Add wandb support

train.py

@@ -23,6 +23,8 @@ from collections import OrderedDict
 from contextlib import suppress
 from datetime import datetime
 
+import wandb 
+
 import torch
 import torch.nn as nn
 import torchvision.utils
@@ -293,7 +295,8 @@ def _parse_args():
 def main():
     setup_default_logging()
     args, args_text = _parse_args()
-
+    wandb.init(project='efficientnet_v2', config=args)
+    wandb.run.name = args.model
     args.prefetcher = not args.no_prefetcher
     args.distributed = False
     if 'WORLD_SIZE' in os.environ:
@@ -572,14 +575,14 @@ def main():
                 epoch, model, loader_train, optimizer, train_loss_fn, args,
                 lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                 amp_autocast=amp_autocast, loss_scaler=loss_scaler, model_ema=model_ema, mixup_fn=mixup_fn)
-
+            wandb.log(train_metrics)
             if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                 if args.local_rank == 0:
                     _logger.info("Distributing BatchNorm running means and vars")
                 distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
 
             eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)
-
+            wandb.log(eval_metrics)
             if model_ema is not None and not args.model_ema_force_cpu:
                 if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
                     distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
@@ -711,7 +714,7 @@ def train_one_epoch(
     if hasattr(optimizer, 'sync_lookahead'):
         optimizer.sync_lookahead()
 
-    return OrderedDict([('loss', losses_m.avg)])
+    return OrderedDict([('train_loss', losses_m.avg)])
 
 
 def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
@@ -773,7 +776,7 @@ def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix='')
                         log_name, batch_idx, last_idx, batch_time=batch_time_m,
                         loss=losses_m, top1=top1_m, top5=top5_m))
 
-    metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
+    metrics = OrderedDict([('val_loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
 
     return metrics