cavis/.old/nd4j/samediff-import/samediff-import-tensorflow/tensorflow-processes.pbtxt

15885 lines
342 KiB
Plaintext
Raw Normal View History

2021-02-01 06:31:20 +01:00
mappings {
frameworkName: "tensorflow"
opName: "unique"
inputFrameworkOpName: "UniqueV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "UniqueV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "conv2d"
inputFrameworkOpName: "Conv2D"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "filter"
outputTensorName: "input"
outputTensorName: "weights"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "weights"
value: "filter"
}
ruleType: "tensor"
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "wFormat"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "wFormat"
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 9
stringValue: "NCHW"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kH"
int64Value: -1
argType: INT64
}
}
inputFrameworkOpName: "Conv2D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kW"
int64Value: -1
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "Conv2D"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_poisson"
inputFrameworkOpName: "RandomPoisson"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
inputTensorName: "rate"
outputTensorName: "shape"
outputTensorName: "lambda"
inputToOutput {
key: "shape"
value: "shape"
}
inputToOutput {
key: "lambda"
value: "rate"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomPoisson"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
2021-02-01 09:47:29 +01:00
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "seed"
value: "seed"
}
2021-02-01 09:47:29 +01:00
inputToOutput {
key: "dtype"
value: "dtype"
}
2021-02-01 06:31:20 +01:00
ruleType: "attribute"
inputFrameworkOpName: "RandomPoisson"
}
}
mappings {
frameworkName: "tensorflow"
opName: "maxpool2d"
inputFrameworkOpName: "MaxPool"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "extraParam0"
int64Value: 1
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 10
stringValue: "NCHW"
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "kH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
inputFrameworkOpName: "MaxPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "kW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
inputFrameworkOpName: "MaxPool"
}
}
mappings {
frameworkName: "tensorflow"
opName: "size"
inputFrameworkOpName: "Size"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Size"
}
2021-02-07 11:27:41 +01:00
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "out_type"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "out_type"
}
ruleType: "attribute"
inputFrameworkOpName: "Size"
}
2021-02-01 06:31:20 +01:00
}
mappings {
frameworkName: "tensorflow"
opName: "squaredsubtract"
inputFrameworkOpName: "SquaredDifference"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "SquaredDifference"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "SquaredDifference"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "SquaredDifference"
}
}
mappings {
frameworkName: "tensorflow"
opName: "randomuniform"
inputFrameworkOpName: "StatelessRandomUniform"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
outputTensorName: "shape"
inputToOutput {
key: "shape"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "StatelessRandomUniform"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "max"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "max"
doubleValue: 1.0
argType: DOUBLE
argIndex: 1
}
}
2021-02-15 08:16:40 +01:00
inputFrameworkOpName: "StatelessRandomUniform"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "min"
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
transformerArgs {
key: "value"
transformerArgs {
name: "min"
argType: DOUBLE
}
}
2021-02-15 08:16:40 +01:00
inputFrameworkOpName: "StatelessRandomUniform"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "seed"
inputToOutput {
key: "seed"
value: "seed"
2021-02-01 06:31:20 +01:00
}
2021-02-15 08:16:40 +01:00
ruleType: "attribute"
inputFrameworkOpName: "StatelessRandomUniform"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "dtype"
inputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-15 08:16:40 +01:00
ruleType: "attribute"
inputFrameworkOpName: "StatelessRandomUniform"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "dtype"
}
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "StatelessRandomUniform"
}
}
mappings {
frameworkName: "tensorflow"
opName: "shift_bits"
inputFrameworkOpName: "LeftShift"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "LeftShift"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "LeftShift"
}
}
mappings {
frameworkName: "tensorflow"
opName: "isinf"
inputFrameworkOpName: "IsInf"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "IsInf"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "IsInf"
}
}
mappings {
frameworkName: "tensorflow"
opName: "digamma"
inputFrameworkOpName: "Digamma"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Digamma"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_shuffle"
inputFrameworkOpName: "RandomShuffle"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "value"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "value"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomShuffle"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seeds"
inputToOutput {
key: "seeds"
value: "seed"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomShuffle"
}
}
mappings {
frameworkName: "tensorflow"
opName: "adjust_hue"
inputFrameworkOpName: "AdjustHue"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "delta"
outputTensorName: "input"
outputTensorName: "delta"
inputToOutput {
key: "input"
value: "images"
}
inputToOutput {
key: "delta"
value: "delta"
}
ruleType: "tensor"
inputFrameworkOpName: "AdjustHue"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dimC"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dimC"
int64Value: -1
argType: INT64
}
}
inputFrameworkOpName: "AdjustHue"
}
}
mappings {
frameworkName: "tensorflow"
opName: "Assert"
inputFrameworkOpName: "Assert"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "condition"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "condition"
}
ruleType: "tensor"
inputFrameworkOpName: "Assert"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_determinant"
inputFrameworkOpName: "MatrixDeterminant"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixDeterminant"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "MatrixDeterminant"
}
}
mappings {
frameworkName: "tensorflow"
opName: "adjust_saturation"
inputFrameworkOpName: "AdjustSaturation"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "scale"
outputTensorName: "input"
outputTensorName: "factor"
inputToOutput {
key: "input"
value: "images"
}
inputToOutput {
key: "factor"
value: "scale"
}
ruleType: "tensor"
inputFrameworkOpName: "AdjustSaturation"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dimC"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dimC"
int64Value: -1
argType: INT64
}
}
inputFrameworkOpName: "AdjustSaturation"
}
}
mappings {
frameworkName: "tensorflow"
opName: "ones_as"
inputFrameworkOpName: "OnesLike"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "OnesLike"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
outputIntName: "dataType"
inputDataTypeName: "T"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "OnesLike"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_min"
inputFrameworkOpName: "TensorScatterMin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "tensor"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "TensorScatterMin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "squeeze"
inputFrameworkOpName: "Squeeze"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Squeeze"
}
rule {
ruleName: "listnumbertolistnumber"
functionName: "listnumbertolistnumber"
outputIntName: "_a"
inputToOutput {
key: "_a"
value: "squeeze_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Squeeze"
}
}
mappings {
frameworkName: "tensorflow"
opName: "stack"
inputFrameworkOpName: "Pack"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "multiinputindex"
functionName: "multiinputindex"
2021-02-01 06:31:20 +01:00
inputTensorName: "values"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "values"
}
ruleType: "tensor"
inputFrameworkOpName: "Pack"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "axis"
outputIntName: "dimensions"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dimensions"
value: "axis"
}
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Pack"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unsorted_segment_prod"
inputFrameworkOpName: "UnsortedSegmentProd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
2021-02-15 08:16:40 +01:00
inputTensorName: "num_segments"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
outputTensorName: "idxSegments"
2021-02-15 08:16:40 +01:00
outputTensorName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "UnsortedSegmentProd"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
ruleType: "attribute"
inputFrameworkOpName: "UnsortedSegmentProd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "subtract"
inputFrameworkOpName: "Sub"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Sub"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sub"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sub"
}
}
mappings {
frameworkName: "tensorflow"
opName: "not_equals"
inputFrameworkOpName: "NotEqual"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "NotEqual"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "NotEqual"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "NotEqual"
}
}
mappings {
frameworkName: "tensorflow"
opName: "expm1"
inputFrameworkOpName: "Expm1"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Expm1"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Expm1"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Expm1"
}
}
mappings {
frameworkName: "tensorflow"
opName: "relu6"
inputFrameworkOpName: "Relu6"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Relu6"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Relu6"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "cutoff"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
transformerArgs {
name: "cutoff"
argType: DOUBLE
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
transformerArgs {
name: "cutoff"
argType: DOUBLE
}
}
inputFrameworkOpName: "Relu6"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reduce_sum"
inputFrameworkOpName: "Sum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Sum"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Sum"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Sum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "dynamic_stitch"
inputFrameworkOpName: "DynamicStitch"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "DynamicStitch"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "N"
outputIntName: "numPartitions"
inputToOutput {
key: "numPartitions"
value: "N"
}
ruleType: "attribute"
inputFrameworkOpName: "DynamicStitch"
}
}
mappings {
frameworkName: "tensorflow"
opName: "argmax"
inputFrameworkOpName: "ArgMax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "dimension"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "dimension"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "ArgMax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "keepDims"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "keepDims"
argType: BOOL
}
}
inputFrameworkOpName: "ArgMax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "expand_dims"
inputFrameworkOpName: "ExpandDims"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "ExpandDims"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
inputToOutput {
key: "dimensions"
value: "dim"
}
ruleType: "attribute"
inputFrameworkOpName: "ExpandDims"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reduce_min"
inputFrameworkOpName: "Min"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Min"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Min"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Min"
}
}
mappings {
frameworkName: "tensorflow"
opName: "space_to_batch"
inputFrameworkOpName: "SpaceToBatch"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "paddings"
outputTensorName: "input"
outputTensorName: "padding"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "padding"
value: "paddings"
}
ruleType: "tensor"
inputFrameworkOpName: "SpaceToBatch"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "block_size"
outputIntName: "blockSize"
inputToOutput {
key: "blockSize"
value: "block_size"
}
ruleType: "attribute"
inputFrameworkOpName: "SpaceToBatch"
}
}
mappings {
frameworkName: "tensorflow"
opName: "bitwise_xor"
inputFrameworkOpName: "BitwiseXor"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "BitwiseXor"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BitwiseXor"
}
}
mappings {
frameworkName: "tensorflow"
opName: "concat"
inputFrameworkOpName: "ParallelConcat"
rule {
ruleName: "multiinputindex"
functionName: "multiinputindex"
inputTensorName: "values"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "values"
}
ruleType: "tensor"
inputFrameworkOpName: "ParallelConcat"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "isDynamicAxis"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isDynamicAxis"
argType: BOOL
}
}
inputFrameworkOpName: "ParallelConcat"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "ParallelConcat"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "concatDimension"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "concatDimension"
argType: INT64
}
}
inputFrameworkOpName: "ParallelConcat"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_list"
inputFrameworkOpName: "TensorArrayScatterV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayScatterV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayScatterV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_list"
inputFrameworkOpName: "TensorArrayScatterV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayScatterV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayScatterV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "Pow"
inputFrameworkOpName: "Pow"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Pow"
}
}
mappings {
frameworkName: "tensorflow"
opName: "split"
inputFrameworkOpName: "Split"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "split_dim"
inputTensorName: "value"
outputTensorName: "a"
outputTensorName: "b"
inputToOutput {
key: "a"
value: "split_dim"
}
inputToOutput {
key: "b"
value: "value"
}
ruleType: "tensor"
inputFrameworkOpName: "Split"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_split"
outputIntName: "numSplit"
inputToOutput {
key: "numSplit"
value: "num_split"
}
ruleType: "attribute"
inputFrameworkOpName: "Split"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
inputToOutput {
key: "dimensions"
value: "split_dim"
}
ruleType: "attribute"
inputFrameworkOpName: "Split"
}
}
mappings {
frameworkName: "tensorflow"
opName: "Where"
inputFrameworkOpName: "Where"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "condition"
inputToOutput {
key: "condition"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Where"
}
}
mappings {
frameworkName: "tensorflow"
opName: "svd"
inputFrameworkOpName: "Svd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Svd"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
outputIntName: "fullUV"
2021-02-01 06:31:20 +01:00
inputBooleanName: "compute_uv"
inputBooleanName: "full_matrices"
outputBooleanName: "computeUv"
inputToOutput {
key: "computeUv"
value: "compute_uv"
}
inputToOutput {
key: "fullUV"
value: "full_matrices"
}
ruleType: "attribute"
inputFrameworkOpName: "Svd"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "calcUV"
outputIntName: "fullUV"
2021-02-01 06:31:20 +01:00
inputBooleanName: "compute_uv"
inputBooleanName: "full_matrices"
inputToOutput {
key: "calcUV"
value: "compute_uv"
}
inputToOutput {
key: "fullUV"
value: "full_matrices"
}
ruleType: "attribute"
inputFrameworkOpName: "Svd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "switchNum"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "switchNum"
int64Value: 16
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "Svd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "acosh"
inputFrameworkOpName: "Acosh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Acosh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Acosh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Acosh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "placeholder"
inputFrameworkOpName: "Placeholder"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "Placeholder"
}
}
mappings {
frameworkName: "tensorflow"
opName: "polygamma"
inputFrameworkOpName: "Polygamma"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "x"
outputTensorName: "n"
outputTensorName: "input"
inputToOutput {
key: "n"
value: "a"
}
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Polygamma"
}
}
2021-02-07 11:27:41 +01:00
mappings {
frameworkName: "tensorflow"
opName: "matrix_band_part"
inputFrameworkOpName: "MatrixBandPart"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "num_lower"
inputTensorName: "num_upper"
outputTensorName: "input"
outputTensorName: "minLowerT"
outputTensorName: "maxUpperT"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "minLowerT"
value: "num_lower"
}
inputToOutput {
key: "maxUpperT"
value: "num_upper"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixBandPart"
}
}
2021-02-01 06:31:20 +01:00
mappings {
frameworkName: "tensorflow"
opName: "equals"
inputFrameworkOpName: "ApproximateEqual"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "ApproximateEqual"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "ApproximateEqual"
}
}
mappings {
frameworkName: "tensorflow"
opName: "stop_gradient"
inputFrameworkOpName: "StopGradient"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "StopGradient"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "StopGradient"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_add"
inputFrameworkOpName: "TensorScatterAdd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "tensor"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "TensorScatterAdd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "lock"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "lock"
argType: BOOL
}
}
inputFrameworkOpName: "TensorScatterAdd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
argIndex: 1
}
}
inputFrameworkOpName: "TensorScatterAdd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "avgpool2d"
inputFrameworkOpName: "AvgPool"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "value"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "value"
}
ruleType: "tensor"
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 10
stringValue: "NCHW"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "kH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "kW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
