mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmengine import Config
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Backend, Codebase, load_config
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from mmdeploy.utils.test import WrapModel, check_backend, get_rewrite_outputs
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try:
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import_codebase(Codebase.MMACTION)
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except ImportError:
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pytest.skip(
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f'{Codebase.MMACTION} is not installed.', allow_module_level=True)
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@pytest.mark.parametrize('backend', [Backend.ONNXRUNTIME])
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@pytest.mark.parametrize('model_cfg_path',
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['tests/test_codebase/test_mmaction/data/model.py'])
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def test_forward_of_base_recognizer(model_cfg_path, backend):
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check_backend(backend)
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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='mmaction', task='VideoRecognition'),
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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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model_cfg = load_config(model_cfg_path)[0]
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from mmaction.apis import init_recognizer
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model = init_recognizer(model_cfg, None, device='cpu')
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img = torch.randn(1, 3, 3, 224, 224)
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from mmaction.structures import ActionDataSample
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data_sample = ActionDataSample()
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img_meta = dict(img_shape=(224, 224))
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data_sample.set_metainfo(img_meta)
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rewrite_inputs = {'inputs': img}
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wrapped_model = WrapModel(
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model, 'forward', data_samples=[data_sample], mode='predict')
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rewrite_outputs, _ = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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assert rewrite_outputs is not None
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