diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 13 |
1 files changed, 6 insertions, 7 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 5d6d7071..f3ca1b63 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -871,7 +871,7 @@ def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng): # Add explicit rescaling rescale = ifm.quantization.scale_f32 / ofm.quantization.scale_f32 multiplier, shift = scaling.quantise_scale(rescale) - relu_fused_op.rescale = ExplicitScaling(False, [shift], [multiplier]) + relu_fused_op.explicit_scaling = ExplicitScaling(False, [shift], [multiplier]) # Tidy up and assign the ifm and ofm to the new op ifm.consumer_list.remove(op) @@ -991,8 +991,8 @@ def convert_prelu(op, arch, nng): DebugDatabase.add_optimised(op, relu_op) # Add scaled and alpha multiplied values (without scaling) - add_op = Operation(Op.RescaleAdd, op.name + "_add") - add_op.rescale = (1, 0) # No scale or shift + add_op = Operation(Op.Add, op.name + "_add") + add_op.explicit_scaling = ExplicitScaling(False, shift=[0], multiplier=[1]) # No scaling add_op.add_input_tensor(fm_alpha) add_op.add_input_tensor(fm_scaled) add_op.set_output_tensor(ofm) @@ -1180,8 +1180,8 @@ def convert_lrelu_to_mul_max(op, arch): mul_ifm.dtype = DataType.int32 min_op.set_output_tensor(mul_ifm) min_op.set_ifm_ofm_shapes() - new_op = Op.RescaleAdd - op.rescale = (1, 0) # No scale or shift + new_op = Op.Add + op.explicit_scaling = ExplicitScaling(False, shift=[0], multiplier=[1]) # No scaling DebugDatabase.add_optimised(op, min_op) # Add multiplication with alpha @@ -1196,8 +1196,7 @@ def convert_lrelu_to_mul_max(op, arch): if is_converted_prelu: # The LeakyRelu was the result from convert_prelu and the scaling is provided scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"] - mul_alpha.type = Op.RescaleMul - mul_alpha.rescale = [alpha_scale, alpha_shift] + mul_alpha.explicit_scaling = ExplicitScaling(False, [alpha_shift], [alpha_scale]) elif alpha == 0 or np.isinf(1 / alpha): # Handling of alpha near or at zero quantization.scale_f32 = np.float32(1) |