def Pad(node, value_info, node_name_to_out_var_dict,
innermost_let_ast_node, out_var_count, mtdAST):
node = OnnxNode(node)
if (DEBUG):
print(node)
inputsRef = node.inputs
# Skip constant_val input (last input)
inpLen = len(inputsRef) - 1
assert (inpLen == 2)
inputs = [
AST.ID(node_name_to_out_var_dict[inputsRef[x]])
for x in range(0, inpLen)
]
mode = node.attrs['mode']
assert (mode == 'constant')
seedot_output_ast = AST.UninterpFuncCall(
list(value_info[node.outputs[0]][1]), 'PadONNX', inputs)
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[0]] = output_name
return (innermost_let_ast_node, out_var_count)
def Concat(node, value_info, node_name_to_out_var_dict,
innermost_let_ast_node, out_var_count, mtdAST):
node = OnnxNode(node)
if (DEBUG):
print(node)
inputsRef = node.inputs
N = len(inputsRef)
inputs = [
AST.ID(node_name_to_out_var_dict[inputsRef[x]])
for x in range(0, len(inputsRef))
]
axis = node.attrs['axis']
seedot_output_ast = AST.UninterpFuncCall(
list(value_info[node.outputs[0]][1]),
'Concat' + str(N) + 'T',
inputs + [AST.Int(axis, 32, False)],
outputDiffInpDims=1)
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[0]] = output_name
return (innermost_let_ast_node, out_var_count)
def ReduceMean(node, value_info, node_name_to_out_var_dict,
innermost_let_ast_node, out_var_count, mtdAST):
node = OnnxNode(node)
inputsRef = node.inputs
keepdims = node.attrs['keepdims']
axes = node.attrs['axes']
# currently handling only this case
# currently support only 0 case
assert (keepdims == 0)
assert (len(axes) == 2)
seedot_output_ast = AST.UninterpFuncCall(
value_info[node.outputs[0]][1], 'ReduceMeanO', [
AST.ID(node_name_to_out_var_dict[inputsRef[0]]),
AST.Int(axes[0], 32, False),
AST.Int(axes[1], 32, False)
])
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[0]] = output_name
return (innermost_let_ast_node, out_var_count)
def ExpandDims(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.ExpandDims.name,
list(map(lambda x: AST.ID(dictNodeNameToOutVarStr[x]), inputsRef)))
return (None, retAST)
def MaxPoolGrad(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.MaxPoolGrad.name,
list(
map(lambda x: AST.ID(dictNodeNameToOutVarStr[x]),
inputsRef))))
def ZerosLike(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
curNodeOutputType = curNode.getAttrMapRef()["\"T\""].getDataType()
assert (curNodeOutputType is not Graph.DataTypeEnum.DT_INVALID)
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateTensor.name, [AST.Int(0)],
isSecret=False)
return (None, retAST)
def Tile(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Tile.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]])
]))
def RandomUniform(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
curNodeDataType = curNode.getAttrMapRef()["\"dtype\""].getDataType()
assert (curNodeDataType is not Graph.DataTypeEnum.DT_INVALID)
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
curNodeOutputShape = extraNodeInfoDict[curNode.getName()][0]
return (None,
AST.UninterpFuncCall(
curNodeOutputShape,
TFNodesAST.UninterpFuncCallNames.RandomUniform.name,
[AST.ID(curNodeDataType.name)]))
def Pack(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
N = curNode.getAttrMapRef()["\"N\""].getI()
axis = curNode.getAttrMapRef()["\"axis\""].getI()
assert (len(inputsRef) == N)
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Pack.name,
list(map(lambda x: AST.ID(dictNodeNameToOutVarStr[x]),
inputsRef)) + [AST.Int(axis)])
return (None, retAST)
def Cast(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
sourceType = curNode.getAttrMapRef()["\"SrcT\""].getDataType()
destType = curNode.getAttrMapRef()["\"DstT\""].getDataType()
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Cast.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(sourceType.name),
AST.ID(destType.name)
]))
def ConcatV2(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
N = curNode.getAttrMapRef()["\"N\""].getI()
