forked from Qortal/Brooklyn
274 lines
10 KiB
Python
274 lines
10 KiB
Python
# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
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# SPDX-License-Identifier: MIT
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import numpy as np
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import pytest
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import tflite_runtime.interpreter as tflite
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import os
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from utils import run_mock_model, run_inference, compare_outputs
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def test_external_delegate_unknown_options(delegate_dir):
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print(delegate_dir)
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with pytest.raises(ValueError):
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tflite.load_delegate(
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delegate_dir,
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options={"wrong": "wrong"})
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def test_external_delegate_options_multiple_backends(delegate_dir):
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tflite.load_delegate(
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delegate_dir,
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options={"backends": "GpuAcc,CpuAcc,CpuRef,Unknown"})
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@pytest.mark.GpuAccTest
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def test_external_delegate_options_gpu_tuning(delegate_dir, test_data_folder, tmp_path):
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tuning_file = os.path.join(str(tmp_path), "test_gpu.tuning")
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# cleanup previous test run if necessary
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if os.path.exists(tuning_file):
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os.remove(tuning_file)
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# with tuning level 2 a tuning file should be created
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armnn_delegate = tflite.load_delegate(
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delegate_dir,
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options={
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"backends": "GpuAcc",
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"gpu-tuning-level": "2",
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"gpu-tuning-file": tuning_file,
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"logging-severity": "info"})
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run_mock_model(armnn_delegate, test_data_folder)
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# destroy delegate, otherwise tuning file won't be written to file
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armnn_delegate.__del__()
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assert (os.path.exists(tuning_file))
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# if no tuning level is provided it defaults to 0 which means it will use the tuning parameters from a tuning
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# file if one is provided
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armnn_delegate2 = tflite.load_delegate(
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delegate_dir,
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options={
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"backends": "GpuAcc",
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"gpu-tuning-file": tuning_file,
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"logging-severity": "info"})
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run_mock_model(armnn_delegate2, test_data_folder)
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# cleanup
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os.remove(tuning_file)
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@pytest.mark.GpuAccTest
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def test_external_delegate_options_gpu_cached_network(delegate_dir, test_data_folder, tmp_path):
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binary_file = os.path.join(str(tmp_path), "test_binary.bin")
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# cleanup previous test run if necessary
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if os.path.exists(binary_file):
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os.remove(binary_file)
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# Create blank binary file to write to.
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open(binary_file, 'a').close()
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assert (os.path.exists(binary_file))
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assert (os.stat(binary_file).st_size == 0)
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# Run inference to save cached network.
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armnn_delegate = tflite.load_delegate(
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delegate_dir,
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options={
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"backends": "GpuAcc",
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"save-cached-network": "1",
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"cached-network-filepath": binary_file,
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"logging-severity": "info"})
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run_mock_model(armnn_delegate, test_data_folder)
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# destroy delegate and check if file has been saved.
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armnn_delegate.__del__()
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assert (os.stat(binary_file).st_size != 0)
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# Create second delegate to load in binary file created.
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armnn_delegate2 = tflite.load_delegate(
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delegate_dir,
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options={
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"backends": "GpuAcc",
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"cached-network-filepath": binary_file,
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"logging-severity": "info"})
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run_mock_model(armnn_delegate2, test_data_folder)
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# cleanup
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os.remove(binary_file)
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@pytest.mark.GpuAccTest
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def test_external_delegate_gpu_fastmath(delegate_dir, test_data_folder):
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# create armnn delegate with enable-fast-math
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# fast-math is only enabled on Conv2d layer, so use conv2d model.
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'GpuAcc',
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'enable-fast-math': '1',
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"logging-severity": "info"})
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model_file_name = 'conv2d.tflite'
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inputShape = [ 1, 5, 5, 1 ]
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outputShape = [ 1, 3, 3, 1 ]
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inputValues = [ 1, 5, 2, 3, 5,
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8, 7, 3, 6, 3,
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3, 3, 9, 1, 9,
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4, 1, 8, 1, 3,
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6, 8, 1, 9, 2 ]
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expectedResult = [ 28, 38, 29,
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96, 104, 53,
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31, 55, 24 ]
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input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
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expected_output = np.array(expectedResult, dtype=np.float32).reshape(outputShape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, [input], [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output])
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@pytest.mark.CpuAccTest
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def test_external_delegate_cpu_options(capfd, delegate_dir, test_data_folder):
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# create armnn delegate with enable-fast-math and number-of-threads options
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# fast-math is only enabled on Conv2d layer, so use conv2d model.