inputFrameworkOpName: "AvgPool"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
inputIntName: "pW"
inputIntName: "dW"
inputIntName: "dH"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "AvgPool"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unique_with_counts"
inputFrameworkOpName: "UniqueWithCountsV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "UniqueWithCountsV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "depthwise_conv2d"
inputFrameworkOpName: "DepthwiseConv2dNative"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "filter"
outputTensorName: "input"
outputTensorName: "weights"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "weights"
value: "filter"
}
ruleType: "tensor"
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 9
stringValue: "NCHW"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "ndarraysizeat"
functionName: "ndarraysizeat"
outputIntName: "kH"
inputFloatName: "filter"
inputToOutput {
key: "kH"
value: "filter"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "filter"
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "ndarraysizeat"
functionName: "ndarraysizeat"
outputIntName: "kW"
inputFloatName: "filter"
inputToOutput {
key: "kW"
value: "filter"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "filter"
int64Value: 1
argIndex: 1
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
inputIntName: "pW"
inputIntName: "wFormat"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "wFormat"
argType: INT64
argIndex: 10
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "wFormat"
argType: INT64
argIndex: 10
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
transformerArgs {
name: "wFormat"
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "DepthwiseConv2dNative"
}
}
mappings {
frameworkName: "tensorflow"
opName: "log_matrix_determinant"
inputFrameworkOpName: "LogMatrixDeterminant"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "LogMatrixDeterminant"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "LogMatrixDeterminant"
}
}
mappings {
frameworkName: "tensorflow"
opName: "realdiv"
inputFrameworkOpName: "RealDiv"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "RealDiv"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "RealDiv"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "RealDiv"
}
}
mappings {
frameworkName: "tensorflow"
opName: "abs"
inputFrameworkOpName: "Abs"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Abs"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Abs"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Abs"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "VariableV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "VariableV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "VariableV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "VariableV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_determinant"
inputFrameworkOpName: "BatchMatrixDeterminant"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchMatrixDeterminant"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "MatrixDeterminant"
}
}
mappings {
frameworkName: "tensorflow"
opName: "maxpool3dnew"
inputFrameworkOpName: "MaxPool3D"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 13
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pD"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pD"
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 8
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dD"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dD"
int64Value: 1
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 11
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "isNCDHW"
inputToOutput {
key: "isNCDHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCDHW"
transformerArgs {
name: "data_format"
argType: STRING
argIndex: 14
stringValue: "NDHWC"
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 12
stringValue: "SAME"
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kH"
inputToOutput {
key: "kH"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kW"
inputToOutput {
key: "kW"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kD"
inputToOutput {
key: "kD"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sH"
inputToOutput {
key: "sH"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sW"
inputToOutput {
key: "sW"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "MaxPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sD"
inputToOutput {
key: "sD"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 3
}
}
inputFrameworkOpName: "MaxPool3D"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tensorarraywritev3"
inputFrameworkOpName: "TensorArrayWriteV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayWriteV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "softmax_cross_entropy_loss_with_logits"
inputFrameworkOpName: "SoftmaxCrossEntropyWithLogits"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "labels"
inputTensorName: "features"
outputTensorName: "labels"
outputTensorName: "logits"
inputToOutput {
key: "labels"
value: "labels"
}
inputToOutput {
key: "logits"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "SoftmaxCrossEntropyWithLogits"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "SoftmaxCrossEntropyWithLogits"
}
}
mappings {
frameworkName: "tensorflow"
opName: "segment_max"
inputFrameworkOpName: "SegmentMax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
outputTensorName: "input"
outputTensorName: "idxSegments"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
ruleType: "tensor"
inputFrameworkOpName: "SegmentMax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "conv3dnew"
inputFrameworkOpName: "Conv3D"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "filter"
outputTensorName: "input"
outputTensorName: "weights"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "weights"
value: "filter"
}
ruleType: "tensor"
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "isNCDHW"
inputToOutput {
key: "isNCDHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCDHW"
transformerArgs {
name: "data_format"
argType: STRING
argIndex: 13
stringValue: "NDHWC"
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "paddingMode"
inputToOutput {
key: "paddingMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "paddingMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 12
stringValue: "SAME"
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "ndarraysizeat"
functionName: "ndarraysizeat"
outputIntName: "kD"
inputFloatName: "filter"
inputToOutput {
key: "kD"
value: "filter"
}
ruleType: "attribute"
transformerArgs {
key: "kD"
transformerArgs {
name: "filter"
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "ndarraysizeat"
functionName: "ndarraysizeat"
outputIntName: "kH"
inputFloatName: "filter"
inputToOutput {
key: "kH"
value: "filter"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "filter"
int64Value: 1
argIndex: 1
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "ndarraysizeat"
functionName: "ndarraysizeat"
outputIntName: "kW"
inputFloatName: "filter"
inputToOutput {
key: "kW"
value: "filter"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "filter"
int64Value: 2
argIndex: 2
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sD"
inputToOutput {
key: "sD"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 3
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sH"
inputToOutput {
key: "sH"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sW"
inputToOutput {
key: "sW"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 8
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "dH"
inputToOutput {
key: "dH"
value: "dilations"
}
ruleType: "attribute"
transformerArgs {
key: "dH"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 11
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "dW"
inputToOutput {
key: "dW"
value: "dilations"
}
ruleType: "attribute"
transformerArgs {
key: "dW"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "Conv3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "dD"
inputToOutput {
key: "dD"
value: "dilations"
}
ruleType: "attribute"
transformerArgs {
key: "dD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "Conv3D"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_sub"
inputFrameworkOpName: "ScatterSub"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "ref"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "ref"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterSub"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "lock"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "lock"
argType: BOOL
}
}
inputFrameworkOpName: "ScatterSub"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
argIndex: 1
}
}
inputFrameworkOpName: "ScatterSub"
}
}
mappings {
frameworkName: "tensorflow"
opName: "loop_cond"
inputFrameworkOpName: "LoopCond"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
2021-02-10 14:40:04 +01:00
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "LoopCond"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reverse"
inputFrameworkOpName: "ReverseV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "tensor"
}
ruleType: "tensor"
inputFrameworkOpName: "ReverseV2"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "axis"
}
ruleType: "attribute"
inputFrameworkOpName: "ReverseV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "rank"
inputFrameworkOpName: "Rank"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Rank"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Rank"
}
}
mappings {
frameworkName: "tensorflow"
opName: "erfc"
inputFrameworkOpName: "Erfc"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Erfc"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Erfc"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Erfc"
}
}
mappings {
frameworkName: "tensorflow"
opName: "divide"
inputFrameworkOpName: "Div"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Div"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Div"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Div"
}
}
mappings {
frameworkName: "tensorflow"
opName: "pad"
inputFrameworkOpName: "Pad"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "paddings"
outputTensorName: "input"
outputTensorName: "paddings"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "paddings"
value: "paddings"
}
ruleType: "tensor"
inputFrameworkOpName: "Pad"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "mode"
inputFloatName: "padValue"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "mode"
argType: INT64
}
transformerArgs {
name: "padValue"
argType: DOUBLE
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "mode"
argType: INT64
}
transformerArgs {
name: "padValue"
argType: DOUBLE
}
}
inputFrameworkOpName: "Pad"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sparse_softmax_cross_entropy_loss_with_logits"
inputFrameworkOpName: "SparseSoftmaxCrossEntropyWithLogits"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "labels"
inputTensorName: "features"
outputTensorName: "labels"
outputTensorName: "logits"
inputToOutput {
key: "labels"
value: "labels"
}
inputToOutput {
key: "logits"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "SparseSoftmaxCrossEntropyWithLogits"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "SparseSoftmaxCrossEntropyWithLogits"
}
indexOverrides {
key: 1
value: 0
}
indexOverrides {
key: 0
value: 1
}
}
mappings {
frameworkName: "tensorflow"
opName: "merge"
inputFrameworkOpName: "Merge"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "multiinputindex"
functionName: "multiinputindex"
2021-02-01 06:31:20 +01:00
inputTensorName: "inputs"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "inputs"
}
ruleType: "tensor"
inputFrameworkOpName: "Merge"
}
}
mappings {
frameworkName: "tensorflow"
opName: "resize_nearest_neighbor"
inputFrameworkOpName: "ResizeNearestNeighbor"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "size"
outputTensorName: "image"
outputTensorName: "newImageSize"
inputToOutput {
key: "image"
value: "images"
}
inputToOutput {
key: "newImageSize"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "ResizeNearestNeighbor"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "align_corners"
inputBooleanName: "half_pixel_centers"
outputBooleanName: "alignCorners"
outputBooleanName: "halfPixelCenter"
inputToOutput {
key: "alignCorners"
value: "align_corners"
}
inputToOutput {
key: "halfPixelCenter"
value: "half_pixel_centers"
}
ruleType: "attribute"
inputFrameworkOpName: "ResizeNearestNeighbor"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_min"
inputFrameworkOpName: "ScatterMin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "ref"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterMin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "check_numerics"
inputFrameworkOpName: "CheckNumericsV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "tensor"
}
ruleType: "tensor"
inputFrameworkOpName: "CheckNumericsV2"
}
rule {
ruleName: "convertinputstringtondarray"
functionName: "convertinputstringtondarray"
inputStringAttrName: "message"
inputToOutput {
key: "message"
value: "message"
}
ruleType: "attribute"
inputFrameworkOpName: "CheckNumericsV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "select"
inputFrameworkOpName: "Select"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "condition"
inputTensorName: "t"
inputTensorName: "e"
outputTensorName: "cond"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "cond"
value: "condition"
}
inputToOutput {
key: "input"
value: "t"
}
inputToOutput {
key: "y"
value: "e"
}
ruleType: "tensor"
inputFrameworkOpName: "Select"
}
}
mappings {
frameworkName: "tensorflow"
opName: "assign"
inputFrameworkOpName: "Assign"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "value"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "ref"
}
inputToOutput {
key: "y"
value: "value"
}
ruleType: "tensor"
inputFrameworkOpName: "Assign"
}
}
mappings {
frameworkName: "tensorflow"
opName: "size_list"
inputFrameworkOpName: "TensorArraySize"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySize"
}
}
mappings {
frameworkName: "tensorflow"
opName: "rint"
inputFrameworkOpName: "Rint"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Rint"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Rint"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Rint"
}
}
mappings {
frameworkName: "tensorflow"
opName: "dilation2d"
inputFrameworkOpName: "Dilation2D"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "filter"
outputTensorName: "input"
outputTensorName: "weights"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "weights"
value: "filter"
}
ruleType: "tensor"
inputFrameworkOpName: "Dilation2D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
stringValue: "SAME"
}
}
inputFrameworkOpName: "Dilation2D"
}
rule {
ruleName: "listnumbertolistnumber"
functionName: "listnumbertolistnumber"
outputIntName: "rates"
inputToOutput {
key: "rates"
value: "rates"
}
ruleType: "attribute"
inputFrameworkOpName: "Dilation2D"
}
rule {
ruleName: "listnumbertolistnumber"
functionName: "listnumbertolistnumber"
outputIntName: "strides"
inputToOutput {
key: "strides"
value: "strides"
}
ruleType: "attribute"
inputFrameworkOpName: "Dilation2D"
}
}
mappings {
frameworkName: "tensorflow"
opName: "avgpool3dnew"
inputFrameworkOpName: "AvgPool3D"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 13
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pD"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pD"
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 8
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dD"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dD"
int64Value: 1
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 11
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "isNCDHW"
inputToOutput {
key: "isNCDHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCDHW"
transformerArgs {
name: "data_format"
argType: STRING
argIndex: 14
stringValue: "NDHWC"
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 12
stringValue: "SAME"
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kH"
inputToOutput {
key: "kH"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kW"
inputToOutput {
key: "kW"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kD"
inputToOutput {
key: "kD"
value: "ksize"
}
ruleType: "attribute"
transformerArgs {
key: "kD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sH"
inputToOutput {
key: "sH"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "index"
int64Value: 3
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sW"
inputToOutput {
key: "sW"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "AvgPool3D"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "sD"
inputToOutput {
key: "sD"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "sD"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 3
}
}
inputFrameworkOpName: "AvgPool3D"
}
}
mappings {
frameworkName: "tensorflow"
opName: "add"
inputFrameworkOpName: "Add"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Add"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Add"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Add"
}
}
mappings {
frameworkName: "tensorflow"
opName: "isfinite"
inputFrameworkOpName: "IsFinite"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "IsFinite"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "IsFinite"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_inverse"
inputFrameworkOpName: "BatchMatrixInverse"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchMatrixInverse"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "BatchMatrixInverse"
}
}
mappings {
frameworkName: "tensorflow"
opName: "rshift_bits"
inputFrameworkOpName: "RightShift"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "RightShift"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "RightShift"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "RightShift"
}
}
mappings {
frameworkName: "tensorflow"
opName: "elu"
inputFrameworkOpName: "Elu"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Elu"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "alpha"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "alpha"
doubleValue: 1.0
argType: DOUBLE
}
}
inputFrameworkOpName: "Elu"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_diag"
inputFrameworkOpName: "MatrixDiag"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "diagonal"
outputTensorName: "diagonal"
inputToOutput {
key: "diagonal"
value: "diagonal"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixDiag"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "MatrixDiag"
}
}
mappings {
frameworkName: "tensorflow"
opName: "draw_bounding_boxes"
inputFrameworkOpName: "DrawBoundingBoxesV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "boxes"
inputTensorName: "colors"
outputTensorName: "images"
outputTensorName: "boxes"
outputTensorName: "colors"
inputToOutput {
key: "images"
value: "images"
}
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "colors"