assert (len(inputsRef) == N + 1) # One extra for axis
# TODO : Since the axis of concat is constant, therefore, its known here - the input's sizes along that dim should be
# passed as input to the below function.
# For now hardcoding.
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Concat.name + str(N) + 'T',
list(map(lambda x: AST.ID(dictNodeNameToOutVarStr[x]), inputsRef)),
outputDiffInpDims=1)
return (None, retAST)
def TruncatedNormal(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict):
curNodeDataType = curNode.getAttrMapRef()["\"dtype\""].getDataType()
assert (curNodeDataType is not Graph.DataTypeEnum.DT_INVALID)
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
curNodeOutputShape = extraNodeInfoDict[curNode.getName()][0]
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.TruncatedNormal.name,
[AST.ID(curNodeDataType.name)] +
list(map(lambda x: AST.Int(x), curNodeOutputShape)))
) # TODO
def Identity(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
# In SeeDot, J2=J1 creates a new reference for J1 -- so
# the corresponding code in Seedot cannot simply be J2 = J1.
# Instead create a new tensor first and then assign the old one to the new one.
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
curNodeDataType = curNode.getAttrMapRef()["\"T\""].getDataType()
assert (curNodeDataType is not Graph.DataTypeEnum.DT_INVALID)
curNodeShape = extraNodeInfoDict[curNode.getName()][0]
retAST = AST.UninterpFuncCall(
curNodeShape, TFNodesAST.UninterpFuncCallNames.CreateIdentity.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])])
return (None, retAST)
def Const(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
assert (len(curNode.getInputsRef()) == 0)
tensor = curNode.getAttrMapRef()["\"value\""].getTensor()
curNodeDataType = curNode.getAttrMapRef()["\"dtype\""].getDataType()
# create a different copy to not change the original copy
curNodeShape = tensor.getShapeRef()[:]
tensorConstantVal = tensor.getConstantVal()
if tensorConstantVal is not None:
# Use uinterpreted call of CreateTensor to create the tensor and fill it with a constant value
dataPassed = None
if curNodeDataType == Graph.DataTypeEnum.DT_INT32:
dataPassed = AST.Int(tensorConstantVal, 32)
elif curNodeDataType == Graph.DataTypeEnum.DT_FLOAT:
dataPassed = AST.Float(tensorConstantVal)
else:
assert False
if (len(curNodeShape) == 0):
# This is a constant element
retAST = dataPassed
else:
retAST = AST.UninterpFuncCall(
curNodeShape,
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[dataPassed],
isSecret=False)
else:
# The tensor content is given as byte array. Extract val array from the byte array and create ast.
if curNodeDataType == Graph.DataTypeEnum.DT_INT32:
dataPassed = list(
map(lambda x: AST.Int(x, 32),
tensor.getContentAsValArr()[:]))
elif curNodeDataType == Graph.DataTypeEnum.DT_FLOAT:
dataPassed = list(
map(lambda x: AST.Float(x),
tensor.getContentAsValArr()[:]))
else:
assert False
retAST = AST.Decl(curNodeShape,
None,
None,
dataPassed,
isSecret=False)
return (None, retAST)
def Fill(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
curNodeOutputShape = extraNodeInfoDict[inputsRef[0]][0]
# inputsRef[0] denotes a shape and should have a rank of 1
assert (len(curNodeOutputShape) == 1)
curNodeOutputType = curNode.getAttrMapRef()["\"T\""].getDataType()
assert (curNodeOutputType is not Graph.DataTypeEnum.DT_INVALID)
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[1]])],
isSecret=False)
return (None, retAST)
def Squeeze(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
# TODO : Do this in somewhat better way
inputsRef = curNode.getInputsRef()
inputTensorShape = extraNodeInfoDict[inputsRef[0]][0]
inputTensorRank = len(inputTensorShape)
squeezeDims = curNode.getAttrMapRef()["\"squeeze_dims\""].getList(
).getILi()
squeezeDimsRank = len(squeezeDims)
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Squeeze.name,
