# Construct 4 weights
node_name = value_name + "_nop_bn"
ones = [1.0] * channel
zeros = [0.0] * channel
scale_node = helper.list_to_constant(node_name + "_scale", [channel],
ones)
bias_node = helper.list_to_constant(node_name + "_bias", [channel],
zeros)
mean_node = helper.list_to_constant(node_name + "_mean", [channel],
zeros)
var_node = helper.list_to_constant(node_name + "_var", [channel], ones)
# Construct BN node
bn_node = onnx.helper.make_node("BatchNormalization", [
value_name, scale_node.output[0], bias_node.output[0],
mean_node.output[0], var_node.output[0]
], [node_name],
name=node_name)
# Reconnect the graph
following_nodes = helper.find_following_nodes_by_input_value_name(
g, value_name)
if len(following_nodes) > 0:
for following_node in following_nodes:
replace_node_input(following_node, value_name, node_name)
else:
# If the node is the output, replace the output with the previous input.
new_value = onnx.helper.make_tensor_value_info(
node_name, value.type.tensor_type.elem_type, shape)
output_values = []
while len(g.output):
output_values.append(g.output.pop())
while output_values:
output_value = output_values.pop()
if output_value.name == value_name:
g.output.extend([new_value])
else:
g.output.extend([output_value])
# Add node to the graph
g.node.extend([bn_node, scale_node, bias_node, mean_node, var_node])
topological_sort(g)
def rename_all_node_name(g):
"""
rename all nodes:
new_name = old_name + "_kn"
:param g: the onnx graph
"""
for node in g.node:
new_node_name = node.name + "_kn"
new_node_output0_name = node.output[0] + "_kn"
# in order to keep same output node name, skip if it is output node.
output_value_info = helper.find_output_by_name(g, node.output[0])
if output_value_info != None:
continue
# rename the input of all the following nodes
following_nodes = helper.find_following_nodes_by_input_value_name(
g, node.output[0])
for following_node in following_nodes:
replace_node_input(following_node, node.output[0],
new_node_output0_name)
# rename value info
value_info = helper.find_value_by_name(g, node.output[0])
if value_info != None:
value_info.name = new_node_output0_name
# rename node
node.output[0] = new_node_output0_name
node.name = new_node_name
def find_first_sequential_outputs(g, node):
for value_name in node.output:
value = helper.find_output_by_name(g, value_name)
if value is not None:
return value
return find_first_sequential_outputs(
g,
helper.find_nodes_by_input_name(g, node.output[0])[0])
def add_nop_conv_after(g, value_names):
"""Add do-nothing depthwise Conv nodes after the given value info. It will\\
take the given names as the inputs of the new node and replace the inputs\\
of the following nodes.
:param g: the graph\\
:param value_names: a list of string which are the names of value_info.
"""
for value_name in value_names:
# Find the value first
value = helper.find_value_by_name(g, value_name)
if value is None:
value = helper.find_input_by_name(g, value_name)
if value is None:
value = helper.find_output_by_name(g, value_name)
if value is None:
print("Cannot find an value_info named {}".format(value_name))
continue
# Get the channel number from value info
shape = helper.get_shape_from_value_info(value)
channel = shape[1]
# Construct 4 weights
node_name = value_name + "_nop_conv"
ones = [1.0] * channel
weight_node = helper.list_to_constant(node_name + "_weight",
[channel, 1, 1, 1], ones)
# Construct BN node
conv_node = onnx.helper.make_node("Conv",
[value_name, weight_node.output[0]],
[node_name],
name=node_name,
dilations=[1, 1],
group=channel,
kernel_shape=[1, 1],
pads=[0, 0, 0, 0],
strides=[1, 1])
# Reconnect the graph
following_nodes = helper.find_following_nodes_by_input_value_name(
g, value_name)
if len(following_nodes) > 0:
for following_node in following_nodes:
replace_node_input(following_node, value_name, node_name)
else:
