def parse(self):
logger.debug("Parsing %s...", self.type)
op = self.tflite
opcode = self.model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
assert (opcode in self.TypeMapping)
assert (op.InputsLength() == 2)
assert (op.OutputsLength() == 1)
self.parseInput(0)
self.parseInput(1)
ot = self.parseOutput(0)
self.fakeBroadcast()
# options
op_opt = op.BuiltinOptions()
if opcode in self.OptionMapping:
option = self.OptionMapping[opcode]()
option.Init(op_opt.Bytes, op_opt.Pos)
handleFusedActivation(self, option, ot)
self.setParsed()
def parse(self):
logger.debug("Parsing %s...", self.type)
op = self.tflite
opcode = self.model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
assert (opcode is tflite.BuiltinOperator.FULLY_CONNECTED)
assert (
op.InputsLength() == 3), "TFLite Fullly Connected always has bias"
assert (op.OutputsLength() == 1)
# input
self.parseInput(0)
# weight
self.parseInput(1)
# bias
self.parseInput(2, is_bias=True)
# output
ot = self.parseOutput(0)
# options
op_opt = op.BuiltinOptions()
option = tflite.FullyConnectedOptions()
option.Init(op_opt.Bytes, op_opt.Pos)
assert (not option.KeepNumDims())
assert (option.WeightsFormat() is
tflite.FullyConnectedOptionsWeightsFormat.DEFAULT)
handleFusedActivation(self, option, ot)
self.setParsed()
def parse(self):
logger.debug("Parsing %s...", self.type)
op = self.tflite
opcode = self.model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
assert (opcode in self.TypeMapping)
assert (op.InputsLength() == 1)
assert (op.OutputsLength() == 1)
ilayout = Layout('NHWC', 'NCHW')
self.parseInput(0, ilayout)
op_opt = op.BuiltinOptions()
option = tflite.Pool2DOptions()
option.Init(op_opt.Bytes, op_opt.Pos)
self.attrs['auto_pad'] = PaddingMapping[option.Padding()]
self.attrs['kernel_shape'] = [
option.FilterHeight(), option.FilterWidth()
]
self.attrs['strides'] = [option.StrideH(), option.StrideW()]
olayout = Layout('NHWC', 'NCHW')
ot = self.parseOutput(0, olayout)
handleFusedActivation(self, option, ot)
self.setParsed()
def parse(self):
logger.debug("Parsing %s...", self.type)
op = self.tflite
opcode = self.model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
assert (opcode in self.TypeMapping)
assert (op.InputsLength() == 3), "TFLite Conv always has bias"
assert (op.OutputsLength() == 1)
# input
ilayout = Layout('NHWC', 'NCHW')
it = self.parseInput(0, ilayout)
# weight
wlayout = Layout('CHWM', 'MCHW') if self.isDepthwise else Layout(
'OHWI', 'OIHW')
wt = self.parseInput(1, wlayout)
# bias
self.parseInput(2, is_bias=True)
# output
olayout = Layout('NHWC', 'NCHW')
ot = self.parseOutput(0, olayout)
# options
op_opt = op.BuiltinOptions()
option = tflite.DepthwiseConv2DOptions(
) if self.isDepthwise else tflite.Conv2DOptions()
option.Init(op_opt.Bytes, op_opt.Pos)
self.attrs['dilations'] = [
option.DilationHFactor(),
option.DilationWFactor()
]
self.attrs['group'] = wt.shape[3] if self.isDepthwise else 1
self.attrs['kernel_shape'] = wt.shape[1:3]
self.attrs['strides'] = [option.StrideH(), option.StrideW()]
# XXX Not enabled as ONNXRuntime has limitation to infer pads for non-1 dilation
# self.attrs['auto_pad'] = PaddingMapping[option.Padding()]
if self.isDepthwise:
assert (option.DepthMultiplier() == 1)
self.attrs['pads'] = computePaddingSize(option.Padding(),
it.shape[1:3],
self.attrs['kernel_shape'],
self.attrs['strides'],
self.attrs['dilations'])
handleFusedActivation(self, option, ot)
self.setParsed()
def parse(self):
logger.debug("Parsing %s...", self.shorty)
op = self.tflite
opcode = self.model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
assert(opcode is tflite.BuiltinOperator.CONCATENATION)
assert(op.InputsLength() >= 1)
assert(op.OutputsLength() == 1)
for i in range(op.InputsLength()):
self.parseInput(i)
op_opt = op.BuiltinOptions()
option = tflite.ConcatenationOptions()
option.Init(op_opt.Bytes, op_opt.Pos)
self.attrs['axis'] = option.Axis()
self.parseOutput(0)
handleFusedActivation(self, option, self.outputs[0])
self.setParsed()