More adjustments to ByoaNet models for further experiments.

timm/models/byoanet.py

@@ -46,18 +46,18 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     # GPU-Efficient (ResNet) weights
     'botnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
-    'botnet50t_224': _cfg(url='', fixed_input_size=True),
+    'botnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
-    'botnet50t_c4c5_224': _cfg(url='', fixed_input_size=True),
 
     'halonet_h1': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
     'halonet_h1_c4c5': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
     'halonet26t': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
-    'halonet50t': _cfg(url='', min_input_size=(3, 224, 224)),
+    'halonet50ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
 
     'lambda_resnet26t': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
     'lambda_resnet50t': _cfg(url='', min_input_size=(3, 128, 128)),
 
     'swinnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
+    'swinnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
 }
 
 
@@ -110,38 +110,18 @@ model_cfgs = dict(
         self_attn_fixed_size=True,
         self_attn_kwargs=dict()
     ),
-    botnet50t=ByoaCfg(
+    botnet50ts=ByoaCfg(
         blocks=(
             ByoaBlocksCfg(type='bottle', d=3, c=256, s=2, gs=0, br=0.25),
             ByoaBlocksCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
-            ByoaBlocksCfg(type='bottle', d=6, c=1024, s=2, gs=0, br=0.25),
+            interleave_attn(types=('bottle', 'self_attn'), every=1, d=6, c=1024, s=2, gs=0, br=0.25),
             ByoaBlocksCfg(type='self_attn', d=3, c=2048, s=1, gs=0, br=0.25),
         ),
         stem_chs=64,
         stem_type='tiered',
         stem_pool='',
         num_features=0,
-        self_attn_layer='bottleneck',
+        act_layer='silu',
-        self_attn_fixed_size=True,
-        self_attn_kwargs=dict()
-    ),
-    botnet50t_c4c5=ByoaCfg(
-        blocks=(
-            ByoaBlocksCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
-            ByoaBlocksCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
-            (
-                ByoaBlocksCfg(type='self_attn', d=1, c=1024, s=2, gs=0, br=0.25),
-                ByoaBlocksCfg(type='bottle', d=5, c=1024, s=1, gs=0, br=0.25),
-            ),
-            (
-                ByoaBlocksCfg(type='self_attn', d=1, c=2048, s=2, gs=0, br=0.25),
-                ByoaBlocksCfg(type='bottle', d=2, c=2048, s=1, gs=0, br=0.25),
-            )
-        ),
-        stem_chs=64,
-        stem_type='tiered',
-        stem_pool='maxpool',
-        num_features=0,
         self_attn_layer='bottleneck',
         self_attn_fixed_size=True,
         self_attn_kwargs=dict()
@@ -189,19 +169,20 @@ model_cfgs = dict(
         self_attn_layer='halo',
         self_attn_kwargs=dict(block_size=8, halo_size=2)  # intended for 256x256 res
     ),
-    halonet50t=ByoaCfg(
+    halonet50ts=ByoaCfg(
         blocks=(
             ByoaBlocksCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
             ByoaBlocksCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
-            ByoaBlocksCfg(type='bottle', d=6, c=1024, s=2, gs=0, br=0.25),
+            interleave_attn(types=('bottle', 'self_attn'), every=1, d=6, c=1024, s=2, gs=0, br=0.25),
             ByoaBlocksCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
         ),
         stem_chs=64,
         stem_type='tiered',
         stem_pool='maxpool',
         num_features=0,
+        act_layer='silu',
         self_attn_layer='halo',
-        self_attn_kwargs=dict(block_size=7, halo_size=2)
+        self_attn_kwargs=dict(block_size=8, halo_size=2)
     ),
 
     lambda_resnet26t=ByoaCfg(
@@ -234,9 +215,24 @@ model_cfgs = dict(
     ),
 
     swinnet26t=ByoaCfg(
+        blocks=(
+            ByoaBlocksCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
+            interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=0, br=0.25),
+            interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
+            ByoaBlocksCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
+        ),
+        stem_chs=64,
+        stem_type='tiered',
+        stem_pool='maxpool',
+        num_features=0,
+        self_attn_layer='swin',
+        self_attn_fixed_size=True,
+        self_attn_kwargs=dict(win_size=8)
+    ),
+    swinnet50ts=ByoaCfg(
         blocks=(
             ByoaBlocksCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
-            ByoaBlocksCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
+            interleave_attn(types=('bottle', 'self_attn'), every=1, d=4, c=512, s=2, gs=0, br=0.25),
             interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
             ByoaBlocksCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
         ),
@@ -244,6 +240,7 @@ model_cfgs = dict(
         stem_type='tiered',
         stem_pool='maxpool',
         num_features=0,
+        act_layer='silu',
         self_attn_layer='swin',
         self_attn_fixed_size=True,
         self_attn_kwargs=dict(win_size=8)
@@ -415,19 +412,11 @@ def botnet26t_256(pretrained=False, **kwargs):
 
 
 @register_model
-def botnet50t_224(pretrained=False, **kwargs):
+def botnet50ts_256(pretrained=False, **kwargs):
     """ Bottleneck Transformer w/ ResNet50-T backbone. Bottleneck attn in final stage.
     """
-    kwargs.setdefault('img_size', 224)
+    kwargs.setdefault('img_size', 256)
-    return _create_byoanet('botnet50t_224', 'botnet50t', pretrained=pretrained, **kwargs)
+    return _create_byoanet('botnet50ts_256', 'botnet50ts', pretrained=pretrained, **kwargs)
-
-
-@register_model
-def botnet50t_c4c5_224(pretrained=False, **kwargs):
-    """ Bottleneck Transformer w/ ResNet50-T backbone. Bottleneck attn in last two stages.
-    """
-    kwargs.setdefault('img_size', 224)
-    return _create_byoanet('botnet50t_c4c5_224', 'botnet50t_c4c5', pretrained=pretrained, **kwargs)
 
 
 @register_model
@@ -454,10 +443,10 @@ def halonet26t(pretrained=False, **kwargs):
 
 
 @register_model
-def halonet50t(pretrained=False, **kwargs):
+def halonet50ts(pretrained=False, **kwargs):
     """ HaloNet w/ a ResNet50-t backbone, Hallo attention in final stage
     """
-    return _create_byoanet('halonet50t', pretrained=pretrained, **kwargs)
+    return _create_byoanet('halonet50ts', pretrained=pretrained, **kwargs)
 
 
 @register_model
@@ -480,3 +469,11 @@ def swinnet26t_256(pretrained=False, **kwargs):
     """
     kwargs.setdefault('img_size', 256)
     return _create_byoanet('swinnet26t_256', 'swinnet26t', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def swinnet50ts_256(pretrained=False, **kwargs):
+    """
+    """
+    kwargs.setdefault('img_size', 256)
+    return _create_byoanet('swinnet50ts_256', 'swinnet50ts', pretrained=pretrained, **kwargs)