# If the node is the output, replace the output with the previous input.
new_value = onnx.helper.make_tensor_value_info(
node_name, value.type.tensor_type.elem_type, shape)
output_values = []
while len(g.output):
output_values.append(g.output.pop())
while output_values:
output_value = output_values.pop()
if output_value.name == value_name:
g.output.extend([new_value])
else:
g.output.extend([output_value])
# Add node to the graph
g.node.extend([conv_node, weight_node])
topological_sort(g)
def inference_cov_shape(g):
processed = False
for node in g.node:
if node.op_type != 'Conv':
continue
input_value_info = helper.find_value_by_name(g, node.input[0])
if not input_value_info:
input_value_info = helper.find_input_by_name(g, node.input[0])
if not input_value_info:
continue
kernel_value_info = helper.find_value_by_name(g, node.input[1])
output_value_info = helper.find_value_by_name(g, node.output[0])
if not output_value_info:
output_value_info = helper.find_output_by_name(g, node.output[0])
if output_value_info and \
helper.get_shape_from_value_info(output_value_info):
continue
_, kernel_shape = helper.find_size_shape_from_value(kernel_value_info)
_, input_shape = helper.find_size_shape_from_value(input_value_info)
if not input_shape or not kernel_shape:
continue
strides = helper.get_attribute_by_name(node, 'strides').ints
pads = helper.get_attribute_by_name(node, 'pads').ints
dilation = helper.get_attribute_by_name(node, 'dilations').ints
# Pytorch model has the case where strides only have one number
if len(strides) == 1:
return strides.append(strides[0])
if len(dilation) == 1:
return dilation.append(dilation[0])
H = math.floor((input_shape[2]+pads[0]+pads[2]-\
dilation[0]*(kernel_shape[2]-1)-1)/strides[0]+1)
W = math.floor((input_shape[3]+pads[1]+pads[3]-\
dilation[1]*(kernel_shape[3]-1)-1)/strides[1]+1)
output_shape = [input_shape[0], kernel_shape[0], H, W]
new_output_value_info = onnx.helper.make_tensor_value_info(
node.output[0], input_value_info.type.tensor_type.elem_type,
output_shape)
processed = True
if output_value_info:
g.value_info.remove(output_value_info)
g.value_info.extend([new_output_value_info])
return processed
def rename_output_name(g, original_name, new_name):
# Output
output_value = helper.find_output_by_name(g, original_name)
if output_value is None:
logging.error("Cannot find output value named " + original_name)
return
output_value.name = new_name
# Value Info
value_info = helper.find_value_by_name(g, original_name)
if value_info is not None:
value_info.name = new_name
# Node output
node = helper.find_node_by_output_name(g, original_name)
node.output[0] = new_name
# Node input
nodes = helper.find_nodes_by_input_name(g, original_name)
for node in nodes:
replace_node_input(node, original_name, new_name)
def inference_upsample_shape(g):
"""For onnx v1.4.1+, onnx cannot inference upsample output shape. Let's\\
do it ourselves. This function only inference the next upsample without\\
output shape each time.
:param g: the graph\\
:return: True if any Upsample shape is generated. Otherwise, False.
"""
for node in g.node:
if node.op_type != 'Upsample':
continue
output_value = helper.find_value_by_name(g, node.output[0])
if output_value is None:
output_value = helper.find_output_by_name(g, node.output[0])
if output_value and helper.get_shape_from_value_info(output_value):
continue
# Get input shape
input_value = helper.find_value_by_name(g, node.input[0])
if input_value is None:
continue
#raise RuntimeError("Shape for {} has not been generated.".format(node.input[0]))
if not helper.get_shape_from_value_info(input_value):
continue
#raise RuntimeError("Shape for {} is empty.".format(node.input[0]))
input_shape = helper.get_shape_from_value_info(input_value)
# Get upsample weight
weight_node = helper.find_node_by_output_name(g, node.input[1])
weight_shape, weight = helper.constant_to_list(weight_node)
if len(input_shape) != weight_shape[0]:
raise RuntimeError(
"Unmatch input shape and weight shape: {} vs {}".format(
input_shape, weight_shape))
# Calculate shape
output_shape = list(input_shape)
for i in range(len(output_shape)):
output_shape[i] = int(input_shape[i] * weight[i])
output_value = onnx.helper.make_tensor_value_info(
node.output[0], input_value.type.tensor_type.elem_type,
output_shape)
g.value_info.extend([output_value])
return True
return False
def change_output_shape(g, target_list):
for target in target_list:
try:
name, shape = parse_shape_change_input(target)
output_value = helper.find_output_by_name(g, name)
if output_value is None:
print("Cannot find output {}".format(name))
continue
if len(shape) != len(output_value.type.tensor_type.shape.dim):
print("The dimension doesn't match for output {}".format(name))
continue
for i in range(len(shape)):
output_value.type.tensor_type.shape.dim[i].dim_value = shape[i]
except TypeError:
