# Copyright © 2022 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, run_inference, compare_outputs def test_external_delegate_unknown_options(delegate_dir): print(delegate_dir) with pytest.raises(ValueError): tflite.load_delegate( delegate_dir, options={"wrong": "wrong"}) def test_external_delegate_options_multiple_backends(delegate_dir): tflite.load_delegate( delegate_dir, options={"backends": "GpuAcc,CpuAcc,CpuRef,Unknown"}) @pytest.mark.GpuAccTest def test_external_delegate_options_gpu_tuning(delegate_dir, test_data_folder, tmp_path): tuning_file = os.path.join(str(tmp_path), "test_gpu.tuning") # cleanup previous test run if necessary if os.path.exists(tuning_file): os.remove(tuning_file) # with tuning level 2 a tuning file should be created armnn_delegate = tflite.load_delegate( delegate_dir, options={ "backends": "GpuAcc", "gpu-tuning-level": "2", "gpu-tuning-file": tuning_file, "logging-severity": "info"}) run_mock_model(armnn_delegate, test_data_folder) # destroy delegate, otherwise tuning file won't be written to file armnn_delegate.__del__() assert (os.path.exists(tuning_file)) # if no tuning level is provided it defaults to 0 which means it will use the tuning parameters from a tuning # file if one is provided armnn_delegate2 = tflite.load_delegate( delegate_dir, options={ "backends": "GpuAcc", "gpu-tuning-file": tuning_file, "logging-severity": "info"}) run_mock_model(armnn_delegate2, test_data_folder) # cleanup os.remove(tuning_file) @pytest.mark.GpuAccTest def test_external_delegate_options_gpu_cached_network(delegate_dir, test_data_folder, tmp_path): binary_file = os.path.join(str(tmp_path), "test_binary.bin") # cleanup previous test run if necessary if os.path.exists(binary_file): os.remove(binary_file) # Create blank binary file to write to. open(binary_file, "a").close() assert (os.path.exists(binary_file)) assert (os.stat(binary_file).st_size == 0) # Run inference to save cached network. armnn_delegate = tflite.load_delegate( delegate_dir, options={ "backends": "GpuAcc", "save-cached-network": "1", "cached-network-filepath": binary_file, "logging-severity": "info"}) run_mock_model(armnn_delegate, test_data_folder) # destroy delegate and check if file has been saved. armnn_delegate.__del__() assert (os.stat(binary_file).st_size != 0) # Create second delegate to load in binary file created. armnn_delegate2 = tflite.load_delegate( delegate_dir, options={ "backends": "GpuAcc", "cached-network-filepath": binary_file, "logging-severity": "info"}) run_mock_model(armnn_delegate2, test_data_folder) # cleanup os.remove(binary_file) @pytest.mark.GpuAccTest def test_external_delegate_gpu_fastmath(delegate_dir, test_data_folder): # create armnn delegate with enable-fast-math # fast-math is only enabled on Conv2d layer, so use conv2d model. armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "GpuAcc", "enable-fast-math": "1", "logging-severity": "info"}) 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]) @pytest.mark.CpuAccTest def test_external_delegate_cpu_options(delegate_dir, test_data_folder): # create armnn delegate with enable-fast-math and number-of-threads options # fast-math is only enabled on Conv2d layer, so use conv2d model. armnn_delegate = tflite.load_delegate(delegate_dir, options = {"backends": "CpuAcc", "enable-fast-math": "1", "number-of-threads": "4", "logging-severity": "info"}) 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]) def test_external_delegate_options_wrong_logging_level(delegate_dir): with pytest.raises(ValueError): 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_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])