def add_bn_before_add(g):
for n in g.node:
# Find merge node (Add)
if n.op_type != 'Add':
continue
if len(n.input) != 2:
continue
# Get two inputs
input_node_a = helper.find_node_by_output_name(g, n.input[0])
input_node_b = helper.find_node_by_output_name(g, n.input[1])
# Skip constant input add
if input_node_a is None or input_node_a.op_type == 'Constant':
continue
if input_node_b is None or input_node_b.op_type == 'Constant':
continue
def add_bn_after(prev_node):
# Get the channel number from value info
value_name = prev_node.output[0]
value = helper.find_value_by_name(g, value_name)
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,
epsilon=0.00000001)
# Reconnect the graph
replace_node_input(n, value_name, node_name)
# Add node to the graph
g.node.extend(
[bn_node, scale_node, bias_node, mean_node, var_node])
if not input_node_a.op_type == 'BatchNormalization' or len(
helper.find_following_nodes_by_input_value_name(
g, input_node_a.output[0])) > 1:
add_bn_after(input_node_a)
if not input_node_b.op_type == 'BatchNormalization' or len(
helper.find_following_nodes_by_input_value_name(
g, input_node_b.output[0])) > 1:
add_bn_after(input_node_b)
topological_sort(g)
def duplicate_shared_Flatten(g):
"""To feed our compiler, bind Flatten with Gemm. If the output of one\\
Flatten goes to two Gemm nodes, duplicate the Flatten.
:param g: the graph
"""
for node in g.node:
# Find a Flatten node
if node.op_type != 'Flatten':
continue
# Check Flatten outputs. Get following Gemm
output_nodes = helper.find_following_nodes_by_input_value_name(g, node.output[0])
if len(output_nodes) < 2:
continue
gemm_nodes = []
for output_node in output_nodes:
if output_node.op_type == 'Gemm':
gemm_nodes.append(output_node)
if len(gemm_nodes) < 2:
continue
# Process all the Gemm nodes except for the first one.
for i in range(1, len(gemm_nodes)):
# Duplicate
new_flatten_name = node.name + "_copy" + str(i)
new_flatten_node = onnx.helper.make_node(
"Flatten",
node.input,
[new_flatten_name],
name=new_flatten_name,
axis=1
)
# Connect new graph
replace_node_input(gemm_nodes[i], node.output[0], new_flatten_name)
g.node.extend([new_flatten_node])
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 change_first_conv_from_bgr_to_rgb(m):
"""For input channel format BGR model, use this function to change the first
conv weight to adapt the input into RGB.
:param m: the model proto
"""
# Check for first node.
g = m.graph
input_name = g.input[0].name
first_nodes = helper.find_following_nodes_by_input_value_name(
g, input_name)
if len(first_nodes) > 1:
return False
first_node = first_nodes[0]
# Now we have the first node. Check this first node.
if first_node.op_type != 'Conv':
return False
weight_value = helper.find_value_by_name(g, first_node.input[1])
weight_shape = helper.get_shape_from_value_info(weight_value)
if weight_shape[1] != 3:
return False
# Do weight shuffle
weight_node = helper.find_node_by_output_name(g, weight_value.name)
weight_np = helper.constant_to_numpy(weight_node)
b_channel = np.expand_dims(weight_np[:, 0, :, :], axis=1)
g_channel = np.expand_dims(weight_np[:, 1, :, :], axis=1)
r_channel = np.expand_dims(weight_np[:, 2, :, :], axis=1)
new_np = np.concatenate((r_channel, g_channel, b_channel), axis=1)
new_node = helper.numpy_to_constant(weight_value.name, new_np)
# Replace the weight and topological sort
g.node.remove(weight_node)
g.node.extend([new_node])
other.topological_sort(g)
return True
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 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 replace_Sum_with_Adds(g):
node_to_del = []
for node in g.node:
# Check for sum
if node.op_type != 'Sum':
continue
# Check for input number
if len(node.input) == 1:
# If input number is 1, delete the sum node.
following_nodes = helper.find_following_nodes_by_input_value_name(g, node.output[0])
for following_node in following_nodes:
modhelper.replace_node_input(following_node, node.output[0], node.input[0])
node_to_del.append(node)
if helper.find_value_by_name(node.output[0]) is not None:
g.value_info.remove(helper.find_value_by_name(node.output[0]))
elif len(node.input) == 2:
# If input number is 2, replace it with add.
node.op_type = 'Add'
continue
elif len(node.input) > 2:
# If input number is larger than 2, replace it with n-1 add.
input_count = len(node.input)
# First node has 2 inputs
first_node = onnx.helper.make_node(
"Add",
[node.input[0], node.input[1]],
[node.output[0] + '_replacement_1'],
name=node.name + '_replacement_1'
)
# Last node has the same output as the original sum node
last_node = onnx.helper.make_node(
"Add",
[node.output[0] + '_replacement_' + str(input_count - 2), node.input[input_count - 1]],
[node.output[0]],
name=node.name
)
g.node.extend([first_node, last_node])
for i in range(2, input_count - 1):
new_node = onnx.helper.make_node(
"Add",
[node.output[0] + '_replacement_' + str(i - 1), node.input[i]],
[node.output[0] + '_replacement_' + str(i)],
name=node.name + '_replacement_' + str(i)
)
g.node.extend([new_node])
node_to_del.append(node)
else:
logging.error("Sum node must have at least 1 input.")
quit(1)
while node_to_del:
g.node.remove(node_to_del.pop())
topological_sort(g)
def fuse_BN_into_Gemm(g):
"""Fuse the following BN into the previous Gemm.
:param g: the graph
"""
node_to_remove = []
for node in g.node:
# Check for BN and Gemm
if node.op_type != 'BatchNormalization':
continue
gemm_node = helper.find_node_by_output_name(g, node.input[0])
if gemm_node is None:
continue
if gemm_node.op_type != 'Gemm':
continue
if len(
helper.find_following_nodes_by_input_value_name(
g, gemm_node.output[0])) > 1:
continue
bn_node = node
# 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])
node_to_remove.extend([
gemm_b_node, gemm_c_node, bn_node, bn_scale_node, bn_bias_node,
bn_mean_node, bn_var_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
# Connect the new graph
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_c_value.name = new_gemm_c_node.output[0]
gemm_value = helper.find_value_by_name(g, gemm_node.output[0])
g.value_info.remove(gemm_value)
gemm_node.output[0] = bn_node.output[0]
for i in range(1, 5):
value = helper.find_value_by_name(g, bn_node.input[i])
g.value_info.remove(value)
# Remove useless nodes
for node in node_to_remove:
g.node.remove(node)
topological_sort(g)
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)