# This happens when the parser function returns None.
continue
except ValueError:
# This happens when the input cannot be converter into int
print("Cannot parse {} into name and int".format(target))
continue
def inference_resize_shape(g):
for node in g.node:
if node.op_type != 'Resize':
continue
output_value = helper.find_value_by_name(g, node.output[0])
output_value = helper.find_output_by_name(
g, node.output[0]) if output_value is None else output_value
if output_value is not None:
continue
if len(node.input) == 4: # input: X, roi, scales, sizes
shape_node = helper.find_node_by_output_name(g, node.input[3])
if shape_node.op_type != 'Constant':
continue
_, shape_value = helper.constant_to_list(shape_node)
output_value = onnx.helper.make_tensor_value_info(
node.output[0], onnx.TensorProto.FLOAT,
[int(v) for v in shape_value])
g.value_info.extend([output_value])
return True
else:
# If output shape is not given, inference from scales
# Get the input shape
input_value = helper.find_value_by_name(g, node.input[0])
if input_value is None:
continue
shape_value = helper.get_shape_from_value_info(input_value)
scales_node = helper.find_node_by_output_name(g, node.input[2])
if scales_node.op_type != 'Constant':
continue
_, scales_value = helper.constant_to_list(scales_node)
for i in range(len(shape_value)):
shape_value[i] *= scales_value[i]
output_value = onnx.helper.make_tensor_value_info(
node.output[0], onnx.TensorProto.FLOAT,
[int(v) for v in shape_value])
g.value_info.extend([output_value])
return True
return False
def remove_nodes(g, cut_nodes=[], cut_types=[]):
node_to_delete = []
#Find target nodes
for node in g.node:
if node.name not in cut_nodes and node.op_type not in cut_types:
continue
else:
node_to_delete.append(node)
# Mapping outputs
output_mapping = {}
new_output = set()
for node in node_to_delete:
original_output = find_first_sequential_outputs(g, node)
if original_output.name not in output_mapping:
output_mapping[original_output.name] = []
for input_name in node.input:
value = helper.find_value_by_name(g, input_name)
if value is not None and helper.find_output_by_name(g, input_name) is None and value.name not in new_output:
output_mapping[original_output.name].append(value)
new_output.add(value.name)
# Remove them
while node_to_delete:
g.node.remove(node_to_delete.pop())
# Remove unreachable nodes
visited_values = set()
unused_constant_map = {}
for input_value in g.input:
visited_values.add(input_value.name)
for node in g.node:
if node.op_type == 'Constant':
visited_values.add(node.output[0])
unused_constant_map[node.output[0]] = node
continue
can_reach = True
for input_name in node.input:
if input_name not in visited_values:
can_reach = False
break
if can_reach:
for output_name in node.output:
visited_values.add(output_name)
else:
node_to_delete.append(node)
# Mapping outputs again
for node in node_to_delete:
original_output = find_first_sequential_outputs(g, node)
if original_output.name not in output_mapping:
output_mapping[original_output.name] = []
for input_name in node.input:
value = helper.find_value_by_name(g, input_name)
if value is not None and helper.find_output_by_name(g, input_name) is None and value.name not in new_output:
output_mapping[original_output.name].append(value)
new_output.add(value.name)
# Remove them
while node_to_delete:
g.node.remove(node_to_delete.pop())
#Remove unused constants
for node in g.node:
for input_name in node.input:
if input_name in unused_constant_map:
del unused_constant_map[input_name]
for node in unused_constant_map.values():
g.node.remove(node)
#Remove unreachable value infos
reachable_values = set()
for input_value in g.input:
reachable_values.add(input_value.name)
for node in g.node:
for input_name in node.input:
reachable_values.add(input_name)
for output_name in node.output:
reachable_values.add(output_name)
value_to_remove = []
for value_info in g.value_info:
if value_info.name not in reachable_values:
value_to_remove.append(value_info)
while value_to_remove:
value_info = value_to_remove.pop()
g.value_info.remove(value_info)
# Reorder output
output_values = []
while len(g.output):
output_values.append(g.output.pop())
while output_values:
output_value = output_values.pop()
if output_value.name in reachable_values:
logging.info("Keep output {}".format(output_value.name))
g.output.extend([output_value])
elif output_value.name in output_mapping:
real_outputs = [i for i in output_mapping[output_value.name] if i.name in reachable_values]
logging.info("Replace output {} with {}".format(output_value.name, [i.name for i in real_outputs]))
g.output.extend(real_outputs)
else:
logging.info("Abandon output {}".format(output_value.name))
continue
def inference_cov_shape(g):
processed = False
for node in g.node:
