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

6 files changed, 45 insertions, 36 deletions

diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 15d13522..4806001f 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -35,6 +35,7 @@ from .operation import create_activation_function
 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 needed_total_padding(input_size, stride, filter_size):
 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_padding_and_skirt(padding_type, kernel_size, stride, input_dims):
 
 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 @@ def convert_resizebilinear_to_2x2_pool(op):
     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 @@ def add_attrs_to_resizebilinear(op, arch, nng):
         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
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 32cba365..afc02d41 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -326,6 +326,11 @@ class Op(Enum):
         return self.value.id < other.value.id
 
 
+class Padding(Enum):
+    SAME = 0
+    VALID = 1
+
+
 class ActivationFunction:
     """Fused activation function"""
 
diff --git a/ethosu/vela/operation_util.py b/ethosu/vela/operation_util.py
index 2fc7622c..a267b2ad 100644
--- a/ethosu/vela/operation_util.py
+++ b/ethosu/vela/operation_util.py
@@ -22,6 +22,7 @@ from .high_level_command_to_npu_op import ifm_ifm2_correct_order
 from .operation import ActivationFunction
 from .operation import Op
 from .operation import Operation
+from .operation import Padding
 from .tensor import create_reshape_tensor
 from .tensor import QuantizationParameters
 from .tensor import Tensor
@@ -29,7 +30,7 @@ from .tensor import Tensor
 
 def create_avgpool_nop(name: str) -> Operation:
     op = Operation(Op.AvgPool, name)
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     op.attrs["stride_w"] = 1
     op.attrs["stride_h"] = 1
     op.attrs["filter_width"] = 1
@@ -48,7 +49,7 @@ def create_depthwise_maxpool(
     height = ifm.shape[1] * ifm.shape[2]
     width = ifm.shape[3]
     ifm_shape = [1, height, width, 1]
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     op.attrs["stride_w"] = 1
     op.attrs["stride_h"] = 1
     op.attrs["filter_width"] = width
@@ -67,7 +68,7 @@ def create_reduce_sum(
     name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None
 ) -> Operation:
     op = Operation(Op.ReduceSum, name)
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     op.attrs["stride_w"] = 1
     op.attrs["stride_h"] = 1
     op.attrs["filter_width"] = 1
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index a1fcf6ab..f2c2eb9e 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -24,6 +24,7 @@ from .data_type import DataType
 from .numeric_util import is_integer
 from .operation import get_slice_offsets
 from .operation import Op
+from .operation import Padding
 from .tensor import check_quantized_tens_scaling_equal
 from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN
 from .tflite_mapping import optype_to_builtintype
@@ -569,7 +570,7 @@ class SupportedOperators:
     @staticmethod
     def constraint_tconv_same(op):
         "SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride"
-        if op.attrs["padding"] == b"SAME":
+        if op.attrs["padding"] == Padding.SAME:
             w = op.kernel.stride.x
             h = op.kernel.stride.y
             ifm_shape = op.ifm.shape
@@ -582,7 +583,7 @@ class SupportedOperators:
     def constraint_tconv_valid(op):
         """VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride,
                   minus difference between kernel size and stride"""
-        if op.attrs["padding"] == b"VALID":
+        if op.attrs["padding"] == Padding.VALID:
             s_w = op.kernel.stride.x
             s_h = op.kernel.stride.y
             k_w = op.kernel.width
@@ -626,7 +627,7 @@ class SupportedOperators:
     @docstring_format_args(filter_range)
     def constraint_filter_range(cls, op):
         "Kernel filter values for both width and height must be in the range [{}, {}]"
-        if op.attrs["padding"] == b"SAME":
+        if op.attrs["padding"] == Padding.SAME:
             w = op.kernel.width
             h = op.kernel.height
             filter_min, filter_max = cls.filter_range
@@ -656,7 +657,7 @@ class SupportedOperators:
     @docstring_format_args(filter_height_range)
     def constraint_filter_height_range_valid_pad(op):
         "VALID padding: Kernel filter height must be in the range [{}, {}]"
-        if op.attrs["padding"] == b"VALID":
+        if op.attrs["padding"] == Padding.VALID:
             return SupportedOperators.constraint_filter_height_range(op)
         return True, "Op has padding=SAME"
 
@@ -664,7 +665,7 @@ class SupportedOperators:
     @docstring_format_args(filter_product_range)
     def constraint_filter_product_range_valid_pad(op):
         "VALID padding: Product of kernel filter width and height must be in the range [{}, {}]"
-        if op.attrs["padding"] == b"VALID":
+        if op.attrs["padding"] == Padding.VALID:
             return SupportedOperators.constraint_filter_product_range(op)
         return True, "Op has padding=SAME"
 
