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author | Dwight Lidman <dwight.lidman@arm.com> | 2020-11-05 15:56:08 +0100 |
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committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2020-11-16 09:10:54 +0000 |
commit | e05de456770926fdc057478739d1b96b7f651756 (patch) | |
tree | 784fb9792a959a97c897ece67b23787abbcfcae1 /ethosu/vela/test/test_tflite_reader.py | |
parent | e8a5a78dd16ec979c7a7bb1f5bd87e9b2909c32d (diff) | |
download | ethos-u-vela-e05de456770926fdc057478739d1b96b7f651756.tar.gz |
MLBEDSW-3301: Vela fails ungracefully when reading string buffers
When encountering a sparse string buffer, Vela fails
both due to missing a mapping for a Numpy string type
and also for not being able to read sparse buffers.
The failing line is attempting to reshape a [100]
buffer into a [3, 5] tensor which does not work due
to Vela treating the buffer as non-sparse.
The solution here is to simply not do the reshape
for string buffers (which all appear to be sparse)
since it is not something that will be supported in
the future anyway.
The related operator can then be pushed to the CPU
as expected.
Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: Iea0af6cd60a691f975209014b6aa098dde8d6a4b
Diffstat (limited to 'ethosu/vela/test/test_tflite_reader.py')
-rw-r--r-- | ethosu/vela/test/test_tflite_reader.py | 33 |
1 files changed, 33 insertions, 0 deletions
diff --git a/ethosu/vela/test/test_tflite_reader.py b/ethosu/vela/test/test_tflite_reader.py index 23abb4a0..14c9b204 100644 --- a/ethosu/vela/test/test_tflite_reader.py +++ b/ethosu/vela/test/test_tflite_reader.py @@ -18,9 +18,11 @@ from unittest.mock import MagicMock from unittest.mock import patch +import numpy as np import pytest from ethosu.vela.operation import Op +from ethosu.vela.tflite.TensorType import TensorType from ethosu.vela.tflite_reader import TFLiteSubgraph @@ -79,3 +81,34 @@ class TestTFLiteSubgraph: assert len(created_op.inputs) == expected assert created_op.outputs[0].name == "tensor_{}".format(output) assert inputs[-1] != -1 or not created_op.inputs[-1] + + string_buffer_testdata = [ + (np.array([np.random.randint(256) for _ in range(100)], dtype=np.uint8), [3, 5]), + (np.array([np.random.randint(256) for _ in range(100)], dtype=np.int16), [10, 10]), + (np.array([np.random.randint(256) for _ in range(100)], dtype=np.float32), [100]), + (np.array([], dtype=np.int8), [30]), + ] + + @pytest.mark.parametrize("buffer, tens_shape", string_buffer_testdata) + def test_parse_tensor_with_string_buffer(self, buffer, tens_shape): + tens_data = MagicMock() + tens_data.ShapeAsNumpy = MagicMock(return_value=np.array(tens_shape), dtype=np.int32) + tens_data.Name = MagicMock(return_value=b"test_data") + tens_data.Type = MagicMock(return_value=TensorType.STRING) + tens_data.Quantization = MagicMock(return_value=None) + tens_data.Buffer = MagicMock(return_value=0) + + tfl_sg = MagicMock() + tfl_sg.Name = MagicMock(return_value=b"test_sg") + tfl_sg.TensorsLength = MagicMock(return_value=0) + tfl_sg.OperatorsLength = MagicMock(return_value=0) + tfl_sg.OutputsAsNumpy = MagicMock(return_value=[]) + tfl_sg.InputsAsNumpy = MagicMock(return_value=[]) + + graph = MagicMock() + graph.buffers = [buffer] + + subgraph = TFLiteSubgraph(graph, tfl_sg) + + tens = subgraph.parse_tensor(tens_data) + assert tens.values is None |