value: "colors"
}
ruleType: "tensor"
inputFrameworkOpName: "DrawBoundingBoxesV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "igamma"
inputFrameworkOpName: "Igamma"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "x"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "a"
}
inputToOutput {
key: "y"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Igamma"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matmul"
inputFrameworkOpName: "MatMul"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "b"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "a"
}
inputToOutput {
key: "y"
value: "b"
}
ruleType: "tensor"
inputFrameworkOpName: "MatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "alpha"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "alpha"
doubleValue: 1.0
argType: DOUBLE
}
}
inputFrameworkOpName: "MatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "beta"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "beta"
argType: DOUBLE
argIndex: 1
}
}
inputFrameworkOpName: "MatMul"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "transX"
outputIntName: "transY"
inputBooleanName: "transpose_a"
inputBooleanName: "transpose_b"
inputToOutput {
key: "transX"
value: "transpose_a"
}
inputToOutput {
key: "transY"
value: "transpose_b"
}
ruleType: "attribute"
inputFrameworkOpName: "MatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "transZ"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "transZ"
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "MatMul"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sinh"
inputFrameworkOpName: "Sinh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Sinh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sinh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sinh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "softplus"
inputFrameworkOpName: "Softplus"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Softplus"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Softplus"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "Const"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "Const"
}
rule {
ruleName: "ndarrayinputtondarray"
functionName: "ndarrayinputtondarray"
inputTensorName: "value"
inputToOutput {
key: "input"
value: "value"
}
ruleType: "attribute"
inputFrameworkOpName: "Const"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Const"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "Const"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cumsum"
inputFrameworkOpName: "Cumsum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "axis"
outputTensorName: "input"
outputTensorName: "dimensions"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "dimensions"
value: "axis"
}
ruleType: "tensor"
inputFrameworkOpName: "Cumsum"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "exclusive"
outputIntName: "reverse"
2021-02-01 06:31:20 +01:00
inputBooleanName: "exclusive"
inputBooleanName: "reverse"
inputToOutput {
key: "exclusive"
value: "exclusive"
}
inputToOutput {
key: "reverse"
value: "reverse"
}
ruleType: "attribute"
inputFrameworkOpName: "Cumsum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "zeroslike"
inputFrameworkOpName: "ZerosLike"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "ZerosLike"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "ZerosLike"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
outputIntName: "dataType"
inputDataTypeName: "T"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "ZerosLike"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather"
inputFrameworkOpName: "Gather"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "params"
inputTensorName: "indices"
outputTensorName: "input"
outputTensorName: "indices"
inputToOutput {
key: "input"
value: "params"
}
inputToOutput {
key: "indices"
value: "indices"
}
ruleType: "tensor"
inputFrameworkOpName: "Gather"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
ruleType: "attribute"
inputFrameworkOpName: "Gather"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Gather"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dimensions"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dimensions"
argType: INT64
}
}
inputFrameworkOpName: "Gather"
}
}
mappings {
frameworkName: "tensorflow"
opName: "placeholder"
inputFrameworkOpName: "PlaceholderWithDefault"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "PlaceholderWithDefault"
}
}
2021-02-01 06:31:20 +01:00
mappings {
frameworkName: "tensorflow"
opName: "stack_list"
inputFrameworkOpName: "TensorArrayConcat"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayConcat"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-10 14:40:04 +01:00
key: "dtype"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-10 14:40:04 +01:00
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "TensorArrayConcat"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_nd_add"
inputFrameworkOpName: "ScatterNdAdd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "ref"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "ref"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterNdAdd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "bitcast"
inputFrameworkOpName: "Bitcast"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Bitcast"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "newType"
inputDataTypeName: "type"
inputToOutput {
key: "newType"
value: "type"
}
ruleType: "attribute"
inputFrameworkOpName: "Bitcast"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "type"
2021-02-07 11:27:41 +01:00
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-07 11:27:41 +01:00
key: "dtype"
2021-02-01 06:31:20 +01:00
value: "type"
}
ruleType: "attribute"
inputFrameworkOpName: "Bitcast"
}
}
mappings {
frameworkName: "tensorflow"
opName: "bitwise_or"
inputFrameworkOpName: "BitwiseOr"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "BitwiseOr"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BitwiseOr"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gruCell"
inputFrameworkOpName: "GRUBlockCell"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "h_prev"
inputTensorName: "w_ru"
inputTensorName: "w_c"
inputTensorName: "b_ru"
inputTensorName: "b_c"
outputTensorName: "input"
outputTensorName: "hLast"
outputTensorName: "Wru"
outputTensorName: "Wc"
outputTensorName: "bru"
outputTensorName: "bc"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "hLast"
value: "h_prev"
}
inputToOutput {
key: "Wru"
value: "w_ru"
}
inputToOutput {
key: "Wc"
value: "w_c"
}
inputToOutput {
key: "bru"
value: "b_ru"
}
inputToOutput {
key: "bc"
value: "b_c"
}
ruleType: "tensor"
inputFrameworkOpName: "GRUBlockCell"
}
}
mappings {
frameworkName: "tensorflow"
opName: "randomuniform"
inputFrameworkOpName: "RandomUniform"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
outputTensorName: "shape"
inputToOutput {
key: "shape"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomUniform"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "max"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "max"
doubleValue: 1.0
argType: DOUBLE
argIndex: 1
}
}
2021-02-15 08:16:40 +01:00
inputFrameworkOpName: "RandomUniform"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "min"
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
transformerArgs {
key: "value"
transformerArgs {
name: "min"
argType: DOUBLE
}
}
2021-02-15 08:16:40 +01:00
inputFrameworkOpName: "RandomUniform"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "dtype"
inputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-15 08:16:40 +01:00
ruleType: "attribute"
inputFrameworkOpName: "RandomUniform"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
inputDataTypeName: "dtype"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "seed"
value: "seed"
}
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "RandomUniform"
}
}
mappings {
frameworkName: "tensorflow"
opName: "bitwise_and"
inputFrameworkOpName: "BitwiseAnd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "BitwiseAnd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BitwiseAnd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "enter"
inputFrameworkOpName: "Enter"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "data"
}
ruleType: "tensor"
inputFrameworkOpName: "Enter"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputStringAttrName: "frame_name"
outputStringAttrName: "frameName"
inputBooleanName: "is_constant"
outputBooleanName: "isConstant"
inputToOutput {
key: "isConstant"
value: "is_constant"
}
inputToOutput {
key: "frameName"
value: "frame_name"
}
ruleType: "attribute"
inputFrameworkOpName: "Enter"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sin"
inputFrameworkOpName: "Sin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Sin"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sin"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unique"
inputFrameworkOpName: "Unique"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Unique"
}
}
mappings {
frameworkName: "tensorflow"
opName: "roll"
inputFrameworkOpName: "Roll"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "axis"
2021-02-15 08:16:40 +01:00
inputTensorName: "shift"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-15 08:16:40 +01:00
outputTensorName: "shiftsI"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "axis"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "shiftsI"
value: "shift"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Roll"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "shift"
inputToOutput {
key: "shift"
value: "shift"
}
ruleType: "attribute"
inputFrameworkOpName: "Roll"
}
}
mappings {
frameworkName: "tensorflow"
opName: "in_top_k"
inputFrameworkOpName: "InTopK"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "targets"
inputTensorName: "predictions"
outputTensorName: "target"
outputTensorName: "predictions"
inputToOutput {
key: "target"
value: "targets"
}
inputToOutput {
key: "predictions"
value: "predictions"
}
ruleType: "tensor"
inputFrameworkOpName: "InTopK"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "k"
outputIntName: "k"
inputToOutput {
key: "k"
value: "k"
}
ruleType: "attribute"
inputFrameworkOpName: "InTopK"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "sorted"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "sorted"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "InTopK"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reverse_sequence"
inputFrameworkOpName: "ReverseSequence"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "seq_lengths"
outputTensorName: "input"
outputTensorName: "seqLengths"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "seqLengths"
value: "seq_lengths"
}
ruleType: "tensor"
inputFrameworkOpName: "ReverseSequence"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "batch_dim"
inputIntName: "seq_dim"
outputIntName: "batchDim"
outputIntName: "seqDim"
inputToOutput {
key: "batchDim"
value: "batch_dim"
}
inputToOutput {
key: "seqDim"
value: "seq_dim"
}
ruleType: "attribute"
inputFrameworkOpName: "ReverseSequence"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unsorted_segment_min"
inputFrameworkOpName: "UnsortedSegmentMin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
2021-02-15 08:16:40 +01:00
inputTensorName: "num_segments"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
outputTensorName: "idxSegments"
2021-02-15 08:16:40 +01:00
outputTensorName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "UnsortedSegmentMin"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
ruleType: "attribute"
inputFrameworkOpName: "UnsortedSegmentMin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "rsqrt"
inputFrameworkOpName: "Rsqrt"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Rsqrt"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Rsqrt"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Rsqrt"
}
}
mappings {
frameworkName: "tensorflow"
opName: "split_list"
inputFrameworkOpName: "TensorArraySplit"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySplit"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArraySplit"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_nd_update"
inputFrameworkOpName: "ScatterNdUpdate"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "ref"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "ref"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterNdUpdate"
}
}
mappings {
frameworkName: "tensorflow"
opName: "rgb_to_hsv"
inputFrameworkOpName: "RGBToHSV"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "images"
}
ruleType: "tensor"
inputFrameworkOpName: "RGBToHSV"
}
}
mappings {
frameworkName: "tensorflow"
opName: "create"
inputFrameworkOpName: "Empty"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "Empty"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
2021-02-10 14:40:04 +01:00
outputIntName: "outputType"
2021-02-01 06:31:20 +01:00
inputBooleanName: "init"
outputBooleanName: "init"
inputDataTypeName: "dtype"
inputToOutput {
key: "init"
value: "init"
}
inputToOutput {
key: "outputType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "Empty"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
2021-02-10 14:40:04 +01:00
outputIntName: "outputType"
2021-02-01 06:31:20 +01:00
inputDataTypeName: "dtype"
inputToOutput {
key: "outputType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "Empty"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "order"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "order"
int64Value: 99
argType: INT64
}
}
inputFrameworkOpName: "Empty"
}
}
mappings {
frameworkName: "tensorflow"
opName: "zeta"
inputFrameworkOpName: "Zeta"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "q"
outputTensorName: "input"
outputTensorName: "q"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "q"
value: "q"
}
ruleType: "tensor"
inputFrameworkOpName: "Zeta"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Zeta"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lin_space"
inputFrameworkOpName: "LinSpace"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "start"
inputTensorName: "stop"
inputTensorName: "num"
outputTensorName: "start"
outputTensorName: "finish"
outputTensorName: "numOfElements"
inputToOutput {
key: "start"
value: "start"
}
inputToOutput {
key: "finish"
value: "stop"
}
inputToOutput {
key: "numOfElements"
value: "num"
}
ruleType: "tensor"
inputFrameworkOpName: "LinSpace"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputDoubleName: "start"
2021-02-01 06:31:20 +01:00
outputDoubleName: "stop"
inputToOutput {
key: "start"
value: "start"
}
inputToOutput {
key: "stop"
value: "stop"
}
ruleType: "attribute"
inputFrameworkOpName: "LinSpace"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
2021-02-15 08:16:40 +01:00
outputIntName: "dataType"
2021-02-01 06:31:20 +01:00
inputDataTypeName: "T"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "LinSpace"
}
}
mappings {
frameworkName: "tensorflow"
opName: "boolean_and"
inputFrameworkOpName: "LogicalAnd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "LogicalAnd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_gamma"
inputFrameworkOpName: "RandomGamma"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
inputTensorName: "alpha"
outputTensorName: "shape"
outputTensorName: "alpha"
inputToOutput {
key: "shape"
value: "shape"
}
inputToOutput {
key: "alpha"
value: "alpha"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomGamma"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
inputToOutput {
key: "seed"
value: "seed"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomGamma"
}
}
mappings {
frameworkName: "tensorflow"
opName: "pad"
inputFrameworkOpName: "PadV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "paddings"
outputTensorName: "input"
outputTensorName: "paddings"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "paddings"
value: "paddings"
}
ruleType: "tensor"
inputFrameworkOpName: "PadV2"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputDoubleName: "padValue"
inputToOutput {
key: "padValue"
value: "constant_values"
}
ruleType: "attribute"
inputFrameworkOpName: "PadV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "mode"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "mode"
argType: INT64
}
}
inputFrameworkOpName: "PadV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unsorted_segment_sum"
inputFrameworkOpName: "UnsortedSegmentSum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
2021-02-15 08:16:40 +01:00
inputTensorName: "num_segments"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
outputTensorName: "idxSegments"
2021-02-15 08:16:40 +01:00
outputTensorName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "UnsortedSegmentSum"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
ruleType: "attribute"
inputFrameworkOpName: "UnsortedSegmentSum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "log1p"
inputFrameworkOpName: "Log1p"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Log1p"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Log1p"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Log1p"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_set_diag"
inputFrameworkOpName: "MatrixSetDiag"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "diagonal"
outputTensorName: "input"
outputTensorName: "diagonal"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "diagonal"
value: "diagonal"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixSetDiag"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BatchMatrixSetDiag"
}
}
mappings {
frameworkName: "tensorflow"
opName: "dynamic_partition"
inputFrameworkOpName: "DynamicPartition"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "partitions"
outputTensorName: "input"
outputTensorName: "indices"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "indices"
value: "partitions"
}
ruleType: "tensor"
inputFrameworkOpName: "DynamicPartition"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_partitions"
outputIntName: "numPartitions"
inputToOutput {
key: "numPartitions"
value: "num_partitions"
}
ruleType: "attribute"
inputFrameworkOpName: "DynamicPartition"
}
}
mappings {
frameworkName: "tensorflow"
opName: "mod"
inputFrameworkOpName: "Mod"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Mod"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Mod"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Mod"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_mul"
inputFrameworkOpName: "ScatterMul"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "ref"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "ref"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterMul"
}
}
mappings {
frameworkName: "tensorflow"
opName: "broadcast_to"
inputFrameworkOpName: "BroadcastTo"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "shape"
outputTensorName: "input"
outputTensorName: "shape"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "shape"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "BroadcastTo"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_poisson"
