Newer
Older
self.assertEqual(runner.optim_wrapper['linear1'].param_groups[0]['lr'],
self.assertIsInstance(runner.optim_wrapper['linear2'].optimizer, Adam)
self.assertEqual(runner.optim_wrapper['linear2'].param_groups[0]['lr'],
0.0002)
self.assertIsInstance(runner.param_schedulers, dict)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertEqual(runner.param_schedulers['linear1'][0].milestones, {
1: 1,
2: 1
})
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear2'][0],
MultiStepLR)
self.assertEqual(runner.param_schedulers['linear2'][0].milestones, {
1: 1,
2: 1
})
# 2. test iter based
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint8'
# 2.1 test `save_checkpoint` which is called by `CheckpointHook`
path = osp.join(self.temp_dir, 'iter_12.pth')
self.assertTrue(osp.exists(path))
self.assertFalse(osp.exists(osp.join(self.temp_dir, 'epoch_13.pth')))
ckpt = torch.load(path)
self.assertEqual(ckpt['meta']['epoch'], 0)
self.assertEqual(ckpt['meta']['iter'], 12)
assert isinstance(ckpt['optimizer'], dict)
assert isinstance(ckpt['param_schedulers'], list)
self.assertIsInstance(ckpt['message_hub'], dict)
message_hub.load_state_dict(ckpt['message_hub'])
self.assertEqual(message_hub.get_info('epoch'), 0)
self.assertEqual(message_hub.get_info('iter'), 11)
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint9'
runner.load_checkpoint(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 0)
self.assertTrue(runner._has_loaded)
# 2.3 test `resume`
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint10'
runner.resume(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
self.assertEqual(runner.message_hub.get_info('epoch'), 0)
self.assertEqual(runner.message_hub.get_info('iter'), 11)
# 2.4 test auto resume
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint11'
cfg.resume = True
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 2.5 test resume from a specified checkpoint
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint12'
cfg.resume = True
cfg.load_from = osp.join(self.temp_dir, 'iter_3.pth')
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 3)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 2.6 test resumed message_hub has the history value.
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint13'
cfg.resume = True
cfg.load_from = osp.join(self.temp_dir, 'iter_3.pth')
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
assert len(runner.message_hub.log_scalars['train/lr'].data[1]) == 3
assert len(MessageHub.get_current_instance().log_scalars['train/lr'].
data[1]) == 3
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
# 2.7.1 test `resume` 2 optimizers and 1 scheduler list.
path = osp.join(self.temp_dir, 'epoch_3.pth')
optim_cfg = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint14'
cfg.optim_wrapper = optim_cfg
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2, 3])
cfg.model = dict(type='ToyGANModel')
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint15'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertIsInstance(runner.param_schedulers['linear2'][0],
MultiStepLR)
# 2.7.2 test `resume` 2 optimizers and 2 scheduler list.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint16'
cfg.optim_wrapper = optim_cfg
cfg.param_scheduler = dict(
linear1=dict(type='MultiStepLR', milestones=[1, 2, 3]),
linear2=dict(type='StepLR', gamma=0.1, step_size=3))
cfg.model = dict(type='ToyGANModel')
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint17'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertIsInstance(runner.param_schedulers['linear2'][0], StepLR)
# 2.7.3 test `resume` 2 optimizers and 0 sheduler list.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint18'
cfg.optim_wrapper = optim_cfg
cfg.model = dict(type='ToyGANModel')
cfg.param_scheduler = None
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint19'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertIsNone(runner.param_schedulers)
def test_build_runner(self):
# No need to test other cases which have been tested in
# `test_build_from_cfg`
# test custom runner
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_runner1'
cfg.runner_type = 'CustomRunner'
assert isinstance(RUNNERS.build(cfg), CustomRunner)
# test default runner
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_runner2'
assert isinstance(RUNNERS.build(cfg), Runner)