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 set_upsample_mode_to_align_corner(g):
"""Set all the upsample nodes mode to align_corner.
"""
for node in g.node:
if node.op_type != 'Upsample':
continue
# Find a upsample node
attribute = helper.get_attribute_by_name(node, "mode")
if type(attribute.s) == type(b'abc'):
attribute.s = "align_corner".encode('utf-8')
else:
attribute.s = "align_corner"
def replace_ConstantOfShape_with_constant(g):
"""Replace Shape with Constant.\\
This is the first step of reshape constant folding.
:param g: the input graph\\
:return: if anything modified, return true.
"""
node_to_remove = []
for node in g.node:
# Find a Shape
if node.op_type != 'ConstantOfShape':
continue
# Check input
input_value = helper.find_value_by_name(g, node.input[0])
if input_value is None:
input_value = helper.find_input_by_name(g, node.input[0])
if input_value is None or len(
input_value.type.tensor_type.shape.dim) == 0:
continue
# Replace to constant node
pre_node = helper.find_node_by_output_name(g, node.input[0])
_, target_shape = helper.constant_to_list(pre_node)
value = helper.get_attribute_by_name(node, 'value').i
node_name = node.output[0]
new_node = helper.list_to_constant(node_name, [target_shape[0]],
[value] * target_shape[0])
g.node.extend([new_node])
# remove old node
node_to_remove.append(node)
# delete value_info
val_info_used = sum(
[input_value.name in node.input for node in g.node])
if val_info_used == 1:
g.value_info.remove(input_value)
replaced = True if len(node_to_remove) > 0 else False
for node in node_to_remove:
g.node.remove(node)
topological_sort(g)
return replaced
def split_ConvTranspose(model):
"""To feed our compiler, split ConvTranspose into Upsample and Conv.
:param model: the model
"""
node_to_delete = []
# Change model properties for upsample.
if model.ir_version < 3:
print("Warning: Current model IR version is not fully supported.")
model.ir_version = 4
model.opset_import[0].version = 9
g = model.graph
# Get a Convtranspose layer
for node in g.node:
# Find a Flatten node
if node.op_type != 'ConvTranspose':
continue
# Check auto_pad
auto_pad_proto = helper.get_attribute_by_name(node, "auto_pad")
if auto_pad_proto is not None:
print("Currently not split auto_pad ConvTranspose")
continue
# Check output_shape
output_shape_proto = helper.get_attribute_by_name(node, "output_shape")
if output_shape_proto is not None:
print("Currently not split output_shape ConvTranspose")
continue
# Get input shape
input_value = helper.find_value_by_name(g, node.input[0])
if input_value is None:
input_value = helper.find_input_by_name(g, node.input[0])
if input_value is None:
print("Cannot get value info named {}.".format(node.input[0]))
exit(1)
input_shape = helper.get_shape_from_value_info(input_value)
# Get attrbutes
attr = deconv_to_conv_info_extraction(input_shape, node)
# Generate Upsample scales
upsample_output_shape = list(input_shape)
upsample_output_shape[2] = (input_shape[2] - 1) * attr["strides"][0] + 1
upsample_output_shape[3] = (input_shape[3] - 1) * attr["strides"][1] + 1
upsample_node_name = node.name + "_inner_upsample"
upsample_scale_name = upsample_node_name + "_scales"
scales_np = np.ones([4]).astype('float32')
scales_np[2] = float(upsample_output_shape[2]) / input_shape[2]
scales_np[3] = float(upsample_output_shape[3]) / input_shape[3]
scales_node = helper.numpy_to_constant(upsample_scale_name, scales_np)
# Generate a Upsample layer and an internal value info
upsample_node = onnx.helper.make_node(
"Upsample",
[node.input[0], upsample_scale_name],
[upsample_node_name],
name=upsample_node_name,
mode="zeros"
)
upsample_value_info = onnx.helper.make_tensor_value_info(
upsample_node_name,
input_value.type.tensor_type.elem_type,
upsample_output_shape
)
# Check the weight layer, it may need a transpose
if attr["group"] != input_shape[1]:
weight_node = helper.find_node_by_output_name(g, node.input[1])
weight_np = helper.constant_to_numpy(weight_node)
new_weight_np = np.transpose(weight_np, [1, 0, 2, 3])
new_weight_node = helper.numpy_to_constant(node.input[1], new_weight_np)
node_to_delete.append(weight_node)
g.node.extend([new_weight_node])
value = helper.find_value_by_name(g, node.input[1])
g.value_info.remove(value)
# Generate a Conv layer
conv_node_name = node.name + "_inner_conv"
conv_node_input = [upsample_node_name]
conv_node_input.extend(node.input[1:])
conv_node = onnx.helper.make_node(
"Conv",
conv_node_input,
[node.output[0]],
name=conv_node_name,
pads=[int(i) for i in attr["conv_pads"]],
dilations=[int(i) for i in attr["dilations"]],
group=int(attr["group"]),
kernel_shape=[int(i) for i in attr["kernel_shape"]],
strides=[int(1), int(1)]
)
# Reconnect the graph
g.node.extend([scales_node, upsample_node, conv_node])
g.value_info.extend([upsample_value_info])
node_to_delete.append(node)
# Delete useless nodes
for node in node_to_delete:
g.node.remove(node)
topological_sort(g)
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_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_Gemm_into_Gemm(g):
"""Fuse the previous Gemm into the following Gemm.
