Add new weights for ecaresnet26t/50t/269d models. Remove distinction between 't' and 'tn' (tiered models), tn is now t. Add test time img size spec to default cfg.

Author: rwightman

sotabench.py

@@ -122,8 +122,6 @@ def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE,
            model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
     _entry('seresnext26t_32x4d', 'SE-ResNeXt-26-T 32x4d', '1812.01187',
            model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
-    _entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
-           model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
     _entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
 
     _entry('skresnet18', 'SK-ResNet-18', '1903.06586'),

timm/data/config.py

@@ -5,7 +5,7 @@
 _logger = logging.getLogger(__name__)
 
 
-def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
+def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=True):
     new_config = {}
     default_cfg = default_cfg
     if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
@@ -25,8 +25,11 @@ def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
     elif 'img_size' in args and args['img_size'] is not None:
         assert isinstance(args['img_size'], int)
         input_size = (in_chans, args['img_size'], args['img_size'])
-    elif 'input_size' in default_cfg:
-        input_size = default_cfg['input_size']
+    else:
+        if use_test_size and 'test_input_size' in default_cfg:
+            input_size = default_cfg['test_input_size']
+        elif 'input_size' in default_cfg:
+            input_size = default_cfg['input_size']
     new_config['input_size'] = input_size
 
     # resolve interpolation method

timm/models/layers/test_time_pool.py

@@ -36,14 +36,17 @@ def forward(self, x):
         return x.view(x.size(0), -1)
 
 
-def apply_test_time_pool(model, config):
+def apply_test_time_pool(model, config, use_test_size=True):
     test_time_pool = False
     if not hasattr(model, 'default_cfg') or not model.default_cfg:
         return model, False
-    if (config['input_size'][-1] > model.default_cfg['input_size'][-1] and
-            config['input_size'][-2] > model.default_cfg['input_size'][-2]):
+    if use_test_size and 'test_input_size' in model.default_cfg:
+        df_input_size = model.default_cfg['test_input_size']
+    else:
+        df_input_size = model.default_cfg['input_size']
+    if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]:
         _logger.info('Target input size %s > pretrained default %s, using test time pooling' %
-                     (str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
+                     (str(config['input_size'][-2:]), str(df_input_size[-2:])))
         model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
         test_time_pool = True
     return model, test_time_pool