Add weights for new tiny test models

Author: rwightman

timm/models/byobnet.py

@@ -2355,7 +2355,7 @@ def _cfgr(url='', **kwargs):
     'test_byobnet.r160_in1k': _cfgr(
         hf_hub_id='timm/',
         first_conv='stem.conv',
-        input_size=(3, 160, 160), crop_pct=0.875, pool_size=(5, 5),
+        input_size=(3, 160, 160), crop_pct=0.95, pool_size=(5, 5),
     ),
 })
 

timm/models/convnext.py

@@ -953,14 +953,17 @@ def _cfgv2(url='', **kwargs):
         input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=1024),
 
     "test_convnext.r160_in1k": _cfg(
-        # hf_hub_id='timm/',
-        input_size=(3, 160, 160), pool_size=(5, 5), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+        hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
     "test_convnext2.r160_in1k": _cfg(
-        # hf_hub_id='timm/',
-        input_size=(3, 160, 160), pool_size=(5, 5), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+        hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
     "test_convnext3.r160_in1k": _cfg(
-        # hf_hub_id='timm/',
-        input_size=(3, 160, 160), pool_size=(5, 5), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+        hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
 
 })
 

timm/models/efficientnet.py

@@ -1804,19 +1804,18 @@ def _cfg(url='', **kwargs):
 
     "test_efficientnet.r160_in1k": _cfg(
         hf_hub_id='timm/',
-        input_size=(3, 160, 160), pool_size=(5, 5)),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
+    "test_efficientnet_ln.r160_in1k": _cfg(
+        hf_hub_id='timm/',
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
     "test_efficientnet_gn.r160_in1k": _cfg(
         hf_hub_id='timm/',
         mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-        input_size=(3, 160, 160), pool_size=(5, 5)),
-    "test_efficientnet_ln.r160_in1k": _cfg(
-        #hf_hub_id='timm/',
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-        input_size=(3, 160, 160), pool_size=(5, 5)),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
     "test_efficientnet_evos.r160_in1k": _cfg(
-        #hf_hub_id='timm/',
+        hf_hub_id='timm/',
         mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-        input_size=(3, 160, 160), pool_size=(5, 5)),
+        input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.95),
 })
 
 

timm/models/nfnet.py

@@ -736,9 +736,9 @@ def _dcfg(url='', **kwargs):
     'nf_ecaresnet101': _dcfg(url='', first_conv='stem.conv'),
 
     'test_nfnet.r160_in1k': _dcfg(
-        # hf_hub_id='timm/',
+        hf_hub_id='timm/',
         mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
-        crop_pct=0.875, input_size=(3, 160, 160), pool_size=(5, 5)),
+        crop_pct=0.95, input_size=(3, 160, 160), pool_size=(5, 5)),
 })
 
 

timm/models/resnet.py

@@ -1304,8 +1304,8 @@ def _gcfg(url='', **kwargs):
         first_conv='conv1.0'),
 
     'test_resnet.r160_in1k': _cfg(
-        #hf_hub_id='timm/',
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.95,
         input_size=(3, 160, 160), pool_size=(5, 5), first_conv='conv1.0'),
 })
 

timm/models/vision_transformer.py

@@ -2014,13 +2014,13 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
 
     'test_vit.r160_in1k': _cfg(
         hf_hub_id='timm/',
-        input_size=(3, 160, 160), crop_pct=0.875),
+        input_size=(3, 160, 160), crop_pct=0.95),
     'test_vit2.r160_in1k': _cfg(
-        #hf_hub_id='timm/',
-        input_size=(3, 160, 160), crop_pct=0.875),
+        hf_hub_id='timm/',
+        input_size=(3, 160, 160), crop_pct=0.95),
     'test_vit3.r160_in1k': _cfg(
         #hf_hub_id='timm/',
-        input_size=(3, 160, 160), crop_pct=0.875),
+        input_size=(3, 160, 160), crop_pct=0.95),
 }
 
 _quick_gelu_cfgs = [
@@ -3217,21 +3217,23 @@ def vit_so150m_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
 def test_vit(pretrained: bool = False, **kwargs) -> VisionTransformer:
     """ ViT Test
     """
-    model_args = dict(patch_size=16, embed_dim=64, depth=6, num_heads=2, mlp_ratio=3)
+    model_args = dict(patch_size=16, embed_dim=64, depth=6, num_heads=2, mlp_ratio=3, dynamic_img_size=True)
     model = _create_vision_transformer('test_vit', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
+@register_model
 def test_vit2(pretrained: bool = False, **kwargs) -> VisionTransformer:
     """ ViT Test
     """
     model_args = dict(
         patch_size=16, embed_dim=64, depth=8, num_heads=2, mlp_ratio=3,
-        class_token=False, reg_tokens=1, global_pool='avg', init_values=1e-5)
+        class_token=False, reg_tokens=1, global_pool='avg', init_values=1e-5, dynamic_img_size=True)
     model = _create_vision_transformer('test_vit2', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
+@register_model
 def test_vit3(pretrained: bool = False, **kwargs) -> VisionTransformer:
     """ ViT Test
     """