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index f132eef7..583821a2 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -21,6 +21,7 @@ import numpy as np
 from ethosu.vela.data_type import DataType
 from ethosu.vela.operation import ActivationFunction
 from ethosu.vela.operation import Op
+from ethosu.vela.operation import Padding
 from ethosu.vela.supported_operators import SupportedOperators
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import QuantizationParameters
@@ -276,7 +277,7 @@ def test_constraint_depth_multiplier():
 def test_constraint_tconv_stride():
     # Strides must be 2
     op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
-    op.attrs = {"stride_w": 1, "stride_h": 1, "padding": b"SAME"}
+    op.attrs = {"stride_w": 1, "stride_h": 1, "padding": Padding.SAME}
     ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
     ifm.quantization = testutil.default_quant_params()
     op.add_input_tensor(ifm)
@@ -286,14 +287,14 @@ def test_constraint_tconv_stride():
 def test_constraint_tconv_same():
     # Valid
     op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME}
     ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
     ifm.quantization = testutil.default_quant_params()
     op.add_input_tensor(ifm)
     assert support.is_operator_supported(op)
     # Invalid
     op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[1, 1, 1, 1])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME}
     ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
     ifm.quantization = testutil.default_quant_params()
     op.add_input_tensor(ifm)
@@ -303,14 +304,14 @@ def test_constraint_tconv_same():
 def test_constraint_tconv_valid():
     # Valid
     op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[4, 4, 1, 1])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID}
     ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
     ifm.quantization = testutil.default_quant_params()
     op.add_input_tensor(ifm)
     assert support.is_operator_supported(op)
     # Invalid
     op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[2, 2, 1, 1])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID}
     ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
     ifm.quantization = testutil.default_quant_params()
     op.add_input_tensor(ifm)
@@ -320,7 +321,7 @@ def test_constraint_tconv_valid():
 def test_constraint_matching_in_out_types():
     # Valid
     op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 2, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 2, "padding": Padding.SAME}
     assert support.is_operator_supported(op)
     # Invalid. datatypes for ifm and ofm must match (default uint8)
     op.ifm.dtype = DataType.int8
@@ -330,7 +331,7 @@ def test_constraint_matching_in_out_types():
 def test_constraint_filter_type():
     # Filter width/height must be integers
     op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2.5, "filter_height": "2", "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2.5, "filter_height": "2", "padding": Padding.SAME}
     assert not support.is_operator_supported(op)
 
 
@@ -338,17 +339,17 @@ def test_constraint_filter_range():
     # Avg pool restrictions are dependent on padding:
     # SAME padding restricts both W and H to max 8
     op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 20, "filter_height": 20, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 20, "filter_height": 20, "padding": Padding.SAME}
     assert not support.is_operator_supported(op)
     # VALID padding limits are much larger
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     assert support.is_operator_supported(op)
 
 
 def test_constraint_filter_height_range_valid_pad():
     # Avg pool restrictions are dependent on padding:
     op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": b"VALID"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.VALID}
     assert support.is_operator_supported(op)
     # VALID padding restricts to 256 in filter height
     op.attrs["filter_height"] = 257
@@ -358,7 +359,7 @@ def test_constraint_filter_height_range_valid_pad():
 def test_constraint_filter_product_height_range_valid_pad():
     # Avg pool restrictions are dependent on padding:
     op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": b"VALID"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.VALID}
     assert support.is_operator_supported(op)
     # VALID padding restricts filter W x H to 256x256
     op.attrs["filter_width"] = 257
@@ -368,26 +369,26 @@ def test_constraint_filter_product_height_range_valid_pad():
 def test_constraint_filter_height_range():
     # Max pool restrictions arent dependent on padding
     op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.SAME}
     assert support.is_operator_supported(op)
     # Restricts to 256 in filter height
     op.attrs["filter_height"] = 257
     assert not support.is_operator_supported(op)
     # Doesnt matter if SAME or VALID
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     assert not support.is_operator_supported(op)
 
 
 def test_constraint_filter_product_height_range():
     # Max pool restrictions arent dependent on padding
     op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": b"SAME"}
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.SAME}
     assert support.is_operator_supported(op)
     # Restricts filter W x H to 256x256
     op.attrs["filter_width"] = 257
     assert not support.is_operator_supported(op)
     # Doesnt matter if SAME or VALID
-    op.attrs["padding"] = b"VALID"
+    op.attrs["padding"] = Padding.VALID
     assert not support.is_operator_supported(op)
 
 
diff --git a/ethosu/vela/tflite_mapping.py b/ethosu/vela/tflite_mapping.py
index fe582614..cc6053c0 100644
--- a/ethosu/vela/tflite_mapping.py
+++ b/ethosu/vela/tflite_mapping.py
@@ -24,6 +24,7 @@ import numpy as np
 from .data_type import DataType
 from .operation import CustomType
 from .operation import Op
+from .operation import Padding as opPad
 from .tflite import AbsOptions
 from .tflite import AddNOptions
 from .tflite import AddOptions
@@ -425,8 +426,8 @@ class CustomOptionsSerializer:
 
 
 padding_map = {
-    Padding.SAME: b"SAME",
-    Padding.VALID: b"VALID",
+    Padding.SAME: opPad.SAME,
+    Padding.VALID: opPad.VALID,
 }
 
 padding_inv_map = inverse_map(padding_map)