inputFrameworkOpName: "RandomPoissonV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
inputTensorName: "rate"
outputTensorName: "shape"
outputTensorName: "lambda"
inputToOutput {
key: "shape"
value: "shape"
}
inputToOutput {
key: "lambda"
value: "rate"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomPoissonV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
2021-02-01 09:47:29 +01:00
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "seed"
value: "seed"
}
2021-02-01 09:47:29 +01:00
inputToOutput {
key: "dtype"
value: "dtype"
}
2021-02-01 06:31:20 +01:00
ruleType: "attribute"
inputFrameworkOpName: "RandomPoissonV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "asin"
inputFrameworkOpName: "Asin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Asin"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Asin"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Asin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "space_to_depth"
inputFrameworkOpName: "SpaceToDepth"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "SpaceToDepth"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "block_size"
outputIntName: "block_size"
inputToOutput {
key: "block_size"
value: "block_size"
}
ruleType: "attribute"
inputFrameworkOpName: "SpaceToDepth"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "isNHWC"
inputToOutput {
key: "isNHWC"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNHWC"
transformerArgs {
name: "data_format"
argType: STRING
argIndex: 1
stringValue: "NHWC"
}
}
inputFrameworkOpName: "SpaceToDepth"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tile"
inputFrameworkOpName: "Tile"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "multiples"
outputTensorName: "input"
outputTensorName: "reps_vector"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "reps_vector"
value: "multiples"
}
ruleType: "tensor"
inputFrameworkOpName: "Tile"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dimensions"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dimensions"
argType: INT64
}
}
inputFrameworkOpName: "Tile"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "is_static_reps"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "is_static_reps"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "Tile"
}
}
mappings {
frameworkName: "tensorflow"
opName: "depth_to_space"
inputFrameworkOpName: "DepthToSpace"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "DepthToSpace"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "block_size"
outputIntName: "block_size"
inputToOutput {
key: "block_size"
value: "block_size"
}
ruleType: "attribute"
inputFrameworkOpName: "DepthToSpace"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "isNHWC"
inputToOutput {
key: "isNHWC"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNHWC"
transformerArgs {
name: "data_format"
argType: STRING
argIndex: 1
stringValue: "NHWC"
}
}
inputFrameworkOpName: "DepthToSpace"
}
}
mappings {
frameworkName: "tensorflow"
opName: "invert_permutation"
inputFrameworkOpName: "InvertPermutation"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "InvertPermutation"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "InvertPermutation"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "InvertPermutation"
}
}
mappings {
frameworkName: "tensorflow"
opName: "crop_and_resize"
inputFrameworkOpName: "CropAndResize"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "image"
inputTensorName: "boxes"
inputTensorName: "box_ind"
inputTensorName: "crop_size"
outputTensorName: "image"
outputTensorName: "boxes"
outputTensorName: "boxIndexes"
outputTensorName: "newImageSize"
inputToOutput {
key: "image"
value: "image"
}
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "boxIndexes"
value: "box_ind"
}
inputToOutput {
key: "newImageSize"
value: "crop_size"
}
ruleType: "tensor"
inputFrameworkOpName: "CropAndResize"
}
rule {
ruleName: "stringtoindex"
functionName: "stringtoindex"
inputStringAttrName: "method"
outputIntName: "method"
inputFloatName: "bilinear"
inputFloatName: "nearest"
inputToOutput {
key: "method"
value: "method"
}
ruleType: "attribute"
transformerArgs {
key: "method"
transformerArgs {
name: "bilinear"
stringValue: "bilinear"
}
transformerArgs {
name: "nearest"
stringValue: "nearest"
}
}
transformerArgs {
key: "method"
transformerArgs {
name: "bilinear"
stringValue: "bilinear"
}
transformerArgs {
name: "nearest"
stringValue: "nearest"
}
}
inputFrameworkOpName: "CropAndResize"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "extrapolation_value"
outputDoubleName: "extrapolationVal"
inputToOutput {
key: "extrapolationVal"
value: "extrapolation_value"
}
ruleType: "attribute"
inputFrameworkOpName: "CropAndResize"
}
}
mappings {
frameworkName: "tensorflow"
opName: "read_list"
inputFrameworkOpName: "TensorArrayRead"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayRead"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "importDataType"
inputToOutput {
key: "importDataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayRead"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_nd"
inputFrameworkOpName: "ScatterNd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "shape"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "shape"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "shape"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterNd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "lock"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "lock"
argType: BOOL
}
}
inputFrameworkOpName: "ScatterNd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
argIndex: 1
}
}
inputFrameworkOpName: "ScatterNd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "strided_slice"
inputFrameworkOpName: "StridedSlice"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "begin"
inputTensorName: "end"
inputTensorName: "strides"
outputTensorName: "input"
outputTensorName: "v_begin"
outputTensorName: "v_end"
outputTensorName: "v_stride"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "v_begin"
value: "begin"
}
inputToOutput {
key: "v_end"
value: "end"
}
inputToOutput {
key: "v_stride"
value: "strides"
}
ruleType: "tensor"
inputFrameworkOpName: "StridedSlice"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "begin_mask"
inputIntName: "end_mask"
inputIntName: "ellipsis_mask"
inputIntName: "new_axis_mask"
inputIntName: "shrink_axis_mask"
outputIntName: "begin_mask"
outputIntName: "end_mask"
outputIntName: "ellipsis_mask"
outputIntName: "new_axis_mask"
outputIntName: "shrink_axis_mask"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "begin_mask"
value: "begin_mask"
}
inputToOutput {
key: "end_mask"
value: "end_mask"
}
inputToOutput {
key: "ellipsis_mask"
value: "ellipsis_mask"
}
inputToOutput {
key: "new_axis_mask"
value: "new_axis_mask"
}
inputToOutput {
key: "shrink_axis_mask"
value: "shrink_axis_mask"
}
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "StridedSlice"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_list"
inputFrameworkOpName: "TensorArrayScatter"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayScatter"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayScatter"
}
}
mappings {
frameworkName: "tensorflow"
opName: "size_list"
inputFrameworkOpName: "TensorArraySizeV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySizeV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "size_list"
inputFrameworkOpName: "TensorArraySizeV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySizeV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "next_iteration"
inputFrameworkOpName: "NextIteration"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "data"
}
ruleType: "tensor"
inputFrameworkOpName: "NextIteration"
}
}
mappings {
frameworkName: "tensorflow"
opName: "solve"
inputFrameworkOpName: "MatrixSolve"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "matrix"
inputTensorName: "rhs"
outputTensorName: "a"
outputTensorName: "b"
inputToOutput {
key: "a"
value: "matrix"
}
inputToOutput {
key: "b"
value: "rhs"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixSolve"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "adjoint"
outputBooleanName: "useAdjoint"
inputToOutput {
key: "useAdjoint"
value: "adjoint"
}
ruleType: "attribute"
inputFrameworkOpName: "MatrixSolve"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fused_batch_norm"
inputFrameworkOpName: "FusedBatchNorm"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "scale"
inputTensorName: "offset"
inputTensorName: "mean"
inputTensorName: "variance"
outputTensorName: "input"
outputTensorName: "scale"
outputTensorName: "offset"
outputTensorName: "mean"
outputTensorName: "variance"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "scale"
value: "scale"
}
inputToOutput {
key: "offset"
value: "offset"
}
inputToOutput {
key: "mean"
value: "mean"
}
inputToOutput {
key: "variance"
value: "variance"
}
ruleType: "tensor"
inputFrameworkOpName: "FusedBatchNorm"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "epsilon"
outputDoubleName: "epsilon"
2021-02-08 23:44:23 +01:00
inputDataTypeName: "T"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "epsilon"
value: "epsilon"
}
2021-02-08 23:44:23 +01:00
inputToOutput {
key: "dtype"
value: "T"
}
2021-02-01 06:31:20 +01:00
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNorm"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "isTraining"
inputBooleanName: "is_training"
inputToOutput {
key: "isTraining"
value: "is_training"
}
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNorm"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "dataFormat"
inputToOutput {
key: "dataFormat"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dataFormat"
transformerArgs {
name: "data_format"
argType: STRING
stringValue: "NCHW"
}
}
inputFrameworkOpName: "FusedBatchNorm"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_max"
inputFrameworkOpName: "TensorScatterMax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "tensor"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "TensorScatterMax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "greater_equal"
inputFrameworkOpName: "GreaterEqual"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "GreaterEqual"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "GreaterEqual"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "GreaterEqual"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_nd_sub"
inputFrameworkOpName: "ScatterNdSub"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "ref"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "ref"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterNdSub"
}
}
mappings {
frameworkName: "tensorflow"
opName: "equals"
inputFrameworkOpName: "Equal"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Equal"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Equal"
}
}
mappings {
frameworkName: "tensorflow"
opName: "read_list"
inputFrameworkOpName: "TensorArrayReadV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayReadV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "importDataType"
inputToOutput {
key: "importDataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayReadV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "floormod"
inputFrameworkOpName: "FloorMod"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "FloorMod"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "FloorMod"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "FloorMod"
}
}
mappings {
frameworkName: "tensorflow"
opName: "read_list"
inputFrameworkOpName: "TensorArrayReadV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayReadV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "importDataType"
inputToOutput {
key: "importDataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayReadV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "biasadd"
inputFrameworkOpName: "BiasAdd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "value"
inputTensorName: "bias"
outputTensorName: "input"
outputTensorName: "bias"
inputToOutput {
key: "input"
value: "value"
}
inputToOutput {
key: "bias"
value: "bias"
}
ruleType: "tensor"
inputFrameworkOpName: "BiasAdd"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputBooleanName: "nchw"
inputToOutput {
key: "nchw"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "nchw"
transformerArgs {
name: "data_format"
argType: STRING
stringValue: "NCHW"
}
}
inputFrameworkOpName: "BiasAdd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "Identity"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Identity"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Identity"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Identity"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unstack"
inputFrameworkOpName: "Unpack"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "value"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "value"
}
ruleType: "tensor"
inputFrameworkOpName: "Unpack"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "axis"
inputIntName: "num"
outputIntName: "dimensions"
outputIntName: "num"
inputToOutput {
key: "dimensions"
value: "axis"
}
inputToOutput {
key: "num"
value: "num"
}
ruleType: "attribute"
inputFrameworkOpName: "Unpack"
}
}
mappings {
frameworkName: "tensorflow"
opName: "exit"
inputFrameworkOpName: "Exit"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "data"
}
ruleType: "tensor"
inputFrameworkOpName: "Exit"
}
}
mappings {
frameworkName: "tensorflow"
opName: "add"
inputFrameworkOpName: "AddV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "AddV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "AddV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "AddV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tanh"
inputFrameworkOpName: "Tanh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Tanh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Tanh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Tanh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "toggle_bits"
inputFrameworkOpName: "Invert"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Invert"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lstmBlockCell"
inputFrameworkOpName: "LSTMBlockCell"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "cs_prev"
inputTensorName: "h_prev"
inputTensorName: "w"
inputTensorName: "wci"
inputTensorName: "wcf"
inputTensorName: "wco"
inputTensorName: "b"
outputTensorName: "xt"
outputTensorName: "cLast"
outputTensorName: "yLast"
outputTensorName: "W"
outputTensorName: "Wci"
outputTensorName: "Wcf"
outputTensorName: "Wco"
outputTensorName: "b"
inputToOutput {
key: "xt"
value: "x"
}
inputToOutput {
key: "cLast"
value: "cs_prev"
}
inputToOutput {
key: "yLast"
value: "h_prev"
}
inputToOutput {
key: "W"
value: "w"
}
inputToOutput {
key: "Wci"
value: "wci"
}
inputToOutput {
key: "Wcf"
value: "wcf"
}
inputToOutput {
key: "Wco"
value: "wco"
}
inputToOutput {
key: "b"
value: "b"
}
ruleType: "tensor"
inputFrameworkOpName: "LSTMBlockCell"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "forget_bias"
inputFloatName: "cell_clip"
outputDoubleName: "forgetBias"
outputDoubleName: "clippingCellValue"
inputToOutput {
key: "forgetBias"
value: "forget_bias"
}
inputToOutput {
key: "clippingCellValue"
value: "cell_clip"
}
ruleType: "attribute"
inputFrameworkOpName: "LSTMBlockCell"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "peephole"
inputBooleanName: "use_peephole"
inputToOutput {
key: "peephole"
value: "use_peephole"
}
ruleType: "attribute"
inputFrameworkOpName: "LSTMBlockCell"
}
}
mappings {
frameworkName: "tensorflow"
opName: "log"
inputFrameworkOpName: "Log"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Log"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Log"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Log"
}
}
mappings {
frameworkName: "tensorflow"
opName: "non_max_suppression_v3"
inputFrameworkOpName: "NonMaxSuppressionV4"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "boxes"
inputTensorName: "scores"
inputTensorName: "max_output_size"
inputTensorName: "iou_threshold"
inputTensorName: "score_threshold"
outputTensorName: "boxes"
outputTensorName: "scales"
outputTensorName: "maxOutSize"
outputTensorName: "iouThreshold"
outputTensorName: "scoreThreshold"
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "scales"
value: "scores"
}
inputToOutput {
key: "maxOutSize"
value: "max_output_size"
}
inputToOutput {
key: "iouThreshold"
value: "iou_threshold"
}
inputToOutput {
key: "scoreThreshold"
value: "score_threshold"
}
ruleType: "tensor"
inputFrameworkOpName: "NonMaxSuppressionV4"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "maxOutputSize"
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppressionV4"
}
}
mappings {
frameworkName: "tensorflow"
opName: "less_equal"
inputFrameworkOpName: "LessEqual"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "LessEqual"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "LessEqual"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "LessEqual"
}
}
mappings {
frameworkName: "tensorflow"
opName: "non_max_suppression"
inputFrameworkOpName: "NonMaxSuppressionV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "boxes"
inputTensorName: "scores"
inputTensorName: "iou_threshold"
inputTensorName: "max_output_size"
outputTensorName: "boxes"
outputTensorName: "scales"
outputTensorName: "iouThreshold"
2021-02-01 06:31:20 +01:00
outputTensorName: "maxOutputSize"
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "scales"
value: "scores"
}
inputToOutput {
key: "iouThreshold"
2021-02-01 06:31:20 +01:00
value: "iou_threshold"
}
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "tensor"
inputFrameworkOpName: "NonMaxSuppressionV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "scoreThreshold"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "scoreThreshold"
doubleValue: 0.5
argType: DOUBLE
argIndex: 1
}
}
inputFrameworkOpName: "NonMaxSuppressionV2"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "maxOutputSize"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppressionV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "non_max_suppression_v3"
inputFrameworkOpName: "NonMaxSuppressionV3"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "boxes"
inputTensorName: "scores"
inputTensorName: "max_output_size"
inputTensorName: "iou_threshold"
inputTensorName: "score_threshold"
outputTensorName: "boxes"
outputTensorName: "scales"
outputTensorName: "maxOutSize"
outputTensorName: "iouThreshold"
outputTensorName: "scoreThreshold"
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "scales"
value: "scores"
}
inputToOutput {
key: "maxOutSize"
value: "max_output_size"
}
inputToOutput {
key: "iouThreshold"
value: "iou_threshold"
}
inputToOutput {
key: "scoreThreshold"
value: "score_threshold"
}
ruleType: "tensor"
inputFrameworkOpName: "NonMaxSuppressionV3"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "maxOutputSize"
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppressionV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "onehot"