# Check for Conv output shape need to be inferrenced.
if node.op_type != 'Conv':
continue
# Input shape is not ready yet. Skip.
input_value_info = helper.find_value_by_name(g, node.input[0])
if not input_value_info:
input_value_info = helper.find_input_by_name(g, node.input[0])
if not input_value_info:
continue
_, input_shape = helper.find_size_shape_from_value(input_value_info)
if not input_shape:
continue
# Output shape is already there. Skip.
output_value_info = helper.find_value_by_name(g, node.output[0])
if not output_value_info:
output_value_info = helper.find_output_by_name(g, node.output[0])
if output_value_info and \
helper.get_shape_from_value_info(output_value_info):
continue
# Now start the inference.
# If auto_pad is set, use the auto_pad.
auto_pad = helper.get_var_attribute_by_name(node, 'auto_pad', 'string')
pads = None
if auto_pad is not None and auto_pad != 'NOTSET':
if auto_pad == 'SAME_LOWER' or auto_pad == 'SAME_UPPER':
new_output_value_info = onnx.helper.make_tensor_value_info(
node.output[0],
input_value_info.type.tensor_type.elem_type,
input_shape
)
if output_value_info:
g.value_info.remove(output_value_info)
g.value_info.extend([new_output_value_info])
processed = True
continue
elif auto_pad == 'VALID':
pads = [0, 0, 0, 0]
else:
print("Unrecognized auto_pad value: " + str(auto_pad))
exit(1)
kernel_value_info = helper.find_value_by_name(g, node.input[1])
_, kernel_shape = helper.find_size_shape_from_value(kernel_value_info)
if not input_shape or not kernel_shape:
continue
strides = helper.get_attribute_by_name(node, 'strides').ints
if not pads:
pads = helper.get_attribute_by_name(node, 'pads').ints
dilation = helper.get_attribute_by_name(node, 'dilations').ints
# Pytorch model has the case where strides only have one number
if len(strides) == 1:
return strides.append(strides[0])
if len(dilation) == 1:
return dilation.append(dilation[0])
H = math.floor((input_shape[2]+pads[0]+pads[2]-\
dilation[0]*(kernel_shape[2]-1)-1)/strides[0]+1)
W = math.floor((input_shape[3]+pads[1]+pads[3]-\
dilation[1]*(kernel_shape[3]-1)-1)/strides[1]+1)
output_shape = [input_shape[0], kernel_shape[0], H, W]
new_output_value_info = onnx.helper.make_tensor_value_info(
node.output[0],
input_value_info.type.tensor_type.elem_type,
output_shape
)
processed = True
if output_value_info:
g.value_info.remove(output_value_info)
g.value_info.extend([new_output_value_info])
return processed
def fuse_BN_with_Reshape_into_Gemm(g):
"""Fuse the following BN into the previous Gemm, even with Reshape or \\
Squeeze and Unsqueeze surrounding.
