diff options
Diffstat (limited to 'ethosu/vela/operation.py')
| -rw-r--r-- | ethosu/vela/operation.py | 15 |
1 files changed, 14 insertions, 1 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index 967d30b..d2b08b5 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -196,7 +196,7 @@ class Op(Enum): Max = OperatorInfo() MaxPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES) Maximum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES) - Mean = OperatorInfo() + Mean = OperatorInfo(indices=IFM_INDICES) Min = OperatorInfo() Minimum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES) MirrorPad = OperatorInfo() @@ -414,6 +414,7 @@ class Operation: "run_on_npu", "activation", "memory_function", + "forced_input_quantization", "forced_output_quantization", "activation_lut", "_kernel", @@ -422,6 +423,7 @@ class Operation: "rescale", "read_offsets", "rounding_mode", + "low_precision_scaling", ) def __init__(self, op_type: Op, name: str): @@ -439,6 +441,7 @@ class Operation: self.memory_function = None # If not none: contains QuantizationParameters to be used as output quantization # (which overrides the ofm tensor's quantization), used in LUT + self.forced_input_quantization = None self.forced_output_quantization = None self.scheduled_pass = None self.op_index = None # input network operator index @@ -451,6 +454,9 @@ class Operation: self.rescale = None self.read_offsets: List[Shape4D] = [None, None] # offset for [ifm, ifm2] self.rounding_mode: Optional[NpuRoundingMode] = None + # The Mean operator (implemented as a depthwise convolution) requires scaling + # to be calculated differently in one case. In that case, this is set to True. + self.low_precision_scaling = False def clone(self, suffix="_clone"): res = Operation(self.type, self.name + suffix) @@ -463,11 +469,13 @@ class Operation: res.run_on_npu = self.run_on_npu res.activation = None if self.activation is None else self.activation.clone() res.memory_function = self.memory_function + res.forced_input_quantization = self.forced_input_quantization res.forced_output_quantization = self.forced_output_quantization res.scheduled_pass = self.scheduled_pass res.op_index = None # not relevant as not part of input network res.read_offsets = list(self.read_offsets) res.rounding_mode = self.rounding_mode + res.low_precision_scaling = self.low_precision_scaling return res @@ -692,6 +700,11 @@ class Operation: if self not in tens.consumer_list: tens.consumer_list.append(self) + def get_input_quantization(self): + if self.forced_input_quantization is not None: + return self.forced_input_quantization + return self.ifm.quantization + def set_output_tensor(self, tens): tens.ops = [self] self.outputs = [tens] |