MLBEDSW-2066 Improve Exception messages

Minor refactoring to use fstrings.
Improve Error classes to correctly inherit the base class.
Use existing exception classes instead of plain exceptions where it
makes sense.

Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I0941c04e91010da1db77299517a8e2d896371e77

1 files changed, 6 insertions, 8 deletions

diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 13f08f26..15d13522 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -42,6 +42,7 @@ from .tensor import create_const_tensor
 from .tensor import create_reshape_tensor
 from .tensor import QuantizationParameters
 from .tensor import Tensor
+from .tflite_mapping import optype_to_builtintype
 
 passthrough_nodes = (Op.Identity,)
 
@@ -157,7 +158,7 @@ def calc_padding_and_skirt(padding_type, kernel_size, stride, input_dims):
         top_pad = 0
         bottom_pad = 0
     else:
-        raise UnsupportedFeatureError("Unknown padding {}".format(str(padding_type)))
+        raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
     padding = (top_pad, left_pad, bottom_pad, right_pad)
     skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad)
     return padding, skirt
@@ -168,19 +169,17 @@ def calc_upscaled_padding_and_skirt(padding_type, kernel_size, stride, input_dim
     if padding_type == b"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":
         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:
-        assert 0, "Unknown padding"
+        raise UnsupportedFeatureError(f"Unknown padding {padding_type.decode('utf-8')}")
 
     padding = (top_pad, left_pad, bottom_pad, right_pad)
     skirt = padding
@@ -504,7 +503,7 @@ def add_padding_fields(op, arch, nng):
                 kernel_size = op.attrs["ksize"][1:3]
                 input_shape = op.inputs[0].shape
             else:
-                raise UnsupportedFeatureError("Unknown operation that uses padding: {}".format(op.type))
+                raise UnsupportedFeatureError(f"Unknown operation that uses padding: {optype_to_builtintype(op.type)}")
 
             if op.type == Op.Conv2DBackpropInputSwitchedBias:
                 upscaling_factor = op.outputs[0].shape[1] // input_shape[1]
@@ -560,9 +559,8 @@ def convert_depthwise_to_conv(op, arch, nng):
             weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape))
         else:
             raise UnsupportedFeatureError(
-                "Unsupported DepthwiseConv2d with depth_multiplier = {}, ifm channels = {}, ofm channels = {}".format(
-                    op.attrs["depth_multiplier"], ifm_tensor.shape[3], ofm_tensor.shape[3]
-                )
+                f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},",
+                f" ifm channels = {ifm_tensor.shape[3]}, ofm channels = {ofm_tensor.shape[3]}",
             )
         DebugDatabase.add_optimised(op, op)
     return op