Default to native PyTorch AMP instead of APEX amp. Too many APEX issues cropping up lately.

timm/models/helpers.py

@@ -177,7 +177,7 @@ def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=Non
     if cfg is None:
         cfg = getattr(model, 'default_cfg')
     if cfg is None or 'url' not in cfg or not cfg['url']:
-        _logger.warning("Pretrained model URL does not exist, using random initialization.")
+        _logger.warning("No pretrained weights exist for this model. Using random initialization.")
         return
 
     state_dict = load_state_dict_from_url(cfg['url'], progress=progress, map_location='cpu')

train.py

@@ -310,11 +310,11 @@ def main():
     # resolve AMP arguments based on PyTorch / Apex availability
     use_amp = None
     if args.amp:
-        # for backwards compat, `--amp` arg tries apex before native amp
-        if has_apex:
-            args.apex_amp = True
-        elif has_native_amp:
+        # `--amp` chooses native amp before apex (APEX ver not actively maintained)
+        if has_native_amp:
             args.native_amp = True
+        elif has_apex:
+            args.apex_amp = True
     if args.apex_amp and has_apex:
         use_amp = 'apex'
     elif args.native_amp and has_native_amp:

validate.py

@@ -116,15 +116,20 @@ def validate(args):
     args.prefetcher = not args.no_prefetcher
     amp_autocast = suppress  # do nothing
     if args.amp:
-        if has_apex:
-            args.apex_amp = True
-        elif has_native_amp:
+        if has_native_amp:
             args.native_amp = True
+        elif has_apex:
+            args.apex_amp = True
         else:
-            _logger.warning("Neither APEX or Native Torch AMP is available, using FP32.")
+            _logger.warning("Neither APEX or Native Torch AMP is available.")
     assert not args.apex_amp or not args.native_amp, "Only one AMP mode should be set."
     if args.native_amp:
         amp_autocast = torch.cuda.amp.autocast
+        _logger.info('Validating in mixed precision with native PyTorch AMP.')
+    elif args.apex_amp:
+        _logger.info('Validating in mixed precision with NVIDIA APEX AMP.')
+    else:
+        _logger.info('Validating in float32. AMP not enabled.')
 
     if args.legacy_jit:
         set_jit_legacy()