| # 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 |