:param g: the graph
"""
node_to_remove = []
for node in g.node:
# Check for BN and Gemm pattern: Gemm A BN B
# Find BatchNorm Node
if node.op_type != 'BatchNormalization':
continue
bn_node = node
# Find A Node
a_node = helper.find_node_by_output_name(g, node.input[0])
if a_node is None or len(a_node.input) == 0:
continue
# Find Gemm Node
gemm_node = helper.find_node_by_output_name(g, a_node.input[0])
if gemm_node is None or gemm_node.op_type != 'Gemm':
continue
# Find B Node
b_node_list = helper.find_following_nodes_by_input_value_name(
g, bn_node.output[0])
if len(b_node_list) == 0:
the_output = helper.find_output_by_name(g, bn_node.output[0])
if the_output is None:
continue
b_node = None
elif len(b_node_list) > 1:
continue
else:
b_node = b_node_list[0]
# Check for branches
if len(
helper.find_following_nodes_by_input_value_name(
g, gemm_node.output[0])) > 1:
continue
if len(
helper.find_following_nodes_by_input_value_name(
g, a_node.output[0])) > 1:
continue
# Check type of A
if a_node.op_type == 'Unsqueeze':
axes = helper.get_attribute_by_name(a_node, 'axes')
if axes.ints != [2]:
continue
elif a_node.op_type == 'Reshape':
a = helper.constant_to_list(
helper.find_node_by_output_name(g, a_node.input[1]))[1]
if len(a) != 3 or a[2] != 1:
continue
else:
continue
# Check type of B
if b_node is None:
pass
elif b_node.op_type == 'Flatten':
pass
elif b_node.op_type == 'Squeeze':
axes = helper.get_attribute_by_name(a_node, 'axes')
if axes.ints != [2]:
continue
elif b_node.op_type == 'Reshape':
a = helper.constant_to_list(
helper.find_node_by_output_name(g, b_node.input[1]))[1]
if len(a) != 2:
continue
else:
continue
# Construct new Nodes
# Get original weights
gemm_b_node = helper.find_node_by_output_name(g, gemm_node.input[1])
gemm_b = helper.constant_to_numpy(gemm_b_node)
gemm_c_node = helper.find_node_by_output_name(g, gemm_node.input[2])
gemm_c = helper.constant_to_numpy(gemm_c_node)
bn_scale_node = helper.find_node_by_output_name(g, bn_node.input[1])
bn_scale = helper.constant_to_numpy(bn_scale_node)
bn_bias_node = helper.find_node_by_output_name(g, bn_node.input[2])
bn_bias = helper.constant_to_numpy(bn_bias_node)
bn_mean_node = helper.find_node_by_output_name(g, bn_node.input[3])
bn_mean = helper.constant_to_numpy(bn_mean_node)
bn_var_node = helper.find_node_by_output_name(g, bn_node.input[4])
bn_var = helper.constant_to_numpy(bn_var_node)
# Apply attributes
# epsilon
epsilon = helper.get_attribute_by_name(bn_node, 'epsilon')
if epsilon is None:
epsilon = 0.00001
else:
epsilon = epsilon.f
bn_var = bn_var + epsilon
# alpha
alpha = helper.get_attribute_by_name(gemm_node, 'alpha')
if alpha is None:
alpha = 1
else:
alpha = alpha.f
gemm_b = gemm_b * alpha
# beta
beta = helper.get_attribute_by_name(gemm_node, 'beta')
if beta is None:
beta = 1
else:
beta = beta.f
gemm_c = gemm_c * beta
# transA
transA = helper.get_attribute_by_name(gemm_node, 'transA')
if transA is not None and transA.i == 1:
raise RuntimeError("Do not support transA")
# transB
transB = helper.get_attribute_by_name(gemm_node, 'transB')
if transB is not None and transB.i == 1:
gemm_b = gemm_b.transpose()
# Calculate new weights
new_gemm_b = gemm_b * bn_scale / np.sqrt(bn_var)
new_gemm_c = (gemm_c - bn_mean) * bn_scale / np.sqrt(bn_var) + bn_bias
# Replace original weights
new_gemm_b_node = helper.numpy_to_constant(gemm_b_node.name + '_fused',
new_gemm_b)
new_gemm_c_node = helper.numpy_to_constant(gemm_c_node.name + '_fused',
new_gemm_c)
g.node.extend([new_gemm_b_node, new_gemm_c_node])
# Modify attributes
# alpha
alpha = helper.get_attribute_by_name(gemm_node, 'alpha')
if alpha is not None:
alpha.f = 1.0
# beta
beta = helper.get_attribute_by_name(gemm_node, 'beta')
if beta is not None:
beta.f = 1.0
# transB
transB = helper.get_attribute_by_name(gemm_node, 'transB')
if transB is not None:
transB.i = 0
# Remove useless nodes
node_to_remove.extend([
gemm_b_node, gemm_c_node, bn_node, bn_scale_node, bn_bias_node,
bn_mean_node, bn_var_node, a_node
])
if a_node.op_type == 'Reshape':
node_to_remove.append(
