mirror of
https://github.com/open-mmlab/mmdeploy.git
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4de5bbb461
* add unittests * lint * modify .gitignore, remove useless files * remove emtpy.py and generate it when use * Update according to comments 1. Use tempfile 2. Delete inference test (which will be tested in each codebase) 3. Refine calibration test * update annotation * Add export_info * Reduce data scale, fix assert * update json blank line * add backend check
135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
import os
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import tempfile
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import mmcv
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import pytest
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from mmdeploy.utils import get_onnx_config, get_task_type, load_config
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from mmdeploy.utils.constants import Task
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from mmdeploy.utils.export_info import dump_info
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correct_model_path = 'tests/data/srgan.py'
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correct_model_cfg = mmcv.Config.fromfile(correct_model_path)
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correct_deploy_path = 'tests/data/super-resolution.py'
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correct_deploy_cfg = mmcv.Config.fromfile(correct_deploy_path)
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empty_file_path = tempfile.NamedTemporaryFile(suffix='.py').name
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empty_path = './a.py'
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@pytest.fixture(autouse=True, scope='module')
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def create_empty_file():
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os.mknod(empty_file_path)
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def test_load_config_none():
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with pytest.raises(AssertionError):
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load_config()
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def test_load_config_type_error():
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with pytest.raises(TypeError):
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load_config(1)
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def test_load_config_file_error():
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with pytest.raises(FileNotFoundError):
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load_config(empty_path)
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@pytest.mark.parametrize('args', [
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[empty_file_path],
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[correct_model_path],
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[correct_model_cfg],
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(correct_model_path, correct_deploy_path),
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(correct_model_path, correct_deploy_cfg),
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(correct_model_cfg, correct_deploy_cfg),
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])
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def test_load_config(args):
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configs = load_config(*args)
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for v in zip(configs, args):
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if isinstance(v[1], str):
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cfg = mmcv.Config.fromfile(v[1])
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else:
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cfg = v[1]
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assert v[0]._cfg_dict == cfg._cfg_dict
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@pytest.mark.parametrize('deploy_cfg, default',
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[(empty_file_path, None),
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(empty_file_path, Task.SUPER_RESOLUTION)])
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def test_get_task_type_default(deploy_cfg, default):
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if default is None:
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res = get_task_type(deploy_cfg)
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else:
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res = get_task_type(deploy_cfg, default)
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assert res == default
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@pytest.mark.parametrize('deploy_cfg, default',
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[(correct_deploy_path, None),
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(correct_deploy_path, Task.TEXT_DETECTION),
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(correct_deploy_cfg, None)])
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def test_get_task_type(deploy_cfg, default):
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if default is None:
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res = get_task_type(deploy_cfg)
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else:
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res = get_task_type(deploy_cfg, default)
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assert res == Task.SUPER_RESOLUTION
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def test_get_onnx_config_error():
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with pytest.raises(Exception):
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get_onnx_config(empty_file_path)
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@pytest.mark.parametrize('deploy_cfg',
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[correct_deploy_path, correct_deploy_cfg])
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def test_get_onnx_config(deploy_cfg):
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onnx_config = dict(
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dynamic_axes={
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'input': {
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0: 'batch',
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2: 'height',
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3: 'width'
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},
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'output': {
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0: 'batch',
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2: 'height',
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3: 'width'
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}
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},
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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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save_file='end2end.onnx',
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input_names=['input'],
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output_names=['output'],
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input_shape=None)
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res = get_onnx_config(deploy_cfg)
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assert res == onnx_config
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def test_AdvancedEnum():
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keys = [
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Task.TEXT_DETECTION, Task.TEXT_RECOGNITION, Task.SEGMENTATION,
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Task.SUPER_RESOLUTION, Task.CLASSIFICATION, Task.OBJECT_DETECTION
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]
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vals = [
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'TextDetection', 'TextRecognition', 'Segmentation', 'SuperResolution',
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'Classification', 'ObjectDetection'
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]
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for k, v in zip(keys, vals):
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assert Task.get(v, None) == k
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assert k.value == v
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assert Task.get('a', Task.TEXT_DETECTION) == Task.TEXT_DETECTION
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def test_export_info():
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with tempfile.TemporaryDirectory() as dir:
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dump_info(correct_deploy_cfg, correct_model_cfg, dir)
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preprocess_json = os.path.join(dir, 'preprocess.json')
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deploy_json = os.path.join(dir, 'deploy_cfg.json')
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assert os.path.exists(preprocess_json)
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assert os.path.exists(deploy_json)
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