deviandice/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Begin a migration to specifying training rate on megasamples instead of arbitrary "steps"

Browse Source 
This should help me greatly in tuning models.  It's also necessary now that batch size isn't really
respected; we simply step once the gradient direction becomes unstable.
This commit is contained in:
James Betker 2022-02-09 17:25:05 -07:00
parent 93ca619267
commit a930f2576e
1 changed files with 21 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
codes/train.py
View File

@ -39,6 +39,8 @@ class Trainer:
self._profile = False
self.val_compute_psnr = opt_get(opt, ['eval', 'compute_psnr'], False)
self.val_compute_fea = opt_get(opt, ['eval', 'compute_fea'], False)
self.current_step = 0
self.total_training_data_encountered = 0
#### loading resume state if exists
if opt['path'].get('resume_state', None):
@ -159,6 +161,7 @@ class Trainer:
self.start_epoch = resume_state['epoch']
self.current_step = resume_state['iter']
self.total_training_data_encountered = opt_get(resume_state, ['total_data_processed'], 0)
self.model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers
else:
self.current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
@ -173,7 +176,11 @@ class Trainer:
_t = time()
opt = self.opt
batch_size = self.opt['datasets']['train']['batch_size'] # It may seem weird to derive this from opt, rather than train_data. The reason this is done is
# because train_data is process-local while the opt variant represents all of the data fed across all GPUs.
self.current_step += 1
self.total_training_data_encountered += batch_size
#### update learning rate
self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter'])
@ -191,7 +198,10 @@ class Trainer:
if self.dataset_debugger is not None:
self.dataset_debugger.update(train_data)
if self.current_step % opt['logger']['print_freq'] == 0 and self.rank <= 0:
logs = self.model.get_current_log(self.current_step)
logs = {'step': self.current_step,
'samples': self.total_training_data_encountered,
'megasamples': self.total_training_data_encountered / 1000000}
logs.update(self.model.get_current_log(self.current_step))
if self.dataset_debugger is not None:
logs.update(self.dataset_debugger.get_debugging_map())
message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(self.epoch, self.current_step)
@ -210,7 +220,10 @@ class Trainer:
self.tb_logger.add_scalar(k, v, self.current_step)
if opt['wandb'] and self.rank <= 0:
import wandb
wandb.log(logs, step=int(self.current_step * opt_get(opt, ['wandb_step_factor'], 1)))
if opt_get(opt, ['wandb_progress_use_raw_steps'], False):
wandb.log(logs, step=self.current_step)
else:
wandb.log(logs, step=self.total_training_data_encountered)
self.logger.info(message)
#### save models and training states
@ -219,7 +232,7 @@ class Trainer:
if self.rank <= 0:
self.logger.info('Saving models and training states.')
self.model.save(self.current_step)
state = {'epoch': self.epoch, 'iter': self.current_step}
state = {'epoch': self.epoch, 'iter': self.current_step, 'total_data_processed': self.total_training_data_encountered}
if self.dataset_debugger is not None:
state['dataset_debugger_state'] = self.dataset_debugger.get_state()
self.model.save_training_state(state)
@ -231,7 +244,11 @@ class Trainer:
shutil.copytree(self.tb_logger_path, alt_tblogger)
#### validation
if opt_get(opt, ['eval', 'pure'], False) and self.current_step % opt['train']['val_freq'] == 0:
if 'val_freq' in opt['train'].keys():
val_freq = opt['train']['val_freq'] * batch_size
else:
val_freq = int(opt['train']['val_freq_megasamples'] * 1000000)
if opt_get(opt, ['eval', 'pure'], False) and self.total_training_data_encountered % val_freq == 0:
metrics = []
for val_data in tqdm(self.val_loader):
self.model.feed_data(val_data, self.current_step, perform_micro_batching=False)