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https://github.com/open-mmlab/mmselfsup.git
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3f530f085e
* [Tools]: MAE Reconstructed Image Visualization] * [Fix]: fix docstring and type hint * [Fix]: fix docstring in MAE clsss * [Fix]: fix docstring in MAE clsss * [Fix]: fix type hint * [Fix]: fix type hint and docstring * [refactor]: refactor super init
60 lines
1.5 KiB
Python
60 lines
1.5 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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import pytest
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import torch
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import torch.nn as nn
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from mmcv import Config
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from PIL import Image
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from mmselfsup.apis import inference_model
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from mmselfsup.models import BaseModel
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class ExampleModel(BaseModel):
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def __init__(self):
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super(ExampleModel, self).__init__()
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self.test_cfg = None
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self.layer = nn.Linear(1, 1)
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self.neck = nn.Identity()
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def extract_feat(self, imgs):
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pass
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def forward_train(self, imgs, **kwargs):
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pass
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def forward_test(self, img, **kwargs):
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out = self.layer(img)
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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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# 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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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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model.cfg.data = dict(
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test=dict(pipeline=[
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dict(type='Resize', size=(1, 1)),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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]))
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data = Image.open(
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osp.join(osp.dirname(__file__), '..', 'data', 'color.jpg'))
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# inference model
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data, output = inference_model(model, data)
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assert data.size() == torch.Size([1, 3, 1, 1])
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assert output.size() == torch.Size([1, 3, 1, 1])
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