| # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # Description: |
| # Contains unit tests for tflite_reader |
| 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 |
| |
| |
| class TestTFLiteSubgraph: |
| |
| # Generate some data for testing len1_array_to_scalar |
| len1_testdata = [ |
| (0, None), |
| pytest.param(1, None, marks=pytest.mark.xfail), |
| ([1, 2, 3], [1, 2, 3]), |
| ([10], 10), |
| ([], []), |
| ] |
| |
| @pytest.mark.parametrize("test_input,expected", len1_testdata) |
| def test_len1_array_to_scalar(self, test_input, expected): |
| output = TFLiteSubgraph.len1_array_to_scalar(test_input) |
| assert output == expected |
| |
| parse_op_testdata = [ |
| # op_type, opt_serializer, inputs, output, expected |
| (Op.FullyConnected, None, [0, 1, 2], 3, 3), # FC |
| (Op.FullyConnected, None, [0, 1, -1], 3, 3), # FC disabled Bias |
| (Op.FullyConnected, None, [0, 1], 3, 3), # FC no Bias |
| (Op.Conv2D, None, [2, 1, 3], 0, 3), # Conv2D |
| (Op.Conv2DBackpropInput, None, [0, 1, 2, 3], 4, 4), # TransposeConv |
| (Op.Conv2DBackpropInput, None, [0, 1, 2], 4, 4), # TransposeConv no Bias |
| pytest.param(Op.Conv2D, None, [0, -1, 1], 3, 3, marks=pytest.mark.xfail), # Conv2D no Weights |
| ] |
| |
| @pytest.mark.parametrize("op_type, opt_serializer, inputs, output, expected", parse_op_testdata) |
| def test_parse_operator(self, op_type, opt_serializer, inputs, output, expected): |
| with patch.object(TFLiteSubgraph, "__init__", lambda self, graph, subraph: None): |
| # Mock a TFLiteSubGraph |
| sg = TFLiteSubgraph(None, None) |
| sg.graph = MagicMock() |
| sg.graph.operator_codes = [(op_type, opt_serializer, "")] |
| |
| # Mock a couple of tensors |
| sg.tensors = [MagicMock() for _ in range(5)] |
| for i, tens in enumerate(sg.tensors): |
| tens.name = "tensor_{}".format(i) |
| tens.ops = [] |
| |
| # Mock op data |
| op_data = MagicMock() |
| op_data.OpcodeIndex.return_value = 0 |
| op_data.InputsAsNumpy.return_value = inputs |
| op_data.OutputsAsNumpy.return_value = [output] |
| |
| sg.parse_operator(0, op_data) |
| |
| # Verify the created Operation |
| created_op = sg.tensors[output].ops[0] |
| assert created_op.type == op_type |
| 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.uint8), [10, 10]), |
| (np.array([np.random.randint(256) for _ in range(100)], dtype=np.uint8), []), |
| (np.array([], dtype=np.uint8), [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 np.array_equal(tens.values, buffer) |