diff options
Diffstat (limited to 'ethosu/vela/operation.py')
| -rw-r--r-- | ethosu/vela/operation.py | 19 |
1 files changed, 10 insertions, 9 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index 277f2de5..5a6423d8 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -15,6 +15,9 @@ # limitations under the License. # Description: # Internal representation of a Neural Network Operation. +# For Class name forward references for the type annotations. (see PEP 563). +from __future__ import annotations + import copy from collections import namedtuple from enum import Enum @@ -24,13 +27,14 @@ 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 from .numeric_util import full_shape from .shape4d import Shape4D - +# Import needed for Type annotations. Only import for Type checking to avoid run-time errors due to cyclic import. if TYPE_CHECKING: from .tensor import Tensor @@ -80,9 +84,6 @@ class Kernel: def area_height(self) -> int: return (self.height - 1) * self.dilation.y + 1 - def dilation(self) -> PointXY: - return self.dilation - def dilated_wh(self) -> Tuple[int, int]: """Returns the dilated kernel width/height""" return self.dilation.x * (self.width - 1) + 1, self.dilation.y * (self.height - 1) + 1 @@ -443,7 +444,7 @@ def create_activation_function(op_type: Op, min=None, max=None) -> ActivationFun return act -def get_slice_offsets(input_shape: List[int], offset_tens: int, offset_mask: int, is_begin: bool = True): +def get_slice_offsets(input_shape: List[int], offset_tens: Tensor, offset_mask: int, is_begin: bool = True): # For strided slice operator: get start or end offsets offsets = len(input_shape) * [0] if is_begin else input_shape[:] for idx in range(len(input_shape)): @@ -493,7 +494,7 @@ class Operation: self.type = op_type self.name = name self.attrs: Dict[str, Any] = {} - self.inputs: List[Tensor] = [] + self.inputs: List[Optional[Tensor]] = [] self.outputs: List[Tensor] = [] self.intermediates: List[Tensor] = [] self.flops = 0 @@ -514,9 +515,9 @@ class Operation: 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 = None - self.read_offsets: List[Shape4D] = [None, None] # offset for [ifm, ifm2] - self.read_shapes: List[Shape4D] = [None, None] # read shape for [ifm, ifm2] + 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 # Rescale op in TOSA supplies explicit multiplier and shift values self.explicit_scaling: Optional[ExplicitScaling] = None |