mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
73cd764b5f
* refactor reconstruction visualization * support simmim visualization * fix reconstruction bug of MAE * support visualization of MaskFeat * refaction mae visualization demo * add unit test * fix lint and ut * update * add docs * set random seed * update * update docstring * add torch version check * update * rename * update version * update * fix lint * add docstring * update docs
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import platform
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from typing import List, Optional
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import pytest
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import torch
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import torch.nn as nn
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from mmengine.config import Config
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from mmselfsup.apis import inference_model
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from mmselfsup.models import BaseModel
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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backbone = dict(
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type='ResNet',
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depth=18,
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in_channels=2,
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out_indices=[4], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN'))
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class ExampleModel(BaseModel):
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def __init__(self, backbone=backbone):
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super(ExampleModel, self).__init__(backbone=backbone)
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self.layer = nn.Linear(1, 1)
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def extract_feat(self,
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inputs: List[torch.Tensor],
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data_samples: Optional[List[SelfSupDataSample]] = None,
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**kwargs) -> SelfSupDataSample:
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out = self.layer(inputs[0])
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return out
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@pytest.mark.skipif(platform.system() == 'Windows', reason='')
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def test_inference_model():
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register_all_modules()
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# Specify the data settings
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cfg = Config.fromfile(
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'configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py' # noqa: E501
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)
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# Build the algorithm
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model = ExampleModel()
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model.cfg = cfg
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model.cfg.test_dataloader = dict(
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dataset=dict(pipeline=[
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dict(
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type='LoadImageFromFile',
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file_client_args=dict(backend='disk')),
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dict(type='Resize', scale=(1, 1)),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]))
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img_path = osp.join(osp.dirname(__file__), '..', 'data', 'color.jpg')
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# inference model
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out = inference_model(model, img_path)
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assert out.size() == torch.Size([1, 3, 1, 1])
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