129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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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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from torch.utils.data import DataLoader
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from mmdeploy.apis import build_task_processor
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from mmdeploy.utils import load_config
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from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper
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model_cfg_path = 'tests/test_codebase/test_mmocr/data/crnn.py'
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@pytest.fixture(scope='module')
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def model_cfg():
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return load_config(model_cfg_path)[0]
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@pytest.fixture(scope='module')
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def deploy_cfg():
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return mmcv.Config(
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dict(
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backend_config=dict(type='onnxruntime'),
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codebase_config=dict(type='mmocr', task='TextRecognition'),
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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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@pytest.fixture(scope='module')
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def task_processor(model_cfg, deploy_cfg):
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return build_task_processor(model_cfg, deploy_cfg, 'cpu')
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img_shape = (32, 32)
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@pytest.fixture(scope='module')
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def img():
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return np.random.rand(*img_shape, 3).astype(np.uint8)
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def test_init_pytorch_model(task_processor):
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from mmocr.models.textrecog.recognizer import BaseRecognizer
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model = task_processor.init_pytorch_model(None)
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assert isinstance(model, BaseRecognizer)
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@pytest.fixture(scope='module')
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def backend_model(task_processor):
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from mmdeploy.backend.onnxruntime import ORTWrapper
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with SwitchBackendWrapper(ORTWrapper) as wrapper:
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wrapper.set(outputs={
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'output': torch.rand(1, 9, 37),
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})
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yield task_processor.init_backend_model([''])
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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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@pytest.fixture(scope='module')
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def model_inputs(task_processor, img):
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return task_processor.create_input(img, input_shape=img_shape)
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def test_create_input(model_inputs):
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assert isinstance(model_inputs, tuple) and len(model_inputs) == 2
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def test_run_inference(backend_model, task_processor, model_inputs):
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input_dict, _ = model_inputs
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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, task_processor, model_inputs, img, tmp_path):
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input_dict, _ = model_inputs
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results = task_processor.run_inference(backend_model, input_dict)
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filename = str(tmp_path / '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_tensort_from_input(task_processor):
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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(task_processor):
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with pytest.raises(NotImplementedError):
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_ = task_processor.get_partition_cfg(partition_type='')
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def test_build_dataset_and_dataloader(task_processor, model_cfg):
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from torch.utils.data import DataLoader, Dataset
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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(task_processor, model_cfg):
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from mmcv.parallel import MMDataParallel
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# Prepare dataloader
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dataloader = DataLoader([])
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# Prepare dummy model
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model = DummyModel(outputs=[torch.rand([1, 1, *img_shape])])
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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, [])
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