'-o', '--output', action='store', dest="out_dir", help="output directory")
args = parser.parse_args()
- filename = sys.argv[1]
filename = args.graph_def
if args.out_dir:
for (const opcode of model.operator_codes) {
opcodeIdxToOpName.push(opcode.builtin_code)
}
-console.debug(opcodeIdxToOpName)
// List: tensor index (number) => producing operator's index (number)
// assume there is only one op that produces given output tensor.
if (opeq(operators[i], "SQUARED_DIFFERENCE"))
squaredDifferenceIdxList.push(i)
}
-console.debug(squaredDifferenceIdxList)
let instanceNormList = [ ]
for (let idx of squaredDifferenceIdxList) {
}
const epsilon = getFloatScalarValueFromInputsOf(add3)
if (hasUndefined(epsilon)) continue
- console.debug(epsilon)
const rsqrt4 = findConsumer(add3, "RSQRT")
if (hasUndefined(rsqrt4)) continue
}
const iGamma = getFloat1DTensorIdxFromInputsOf(mul5)
if (hasUndefined(iGamma)) continue
- console.debug(iGamma)
let mul6, mul7
for (let i of useOps[mul5.outputs[0]]) {