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-rw-r--r--ethosu/vela/operation.py7
1 files changed, 0 insertions, 7 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 8b3c88d9..8189793e 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -27,7 +27,6 @@ from typing import List
from typing import Optional
from typing import Tuple
from typing import TYPE_CHECKING
-from typing import Union
from .api import NpuRoundingMode
from .errors import VelaError
@@ -247,8 +246,6 @@ class Op(Enum):
ReluN1To1 = OperatorInfo(indices=NNG_IFM_INDICES)
ReluN = OperatorInfo(indices=NNG_IFM_INDICES) # TOSA specific
Rescale = OperatorInfo(indices=NNG_IFM_INDICES) # TOSA specific
- RescaleAdd = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
- RescaleMul = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
Reshape = OperatorInfo(indices=NNG_IFM_INDICES)
# resize ops map to pooling operations unless explicitly converted to other operations in the graph optimiser
ResizeBilinear = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
@@ -535,9 +532,6 @@ class Operation:
self._kernel = None
self.ifm_shapes: List[Shape4D] = []
self.ofm_shapes: List[Shape4D] = []
- # If not none: contains rescale to be used as output scaling
- # (which overrides the ofm tensor's scale)
- self.rescale: Optional[Union[Tuple[int, int], ExplicitScaling]] = None
self.read_offsets: List[Optional[Shape4D]] = [None, None] # offset for [ifm, ifm2]
self.read_shapes: List[Optional[Shape4D]] = [None, None] # read shape for [ifm, ifm2]
self.rounding_mode: Optional[NpuRoundingMode] = None
@@ -586,7 +580,6 @@ class Operation:
res.rounding_mode = self.rounding_mode
res.explicit_scaling = self.explicit_scaling
res.low_precision_scaling = self.low_precision_scaling
- res.rescale = self.rescale
res.ifm_resampling_mode = self.ifm_resampling_mode
res.tile_base_offsets_ifm = [_ifm.copy() for _ifm in self.tile_base_offsets_ifm]
res.tile_base_offsets_ofm = self.tile_base_offsets_ofm.copy()