256 lines
8.3 KiB
Python
256 lines
8.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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import math
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import os.path as osp
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import tempfile
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from unittest.mock import Mock
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import torch
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import torch.nn as nn
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from torch.utils.data import Dataset
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from mmengine.evaluator import BaseMetric
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from mmengine.hooks import EarlyStoppingHook
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from mmengine.logging import MMLogger
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from mmengine.model import BaseModel
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from mmengine.optim import OptimWrapper
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from mmengine.runner import Runner
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from mmengine.testing import RunnerTestCase
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class ToyModel(BaseModel):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(2, 1)
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def forward(self, inputs, data_sample, mode='tensor'):
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labels = torch.stack(data_sample)
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inputs = torch.stack(inputs)
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outputs = self.linear(inputs)
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if mode == 'tensor':
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return outputs
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elif mode == 'loss':
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loss = (labels - outputs).sum()
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outputs = dict(loss=loss)
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return outputs
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else:
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return outputs
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class DummyDataset(Dataset):
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METAINFO = dict() # type: ignore
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data = torch.randn(12, 2)
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label = torch.ones(12)
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@property
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def metainfo(self):
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return self.METAINFO
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def __len__(self):
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return self.data.size(0)
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def __getitem__(self, index):
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return dict(inputs=self.data[index], data_sample=self.label[index])
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class DummyMetric(BaseMetric):
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default_prefix: str = 'test'
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def __init__(self, length):
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super().__init__()
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self.length = length
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self.best_idx = length // 2
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self.cur_idx = 0
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self.vals = [90, 91, 92, 88, 89, 90] * 2
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def process(self, *args, **kwargs):
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self.results.append(0)
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def compute_metrics(self, *args, **kwargs):
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acc = self.vals[self.cur_idx]
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self.cur_idx += 1
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return dict(acc=acc)
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def get_mock_runner():
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runner = Mock()
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runner.train_loop = Mock()
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runner.train_loop.stop_training = False
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return runner
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class TestEarlyStoppingHook(RunnerTestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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self.temp_dir.cleanup()
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def test_init(self):
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hook = EarlyStoppingHook(monitor='acc')
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self.assertEqual(hook.rule, 'greater')
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self.assertLess(hook.best_score, 0)
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hook = EarlyStoppingHook(monitor='ACC')
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self.assertEqual(hook.rule, 'greater')
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self.assertLess(hook.best_score, 0)
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hook = EarlyStoppingHook(monitor='mAP_50')
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self.assertEqual(hook.rule, 'greater')
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self.assertLess(hook.best_score, 0)
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hook = EarlyStoppingHook(monitor='loss')
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self.assertEqual(hook.rule, 'less')
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self.assertGreater(hook.best_score, 0)
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hook = EarlyStoppingHook(monitor='Loss')
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self.assertEqual(hook.rule, 'less')
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self.assertGreater(hook.best_score, 0)
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hook = EarlyStoppingHook(monitor='ce_loss')
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self.assertEqual(hook.rule, 'less')
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self.assertGreater(hook.best_score, 0)
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with self.assertRaises(ValueError):
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# `rule` should be passed.
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EarlyStoppingHook(monitor='recall')
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with self.assertRaises(ValueError):
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# Invalid `rule`
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EarlyStoppingHook(monitor='accuracy/top1', rule='the world')
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def test_before_run(self):
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runner = Mock()
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runner.train_loop = object()
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# `train_loop` must contain `stop_training` variable.
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with self.assertRaises(AssertionError):
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hook = EarlyStoppingHook(monitor='accuracy/top1', rule='greater')
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hook.before_run(runner)
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def test_after_val_epoch(self):
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runner = get_mock_runner()
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metrics = {'accuracy/top1': 0.5, 'loss': 0.23}
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hook = EarlyStoppingHook(monitor='acc', rule='greater')
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with self.assertWarns(UserWarning):
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# Skip early stopping process since the evaluation results does not
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# include the key 'acc'
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hook.after_val_epoch(runner, metrics)
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# if `monitor` does not match and strict=True, crash the training.
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with self.assertRaises(RuntimeError):
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metrics = {'accuracy/top1': 0.5, 'loss': 0.23}
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hook = EarlyStoppingHook(
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monitor='acc', rule='greater', strict=True)
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hook.after_val_epoch(runner, metrics)
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# Check largest value
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runner = get_mock_runner()
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metrics = [{'accuracy/top1': i / 9.} for i in range(8)]
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hook = EarlyStoppingHook(monitor='accuracy/top1', rule='greater')
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertAlmostEqual(hook.best_score, 7 / 9)
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# Check smallest value
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runner = get_mock_runner()
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metrics = [{'loss': i / 9.} for i in range(8, 0, -1)]
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hook = EarlyStoppingHook(monitor='loss')
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertAlmostEqual(hook.best_score, 1 / 9)
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# Check stop training
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runner = get_mock_runner()
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metrics = [{'accuracy/top1': i} for i in torch.linspace(98, 99, 8)]
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hook = EarlyStoppingHook(
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monitor='accuracy/top1', rule='greater', min_delta=1)
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertTrue(runner.train_loop.stop_training)
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# Check finite
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runner = get_mock_runner()
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metrics = [{'accuracy/top1': math.inf} for i in range(5)]
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hook = EarlyStoppingHook(
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monitor='accuracy/top1', rule='greater', min_delta=1)
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertTrue(runner.train_loop.stop_training)
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# Check patience
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runner = get_mock_runner()
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metrics = [{'accuracy/top1': i} for i in torch.linspace(98, 99, 8)]
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hook = EarlyStoppingHook(
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monitor='accuracy/top1', rule='greater', min_delta=1, patience=10)
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertFalse(runner.train_loop.stop_training)
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# Check stopping_threshold
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runner = get_mock_runner()
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metrics = [{'accuracy/top1': i} for i in torch.linspace(98, 99, 8)]
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hook = EarlyStoppingHook(
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monitor='accuracy/top1',
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rule='greater',
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stopping_threshold=98.5,
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patience=0)
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for metric in metrics:
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hook.after_val_epoch(runner, metric)
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if runner.train_loop.stop_training:
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break
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self.assertAlmostEqual(hook.best_score.item(), 98 + 4 / 7, places=5)
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def test_with_runner(self):
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max_epoch = 10
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work_dir = osp.join(self.temp_dir.name, 'runner_test')
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early_stop_cfg = dict(
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type='EarlyStoppingHook',
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monitor='test/acc',
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rule='greater',
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min_delta=1,
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patience=3,
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)
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runner = Runner(
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model=ToyModel(),
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work_dir=work_dir,
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train_dataloader=dict(
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dataset=DummyDataset(),
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sampler=dict(type='DefaultSampler', shuffle=True),
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batch_size=3,
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num_workers=0),
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val_dataloader=dict(
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dataset=DummyDataset(),
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sampler=dict(type='DefaultSampler', shuffle=False),
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batch_size=3,
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num_workers=0),
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val_evaluator=dict(type=DummyMetric, length=max_epoch),
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optim_wrapper=OptimWrapper(
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torch.optim.Adam(ToyModel().parameters())),
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train_cfg=dict(
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by_epoch=True, max_epochs=max_epoch, val_interval=1),
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val_cfg=dict(),
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custom_hooks=[early_stop_cfg],
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experiment_name='earlystop_test')
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runner.train()
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self.assertEqual(runner.epoch, 6)
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