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