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author | Patrik Gustavsson <patrik.gustavsson@arm.com> | 2020-11-04 12:43:50 +0100 |
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committer | Patrik Gustavsson <patrik.gustavsson@arm.com> | 2020-11-10 11:19:49 +0100 |
commit | 6ae0e4212abf1b92506fcbb180f647a953a37d89 (patch) | |
tree | 7fc75cdc65619195a437033748acb2ecc5c7e25e /ethosu/vela/tflite_reader.py | |
parent | fd31428db9985fe31811063428ebc609a2b42d05 (diff) | |
download | ethos-u-vela-6ae0e4212abf1b92506fcbb180f647a953a37d89.tar.gz |
MLBEDSW-2868 Refactor separation of scale + bias tensors
Changed so that there is an option to set if Tensor clone should be
seen as unique or not.
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: Ie51c1a5e84b535380d498b105aa18ccba1c8b27c
Diffstat (limited to 'ethosu/vela/tflite_reader.py')
-rw-r--r-- | ethosu/vela/tflite_reader.py | 14 |
1 files changed, 6 insertions, 8 deletions
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py index 82feddd9..24f9f874 100644 --- a/ethosu/vela/tflite_reader.py +++ b/ethosu/vela/tflite_reader.py @@ -41,9 +41,8 @@ def decode_str(s): return s.decode("utf-8") -def clone_and_reshape_tensor(src_tens, reorder): - - tens = src_tens.clone("_reshape") +def clone_and_reshape_tensor(src_tens, reorder, set_unique): + tens = src_tens.clone("_reshape", set_unique) tens.shape = [src_tens.shape[idx] for idx in reorder] tens.bandwidth_shape = tens.shape tens.storage_shape = tens.shape @@ -153,17 +152,16 @@ class TFLiteSubgraph: if op.type.is_depthwise_conv2d_op() or op.type.is_conv2d_op() or op.type == Op.FullyConnected: if inputs[1].values is not None: if op.type == Op.FullyConnected: - inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0)) + inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0), False) else: - inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0)) + 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: - inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,)) # Since bias tensor is used for both bias and scale, - # set different equivalence_id for all bias tensors - inputs[-1].set_random_equivalence_id() + # a clone with a unique equivalence_id is needed + inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,), True) if opt_serializer is not None: op.attrs = opt_serializer.deserialize(op_data) |