129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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from tempfile import NamedTemporaryFile
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import mmcv
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import numpy as np
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import pytest
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import torch
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import mmdeploy.backend.onnxruntime as ort_apis
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Backend, Codebase
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from mmdeploy.utils.test import SwitchBackendWrapper, backend_checker
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NUM_CLASS = 1000
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IMAGE_SIZE = 64
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import_codebase(Codebase.MMCLS)
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@backend_checker(Backend.ONNXRUNTIME)
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class TestEnd2EndModel:
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@classmethod
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def setup_class(cls):
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# force add backend wrapper regardless of plugins
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from mmdeploy.backend.onnxruntime import ORTWrapper
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ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
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# simplify backend inference
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cls.wrapper = SwitchBackendWrapper(ORTWrapper)
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cls.outputs = {
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'outputs': torch.rand(1, 1, IMAGE_SIZE, IMAGE_SIZE),
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}
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cls.wrapper.set(outputs=cls.outputs)
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deploy_cfg = mmcv.Config(
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{'onnx_config': {
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'output_names': ['outputs']
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}})
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from mmdeploy.codebase.mmcls.deploy.classification_model import \
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End2EndModel
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class_names = ['' for i in range(NUM_CLASS)]
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cls.end2end_model = End2EndModel(
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Backend.ONNXRUNTIME, [''],
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device='cpu',
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class_names=class_names,
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deploy_cfg=deploy_cfg)
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@classmethod
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def teardown_class(cls):
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cls.wrapper.recover()
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def test_forward(self):
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imgs = [torch.rand(1, 3, IMAGE_SIZE, IMAGE_SIZE)]
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results = self.end2end_model.forward(imgs)
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assert results is not None, 'failed to get output using '\
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'End2EndModel'
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def test_forward_test(self):
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imgs = torch.rand(2, 3, IMAGE_SIZE, IMAGE_SIZE)
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results = self.end2end_model.forward_test(imgs)
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assert isinstance(results[0], np.ndarray)
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def test_show_result(self):
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input_img = np.zeros([IMAGE_SIZE, IMAGE_SIZE, 3])
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img_path = NamedTemporaryFile(suffix='.jpg').name
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pred_label = torch.randint(0, NUM_CLASS, (1, ))
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pred_score = torch.rand((1, ))
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result = {'pred_label': pred_label, 'pred_score': float(pred_score)}
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self.end2end_model.show_result(
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input_img, result, '', show=False, out_file=img_path)
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assert osp.exists(img_path), 'Fails to create drawn image.'
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@pytest.mark.parametrize('from_file', [True, False])
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@pytest.mark.parametrize('data_type', ['train', 'val', 'test'])
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def test_get_classes_from_config(from_file, data_type):
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from mmcls.datasets import DATASETS
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from mmdeploy.codebase.mmcls.deploy.classification_model import \
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get_classes_from_config
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dataset_type = 'ImageNet'
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data_cfg = mmcv.Config({
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'data': {
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data_type:
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dict(
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type=dataset_type,
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data_root='',
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img_dir='',
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ann_dir='',
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pipeline=None)
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}
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})
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if from_file:
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config_path = NamedTemporaryFile(suffix='.py').name
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with open(config_path, 'w') as file:
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file.write(data_cfg.pretty_text)
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data_cfg = config_path
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classes = get_classes_from_config(data_cfg)
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module = DATASETS.module_dict[dataset_type]
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assert classes == module.CLASSES, \
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f'fail to get CLASSES of dataset: {dataset_type}'
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@backend_checker(Backend.ONNXRUNTIME)
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def test_build_classification_model():
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model_cfg = mmcv.Config(dict(data=dict(test={'type': 'ImageNet'})))
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type='onnxruntime'),
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onnx_config=dict(output_names=['outputs']),
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codebase_config=dict(type='mmcls')))
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from mmdeploy.backend.onnxruntime import ORTWrapper
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ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
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# simplify backend inference
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with SwitchBackendWrapper(ORTWrapper) as wrapper:
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wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
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from mmdeploy.codebase.mmcls.deploy.classification_model import (
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End2EndModel, build_classification_model)
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classifier = build_classification_model([''], model_cfg, deploy_cfg,
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'cpu')
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assert isinstance(classifier, End2EndModel)
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