inputFrameworkOpName: "OneHot"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "indices"
}
ruleType: "tensor"
inputFrameworkOpName: "OneHot"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "depth"
outputDoubleName: "on"
outputDoubleName: "off"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "on"
value: "on_value"
}
inputToOutput {
key: "off"
value: "off_value"
}
inputToOutput {
key: "depth"
value: "depth"
}
ruleType: "attribute"
inputFrameworkOpName: "OneHot"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "axis"
outputIntName: "dimensions"
outputIntName: "dataType"
inputDataTypeName: "T"
inputToOutput {
key: "dimensions"
value: "axis"
}
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "OneHot"
}
}
mappings {
frameworkName: "tensorflow"
opName: "transpose"
inputFrameworkOpName: "Transpose"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "perm"
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "permuteDims"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
2021-02-10 14:40:04 +01:00
key: "permuteDims"
2021-02-01 06:31:20 +01:00
value: "perm"
}
ruleType: "tensor"
inputFrameworkOpName: "Transpose"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Transpose"
}
}
mappings {
frameworkName: "tensorflow"
opName: "square"
inputFrameworkOpName: "Square"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Square"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Square"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Square"
}
}
2021-02-15 08:16:40 +01:00
mappings {
frameworkName: "tensorflow"
opName: "compare_and_bitpack"
inputFrameworkOpName: "CompareAndBitpack"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "threshold"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "y"
value: "threshold"
}
ruleType: "tensor"
inputFrameworkOpName: "CompareAndBitpack"
}
}
2021-02-01 06:31:20 +01:00
mappings {
frameworkName: "tensorflow"
opName: "segment_min"
inputFrameworkOpName: "SegmentMin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
outputTensorName: "input"
outputTensorName: "idxSegments"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
ruleType: "tensor"
inputFrameworkOpName: "SegmentMin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "switch"
inputFrameworkOpName: "Switch"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "pred"
outputTensorName: "input"
outputTensorName: "predicate"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "predicate"
value: "pred"
}
ruleType: "tensor"
inputFrameworkOpName: "Switch"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unsorted_segment_max"
inputFrameworkOpName: "UnsortedSegmentMax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
2021-02-15 08:16:40 +01:00
inputTensorName: "num_segments"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
outputTensorName: "idxSegments"
2021-02-15 08:16:40 +01:00
outputTensorName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "UnsortedSegmentMax"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "numSegments"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "numSegments"
value: "num_segments"
}
ruleType: "attribute"
inputFrameworkOpName: "UnsortedSegmentMax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "segment_sum"
inputFrameworkOpName: "SegmentSum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
outputTensorName: "input"
outputTensorName: "idxSegments"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
ruleType: "tensor"
inputFrameworkOpName: "SegmentSum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "resize_bilinear"
inputFrameworkOpName: "ResizeBilinear"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "size"
outputTensorName: "image"
outputTensorName: "newImageSize"
inputToOutput {
key: "image"
value: "images"
}
inputToOutput {
key: "newImageSize"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "ResizeBilinear"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "align_corners"
inputBooleanName: "half_pixel_centers"
outputBooleanName: "alignCorners"
outputBooleanName: "halfPixelCenter"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "alignCorners"
value: "align_corners"
}
inputToOutput {
key: "halfPixelCenter"
2021-02-01 06:31:20 +01:00
value: "half_pixel_centers"
}
ruleType: "attribute"
inputFrameworkOpName: "ResizeBilinear"
}
}
mappings {
frameworkName: "tensorflow"
opName: "softmax"
inputFrameworkOpName: "Softmax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "logits"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "logits"
}
ruleType: "tensor"
inputFrameworkOpName: "Softmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dimension"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dimension"
int64Value: 1
argType: INT64
}
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
transformerArgs {
key: "value"
transformerArgs {
name: "dimension"
int64Value: 1
argType: INT64
}
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Softmax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "split_list"
inputFrameworkOpName: "TensorArraySplitV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySplitV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArraySplitV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "erf"
inputFrameworkOpName: "Erf"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Erf"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Erf"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Erf"
}
}
mappings {
frameworkName: "tensorflow"
opName: "split_list"
inputFrameworkOpName: "TensorArraySplitV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArraySplitV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArraySplitV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "relu"
inputFrameworkOpName: "Relu"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Relu"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "cutoff"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "cutoff"
argType: DOUBLE
}
}
inputFrameworkOpName: "Relu"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Relu"
}
}
mappings {
frameworkName: "tensorflow"
opName: "ceil"
inputFrameworkOpName: "Ceil"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Ceil"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Ceil"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Ceil"
}
}
mappings {
frameworkName: "tensorflow"
opName: "l2_loss"
inputFrameworkOpName: "L2Loss"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "t"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "t"
}
ruleType: "tensor"
inputFrameworkOpName: "L2Loss"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "L2Loss"
}
}
mappings {
frameworkName: "tensorflow"
opName: "switch"
inputFrameworkOpName: "If"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "cond"
outputTensorName: "input"
outputTensorName: "predicate"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "predicate"
value: "cond"
}
ruleType: "tensor"
inputFrameworkOpName: "If"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cast"
inputFrameworkOpName: "Cast"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Cast"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "DstT"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "DstT"
}
ruleType: "attribute"
inputFrameworkOpName: "Cast"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "dst"
inputDataTypeName: "DstT"
inputToOutput {
key: "dst"
value: "DstT"
}
ruleType: "attribute"
inputFrameworkOpName: "Cast"
}
}
mappings {
frameworkName: "tensorflow"
opName: "minimum"
inputFrameworkOpName: "Minimum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Minimum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "non_max_suppression"
inputFrameworkOpName: "NonMaxSuppression"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "boxes"
inputTensorName: "scores"
inputTensorName: "max_output_size"
outputTensorName: "boxes"
outputTensorName: "scales"
outputTensorName: "maxOutputSize"
inputToOutput {
key: "boxes"
value: "boxes"
}
inputToOutput {
key: "scales"
value: "scores"
}
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "tensor"
inputFrameworkOpName: "NonMaxSuppression"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "scoreThreshold"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "scoreThreshold"
doubleValue: 0.5
argType: DOUBLE
argIndex: 1
}
}
inputFrameworkOpName: "NonMaxSuppression"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "iou_threshold"
inputToOutput {
key: "iouThreshold"
2021-02-01 06:31:20 +01:00
value: "iou_threshold"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppression"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "maxOutputSize"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppression"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lstmBlock"
inputFrameworkOpName: "BlockLSTM"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "seq_len_max"
inputTensorName: "x"
inputTensorName: "cs_prev"
inputTensorName: "h_prev"
inputTensorName: "w"
inputTensorName: "wci"
inputTensorName: "wcf"
inputTensorName: "wco"
inputTensorName: "b"
outputTensorName: "maxTSLength"
outputTensorName: "input"
outputTensorName: "cLast"
outputTensorName: "yLast"
outputTensorName: "W"
outputTensorName: "Wci"
outputTensorName: "Wcf"
outputTensorName: "Wco"
outputTensorName: "b"
inputToOutput {
key: "maxTSLength"
value: "seq_len_max"
}
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "cLast"
value: "cs_prev"
}
inputToOutput {
key: "yLast"
value: "h_prev"
}
inputToOutput {
key: "W"
value: "w"
}
inputToOutput {
key: "Wci"
value: "wci"
}
inputToOutput {
key: "Wcf"
value: "wcf"
}
inputToOutput {
key: "Wco"
value: "wco"
}
inputToOutput {
key: "b"
value: "b"
}
ruleType: "tensor"
inputFrameworkOpName: "BlockLSTM"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "forget_bias"
inputFloatName: "cell_clip"
outputDoubleName: "forgetBias"
outputDoubleName: "clippingCellValue"
inputToOutput {
key: "forgetBias"
value: "forget_bias"
}
inputToOutput {
key: "clippingCellValue"
value: "cell_clip"
}
ruleType: "attribute"
inputFrameworkOpName: "BlockLSTM"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "peephole"
inputBooleanName: "use_peephole"
inputToOutput {
key: "peephole"
value: "use_peephole"
}
ruleType: "attribute"
inputFrameworkOpName: "BlockLSTM"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dataFormat"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dataFormat"
argType: INT64
}
}
inputFrameworkOpName: "BlockLSTM"
}
}
mappings {
frameworkName: "tensorflow"
opName: "shape_of"
inputFrameworkOpName: "Shape"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Shape"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Shape"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "out_type"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "out_type"
}
ruleType: "attribute"
inputFrameworkOpName: "Shape"
}
}
mappings {
frameworkName: "tensorflow"
opName: "check_numerics"
inputFrameworkOpName: "CheckNumerics"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "tensor"
}
ruleType: "tensor"
inputFrameworkOpName: "CheckNumerics"
}
rule {
ruleName: "convertinputstringtondarray"
functionName: "convertinputstringtondarray"
inputStringAttrName: "message"
inputToOutput {
key: "message"
value: "message"
}
ruleType: "attribute"
inputFrameworkOpName: "CheckNumerics"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reduce_max"
inputFrameworkOpName: "Max"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Max"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Max"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Max"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tensorarrayv3"
inputFrameworkOpName: "TensorArrayV3"
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "dataType"
inputDataTypeName: "dtype"
inputToOutput {
key: "dataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_max"
inputFrameworkOpName: "ScatterMax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "ref"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterMax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "isnan"
inputFrameworkOpName: "IsNan"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "IsNan"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "IsNan"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather_list"
inputFrameworkOpName: "TensorArrayGather"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayGather"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayGather"
}
}
mappings {
frameworkName: "tensorflow"
opName: "bincount"
inputFrameworkOpName: "Bincount"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "weights"
inputTensorName: "arr"
2021-02-10 14:40:04 +01:00
inputTensorName: "size"
2021-02-01 06:31:20 +01:00
outputTensorName: "weights"
outputTensorName: "values"
2021-02-10 14:40:04 +01:00
outputTensorName: "min"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "weights"
value: "weights"
}
inputToOutput {
key: "values"
value: "arr"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "min"
value: "size"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Bincount"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
2021-02-10 14:40:04 +01:00
outputIntName: "outputType"
2021-02-01 06:31:20 +01:00
inputDataTypeName: "T"
inputToOutput {
key: "outputType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Bincount"
}
}
mappings {
frameworkName: "tensorflow"
opName: "space_to_batch_nd"
inputFrameworkOpName: "SpaceToBatchND"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "block_shape"
inputTensorName: "paddings"
outputTensorName: "input"
outputTensorName: "blockShape"
outputTensorName: "padding"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "blockShape"
value: "block_shape"
}
inputToOutput {
key: "padding"
value: "paddings"
}
ruleType: "tensor"
inputFrameworkOpName: "SpaceToBatchND"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "blocks"
inputToOutput {
key: "blocks"
value: "block_shape"
}
ruleType: "attribute"
inputFrameworkOpName: "SpaceToBatchND"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "SpaceToBatchND"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reduce_prod"
inputFrameworkOpName: "Prod"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Prod"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Prod"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Prod"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lgamma"
inputFrameworkOpName: "Lgamma"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Lgamma"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matmul"
inputFrameworkOpName: "BatchMatMulV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchMatMulV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "alpha"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "alpha"
doubleValue: 1.0
argType: DOUBLE
}
}
inputFrameworkOpName: "BatchMatMulV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "beta"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "beta"
doubleValue: 1.0
argType: DOUBLE
argIndex: 1
}
}
inputFrameworkOpName: "BatchMatMulV2"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "transX"
outputIntName: "transY"
inputBooleanName: "adj_x"
inputBooleanName: "adj_y"
inputToOutput {
key: "transX"
value: "adj_x"
}
inputToOutput {
key: "transY"
value: "adj_y"
}
ruleType: "attribute"
inputFrameworkOpName: "BatchMatMulV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "transZ"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "transZ"
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "BatchMatMulV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "unique_with_counts"
inputFrameworkOpName: "UniqueWithCounts"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "UniqueWithCounts"
}
}
mappings {
frameworkName: "tensorflow"
opName: "randomuniform"
inputFrameworkOpName: "RandomUniformInt"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
outputTensorName: "shape"
inputToOutput {
key: "shape"
value: "shape"
}
2021-02-15 08:16:40 +01:00
ruleType: "tensor"
inputFrameworkOpName: "RandomUniformInt"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-15 08:16:40 +01:00
key: "seed"
value: "seed"
2021-02-01 06:31:20 +01:00
}
2021-02-15 08:16:40 +01:00
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "RandomUniformInt"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputDoubleName: "min"
outputDoubleName: "max"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "min"
value: "minval"
}
inputToOutput {
key: "max"
value: "maxval"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomUniformInt"
}
rule {
ruleName: "datatypetoint"
functionName: "datatypetoint"
outputIntName: "dtype"
inputDataTypeName: "Tout"
inputToOutput {
key: "dtype"
value: "Tout"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomUniformInt"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "Tout"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "Tout"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomUniformInt"
}
}
mappings {
frameworkName: "tensorflow"
opName: "selu"
inputFrameworkOpName: "Selu"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Selu"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Selu"
}
}
mappings {
frameworkName: "tensorflow"
opName: "argmin"
inputFrameworkOpName: "ArgMin"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "dimension"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "dimension"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "ArgMin"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "keepDims"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "keepDims"
argType: BOOL
}
}
inputFrameworkOpName: "ArgMin"
}
}
mappings {
frameworkName: "tensorflow"
opName: "resize_bicubic"
inputFrameworkOpName: "ResizeBicubic"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "size"
outputTensorName: "image"
outputTensorName: "size"
inputToOutput {
key: "image"
value: "images"
}
inputToOutput {
key: "size"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "ResizeBicubic"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "align_corners"
inputBooleanName: "half_pixel_centers"
outputBooleanName: "alignCorners"
outputBooleanName: "alignPixelCenters"
inputToOutput {
key: "alignCorners"
value: "align_corners"
}
inputToOutput {
key: "alignPixelCenters"
value: "half_pixel_centers"
}
ruleType: "attribute"
inputFrameworkOpName: "ResizeBicubic"
}
}
mappings {
frameworkName: "tensorflow"
opName: "atanh"
inputFrameworkOpName: "Atanh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Atanh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Atanh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Atanh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "split_v"
inputFrameworkOpName: "SplitV"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "value"
inputTensorName: "size_splits"
inputTensorName: "split_dim"
outputTensorName: "input"
outputTensorName: "sizes"
outputTensorName: "_a"
inputToOutput {
key: "input"
value: "value"
}
inputToOutput {
key: "sizes"
value: "size_splits"
}
inputToOutput {