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuAcc',
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'enable-fast-math': '1',
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'number-of-threads': '4',
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"logging-severity": "info"})
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model_file_name = 'conv2d.tflite'
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inputShape = [ 1, 5, 5, 1 ]
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outputShape = [ 1, 3, 3, 1 ]
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inputValues = [ 1, 5, 2, 3, 5,
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8, 7, 3, 6, 3,
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3, 3, 9, 1, 9,
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4, 1, 8, 1, 3,
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6, 8, 1, 9, 2 ]
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expectedResult = [ 28, 38, 29,
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96, 104, 53,
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31, 55, 24 ]
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input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
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expected_output = np.array(expectedResult, dtype=np.float32).reshape(outputShape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, [input], [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output])
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captured = capfd.readouterr()
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assert 'Set CPPScheduler to Linear mode, with 4 threads to use' in captured.out
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def test_external_delegate_options_wrong_logging_level(delegate_dir):
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with pytest.raises(ValueError):
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tflite.load_delegate(
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delegate_dir,
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options={"logging-severity": "wrong"})
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def test_external_delegate_options_debug(capfd, delegate_dir, test_data_folder):
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# create armnn delegate with debug option
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef', 'debug-data': '1'})
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model_file_name = 'fp32_model.tflite'
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tensor_shape = [1, 2, 2, 1]
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input0 = np.array([1, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
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input1 = np.array([2, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
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inputs = [input0, input0, input1]
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expected_output = np.array([1, 2, 2, 2], dtype=np.float32).reshape(tensor_shape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output])
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captured = capfd.readouterr()
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assert 'layerGuid' in captured.out
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def test_external_delegate_options_fp32_to_fp16(capfd, delegate_dir, test_data_folder):
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# create armnn delegate with reduce-fp32-to-fp16 option
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef',
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'debug-data': '1',
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'reduce-fp32-to-fp16': '1'})
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model_file_name = 'fp32_model.tflite'
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tensor_shape = [1, 2, 2, 1]
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input0 = np.array([1, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
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input1 = np.array([2, 2, 3, 4], dtype=np.float32).reshape(tensor_shape)
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inputs = [input0, input0, input1]
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expected_output = np.array([1, 2, 2, 2], dtype=np.float32).reshape(tensor_shape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output])
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captured = capfd.readouterr()
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assert 'convert_fp32_to_fp16' in captured.out
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assert 'convert_fp16_to_fp32' in captured.out
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def test_external_delegate_options_fp32_to_bf16(capfd, delegate_dir, test_data_folder):
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# create armnn delegate with reduce-fp32-to-bf16 option
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuRef',
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'debug-data': '1',
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'reduce-fp32-to-bf16': '1'})
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model_file_name = 'conv2d.tflite'
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inputShape = [ 1, 5, 5, 1 ]
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outputShape = [ 1, 3, 3, 1 ]
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inputValues = [ 1, 5, 2, 3, 5,
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8, 7, 3, 6, 3,
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3, 3, 9, 1, 9,
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4, 1, 8, 1, 3,
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6, 8, 1, 9, 2 ]
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expectedResult = [ 28, 38, 29,
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96, 104, 53,
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31, 55, 24 ]
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input = np.array(inputValues, dtype=np.float32).reshape(inputShape)
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expected_output = np.array(expectedResult, dtype=np.float32).reshape(outputShape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, [input], [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output])
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captured = capfd.readouterr()
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assert 'convert_fp32_to_bf16' in captured.out
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def test_external_delegate_options_memory_import(delegate_dir, test_data_folder):
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# create armnn delegate with memory-import option
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armnn_delegate = tflite.load_delegate(delegate_dir, options = {'backends': 'CpuAcc,CpuRef',
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'memory-import': '1'})
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model_file_name = 'fallback_model.tflite'
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tensor_shape = [1, 2, 2, 1]
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input0 = np.array([1, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape)
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input1 = np.array([2, 2, 3, 4], dtype=np.uint8).reshape(tensor_shape)
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inputs = [input0, input0, input1]
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expected_output = np.array([1, 2, 2, 2], dtype=np.uint8).reshape(tensor_shape)
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# run the inference
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armnn_outputs = run_inference(test_data_folder, model_file_name, inputs, [armnn_delegate])
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# check results
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compare_outputs(armnn_outputs, [expected_output]) |