MLBEDSW-3694 Replace padding with enum
Use an Enum instead of a bytestring to specify VALID or SAME padding
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I4e87f8c32b3bfac176d822a68de061e85a558fce
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 15d1352..4806001 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -35,6 +35,7 @@
from .operation import NpuBlockType
from .operation import Op
from .operation import Operation
+from .operation import Padding
from .operation_util import create_avgpool_nop
from .softmax import SoftMax
from .tensor import check_quantized_tens_scaling_equal
@@ -147,18 +148,18 @@
def calc_padding_and_skirt(padding_type, kernel_size, stride, input_dims):
ypad = needed_total_padding(int(input_dims[1]), int(stride[1]), int(kernel_size[0]))
xpad = needed_total_padding(int(input_dims[2]), int(stride[2]), int(kernel_size[1]))
- if padding_type == b"SAME":
+ if padding_type == Padding.SAME:
left_pad = (xpad + 0) // 2
right_pad = (xpad + 1) // 2
top_pad = (ypad + 0) // 2
bottom_pad = (ypad + 1) // 2
- elif padding_type == b"VALID":
+ elif padding_type == Padding.VALID:
left_pad = 0
right_pad = 0
top_pad = 0
bottom_pad = 0
else:
- raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
+ raise UnsupportedFeatureError(f"Unknown padding")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad)
return padding, skirt
@@ -166,21 +167,20 @@
def calc_upscaled_padding_and_skirt(padding_type, kernel_size, stride, input_dims, upscaling_factor):
kernel_height, kernel_width = kernel_size[0], kernel_size[1]
- if padding_type == b"SAME":
+ if padding_type == Padding.SAME:
ypad = needed_total_padding(int(input_dims[1]) * upscaling_factor, int(stride[1]), int(kernel_height))
xpad = needed_total_padding(int(input_dims[2]) * upscaling_factor, int(stride[2]), int(kernel_width))
right_pad = max(((xpad + 1) // upscaling_factor) - 1, 0)
bottom_pad = max(((ypad + 1) // upscaling_factor) - 1, 0)
left_pad = max(kernel_width - 1 - right_pad, 0)
top_pad = max(kernel_height - 1 - bottom_pad, 0)
- elif padding_type == b"VALID":
+ elif padding_type == Padding.VALID:
right_pad = max(kernel_width - 2, 0)
bottom_pad = max(kernel_height - 2, 0)
left_pad = kernel_width - 1
top_pad = kernel_height - 1
else:
- raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
-
+ raise UnsupportedFeatureError(f"Unknown padding")
padding = (top_pad, left_pad, bottom_pad, right_pad)
skirt = padding
return padding, skirt
@@ -230,10 +230,10 @@
op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 2, 2, 1)})
if op.attrs["align_corners"]:
shape_modifier = 1
- op.attrs["padding"] = b"VALID"
+ op.attrs["padding"] = Padding.VALID
else:
shape_modifier = 0
- op.attrs["padding"] = b"SAME"
+ op.attrs["padding"] = Padding.SAME
op.inputs[0].resampling_mode = resampling_mode.NEAREST
upscaled_shape = np.array(op.inputs[0].shape[1:3])
@@ -1034,11 +1034,11 @@
if not op.attrs["align_corners"] and out_shape == upscaled_shape:
# this means the output is supposed to be a x2 upscale,
# so we need to do SAME padding
- op.attrs["padding"] = b"SAME"
+ op.attrs["padding"] = Padding.SAME
elif op.attrs["align_corners"] and out_shape == [upscaled_shape[0] - 1, upscaled_shape[1] - 1]:
# here we can just run the avg pool without padding and
# produce a (M * 2 - 1, N * 2 - 1) sized output
- op.attrs["padding"] = b"VALID"
+ op.attrs["padding"] = Padding.VALID
else:
return op
input_tensor.resampling_mode = resampling_mode.NEAREST