key: "_a"
value: "split_dim"
}
ruleType: "tensor"
inputFrameworkOpName: "SplitV"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_split"
outputIntName: "numSplit"
inputToOutput {
key: "numSplit"
value: "num_split"
}
ruleType: "attribute"
inputFrameworkOpName: "SplitV"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "dimensions"
inputToOutput {
key: "dimensions"
value: "split_dim"
}
ruleType: "attribute"
inputFrameworkOpName: "SplitV"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
inputToOutput {
key: "dimensions"
value: "split_dim"
}
ruleType: "attribute"
inputFrameworkOpName: "SplitV"
}
}
mappings {
frameworkName: "tensorflow"
opName: "mirror_pad"
inputFrameworkOpName: "MirrorPad"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "paddings"
outputTensorName: "input"
outputTensorName: "paddings"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "paddings"
value: "paddings"
}
ruleType: "tensor"
inputFrameworkOpName: "MirrorPad"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "mode"
outputIntName: "mode"
inputFloatName: "mode"
inputToOutput {
key: "mode"
value: "mode"
}
ruleType: "attribute"
transformerArgs {
key: "mode"
transformerArgs {
name: "mode"
stringValue: "REFLECT"
}
}
inputFrameworkOpName: "MirrorPad"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "isSymmetric"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isSymmetric"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "MirrorPad"
}
}
mappings {
frameworkName: "tensorflow"
opName: "shapes_of"
inputFrameworkOpName: "ShapeN"
rule {
2021-02-15 08:16:40 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "ShapeN"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "ShapeN"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cos"
inputFrameworkOpName: "Cos"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Cos"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Cos"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Cos"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sqrt"
inputFrameworkOpName: "Sqrt"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Sqrt"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sqrt"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sqrt"
}
}
mappings {
frameworkName: "tensorflow"
opName: "deconv2d_tf"
inputFrameworkOpName: "Conv2DBackpropInput"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input_sizes"
inputTensorName: "filter"
2021-02-15 08:16:40 +01:00
inputTensorName: "out_backprop"
2021-02-01 06:31:20 +01:00
outputTensorName: "gradIShape"
outputTensorName: "weights"
2021-02-15 08:16:40 +01:00
outputTensorName: "gradO"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "gradIShape"
value: "input_sizes"
}
inputToOutput {
key: "weights"
value: "filter"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "gradO"
value: "out_backprop"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "wFormat"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "wFormat"
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 9
stringValue: "NCHW"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
transformerArgs {
key: "dH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 6
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 6
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 6
stringValue: "dilations"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "conditionalfieldvalueintindex"
functionName: "conditionalfieldvalueintindex"
inputStringAttrName: "data_format"
outputIntName: "dW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "dW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
transformerArgs {
key: "dW"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 7
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 7
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 7
stringValue: "dilations"
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kH"
int64Value: -1
argType: INT64
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kW"
int64Value: -1
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "Conv2DBackpropInput"
}
2021-02-10 14:40:04 +01:00
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Conv2DBackpropInput"
}
2021-02-01 06:31:20 +01:00
}
mappings {
frameworkName: "tensorflow"
opName: "floordiv"
inputFrameworkOpName: "FloorDiv"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "FloorDiv"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "FloorDiv"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "FloorDiv"
}
}
mappings {
frameworkName: "tensorflow"
opName: "stack_list"
inputFrameworkOpName: "TensorArrayConcatV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayConcatV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-10 14:40:04 +01:00
key: "dtype"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-10 14:40:04 +01:00
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "TensorArrayConcatV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "stack_list"
inputFrameworkOpName: "TensorArrayConcatV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayConcatV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-10 14:40:04 +01:00
key: "dtype"
value: "dtype"
2021-02-01 06:31:20 +01:00
}
2021-02-10 14:40:04 +01:00
ruleType: "attribute"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "TensorArrayConcatV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "CopyHost"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "CopyHost"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "CopyHost"
}
2021-02-15 08:16:40 +01:00
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "CopyHost"
}
2021-02-01 06:31:20 +01:00
}
mappings {
frameworkName: "tensorflow"
opName: "neg"
inputFrameworkOpName: "Neg"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Neg"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Neg"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Neg"
}
}
mappings {
frameworkName: "tensorflow"
opName: "top_k"
inputFrameworkOpName: "TopKV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "TopKV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "sorted"
outputBooleanName: "needSort"
inputToOutput {
key: "needSort"
value: "sorted"
}
ruleType: "attribute"
inputFrameworkOpName: "TopKV2"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "k"
inputToOutput {
key: "k"
value: "k"
}
ruleType: "attribute"
inputFrameworkOpName: "TopKV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "resize_area"
inputFrameworkOpName: "ResizeArea"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "size"
outputTensorName: "image"
outputTensorName: "size"
inputToOutput {
key: "image"
value: "images"
}
inputToOutput {
key: "size"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "ResizeArea"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "align_corners"
outputBooleanName: "alignCorners"
inputToOutput {
key: "alignCorners"
value: "align_corners"
}
ruleType: "attribute"
inputFrameworkOpName: "ResizeArea"
}
}
mappings {
frameworkName: "tensorflow"
opName: "triangular_solve"
inputFrameworkOpName: "MatrixTriangularSolve"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "matrix"
inputTensorName: "rhs"
outputTensorName: "a"
outputTensorName: "b"
inputToOutput {
key: "a"
value: "matrix"
}
inputToOutput {
key: "b"
value: "rhs"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixTriangularSolve"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "adjoint"
inputBooleanName: "lower"
outputBooleanName: "useAdjoint"
outputBooleanName: "isLower"
inputToOutput {
key: "useAdjoint"
value: "adjoint"
}
inputToOutput {
key: "isLower"
value: "lower"
}
ruleType: "attribute"
inputFrameworkOpName: "MatrixTriangularSolve"
}
}
mappings {
frameworkName: "tensorflow"
opName: "softsign"
inputFrameworkOpName: "Softsign"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "Softsign"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Softsign"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather"
inputFrameworkOpName: "GatherV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "params"
inputTensorName: "indices"
outputTensorName: "input"
outputTensorName: "indices"
inputToOutput {
key: "input"
value: "params"
}
inputToOutput {
key: "indices"
value: "indices"
}
ruleType: "tensor"
inputFrameworkOpName: "GatherV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "GatherV2"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
inputToOutput {
key: "dimensions"
value: "axis"
}
ruleType: "attribute"
inputFrameworkOpName: "GatherV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fake_quant_with_min_max_args"
inputFrameworkOpName: "FakeQuantWithMinMaxArgs"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "inputs"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "inputs"
}
ruleType: "tensor"
inputFrameworkOpName: "FakeQuantWithMinMaxArgs"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_bits"
outputIntName: "numBits"
inputFloatName: "min"
inputFloatName: "max"
outputDoubleName: "min"
outputDoubleName: "max"
inputBooleanName: "narrow_range"
outputBooleanName: "narrowRange"
inputToOutput {
key: "min"
value: "min"
}
inputToOutput {
key: "max"
value: "max"
}
inputToOutput {
key: "numBits"
value: "num_bits"
}
inputToOutput {
key: "narrowRange"
value: "narrow_range"
}
ruleType: "attribute"
inputFrameworkOpName: "FakeQuantWithMinMaxArgs"
}
}
mappings {
frameworkName: "tensorflow"
opName: "all"
inputFrameworkOpName: "All"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "All"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "All"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "All"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tan"
inputFrameworkOpName: "Tan"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Tan"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Tan"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Tan"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fill"
inputFrameworkOpName: "Fill"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "dims"
2021-02-15 08:16:40 +01:00
inputTensorName: "value"
outputTensorName: "shape"
outputTensorName: "outputs"
2021-02-01 06:31:20 +01:00
inputToOutput {
2021-02-15 08:16:40 +01:00
key: "shape"
2021-02-01 06:31:20 +01:00
value: "dims"
}
2021-02-15 08:16:40 +01:00
inputToOutput {
key: "outputs"
value: "value"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Fill"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputDoubleName: "value"
inputToOutput {
key: "value"
value: "value"
}
ruleType: "attribute"
inputFrameworkOpName: "Fill"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
2021-02-10 14:40:04 +01:00
outputIntName: "dtype"
2021-02-01 06:31:20 +01:00
inputDataTypeName: "T"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Fill"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_add"
inputFrameworkOpName: "ScatterAdd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "ref"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterAdd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "lock"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "lock"
argType: BOOL
}
}
inputFrameworkOpName: "ScatterAdd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
argIndex: 1
}
}
inputFrameworkOpName: "ScatterAdd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "max_pool_with_argmax"
inputFrameworkOpName: "MaxPoolWithArgmax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kH"
int64Value: 1
argType: INT64
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "kW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "kW"
int64Value: 1
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "sH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "sH"
int64Value: 1
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "sW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "sW"
int64Value: 1
argType: INT64
argIndex: 3
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
int64Value: 1
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
int64Value: 1
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "isNHWC"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isNHWC"
int64Value: 1
argType: INT64
argIndex: 10
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "sameMode"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "sameMode"
int64Value: 8
argType: INT64
argIndex: 8
}
}
inputFrameworkOpName: "MaxPoolWithArgmax"
}
2021-02-07 11:27:41 +01:00
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
2021-02-15 08:16:40 +01:00
inputDataTypeName: "Targmax"
2021-02-07 11:27:41 +01:00
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
2021-02-15 08:16:40 +01:00
value: "Targmax"
2021-02-07 11:27:41 +01:00
}
ruleType: "attribute"
inputFrameworkOpName: "MaxPoolWithArgmax"
}
2021-02-01 06:31:20 +01:00
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_diag_part"
inputFrameworkOpName: "MatrixDiagPart"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixDiagPart"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "MatrixDiagPart"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fused_batch_norm"
inputFrameworkOpName: "FusedBatchNormV3"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "scale"
inputTensorName: "offset"
inputTensorName: "mean"
inputTensorName: "variance"
outputTensorName: "input"
outputTensorName: "scale"
outputTensorName: "offset"
outputTensorName: "mean"
outputTensorName: "variance"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "scale"
value: "scale"
}
inputToOutput {
key: "offset"
value: "offset"
}
inputToOutput {
key: "mean"
value: "mean"
}
inputToOutput {
key: "variance"
value: "variance"
}
ruleType: "tensor"
inputFrameworkOpName: "FusedBatchNormV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "epsilon"
outputDoubleName: "epsilon"
2021-02-08 23:44:23 +01:00
inputDataTypeName: "T"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "epsilon"
value: "epsilon"
}
2021-02-08 23:44:23 +01:00
inputToOutput {
key: "dtype"
value: "T"
}
2021-02-01 06:31:20 +01:00
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNormV3"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "isTraining"
inputBooleanName: "is_training"
inputToOutput {
key: "isTraining"
value: "is_training"
}
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNormV3"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "dataFormat"
inputToOutput {
key: "dataFormat"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dataFormat"
transformerArgs {
name: "data_format"
argType: STRING
stringValue: "NCHW"
}
}
inputFrameworkOpName: "FusedBatchNormV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather_list"
inputFrameworkOpName: "TensorArrayGatherV2"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayGatherV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayGatherV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "noop"
inputFrameworkOpName: "NoOp"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "NoOp"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather_list"
inputFrameworkOpName: "TensorArrayGatherV3"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "TensorArrayGatherV3"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "TensorArrayGatherV3"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lrn"
inputFrameworkOpName: "LRN"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "LRN"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "depth_radius"
outputIntName: "depth"
inputFloatName: "alpha"
inputFloatName: "bias"
inputFloatName: "beta"
outputDoubleName: "alpha"
outputDoubleName: "bias"
outputDoubleName: "beta"
inputToOutput {
key: "depth"
value: "depth_radius"
}
inputToOutput {
key: "alpha"
value: "alpha"
}
inputToOutput {
key: "bias"
value: "bias"
}
inputToOutput {
key: "beta"
value: "beta"
}
ruleType: "attribute"
inputFrameworkOpName: "LRN"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "LRN"
}
}
mappings {
frameworkName: "tensorflow"
opName: "betainc"
inputFrameworkOpName: "Betainc"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "b"
inputTensorName: "x"
outputTensorName: "a"
outputTensorName: "b"
outputTensorName: "input"
inputToOutput {
key: "a"
value: "a"
}
inputToOutput {
key: "b"
value: "b"
}
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Betainc"
}
}
mappings {
frameworkName: "tensorflow"
opName: "diag_part"
inputFrameworkOpName: "DiagPart"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "DiagPart"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "DiagPart"
}
}
mappings {
frameworkName: "tensorflow"
opName: "concat"
inputFrameworkOpName: "Concat"
rule {
ruleName: "multiinputindex"
functionName: "multiinputindex"
inputTensorName: "values"
inputTensorName: "concat_dim"
outputTensorName: "input"
outputTensorName: "concatDimension"
inputToOutput {
key: "input"
value: "values"
}
inputToOutput {
key: "concatDimension"
value: "concat_dim"
}
ruleType: "tensor"
inputFrameworkOpName: "Concat"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "concatDimension"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "concatDimension"
value: "concat_dim"
}
ruleType: "attribute"
inputFrameworkOpName: "Concat"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "isDynamicAxis"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isDynamicAxis"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "Concat"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Concat"
}
}
mappings {
frameworkName: "tensorflow"
opName: "segment_prod"
inputFrameworkOpName: "SegmentProd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
outputTensorName: "input"
outputTensorName: "idxSegments"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
ruleType: "tensor"
inputFrameworkOpName: "SegmentProd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "top_k"
inputFrameworkOpName: "TopK"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "TopK"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "k"
outputIntName: "k"
inputBooleanName: "sorted"
outputBooleanName: "needSort"
inputToOutput {
key: "needSort"
value: "sorted"
}
inputToOutput {
key: "k"
value: "k"
}
ruleType: "attribute"
inputFrameworkOpName: "TopK"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fake_quant_with_min_max_vars_per_channel"
inputFrameworkOpName: "FakeQuantWithMinMaxVarsPerChannel"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "inputs"
inputTensorName: "min"
inputTensorName: "max"
outputTensorName: "input"
outputTensorName: "min"
outputTensorName: "max"
inputToOutput {
key: "input"
value: "inputs"
}
inputToOutput {
key: "min"
value: "min"
}
inputToOutput {
key: "max"
value: "max"
}
ruleType: "tensor"
inputFrameworkOpName: "FakeQuantWithMinMaxVarsPerChannel"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_bits"
outputIntName: "numBits"
inputBooleanName: "narrow_range"
outputBooleanName: "narrowed"
inputToOutput {
key: "numBits"
value: "num_bits"
}
inputToOutput {
key: "narrowed"
value: "narrow_range"
}
ruleType: "attribute"
inputFrameworkOpName: "FakeQuantWithMinMaxVarsPerChannel"
}
}
mappings {
frameworkName: "tensorflow"
opName: "maximum"
