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46 lines
1.1 KiB
46 lines
1.1 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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import unittest.mock as mock
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import pytest
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import torch
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from torch.utils.data import DataLoader, Dataset
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from mmpose.core import DistEvalHook, EvalHook
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class ExampleDataset(Dataset):
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def __init__(self):
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self.index = 0
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self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6]
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def __getitem__(self, idx):
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results = dict(imgs=torch.tensor([1]))
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return results
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def __len__(self):
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return 1
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@mock.create_autospec
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def evaluate(self, results, res_folder=None, logger=None):
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pass
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def test_old_fashion_eval_hook_parameters():
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data_loader = DataLoader(
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ExampleDataset(),
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batch_size=1,
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sampler=None,
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num_workers=0,
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shuffle=False)
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# test argument "key_indicator"
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with pytest.warns(DeprecationWarning):
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_ = EvalHook(data_loader, key_indicator='AP')
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with pytest.warns(DeprecationWarning):
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_ = DistEvalHook(data_loader, key_indicator='AP')
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# test argument "gpu_collect"
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with pytest.warns(DeprecationWarning):
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_ = EvalHook(data_loader, save_best='AP', gpu_collect=False)
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