diff options
Diffstat (limited to 'delegate/python/test/test_external_delegate.py')
| -rw-r--r-- | delegate/python/test/test_external_delegate.py | 106 |
1 files changed, 103 insertions, 3 deletions
diff --git a/delegate/python/test/test_external_delegate.py b/delegate/python/test/test_external_delegate.py index 995de8cd70..93d373d0a1 100644 --- a/delegate/python/test/test_external_delegate.py +++ b/delegate/python/test/test_external_delegate.py @@ -1,11 +1,11 @@ # Copyright © 2020 Arm Ltd and Contributors. All rights reserved. # SPDX-License-Identifier: MIT +import numpy as np import pytest import tflite_runtime.interpreter as tflite import os -from utils import run_mock_model - +from utils import run_mock_model, run_inference, compare_outputs def test_external_delegate_unknown_options(delegate_dir): print(delegate_dir) @@ -14,7 +14,6 @@ def test_external_delegate_unknown_options(delegate_dir): delegate_dir, options={"wrong": "wrong"}) - def test_external_delegate_options_multiple_backends(delegate_dir): tflite.load_delegate( delegate_dir, @@ -63,3 +62,104 @@ def test_external_delegate_options_wrong_logging_level(delegate_dir): tflite.load_delegate( delegate_dir, options={"logging-severity": "wrong"}) + +def test_external_delegate_options_debug(capfd, delegate_dir, test_data_folder): + # create armnn delegate with debug option + armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef', 'debug-data': '1'}) + + model_file_name = 'fp32_model.tflite' + + tensor_shape = [1, 2, 2, 1] + + input0 = np.array([1, 2, 3, 4], dtype=np.float32).reshape(tensor_shape) + input1 = np.array([2, 2, 3, 4], dtype=np.float32).reshape(tensor_shape) + inputs = [input0, input0, input1] + expected_output = np.array([1, 2, 2, 2], dtype=np.float32).reshape(tensor_shape) + + # run the inference + armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate]) + + # check results + compare_outputs(armnn_outputs, [expected_output]) + + captured = capfd.readouterr() + assert 'layerGuid' in captured.out + + +def test_external_delegate_options_fp32_to_fp16(capfd, delegate_dir, test_data_folder): + # create armnn delegate with reduce-fp32-to-fp16 option + armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef', + 'debug-data': '1', + 'reduce-fp32-to-fp16': '1'}) + + model_file_name = 'fp32_model.tflite' + + tensor_shape = [1, 2, 2, 1] + + input0 = np.array([1, 2, 3, 4], dtype=np.float32).reshape(tensor_shape) + input1 = np.array([2, 2, 3, 4], dtype=np.float32).reshape(tensor_shape) + inputs = [input0, input0, input1] + expected_output = np.array([1, 2, 2, 2], dtype=np.float32).reshape(tensor_shape) + + # run the inference + armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate]) + + # check results + compare_outputs(armnn_outputs, [expected_output]) + + captured = capfd.readouterr() + assert 'convert_fp32_to_fp16' in captured.out + assert 'convert_fp16_to_fp32' in captured.out + +def test_external_delegate_options_fp32_to_bf16(capfd, delegate_dir, test_data_folder): + # create armnn delegate with reduce-fp32-to-bf16 option + armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef', + 'debug-data': '1', + 'reduce-fp32-to-bf16': '1'}) + + model_file_name = 'conv2d.tflite' + + inputShape = [ 1, 5, 5, 1 ] + outputShape = [ 1, 3, 3, 1 ] + + inputValues = [ 1, 5, 2, 3, 5, + 8, 7, 3, 6, 3, + 3, 3, 9, 1, 9, + 4, 1, 8, 1, 3, + 6, 8, 1, 9, 2 ] + + expectedResult = [ 28, 38, 29, + 96, 104, 53, + 31, 55, 24 ] + + input = np.array(inputValues, dtype=np.float32).reshape(inputShape) + expected_output = np.array(expectedResult, dtype=np.float32).reshape(outputShape) + + # run the inference + armnn_outputs = run_inference(test_data_folder, model_file_name, [input], [armnn_delegate]) + + # check results + compare_outputs(armnn_outputs, [expected_output]) + + captured = capfd.readouterr() + assert 'convert_fp32_to_bf16' in captured.out + +def test_external_delegate_options_memory_import(delegate_dir, test_data_folder): + # create armnn delegate with memory-import option + armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuAcc,CpuRef', + 'memory-import': '1'}) + + model_file_name = 'fallback_model.tflite' + + tensor_shape = [1, 2, 2, 1] + + input0 = np.array([1, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape) + input1 = np.array([2, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape) + inputs = [input0, input0, input1] + expected_output = np.array([1, 2, 2, 2], dtype=np.uint8).reshape(tensor_shape) + + # run the inference + armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate]) + + # check results + compare_outputs(armnn_outputs, [expected_output])
\ No newline at end of file |