make wandb not required but rather optional as huggingface_hub

requirements.txt

@@ -1,4 +1,3 @@
 torch>=1.4.0
 torchvision>=0.5.0
 pyyaml
-wandb

timm/utils/summary.py

@@ -4,9 +4,11 @@ Hacked together by / Copyright 2020 Ross Wightman
 """
 import csv
 import os
-import wandb
 from collections import OrderedDict
-
+try: 
+    import wandb
+except ImportError:
+    pass
 
 def get_outdir(path, *paths, inc=False):
     outdir = os.path.join(path, *paths)
@@ -28,8 +30,6 @@ def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=Fa
     rowd = OrderedDict(epoch=epoch)
     rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
     rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
-    if log_wandb:
-        wandb.log(rowd)
     with open(filename, mode='a') as cf:
         dw = csv.DictWriter(cf, fieldnames=rowd.keys())
         if write_header:  # first iteration (epoch == 1 can't be used)

train.py

@@ -23,8 +23,6 @@ from collections import OrderedDict
 from contextlib import suppress
 from datetime import datetime
 
-import wandb 
-
 import torch
 import torch.nn as nn
 import torchvision.utils
@@ -54,6 +52,12 @@ try:
 except AttributeError:
     pass
 
+try:
+    import wandb
+    has_wandb = True
+except ModuleNotFoundError: 
+    has_wandb = False
+
 torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('train')
 
@@ -274,7 +278,7 @@ parser.add_argument('--use-multi-epochs-loader', action='store_true', default=Fa
 parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                     help='convert model torchscript for inference')
 parser.add_argument('--log-wandb', action='store_true', default=False,
-                    help='use wandb for training and validation logs')
+                    help='log training and validation metrics to wandb')
 
 
 def _parse_args():
@@ -299,7 +303,11 @@ def main():
     args, args_text = _parse_args()
     
     if args.log_wandb:
+        if has_wandb:
             wandb.init(project=args.experiment, config=args)
+        else: 
+            _logger.warning("You've requested to log metrics to wandb but package not found. "
+                            "Metrics not being logged to wandb, try `pip install wandb`")
              
     args.prefetcher = not args.no_prefetcher
     args.distributed = False
@@ -600,7 +608,7 @@ def main():
 
             update_summary(
                 epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
-                write_header=best_metric is None, log_wandb=args.log_wandb)
+                write_header=best_metric is None, log_wandb=args.log_wandb and has_wandb)
 
             if saver is not None:
                 # save proper checkpoint with eval metric