helper.find_node_by_output_name(g, a_node.input[1]))
if b_node is not None:
node_to_remove.append(b_node)
if b_node.op_type == 'Reshape':
node_to_remove.append(
helper.find_node_by_output_name(g, b_node.input[1]))
# Delete useless value infos
value = helper.find_value_by_name(g, a_node.output[0])
g.value_info.remove(value)
if a_node.op_type == 'Reshape':
value = helper.find_value_by_name(g, a_node.input[1])
g.value_info.remove(value)
for i in range(1, 5):
value = helper.find_value_by_name(g, bn_node.input[i])
g.value_info.remove(value)
value = helper.find_value_by_name(g, bn_node.output[0])
if value is not None:
g.value_info.remove(value)
if b_node is not None:
value = helper.find_value_by_name(g, gemm_node.output[0])
g.value_info.remove(value)
if b_node.op_type == 'Reshape':
value = helper.find_value_by_name(g, b_node.input[1])
g.value_info.remove(value)
# Connect the new graph
# Connect Gemm new weights
gemm_node.input[1] = new_gemm_b_node.output[0]
gemm_node.input[2] = new_gemm_c_node.output[0]
gemm_b_value = helper.find_value_by_name(g, gemm_b_node.output[0])
gemm_c_value = helper.find_value_by_name(g, gemm_c_node.output[0])
gemm_b_value.name = new_gemm_b_node.output[0]
gemm_b_value.type.tensor_type.shape.dim[
0].dim_value = new_gemm_b.shape[0]
gemm_b_value.type.tensor_type.shape.dim[
1].dim_value = new_gemm_b.shape[1]
gemm_c_value.name = new_gemm_c_node.output[0]
if b_node is None:
# If b node is None, set the Gemm output as the graph output
output_value = helper.find_output_by_name(g, bn_node.output[0])
g.output.remove(output_value)
g.output.extend(
[helper.find_value_by_name(g, gemm_node.output[0])])
else:
# Else, set node B output as gemm output
gemm_node.output[0] = b_node.output[0]
# Remove useless nodes
for node in node_to_remove:
g.node.remove(node)
topological_sort(g)
def remove_nodes(g, cut_nodes=[], cut_types=[]):
node_to_delete = []
#Find target nodes
for node in g.node:
if node.name not in cut_nodes and node.op_type not in cut_types:
continue
else:
node_to_delete.append(node)
#Remove them and add new outputs
new_output = []
while node_to_delete:
node = node_to_delete.pop()
for input_name in node.input:
value = helper.find_value_by_name(g, input_name)
if value is not None and helper.find_output_by_name(
g, input_name) is None:
new_output.append(value)
g.node.remove(node)
g.output.extend(new_output)
#Remove unreachable nodes
visited_values = set()
unused_constant_map = {}
for input_value in g.input:
visited_values.add(input_value.name)
for node in g.node:
if node.op_type == 'Constant':
visited_values.add(node.output[0])
unused_constant_map[node.output[0]] = node
continue
can_reach = True
for input_name in node.input:
if input_name not in visited_values:
can_reach = False
break
if can_reach:
for output_name in node.output:
visited_values.add(output_name)
else:
node_to_delete.append(node)
new_output = []
while node_to_delete:
node = node_to_delete.pop()
for input_name in node.input:
value = helper.find_value_by_name(g, input_name)
if value is not None and helper.find_output_by_name(
g, input_name) is None:
new_output.append(value)
g.node.remove(node)
g.output.extend(new_output)
#Remove unused constants
for node in g.node:
for input_name in node.input:
if input_name in unused_constant_map:
del unused_constant_map[input_name]
for node in unused_constant_map.values():
g.node.remove(node)
#Remove unreachable value infos and outputs
reachable_values = set()
for input_value in g.input:
reachable_values.add(input_value.name)
for node in g.node:
for input_name in node.input:
reachable_values.add(input_name)
for output_name in node.output:
reachable_values.add(output_name)
value_to_remove = []
for value_info in g.value_info:
if value_info.name not in reachable_values:
value_to_remove.append(value_info)
while value_to_remove:
value_info = value_to_remove.pop()
g.value_info.remove(value_info)
for value_info in g.output:
if value_info.name not in reachable_values:
value_to_remove.append(value_info)
while value_to_remove:
value_info = value_to_remove.pop()
g.output.remove(value_info)
