163 lines
5.3 KiB
Python
163 lines
5.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os
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from tempfile import NamedTemporaryFile, TemporaryDirectory
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from typing import Any
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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 mmengine import Config
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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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try:
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import_codebase(Codebase.MMCLS)
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except ImportError:
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pytest.skip(f'{Codebase.MMCLS} is not installed.', allow_module_level=True)
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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 = 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 = None
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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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@pytest.fixture(autouse=True)
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def init_task_processor():
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global task_processor
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task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
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@pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0])
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def test_build_pytorch_model(from_mmrazor: Any):
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from mmcls.models.classifiers.base import BaseClassifier
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if from_mmrazor is False:
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_task_processor = task_processor
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else:
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_model_cfg_path = 'tests/test_codebase/test_mmcls/data/' \
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'mmrazor_model.py'
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_model_cfg = load_config(_model_cfg_path)[0]
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_model_cfg.algorithm.architecture.model.type = 'mmcls.ImageClassifier'
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_model_cfg.algorithm.architecture.model.backbone = dict(
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type='SearchableShuffleNetV2', widen_factor=1.0)
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_deploy_cfg = copy.deepcopy(deploy_cfg)
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_deploy_cfg.codebase_config['from_mmrazor'] = from_mmrazor
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_task_processor = build_task_processor(_model_cfg, _deploy_cfg, 'cpu')
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if not isinstance(from_mmrazor, bool):
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with pytest.raises(
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TypeError,
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match='`from_mmrazor` attribute must be '
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'boolean type! '
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f'but got: {from_mmrazor}'):
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_ = _task_processor.from_mmrazor
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return
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assert from_mmrazor == _task_processor.from_mmrazor
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if from_mmrazor:
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pytest.importorskip('mmrazor', reason='mmrazor is not installed.')
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model = _task_processor.build_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.build_backend_model([''])
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wrapper.recover()
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def test_build_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_visualize(backend_model):
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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results = backend_model.test_step(input_dict)[0]
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with TemporaryDirectory() as dir:
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filename = dir + '/tmp.jpg'
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task_processor.visualize(img, results, filename, 'window')
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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 = {'inputs': 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 DataLoader, Dataset
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dataset = task_processor.build_dataset(
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dataset_cfg=model_cfg.test_dataloader.dataset)
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assert isinstance(dataset, Dataset), 'Failed to build dataset'
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dataloader_cfg = task_processor.model_cfg.test_dataloader
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dataloader = task_processor.build_dataloader(dataloader_cfg)
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assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
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def test_build_test_runner():
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# Prepare dummy model
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from mmcls.structures import ClsDataSample
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from mmengine.structures import LabelData
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label = LabelData(
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label=torch.tensor([0]),
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score=torch.rand(10),
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metainfo=dict(num_classes=10))
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outputs = [
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ClsDataSample(
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pred_label=label,
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_pred_label=label,
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gt_label=label,
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_gt_label=label,
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metainfo=dict(
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img_shape=(224, 224),
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img_path='',
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ori_shape=(300, 400),
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scale_factor=(0.8525, 0.8533333333333334)))
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]
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model = DummyModel(outputs=outputs)
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assert model is not None
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# Run test
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with TemporaryDirectory() as dir:
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runner = task_processor.build_test_runner(model, dir)
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runner.test()
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