:param g: the graph
"""
node_to_remove = []
for node in g.node:
# Check for Gemm and Gemm
if node.op_type != 'Gemm':
continue
prev_node = helper.find_node_by_output_name(g, node.input[0])
if prev_node is None:
continue
if prev_node.op_type != 'Gemm':
continue
# Get original weights
prev_b_node = helper.find_node_by_output_name(g, prev_node.input[1])
prev_b = helper.constant_to_numpy(prev_b_node)
prev_c_node = helper.find_node_by_output_name(g, prev_node.input[2])
prev_c = helper.constant_to_numpy(prev_c_node)
b_node = helper.find_node_by_output_name(g, node.input[1])
b = helper.constant_to_numpy(b_node)
c_node = helper.find_node_by_output_name(g, node.input[2])
c = helper.constant_to_numpy(c_node)
# Apply attributes
# alpha
alpha = helper.get_attribute_by_name(node, 'alpha')
if alpha is None:
alpha = 1
else:
alpha = alpha.f
b = b * alpha
alpha = helper.get_attribute_by_name(prev_node, 'alpha')
if alpha is None:
alpha = 1
else:
alpha = alpha.f
prev_b = prev_b * alpha
# beta
beta = helper.get_attribute_by_name(node, 'beta')
if beta is None:
beta = 1
else:
beta = beta.f
c = c * beta
beta = helper.get_attribute_by_name(prev_node, 'beta')
if beta is None:
beta = 1
else:
beta = beta.f
prev_c = prev_c * beta
# transA
transA = helper.get_attribute_by_name(node, 'transA')
if transA is not None and transA.i == 1:
raise RuntimeError("Do not support transA")
transA = helper.get_attribute_by_name(prev_node, 'transA')
if transA is not None and transA.i == 1:
raise RuntimeError("Do not support transA")
# transB
transB = helper.get_attribute_by_name(node, 'transB')
if transB is not None and transB.i == 1:
b = b.transpose()
transB = helper.get_attribute_by_name(prev_node, 'transB')
if transB is not None and transB.i == 1:
prev_b = prev_b.transpose()
# Calculate new weights
new_b = prev_b.dot(b)
new_c = prev_c.dot(b) + c
# Replace original weights
new_b_node = helper.numpy_to_constant(b_node.name + '_fused', new_b)
new_c_node = helper.numpy_to_constant(c_node.name + '_fused', new_c)
g.node.extend([new_b_node, new_c_node])
node_to_remove.extend(
[b_node, c_node, prev_b_node, prev_c_node, prev_node])
# Modify attributes
# alpha
alpha = helper.get_attribute_by_name(node, 'alpha')
if alpha is not None:
alpha.f = 1.0
# beta
beta = helper.get_attribute_by_name(node, 'beta')
if beta is not None:
beta.f = 1.0
# transB
transB = helper.get_attribute_by_name(node, 'transB')
if transB is not None:
transB.i = 0
# Connect the new graph
node.input[0] = prev_node.input[0]
prev_value = helper.find_value_by_name(g, prev_node.output[0])
g.value_info.remove(prev_value)
for i in range(1, 3):
value = helper.find_value_by_name(g, prev_node.input[i])
g.value_info.remove(value)
value = helper.find_value_by_name(g, node.input[i])
g.value_info.remove(value)
node.input[1] = new_b_node.output[0]
node.input[2] = new_c_node.output[0]
# 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)
