Add parsable json results output for train.py, tweak --pretrained-path to force head adaptation

timm/models/_builder.py

@@ -261,7 +261,7 @@ def _filter_kwargs(kwargs, names):
         kwargs.pop(n, None)
 
 
-def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
+def _update_default_model_kwargs(pretrained_cfg, kwargs, kwargs_filter):
     """ Update the default_cfg and kwargs before passing to model
 
     Args:
@@ -288,6 +288,11 @@ def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
             if input_size is not None:
                 assert len(input_size) == 3
                 kwargs.setdefault(n, input_size[0])
+        elif n == 'num_classes':
+            default_val = pretrained_cfg.get(n, None)
+            # if default is < 0, don't pass through to model
+            if default_val is not None and default_val >= 0:
+                kwargs.setdefault(n, pretrained_cfg[n])
         else:
             default_val = pretrained_cfg.get(n, None)
             if default_val is not None:
@@ -379,7 +384,7 @@ def build_model_with_cfg(
     # FIXME converting back to dict, PretrainedCfg use should be propagated further, but not into model
     pretrained_cfg = pretrained_cfg.to_dict()
 
-    _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter)
+    _update_default_model_kwargs(pretrained_cfg, kwargs, kwargs_filter)
 
     # Setup for feature extraction wrapper done at end of this fn
     if kwargs.pop('features_only', False):

train.py

@@ -15,6 +15,7 @@ NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
 Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
 """
 import argparse
+import json
 import logging
 import os
 import time
@@ -425,7 +426,10 @@ def main():
     factory_kwargs = {}
     if args.pretrained_path:
         # merge with pretrained_cfg of model, 'file' has priority over 'url' and 'hf_hub'.
-        factory_kwargs['pretrained_cfg_overlay'] = dict(file=args.pretrained_path)
+        factory_kwargs['pretrained_cfg_overlay'] = dict(
+            file=args.pretrained_path,
+            num_classes=-1,  # force head adaptation
+        )
 
     model = create_model(
         args.model,
@@ -770,6 +774,7 @@ def main():
         _logger.info(
             f'Scheduled epochs: {num_epochs}. LR stepped per {"epoch" if lr_scheduler.t_in_epochs else "update"}.')
 
+    results = []
     try:
         for epoch in range(start_epoch, num_epochs):
             if hasattr(dataset_train, 'set_epoch'):
@@ -841,11 +846,20 @@ def main():
                 # step LR for next epoch
                 lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
 
+            results.append({
+                'epoch': epoch,
+                'train': train_metrics,
+                'validation': eval_metrics,
+            })
+
     except KeyboardInterrupt:
         pass
 
+    results = {'all': results}
     if best_metric is not None:
+        results['best'] = results['all'][best_epoch]
         _logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))
+    print(f'--result\n{json.dumps(results, indent=4)}')
 
 
 def train_one_epoch(