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author | Richard Burton <richard.burton@arm.com> | 2020-04-08 16:39:05 +0100 |
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committer | Jim Flynn <jim.flynn@arm.com> | 2020-04-10 16:11:09 +0000 |
commit | dc0c6ed9f8b993e63f492f203d7d7080ab4c835c (patch) | |
tree | ea8541990b13ebf1a038009aa6b8b4b1ea8c3f55 /python/pyarmnn/test/test_tensor_conversion.py | |
parent | fe5a24beeef6e9a41366e694f41093565e748048 (diff) | |
download | armnn-dc0c6ed9f8b993e63f492f203d7d7080ab4c835c.tar.gz |
Add PyArmNN to work with ArmNN API of 20.02
* Add Swig rules for generating python wrapper
* Add documentation
* Add tests and testing data
Change-Id: If48eda08931514fa21e72214dfead2835f07237c
Signed-off-by: Richard Burton <richard.burton@arm.com>
Signed-off-by: Derek Lamberti <derek.lamberti@arm.com>
Diffstat (limited to 'python/pyarmnn/test/test_tensor_conversion.py')
-rw-r--r-- | python/pyarmnn/test/test_tensor_conversion.py | 99 |
1 files changed, 99 insertions, 0 deletions
diff --git a/python/pyarmnn/test/test_tensor_conversion.py b/python/pyarmnn/test/test_tensor_conversion.py new file mode 100644 index 0000000000..a48b00f431 --- /dev/null +++ b/python/pyarmnn/test/test_tensor_conversion.py @@ -0,0 +1,99 @@ +# Copyright © 2020 Arm Ltd. All rights reserved. +# SPDX-License-Identifier: MIT +import os + +import pytest +import pyarmnn as ann +import numpy as np + + +@pytest.fixture(scope="function") +def get_tensor_info_input(shared_data_folder): + """ + Sample input tensor information. + """ + parser = ann.ITfLiteParser() + parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.tflite')) + graph_id = 0 + + input_binding_info = [parser.GetNetworkInputBindingInfo(graph_id, 'input_1')] + + yield input_binding_info + + +@pytest.fixture(scope="function") +def get_tensor_info_output(shared_data_folder): + """ + Sample output tensor information. + """ + parser = ann.ITfLiteParser() + parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.tflite')) + graph_id = 0 + + output_names = parser.GetSubgraphOutputTensorNames(graph_id) + outputs_binding_info = [] + + for output_name in output_names: + outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(graph_id, output_name)) + + yield outputs_binding_info + + +def test_make_input_tensors(get_tensor_info_input): + input_tensor_info = get_tensor_info_input + input_data = [] + + for tensor_id, tensor_info in input_tensor_info: + input_data.append(np.random.randint(0, 255, size=(1, tensor_info.GetNumElements())).astype(np.uint8)) + + input_tensors = ann.make_input_tensors(input_tensor_info, input_data) + assert len(input_tensors) == 1 + + for tensor, tensor_info in zip(input_tensors, input_tensor_info): + # Because we created ConstTensor function, we cannot check type directly. + assert type(tensor[1]).__name__ == 'ConstTensor' + assert str(tensor[1].GetInfo()) == str(tensor_info[1]) + + +def test_make_output_tensors(get_tensor_info_output): + output_binding_info = get_tensor_info_output + + output_tensors = ann.make_output_tensors(output_binding_info) + assert len(output_tensors) == 1 + + for tensor, tensor_info in zip(output_tensors, output_binding_info): + assert type(tensor[1]) == ann.Tensor + assert str(tensor[1].GetInfo()) == str(tensor_info[1]) + + +def test_workload_tensors_to_ndarray(get_tensor_info_output): + # Check shape and size of output from workload_tensors_to_ndarray matches expected. + output_binding_info = get_tensor_info_output + output_tensors = ann.make_output_tensors(output_binding_info) + + data = ann.workload_tensors_to_ndarray(output_tensors) + + for i in range(0, len(output_tensors)): + assert data[i].shape == tuple(output_tensors[i][1].GetShape()) + assert data[i].size == output_tensors[i][1].GetNumElements() + + +def test_make_input_tensors_fp16(get_tensor_info_input): + # Check ConstTensor with float16 + input_tensor_info = get_tensor_info_input + input_data = [] + + for tensor_id, tensor_info in input_tensor_info: + input_data.append(np.random.randint(0, 255, size=(1, tensor_info.GetNumElements())).astype(np.float16)) + tensor_info.SetDataType(ann.DataType_Float16) # set datatype to float16 + + input_tensors = ann.make_input_tensors(input_tensor_info, input_data) + assert len(input_tensors) == 1 + + for tensor, tensor_info in zip(input_tensors, input_tensor_info): + # Because we created ConstTensor function, we cannot check type directly. + assert type(tensor[1]).__name__ == 'ConstTensor' + assert str(tensor[1].GetInfo()) == str(tensor_info[1]) + assert tensor[1].GetDataType() == ann.DataType_Float16 + assert tensor[1].GetNumElements() == 28*28*1 + assert tensor[1].GetNumBytes() == (28*28*1)*2 # check each element is two byte |