Add --use-train-size flag to force use of train input_size (over test input size) for validation. Default test-time pooling to use train input size (fixes issues).

timm/models/layers/test_time_pool.py

@@ -36,7 +36,7 @@ class TestTimePoolHead(nn.Module):
         return x.view(x.size(0), -1)
 
 
-def apply_test_time_pool(model, config, use_test_size=True):
+def apply_test_time_pool(model, config, use_test_size=False):
     test_time_pool = False
     if not hasattr(model, 'default_cfg') or not model.default_cfg:
         return model, False

validate.py

@@ -67,6 +67,8 @@ parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
 parser.add_argument('--input-size', default=None, nargs=3, type=int,
                     metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+parser.add_argument('--use-train-size', action='store_true', default=False,
+                    help='force use of train input size, even when test size is specified in pretrained cfg')
 parser.add_argument('--crop-pct', default=None, type=float,
                     metavar='N', help='Input image center crop pct')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
@@ -164,10 +166,15 @@ def validate(args):
     param_count = sum([m.numel() for m in model.parameters()])
     _logger.info('Model %s created, param count: %d' % (args.model, param_count))
 
-    data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True)
+    data_config = resolve_data_config(
+        vars(args),
+        model=model,
+        use_test_size=not args.use_train_size,
+        verbose=True
+    )
     test_time_pool = False
     if args.test_pool:
-        model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)
+        model, test_time_pool = apply_test_time_pool(model, data_config)
 
     if args.torchscript:
         torch.jit.optimized_execution(True)