inputFrameworkOpName: "Maximum"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Maximum"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Maximum"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Maximum"
}
}
mappings {
frameworkName: "tensorflow"
opName: "mergeadd"
inputFrameworkOpName: "AccumulateNV2"
rule {
ruleName: "multiinputindex"
functionName: "multiinputindex"
inputTensorName: "inputs"
outputTensorName: "inArrs"
inputToOutput {
key: "inArrs"
value: "inputs"
}
ruleType: "tensor"
inputFrameworkOpName: "AccumulateNV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "AccumulateNV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "asinh"
inputFrameworkOpName: "Asinh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Asinh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Asinh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Asinh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fused_batch_norm"
inputFrameworkOpName: "FusedBatchNormV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "scale"
inputTensorName: "offset"
inputTensorName: "mean"
inputTensorName: "variance"
outputTensorName: "input"
outputTensorName: "scale"
outputTensorName: "offset"
outputTensorName: "mean"
outputTensorName: "variance"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "scale"
value: "scale"
}
inputToOutput {
key: "offset"
value: "offset"
}
inputToOutput {
key: "mean"
value: "mean"
}
inputToOutput {
key: "variance"
value: "variance"
}
ruleType: "tensor"
inputFrameworkOpName: "FusedBatchNormV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "epsilon"
outputDoubleName: "epsilon"
2021-02-08 23:44:23 +01:00
inputDataTypeName: "T"
outputDataTypeName: "dtype"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "epsilon"
value: "epsilon"
}
2021-02-08 23:44:23 +01:00
inputToOutput {
key: "dtype"
value: "T"
}
2021-02-01 06:31:20 +01:00
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNormV2"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "isTraining"
inputBooleanName: "is_training"
inputToOutput {
key: "isTraining"
value: "is_training"
}
ruleType: "attribute"
inputFrameworkOpName: "FusedBatchNormV2"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "data_format"
inputStringAttrName: "data_format"
outputIntName: "dataFormat"
inputToOutput {
key: "dataFormat"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "dataFormat"
transformerArgs {
name: "data_format"
argType: STRING
stringValue: "NCHW"
}
}
inputFrameworkOpName: "FusedBatchNormV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "Reciprocal"
inputFrameworkOpName: "Reciprocal"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Reciprocal"
}
}
mappings {
frameworkName: "tensorflow"
opName: "in_top_k"
inputFrameworkOpName: "InTopKV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "targets"
inputTensorName: "predictions"
outputTensorName: "target"
outputTensorName: "predictions"
inputToOutput {
key: "target"
value: "targets"
}
inputToOutput {
key: "predictions"
value: "predictions"
}
ruleType: "tensor"
inputFrameworkOpName: "InTopKV2"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "k"
inputToOutput {
key: "k"
value: "k"
}
ruleType: "attribute"
inputFrameworkOpName: "InTopKV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "sorted"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "sorted"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "InTopKV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "less"
inputFrameworkOpName: "Less"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Less"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Less"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Less"
}
}
mappings {
frameworkName: "tensorflow"
opName: "nth_element"
inputFrameworkOpName: "NthElement"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "n"
inputTensorName: "input"
outputTensorName: "n"
outputTensorName: "input"
inputToOutput {
key: "n"
value: "n"
}
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "NthElement"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "reverse"
2021-02-01 06:31:20 +01:00
inputBooleanName: "reverse"
inputToOutput {
key: "reverse"
value: "reverse"
}
ruleType: "attribute"
inputFrameworkOpName: "NthElement"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matmul"
inputFrameworkOpName: "BatchMatMul"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchMatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "alpha"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "alpha"
doubleValue: 1.0
argType: DOUBLE
}
}
inputFrameworkOpName: "BatchMatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "beta"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "beta"
doubleValue: 1.0
argType: DOUBLE
argIndex: 1
}
}
inputFrameworkOpName: "BatchMatMul"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "transX"
outputIntName: "transY"
inputBooleanName: "adj_x"
inputBooleanName: "adj_y"
inputToOutput {
key: "transX"
value: "adj_x"
}
inputToOutput {
key: "transY"
value: "adj_y"
}
ruleType: "attribute"
inputFrameworkOpName: "BatchMatMul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "transZ"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "transZ"
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "BatchMatMul"
}
}
mappings {
frameworkName: "tensorflow"
opName: "multiply"
inputFrameworkOpName: "Mul"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Mul"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Mul"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Mul"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity_n"
inputFrameworkOpName: "IdentityN"
rule {
2021-02-15 08:16:40 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "IdentityN"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lu"
inputFrameworkOpName: "Lu"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Lu"
}
}
mappings {
frameworkName: "tensorflow"
opName: "diag"
inputFrameworkOpName: "Diag"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "diagonal"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "diagonal"
}
ruleType: "tensor"
inputFrameworkOpName: "Diag"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Diag"
}
}
mappings {
frameworkName: "tensorflow"
opName: "range"
inputFrameworkOpName: "Range"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "start"
inputTensorName: "limit"
inputTensorName: "delta"
outputTensorName: "from"
outputTensorName: "to"
outputTensorName: "step"
inputToOutput {
key: "from"
value: "start"
}
inputToOutput {
key: "to"
value: "limit"
}
inputToOutput {
key: "step"
value: "delta"
}
ruleType: "tensor"
inputFrameworkOpName: "Range"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "from"
outputIntName: "to"
outputIntName: "step"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "from"
value: "start"
}
inputToOutput {
key: "to"
value: "limit"
}
inputToOutput {
key: "step"
value: "delta"
}
ruleType: "attribute"
inputFrameworkOpName: "Range"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "Tidx"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "Tidx"
}
ruleType: "attribute"
inputFrameworkOpName: "Range"
}
}
mappings {
frameworkName: "tensorflow"
opName: "histogram_fixed_width"
inputFrameworkOpName: "HistogramFixedWidth"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "values"
inputTensorName: "value_range"
inputTensorName: "nbins"
outputTensorName: "input"
outputTensorName: "range"
outputTensorName: "numBins"
inputToOutput {
key: "input"
value: "values"
}
inputToOutput {
key: "range"
value: "value_range"
}
inputToOutput {
key: "numBins"
value: "nbins"
}
ruleType: "tensor"
inputFrameworkOpName: "HistogramFixedWidth"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "nbins"
inputToOutput {
key: "nbins"
value: "nbins"
}
ruleType: "attribute"
inputFrameworkOpName: "HistogramFixedWidth"
}
}
mappings {
frameworkName: "tensorflow"
opName: "divide_no_nan"
inputFrameworkOpName: "DivNoNan"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "DivNoNan"
}
}
mappings {
frameworkName: "tensorflow"
opName: "broadcast_dynamic_shape"
inputFrameworkOpName: "BroadcastArgs"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "s0"
inputTensorName: "s1"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "s0"
}
inputToOutput {
key: "y"
value: "s1"
}
ruleType: "tensor"
inputFrameworkOpName: "BroadcastArgs"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_div"
inputFrameworkOpName: "ScatterDiv"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "indices"
inputTensorName: "updates"
outputTensorName: "input"
outputTensorName: "indices"
outputTensorName: "updates"
inputToOutput {
key: "input"
value: "ref"
}
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
ruleType: "tensor"
inputFrameworkOpName: "ScatterDiv"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reshape"
inputFrameworkOpName: "Reshape"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
inputTensorName: "shape"
outputTensorName: "input"
outputTensorName: "shape"
inputToOutput {
key: "input"
value: "tensor"
}
inputToOutput {
key: "shape"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "Reshape"
}
}
mappings {
frameworkName: "tensorflow"
opName: "copy"
inputFrameworkOpName: "Copy"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Copy"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Copy"
}
}
mappings {
frameworkName: "tensorflow"
opName: "slice"
inputFrameworkOpName: "Slice"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "begin"
inputTensorName: "size"
outputTensorName: "input"
outputTensorName: "b"
outputTensorName: "e"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "b"
value: "begin"
}
inputToOutput {
key: "e"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "Slice"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "size"
inputToOutput {
key: "size"
value: "size"
}
ruleType: "attribute"
inputFrameworkOpName: "Slice"
}
}
mappings {
frameworkName: "tensorflow"
opName: "leakyrelu"
inputFrameworkOpName: "LeakyRelu"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "features"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "features"
}
ruleType: "tensor"
inputFrameworkOpName: "LeakyRelu"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "alpha"
outputDoubleName: "alpha"
inputToOutput {
key: "alpha"
value: "alpha"
}
ruleType: "attribute"
inputFrameworkOpName: "LeakyRelu"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_inverse"
inputFrameworkOpName: "MatrixInverse"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MatrixInverse"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "BatchMatrixInverse"
}
}
mappings {
frameworkName: "tensorflow"
opName: "tf_atan2"
inputFrameworkOpName: "Atan2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Atan2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Atan2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Atan2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "batch_to_space"
inputFrameworkOpName: "BatchToSpace"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "crops"
outputTensorName: "input"
outputTensorName: "crop"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "crop"
value: "crops"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchToSpace"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "block_size"
outputIntName: "blockSize"
inputToOutput {
key: "blockSize"
value: "block_size"
}
ruleType: "attribute"
inputFrameworkOpName: "BatchToSpace"
}
}
mappings {
frameworkName: "tensorflow"
opName: "acos"
inputFrameworkOpName: "Acos"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Acos"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Acos"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Acos"
}
}
mappings {
frameworkName: "tensorflow"
opName: "gather_nd"
inputFrameworkOpName: "GatherNd"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "params"
inputTensorName: "indices"
outputTensorName: "input"
outputTensorName: "indices"
inputToOutput {
key: "input"
value: "params"
}
inputToOutput {
key: "indices"
value: "indices"
}
ruleType: "tensor"
inputFrameworkOpName: "GatherNd"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
ruleType: "attribute"
inputFrameworkOpName: "GatherNd"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
}
}
inputFrameworkOpName: "GatherNd"
}
}
mappings {
frameworkName: "tensorflow"
opName: "maxpool2d"
inputFrameworkOpName: "MaxPoolV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "extraParam0"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "extraParam0"
argType: INT64
argIndex: 9
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pH"
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "pW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "pW"
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dW"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dW"
int64Value: 1
argType: INT64
argIndex: 6
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dH"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dH"
int64Value: 1
argType: INT64
argIndex: 7
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "stringnotequalsadapterrule"
functionName: "stringnotequalsadapterrule"
inputStringAttrName: "data_format"
outputIntName: "isNCHW"
inputFloatName: "data_format"
inputToOutput {
key: "isNCHW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "isNCHW"
transformerArgs {
name: "data_format"
argIndex: 10
stringValue: "NCHW"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
outputIntName: "isSameMode"
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
argIndex: 8
stringValue: "SAME"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "conditionalfieldvalueintindexndarray"
functionName: "conditionalfieldvalueintindexndarray"
inputStringAttrName: "data_format"
outputIntName: "sH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
argIndex: 2
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
argIndex: 2
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
argIndex: 2
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
transformerArgs {
key: "sH"
transformerArgs {
name: "targetValue"
argIndex: 2
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
argIndex: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
argIndex: 2
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 2
stringValue: "strides"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "conditionalfieldvalueintindexndarray"
functionName: "conditionalfieldvalueintindexndarray"
inputStringAttrName: "data_format"
outputIntName: "sW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "sW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
argIndex: 3
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
argIndex: 3
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
argIndex: 3
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
transformerArgs {
key: "sW"
transformerArgs {
name: "targetValue"
argIndex: 3
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 3
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 3
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 3
stringValue: "strides"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "conditionalfieldvalueintindexndarray"
functionName: "conditionalfieldvalueintindexndarray"
inputStringAttrName: "data_format"
outputIntName: "kH"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kH"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
transformerArgs {
key: "kH"
transformerArgs {
name: "targetValue"
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 2
}
transformerArgs {
name: "falseIndex"
int64Value: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
stringValue: "ksize"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
rule {
ruleName: "conditionalfieldvalueintindexndarray"
functionName: "conditionalfieldvalueintindexndarray"
inputStringAttrName: "data_format"
outputIntName: "kW"
inputFloatName: "targetValue"
inputFloatName: "trueIndex"
inputFloatName: "falseIndex"
inputFloatName: "attributeNameOfListAttribute"
inputToOutput {
key: "kW"
value: "data_format"
}
ruleType: "attribute"
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
argIndex: 1
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
argIndex: 1
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
argIndex: 1
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
transformerArgs {
key: "kW"
transformerArgs {
name: "targetValue"
argIndex: 1
stringValue: "NCHW"
}
transformerArgs {
name: "trueIndex"
int64Value: 3
argIndex: 1
}
transformerArgs {
name: "falseIndex"
int64Value: 2
argIndex: 1
}
transformerArgs {
name: "attributeNameOfListAttribute"
argIndex: 1
stringValue: "ksize"
}
}
inputFrameworkOpName: "MaxPoolV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cholesky"
inputFrameworkOpName: "Cholesky"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "Cholesky"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_crop"
inputFrameworkOpName: "RandomCrop"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "image"
inputTensorName: "size"
outputTensorName: "input"
outputTensorName: "shape"
inputToOutput {
key: "input"
value: "image"
}
inputToOutput {
key: "shape"
value: "size"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomCrop"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "seed"
outputIntName: "seed"
inputToOutput {
key: "seed"
value: "seed"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomCrop"
}
}
mappings {
frameworkName: "tensorflow"
opName: "batch_to_space_nd"
inputFrameworkOpName: "BatchToSpaceND"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "crops"
inputTensorName: "block_shape"
outputTensorName: "input"
outputTensorName: "crop"
outputTensorName: "blockShape"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "crop"
value: "crops"
}
inputToOutput {
key: "blockShape"
value: "block_shape"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchToSpaceND"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "blocks"
inputToOutput {
key: "blocks"
value: "block_shape"
}
ruleType: "attribute"
inputFrameworkOpName: "BatchToSpaceND"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BatchToSpaceND"
}
}
mappings {
frameworkName: "tensorflow"
opName: "reduce_mean"
inputFrameworkOpName: "Mean"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Mean"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Mean"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Mean"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cosh"
inputFrameworkOpName: "Cosh"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Cosh"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Cosh"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Cosh"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "Variable"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
ruleType: "tensor"
inputFrameworkOpName: "Variable"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Variable"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "Variable"
}
}
mappings {
frameworkName: "tensorflow"
opName: "log_softmax"
inputFrameworkOpName: "LogSoftmax"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "logits"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "logits"
