diff options
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 14 |
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 |