diff options
| -rw-r--r-- | ethosu/vela/tflite_reader.py | 17 | ||||
| -rw-r--r-- | ethosu/vela/tflite_writer.py | 8 |
2 files changed, 15 insertions, 10 deletions
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py index 85acb6b..e732f19 100644 --- a/ethosu/vela/tflite_reader.py +++ b/ethosu/vela/tflite_reader.py @@ -153,18 +153,21 @@ class TFLiteSubgraph: self.virtual_outputs.append(tens) if op.type.is_depthwise_conv2d_op() or op.type.is_conv2d_op() or op.type == Op.FullyConnected: + # Reshape and add bias for ops with constant weights + # Do not modify ops with dynamic data since they will run on CPU if inputs[1].values is not None: if op.type == Op.FullyConnected: inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0), False) else: inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0), False) - if op.type.needs_bias() and len(inputs) <= op_type.info.indices.biases[0]: - # No Bias tensor - inputs.append(None) - if inputs[-1] and inputs[-1].values is not None: - # Since bias tensor is used for both bias and scale, - # a clone with a unique equivalence_id is needed. - inputs[-1] = clone_and_reshape_tensor(inputs[-1], None, True) + + if op.type.needs_bias() and len(inputs) <= op_type.info.indices.biases[0]: + # No Bias tensor + inputs.append(None) + if inputs[-1] and inputs[-1].values is not None: + # Since bias tensor is used for both bias and scale, + # a clone with a unique equivalence_id is needed. + inputs[-1] = clone_and_reshape_tensor(inputs[-1], None, True) if opt_serializer is not None: op.attrs = opt_serializer.deserialize(op_data) diff --git a/ethosu/vela/tflite_writer.py b/ethosu/vela/tflite_writer.py index 44ce711..d4e24a2 100644 --- a/ethosu/vela/tflite_writer.py +++ b/ethosu/vela/tflite_writer.py @@ -105,9 +105,11 @@ class TFLiteSerialiser: if op.type.is_conv2d_op() or op.type.is_depthwise_conv2d_op() or op.type == Op.FullyConnected: # Op is run on CPU, make sure the original weight and bias tensors are written back # instead of the cloned/reshaped (see tflite_reader) - for idx, inp in enumerate(op.inputs): - if inp != op.ifm and inp is not None and inp.src_tensor is not None: - op.inputs[idx] = inp.src_tensor + # Do nothing when values are None (dynamic weights) + if op.inputs[1].values is not None: + for idx, inp in enumerate(op.inputs): + if inp != op.ifm and inp is not None and inp.src_tensor is not None: + op.inputs[idx] = inp.src_tensor # list of tuple(Op, string, op.version); the custom code is only used for 3rd party custom operators self.operator_codes = sorted(set((op.type, op.attrs.get("custom_code", ""), op.version) for op in all_ops)) |