list(map(lambda x: AST.Int(x, 32), squeezeDims)) +
[AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])]))
def Slice(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 3)
curNodeDataType = curNode.getAttrMapRef()["\"T\""].getDataType()
curNodeShapeASTLi = list(
map(lambda x: AST.Int(x), extraNodeInfoDict[curNode.getName()][0]))
retAST = AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateCopy.name,
[
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]), # of this
# begin idx
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
# size
AST.ID(dictNodeNameToOutVarStr[inputsRef[2]])
])
return (None, retAST)
def AvgPool(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 1)
options = {}
stridesUsed = curNode.getAttrMapRef()["\"strides\""].getList().getILi()
assert ((stridesUsed[0] == 1) and (stridesUsed[3] == 1))
strideH = stridesUsed[1]
strideW = stridesUsed[2]
kSizeUsed = curNode.getAttrMapRef()["\"ksize\""].getList().getILi()
assert ((kSizeUsed[0] == 1) and (kSizeUsed[3] == 1))
kSizeH = kSizeUsed[1]
kSizeW = kSizeUsed[2]
paddingUsedStr = curNode.getAttrMapRef()["\"padding\""].getS()
zPadH = zPadW = -1
if (paddingUsedStr == "\"SAME\""):
zPadH = int((kSizeH - 1) / 2)
zPadW = int((kSizeW - 1) / 2)
elif (paddingUsedStr == "\"VALID\""):
zPadH = zPadW = 0
else:
zPadH = zPadW = -1
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgH = inputShape[1]
imgW = inputShape[2]
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.AvgPool.name, [
AST.Int(kSizeH, 32),
AST.Int(kSizeW, 32),
AST.Int(zPadH, 32),
AST.Int(zPadW, 32),
AST.Int(strideH, 32),
AST.Int(strideW, 32),
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])
]))
def Split(node, value_info, node_name_to_out_var_dict,
innermost_let_ast_node, out_var_count, mtdAST):
node = OnnxNode(node)
inputsRef = node.inputs
output_count = len(node.outputs)
for cur_count in range(output_count):
seedot_output_ast = AST.UninterpFuncCall(
list(value_info[node.outputs[cur_count]][1]), 'Split', [
AST.ID(node_name_to_out_var_dict[inputsRef[0]]),
AST.Int(node.attrs['axis'], 32, False),
AST.Int(cur_count, 32, False),
AST.Int(output_count, 32, False)
])
output_name = get_new_var_name(out_var_count)
innermost_let_ast_node = update_program_with_new_node(
innermost_let_ast_node, seedot_output_ast, output_name, mtdAST)
out_var_count += 1
node_name_to_out_var_dict[node.outputs[cur_count]] = output_name
return (innermost_let_ast_node, out_var_count)
def FusedBatchNorm(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
# NOTE : Since the weights to this layer will be scaled appropriately, this op will become identity.
inputsRef = curNode.getInputsRef()
# TODO : This below thing is the right way of implementing the operator
# For now using uninterpreted function call.
# tempAst = AST.BOp(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
# TFNodesAST.getOperatorsIdx('*'),
# AST.ID(dictNodeNameToOutVarStr[inputsRef[1]])
# )
# return (None, AST.BOp(tempAst,
# TFNodesAST.getOperatorsIdx('+'),
# AST.ID(dictNodeNameToOutVarStr[inputsRef[2]])
# ))
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.TempFusedBatchNorm.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[2]]),
]))
def Pad(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
# Mode refers to 'CONSTANT', 'REFLECT' or 'SYMMETRIC'
mode = 0
if ("\"mode\"" in curNode.getAttrMapRef()):
mode = curNode.getAttrMapRef()["\"mode\""].getI()
constant_values = 0
if ("\"constant_values\"" in curNode.getAttrMapRef()):
constant_values = curNode.getAttrMapRef(
)["\"constant_values\""].getI()
# For now to make life easy - deal with SYMMETRIC AND REFLECT when time comes
assert (mode == 0 and constant_values == 0)
inputsRef = curNode.getInputsRef()
inputTensorShapeLi = extraNodeInfoDict[inputsRef[0]][0]
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Pad.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]])
],
outputDiffInpDims=1))