}
ruleType: "tensor"
inputFrameworkOpName: "LogSoftmax"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cross"
inputFrameworkOpName: "Cross"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "b"
outputTensorName: "a"
outputTensorName: "b"
inputToOutput {
key: "a"
value: "a"
}
inputToOutput {
key: "b"
value: "b"
}
ruleType: "tensor"
inputFrameworkOpName: "Cross"
}
}
mappings {
frameworkName: "tensorflow"
opName: "matrix_set_diag"
inputFrameworkOpName: "BatchMatrixSetDiag"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
inputTensorName: "diagonal"
outputTensorName: "input"
outputTensorName: "diagonal"
inputToOutput {
key: "input"
value: "input"
}
inputToOutput {
key: "diagonal"
value: "diagonal"
}
ruleType: "tensor"
inputFrameworkOpName: "BatchMatrixSetDiag"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "BatchMatrixSetDiag"
}
}
mappings {
frameworkName: "tensorflow"
opName: "non_max_suppression_overlaps"
inputFrameworkOpName: "NonMaxSuppressionWithOverlaps"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "scores"
inputTensorName: "overlaps"
outputTensorName: "scales"
outputTensorName: "boxes"
inputToOutput {
key: "scales"
value: "scores"
}
inputToOutput {
key: "boxes"
value: "overlaps"
}
ruleType: "tensor"
inputFrameworkOpName: "NonMaxSuppressionWithOverlaps"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "maxOutputSize"
outputDoubleName: "overlapThreshold"
2021-02-07 11:27:41 +01:00
outputDoubleName: "scoreThreshold"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "maxOutputSize"
value: "max_output_size"
}
inputToOutput {
key: "overlapThreshold"
value: "overlap_threshold"
}
inputToOutput {
key: "scoreThreshold"
value: "score_threshold"
}
ruleType: "attribute"
inputFrameworkOpName: "NonMaxSuppressionWithOverlaps"
}
}
mappings {
frameworkName: "tensorflow"
opName: "concat"
inputFrameworkOpName: "ConcatV2"
rule {
2021-02-15 08:16:40 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "ConcatV2"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputIntName: "concatDimension"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "concatDimension"
value: "axis"
}
ruleType: "attribute"
inputFrameworkOpName: "ConcatV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "isDynamicAxis"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isDynamicAxis"
boolValue: true
argType: BOOL
}
}
inputFrameworkOpName: "ConcatV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "truncatediv"
inputFrameworkOpName: "TruncateDiv"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "TruncateDiv"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "TruncateDiv"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "TruncateDiv"
}
}
mappings {
frameworkName: "tensorflow"
opName: "any"
inputFrameworkOpName: "Any"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
2021-02-10 14:40:04 +01:00
inputTensorName: "reduction_indices"
2021-02-01 06:31:20 +01:00
outputTensorName: "input"
2021-02-10 14:40:04 +01:00
outputTensorName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "input"
value: "input"
}
2021-02-10 14:40:04 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "Any"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputBooleanName: "keep_dims"
outputBooleanName: "keepDims"
inputToOutput {
key: "keepDims"
value: "keep_dims"
}
ruleType: "attribute"
inputFrameworkOpName: "Any"
}
rule {
ruleName: "ndarraytointattributevalue"
functionName: "ndarraytointattributevalue"
outputIntName: "dimensions"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "dimensions"
value: "reduction_indices"
}
ruleType: "attribute"
inputFrameworkOpName: "Any"
}
}
mappings {
frameworkName: "tensorflow"
opName: "boolean_or"
inputFrameworkOpName: "LogicalOr"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "LogicalOr"
}
}
mappings {
frameworkName: "tensorflow"
opName: "Reciprocal"
inputFrameworkOpName: "Inv"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Inv"
}
}
mappings {
frameworkName: "tensorflow"
opName: "boolean_not"
inputFrameworkOpName: "LogicalNot"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "LogicalNot"
}
}
mappings {
frameworkName: "tensorflow"
opName: "igammac"
inputFrameworkOpName: "Igammac"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "a"
inputTensorName: "x"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "a"
}
inputToOutput {
key: "y"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Igammac"
}
}
mappings {
frameworkName: "tensorflow"
opName: "extract_image_patches"
inputFrameworkOpName: "ExtractImagePatches"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "images"
}
ruleType: "tensor"
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "ksizeRows"
inputToOutput {
key: "ksizeRows"
value: "ksizes"
}
ruleType: "attribute"
transformerArgs {
key: "ksizeRows"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "ksizeCols"
inputToOutput {
key: "ksizeCols"
value: "ksizes"
}
ruleType: "attribute"
transformerArgs {
key: "ksizeCols"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 1
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kstrideRows"
inputToOutput {
key: "kstrideRows"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "kstrideRows"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 2
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "kstrideCols"
inputToOutput {
key: "kstrideCols"
value: "strides"
}
ruleType: "attribute"
transformerArgs {
key: "kstrideCols"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 3
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "krateRows"
inputToOutput {
key: "krateRows"
value: "rates"
}
ruleType: "attribute"
transformerArgs {
key: "krateRows"
transformerArgs {
name: "index"
int64Value: 1
argType: INT64
argIndex: 4
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "listattributevaluelookuptoindex"
functionName: "listattributevaluelookuptoindex"
inputIntName: "index"
outputIntName: "krateCols"
inputToOutput {
key: "krateCols"
value: "rates"
}
ruleType: "attribute"
transformerArgs {
key: "krateCols"
transformerArgs {
name: "index"
int64Value: 2
argType: INT64
argIndex: 5
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "stringequals"
functionName: "stringequals"
inputStringAttrName: "padding"
inputStringAttrName: "padding"
2021-02-07 11:27:41 +01:00
outputIntName: "isSameMode"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "isSameMode"
value: "padding"
}
ruleType: "attribute"
transformerArgs {
key: "isSameMode"
transformerArgs {
name: "padding"
argType: STRING
stringValue: "SAME"
}
}
inputFrameworkOpName: "ExtractImagePatches"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "ExtractImagePatches"
}
}
mappings {
frameworkName: "tensorflow"
opName: "fake_quant_with_min_max_vars"
inputFrameworkOpName: "FakeQuantWithMinMaxVars"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "inputs"
inputTensorName: "min"
inputTensorName: "max"
outputTensorName: "input"
outputTensorName: "min"
outputTensorName: "max"
inputToOutput {
key: "input"
value: "inputs"
}
inputToOutput {
key: "min"
value: "min"
}
inputToOutput {
key: "max"
value: "max"
}
ruleType: "tensor"
inputFrameworkOpName: "FakeQuantWithMinMaxVars"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "num_bits"
outputIntName: "numBits"
inputBooleanName: "narrow_range"
outputBooleanName: "narrowed"
inputToOutput {
key: "numBits"
value: "num_bits"
}
inputToOutput {
key: "narrowed"
value: "narrow_range"
}
ruleType: "attribute"
inputFrameworkOpName: "FakeQuantWithMinMaxVars"
}
}
mappings {
frameworkName: "tensorflow"
opName: "round"
inputFrameworkOpName: "Round"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Round"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Round"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Round"
}
}
mappings {
frameworkName: "tensorflow"
opName: "dynamic_stitch"
inputFrameworkOpName: "ParallelDynamicStitch"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "passthrough"
functionName: "passthrough"
2021-02-01 06:31:20 +01:00
ruleType: "tensor"
inputFrameworkOpName: "ParallelDynamicStitch"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputIntName: "N"
outputIntName: "numPartitions"
inputToOutput {
key: "numPartitions"
value: "N"
}
ruleType: "attribute"
inputFrameworkOpName: "ParallelDynamicStitch"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sigmoid"
inputFrameworkOpName: "Sigmoid"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Sigmoid"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sigmoid"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sigmoid"
}
}
mappings {
frameworkName: "tensorflow"
opName: "lstmBlock"
inputFrameworkOpName: "BlockLSTMV2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "seq_len_max"
inputTensorName: "x"
inputTensorName: "cs_prev"
inputTensorName: "h_prev"
inputTensorName: "w"
inputTensorName: "wci"
inputTensorName: "wcf"
inputTensorName: "wco"
inputTensorName: "b"
outputTensorName: "maxTSLength"
outputTensorName: "input"
outputTensorName: "cLast"
outputTensorName: "yLast"
outputTensorName: "W"
outputTensorName: "Wci"
outputTensorName: "Wcf"
outputTensorName: "Wco"
outputTensorName: "b"
inputToOutput {
key: "maxTSLength"
value: "seq_len_max"
}
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "cLast"
value: "cs_prev"
}
inputToOutput {
key: "yLast"
value: "h_prev"
}
inputToOutput {
key: "W"
value: "w"
}
inputToOutput {
key: "Wci"
value: "wci"
}
inputToOutput {
key: "Wcf"
value: "wcf"
}
inputToOutput {
key: "Wco"
value: "wco"
}
inputToOutput {
key: "b"
value: "b"
}
ruleType: "tensor"
inputFrameworkOpName: "BlockLSTMV2"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputFloatName: "cell_clip"
outputDoubleName: "clippingCellValue"
inputToOutput {
key: "clippingCellValue"
value: "cell_clip"
}
ruleType: "attribute"
inputFrameworkOpName: "BlockLSTMV2"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "peephole"
inputBooleanName: "use_peephole"
inputToOutput {
key: "peephole"
value: "use_peephole"
}
ruleType: "attribute"
inputFrameworkOpName: "BlockLSTMV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputFloatName: "forgetBias"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "forgetBias"
doubleValue: 3.0
argType: DOUBLE
}
}
inputFrameworkOpName: "BlockLSTMV2"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputIntName: "dataFormat"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "dataFormat"
argType: INT64
}
}
inputFrameworkOpName: "BlockLSTMV2"
}
}
mappings {
frameworkName: "tensorflow"
opName: "atan"
inputFrameworkOpName: "Atan"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Atan"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Atan"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Atan"
}
}
mappings {
frameworkName: "tensorflow"
opName: "ClipByValue"
inputFrameworkOpName: "ClipByValue"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "t"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "t"
}
ruleType: "tensor"
inputFrameworkOpName: "ClipByValue"
}
rule {
ruleName: "ndarrayinputtonumericalattribute"
functionName: "ndarrayinputtonumericalattribute"
outputDoubleName: "clipValueMin"
outputDoubleName: "clipValueMax"
inputToOutput {
key: "clipValueMin"
value: "clip_value_min"
}
inputToOutput {
key: "clipValueMax"
value: "clip_value_max"
}
ruleType: "attribute"
inputFrameworkOpName: "ClipByValue"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "ClipByValue"
}
}
mappings {
frameworkName: "tensorflow"
opName: "segment_mean"
inputFrameworkOpName: "SegmentMean"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "data"
inputTensorName: "segment_ids"
outputTensorName: "input"
outputTensorName: "idxSegments"
inputToOutput {
key: "input"
value: "data"
}
inputToOutput {
key: "idxSegments"
value: "segment_ids"
}
ruleType: "tensor"
inputFrameworkOpName: "SegmentMean"
}
}
mappings {
frameworkName: "tensorflow"
opName: "floor"
inputFrameworkOpName: "Floor"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Floor"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Floor"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Floor"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_update"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "ScatterUpdate"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "ref"
inputTensorName: "updates"
inputTensorName: "indices"
outputTensorName: "operand"
2021-02-01 06:31:20 +01:00
outputTensorName: "updates"
outputTensorName: "indices"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "operand"
2021-02-01 06:31:20 +01:00
value: "ref"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "indices"
value: "indices"
}
ruleType: "tensor"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "ScatterUpdate"
}
}
mappings {
frameworkName: "tensorflow"
opName: "identity"
inputFrameworkOpName: "DeepCopy"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "DeepCopy"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "DeepCopy"
}
2021-02-15 08:16:40 +01:00
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "DeepCopy"
}
2021-02-01 06:31:20 +01:00
}
mappings {
frameworkName: "tensorflow"
opName: "hsv_to_rgb"
inputFrameworkOpName: "HSVToRGB"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "images"
}
ruleType: "tensor"
inputFrameworkOpName: "HSVToRGB"
}
}
mappings {
frameworkName: "tensorflow"
opName: "listdiff"
inputFrameworkOpName: "ListDiff"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "values"
outputTensorName: "keep"
inputToOutput {
key: "values"
value: "x"
}
inputToOutput {
key: "keep"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "ListDiff"
}
}
mappings {
frameworkName: "tensorflow"
opName: "While"
inputFrameworkOpName: "While"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "input"
outputTensorName: "condition"
inputToOutput {
key: "condition"
value: "input"
}
ruleType: "tensor"
inputFrameworkOpName: "While"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "isConstant"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "isConstant"
argType: BOOL
}
}
inputFrameworkOpName: "While"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_update"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "TensorScatterUpdate"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "tensor"
inputTensorName: "updates"
inputTensorName: "indices"
outputTensorName: "operand"
2021-02-01 06:31:20 +01:00
outputTensorName: "updates"
outputTensorName: "indices"
2021-02-01 06:31:20 +01:00
inputToOutput {
key: "operand"
2021-02-01 06:31:20 +01:00
value: "tensor"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "indices"
value: "indices"
}
ruleType: "tensor"
2021-02-01 06:31:20 +01:00
inputFrameworkOpName: "TensorScatterUpdate"
}
}
mappings {
frameworkName: "tensorflow"
opName: "scatter_sub"
inputFrameworkOpName: "TensorScatterSub"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "indices"
inputTensorName: "updates"
inputTensorName: "tensor"
outputTensorName: "indices"
outputTensorName: "updates"
outputTensorName: "input"
inputToOutput {
key: "indices"
value: "indices"
}
inputToOutput {
key: "updates"
value: "updates"
}
inputToOutput {
key: "input"
value: "tensor"
}
ruleType: "tensor"
inputFrameworkOpName: "TensorScatterSub"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "lock"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "lock"
argType: BOOL
}
}
inputFrameworkOpName: "TensorScatterSub"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "checkIndices"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "checkIndices"
argType: BOOL
argIndex: 1
}
}
inputFrameworkOpName: "TensorScatterSub"
}
}
mappings {
frameworkName: "tensorflow"
opName: "cumprod"
inputFrameworkOpName: "Cumprod"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "axis"
outputTensorName: "input"
outputTensorName: "dimensions"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "dimensions"
value: "axis"
}
ruleType: "tensor"
inputFrameworkOpName: "Cumprod"
}
rule {
ruleName: "invertbooleannumber"
functionName: "invertbooleannumber"
outputIntName: "exclusive"
outputIntName: "reverse"
2021-02-01 06:31:20 +01:00
inputBooleanName: "exclusive"
inputBooleanName: "reverse"
inputToOutput {
key: "exclusive"
value: "exclusive"
}
inputToOutput {
key: "reverse"
value: "reverse"
}
ruleType: "attribute"
inputFrameworkOpName: "Cumprod"
}
}
mappings {
frameworkName: "tensorflow"
opName: "mergesum"
inputFrameworkOpName: "AddN"
rule {
2021-02-10 14:40:04 +01:00
ruleName: "multiinputindex"
functionName: "multiinputindex"
2021-02-01 06:31:20 +01:00
inputTensorName: "inputs"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "inputs"
}
ruleType: "tensor"
inputFrameworkOpName: "AddN"
}
}
mappings {
frameworkName: "tensorflow"
opName: "random_normal"
inputFrameworkOpName: "RandomStandardNormal"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "shape"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "shape"
}
ruleType: "tensor"
inputFrameworkOpName: "RandomStandardNormal"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "dtype"
outputDataTypeName: "dtype"
inputToOutput {
key: "dtype"
value: "dtype"
}
ruleType: "attribute"
inputFrameworkOpName: "RandomStandardNormal"
}
}
mappings {
frameworkName: "tensorflow"
opName: "sign"
inputFrameworkOpName: "Sign"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Sign"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Sign"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Sign"
}
}
mappings {
frameworkName: "tensorflow"
opName: "greater"
inputFrameworkOpName: "Greater"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
inputTensorName: "y"
outputTensorName: "input"
outputTensorName: "y"
inputToOutput {
key: "input"
value: "x"
}
inputToOutput {
key: "y"
value: "y"
}
ruleType: "tensor"
inputFrameworkOpName: "Greater"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Greater"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Greater"
}
}
mappings {
frameworkName: "tensorflow"
opName: "exp"
inputFrameworkOpName: "Exp"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "x"
outputTensorName: "input"
inputToOutput {
key: "input"
value: "x"
}
ruleType: "tensor"
inputFrameworkOpName: "Exp"
}
rule {
ruleName: "valuemapping"
functionName: "valuemapping"
inputDataTypeName: "T"
outputDataTypeName: "dataType"
inputToOutput {
key: "dataType"
value: "T"
}
ruleType: "attribute"
inputFrameworkOpName: "Exp"
}
rule {
ruleName: "argdescriptorconstant"
functionName: "argdescriptorconstant"
inputBooleanName: "inPlace"
ruleType: "attribute"
transformerArgs {
key: "value"
transformerArgs {
name: "inPlace"
argType: BOOL
}
}
inputFrameworkOpName: "Exp"
}
}
mappings {
frameworkName: "tensorflow"
opName: "adjust_contrast_v2"
inputFrameworkOpName: "AdjustContrastv2"
rule {
ruleName: "ndarraymapping"
functionName: "ndarraymapping"
inputTensorName: "images"
inputTensorName: "contrast_factor"
outputTensorName: "input"
outputTensorName: "factor"
inputToOutput {
key: "input"
value: "images"
}
inputToOutput {
key: "factor"
value: "contrast_factor"
}
ruleType: "tensor"
inputFrameworkOpName: "AdjustContrastv2"
}
}