120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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from tempfile import NamedTemporaryFile, TemporaryDirectory
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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.apis import build_task_processor
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Codebase, load_config
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from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper
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import_codebase(Codebase.MMCLS)
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model_cfg_path = 'tests/test_codebase/test_mmcls/data/model.py'
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model_cfg = load_config(model_cfg_path)[0]
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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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codebase_config=dict(type='mmcls', task='Classification'),
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onnx_config=dict(
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type='onnx',
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export_params=True,
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keep_initializers_as_inputs=False,
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opset_version=11,
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input_shape=None,
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input_names=['input'],
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output_names=['output'])))
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onnx_file = NamedTemporaryFile(suffix='.onnx').name
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task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
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img_shape = (64, 64)
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num_classes = 1000
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img = np.random.rand(*img_shape, 3)
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def test_init_pytorch_model():
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from mmcls.models.classifiers.base import BaseClassifier
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model = task_processor.init_pytorch_model(None)
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assert isinstance(model, BaseClassifier)
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@pytest.fixture
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def backend_model():
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from mmdeploy.backend.onnxruntime import ORTWrapper
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ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
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wrapper = SwitchBackendWrapper(ORTWrapper)
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wrapper.set(outputs={
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'output': torch.rand(1, num_classes),
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})
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yield task_processor.init_backend_model([''])
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wrapper.recover()
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def test_init_backend_model(backend_model):
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assert isinstance(backend_model, torch.nn.Module)
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def test_create_input():
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inputs = task_processor.create_input(img, input_shape=img_shape)
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assert isinstance(inputs, tuple) and len(inputs) == 2
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def test_run_inference(backend_model):
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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results = task_processor.run_inference(backend_model, input_dict)
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assert results is not None
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def test_visualize(backend_model):
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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results = task_processor.run_inference(backend_model, input_dict)
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with TemporaryDirectory() as dir:
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filename = dir + 'tmp.jpg'
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task_processor.visualize(backend_model, img, results[0], filename, '')
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assert os.path.exists(filename)
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def test_get_tensor_from_input():
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input_data = {'img': torch.ones(3, 4, 5)}
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inputs = task_processor.get_tensor_from_input(input_data)
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assert torch.equal(inputs, torch.ones(3, 4, 5))
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def test_get_partition_cfg():
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try:
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_ = task_processor.get_partition_cfg(partition_type='')
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except NotImplementedError:
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pass
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def test_build_dataset_and_dataloader():
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from torch.utils.data import Dataset, DataLoader
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dataset = task_processor.build_dataset(
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dataset_cfg=model_cfg, dataset_type='test')
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assert isinstance(dataset, Dataset), 'Failed to build dataset'
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dataloader = task_processor.build_dataloader(dataset, 1, 1)
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assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
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def test_single_gpu_test_and_evaluate():
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from mmcv.parallel import MMDataParallel
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dataset = task_processor.build_dataset(
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dataset_cfg=model_cfg, dataset_type='test')
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dataloader = task_processor.build_dataloader(dataset, 1, 1)
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# Prepare dummy model
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model = DummyModel(outputs=[torch.rand([1, 1000])])
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model = MMDataParallel(model, device_ids=[0])
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assert model is not None
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# Run test
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outputs = task_processor.single_gpu_test(model, dataloader)
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assert outputs is not None
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task_processor.evaluate_outputs(model_cfg, outputs, dataset)
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