Logger Cleanup (#9828)

segment/train.py

@@ -91,17 +91,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     data_dict = None
     if RANK in {-1, 0}:
         logger = GenericLogger(opt=opt, console_logger=LOGGER)
-        # loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-        # if loggers.clearml:
-        #     data_dict = loggers.clearml.data_dict  # None if no ClearML dataset or filled in by ClearML
-        # if loggers.wandb:
-        #     data_dict = loggers.wandb.data_dict
-        #     if resume:
-        #         weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
-        #
-        # # Register actions
-        # for k in methods(loggers):
-        #     callbacks.register_action(k, callback=getattr(loggers, k))
 
     # Config
     plots = not evolve and not opt.noplots  # create plots
@@ -400,7 +389,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     'ema': deepcopy(ema.ema).half(),
                     'updates': ema.updates,
                     'optimizer': optimizer.state_dict(),
-                    # 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
                     'opt': vars(opt),
                     'date': datetime.now().isoformat()}
 

train.py

@@ -53,7 +53,6 @@ from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_g
                            one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
 from utils.loggers import Loggers
 from utils.loggers.comet.comet_utils import check_comet_resume
-from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
 from utils.plots import plot_evolve
@@ -375,7 +374,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     'ema': deepcopy(ema.ema).half(),
                     'updates': ema.updates,
                     'optimizer': optimizer.state_dict(),
-                    'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
                     'opt': vars(opt),
                     'date': datetime.now().isoformat()}
 
@@ -483,7 +481,7 @@ def main(opt, callbacks=Callbacks()):
         check_requirements()
 
     # Resume (from specified or most recent last.pt)
-    if opt.resume and not check_wandb_resume(opt) and not check_comet_resume(opt) and not opt.evolve:
+    if opt.resume and not check_comet_resume(opt) and not opt.evolve:
         last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
         opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
         opt_data = opt.data  # original dataset

utils/general.py

@@ -956,7 +956,7 @@ def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_op
     x = torch.load(f, map_location=torch.device('cpu'))
     if x.get('ema'):
         x['model'] = x['ema']  # replace model with ema
-    for k in 'optimizer', 'best_fitness', 'wandb_id', 'ema', 'updates':  # keys
+    for k in 'optimizer', 'best_fitness', 'ema', 'updates':  # keys
         x[k] = None
     x['epoch'] = -1
     x['model'].half()  # to FP16