AttributeError: 'MultiStepLR' object has no attribute 'get_epoch_values'

[martinenkoEduard]

\AdaFace with the following property self.m 0.4 self.h 0.333 self.s 64.0 self.t_alpha 0.01 Global seed set to 42 /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:441: LightningDeprecationWarning: Setting Trainer(gpus=1) is deprecated in v1.7 and will be removed in v2.0. Please use Trainer(accelerator='gpu', devices=1) instead. rank_zero_deprecation( Using 16bit native Automatic Mixed Precision (AMP) GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs Trainer(limit_train_batches=1.0) was configured so 100% of the batches per epoch will be used.. Trainer(val_check_interval=1.0) was configured so validation will run at the end of the training epoch.. start training making validation data memfile [rank: 0] Global seed set to 42 Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1

distributed_backend=nccl All distributed processes registered. Starting with 1 processes

creating train dataset record file length 490623 creating val dataset laoding validation data memfile laoding validation data memfile laoding validation data memfile laoding validation data memfile laoding validation data memfile LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

| Name | Type | Params

0 | model | Backbone | 43.6 M 1 | head | AdaFace | 5.4 M 2 | cross_entropy_loss | CrossEntropyLoss | 0

49.0 M Trainable params 0 Non-trainable params 49.0 M Total params 97.997 Total estimated model params size (MB) Sanity Checking DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:01<00:00, 9.94it/s]/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:233: UserWarning: You called self.log('agedb_30_num_val_samples', ...) in your validation_epoch_end but the value needs to be floating point. Converting it to torch.float32. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:233: UserWarning: You called self.log('epoch', ...) in your validation_epoch_end but the value needs to be floating point. Converting it to torch.float32. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:537: PossibleUserWarning: It is recommended to use self.log('agedb_30_val_acc', ..., sync_dist=True) when logging on epoch level in distributed setting to accumulate the metric across devices. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:537: PossibleUserWarning: It is recommended to use self.log('agedb_30_best_threshold', ..., sync_dist=True) when logging on epoch level in distributed setting to accumulate the metric across devices. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:537: PossibleUserWarning: It is recommended to use self.log('agedb_30_num_val_samples', ..., sync_dist=True) when logging on epoch level in distributed setting to accumulate the metric across devices. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:537: PossibleUserWarning: It is recommended to use self.log('val_acc', ..., sync_dist=True) when logging on epoch level in distributed setting to accumulate the metric across devices. warning_cache.warn( /home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:537: PossibleUserWarning: It is recommended to use self.log('epoch', ..., sync_dist=True) when logging on epoch level in distributed setting to accumulate the metric across devices. warning_cache.warn( Epoch 0: 0%| | 0/8635 [00:00<?, ?it/s]Traceback (most recent call last): File "main.py", line 109, in main(args) File "main.py", line 81, in main trainer.fit(trainer_mod, data_mod) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 603, in fit call._call_and_handle_interrupt( File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 90, in launch return function(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl self._run(model, ckpt_path=self.ckpt_path) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run results = self._run_stage() File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage self._run_train() File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1200, in _run_train self.fit_loop.run() File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance self._outputs = self.epoch_loop.run(self._data_fetcher) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 214, in advance batch_output = self.batch_loop.run(kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance outputs = self.optimizer_loop.run(optimizers, kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 200, in advance result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position]) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 247, in _run_optimization self._optimizer_step(optimizer, opt_idx, kwargs.get("batch_idx", 0), closure) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 357, in _optimizer_step self.trainer._call_lightning_module_hook( File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1342, in _call_lightning_module_hook output = fn(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/core/module.py", line 1661, in optimizer_step optimizer.step(closure=optimizer_closure) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 169, in step step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 281, in optimizer_step optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 234, in optimizer_step return self.precision_plugin.optimizer_step( File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/native_amp.py", line 85, in optimizer_step closure_result = closure() File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 147, in call self._result = self.closure(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 133, in closure step_output = self._step_fn() File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 406, in _training_step training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values()) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1480, in _call_strategy_hook output = fn(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 352, in training_step return self.model(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1156, in forward output = self._run_ddp_forward(*inputs, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1110, in _run_ddp_forward return module_to_run(*inputs[0], **kwargs[0]) # type: ignore[index] File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/administrator/anaconda3/envs/py388/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 98, in forward output = self._forward_module.training_step(*inputs, **kwargs) File "/home/administrator/adaface/AdaFace/train_val.py", line 78, in training_step lr = self.get_current_lr() File "/home/administrator/adaface/AdaFace/train_val.py", line 60, in get_current_lr lr = scheduler.get_epoch_values(self.current_epoch)[0] AttributeError: 'MultiStepLR' object has no attribute 'get_epoch_values' Epoch 0: 0%| | 0/8635 [00:00<?, ?it/s]

Is it a pytorch-lighting error?

[WakingHours-GitHub]

same errors occur.

[Daishinkan002]

use lr = scheduler.get_last_lr()[0] instead of lr = scheduler.get_epoch_values(self.current_epoch)[0], it worked for me :)

[ayush0x00]

use lr = scheduler.get_last_lr()[0] instead of lr = scheduler.get_epoch_values(self.current_epoch)[0], it worked for me :)

Yup. get_epoch_values() is deprecated