Add ResNet-50 w/ GN (resnet50_gn) and SEBotNet-33-TS (sebotnet33ts_256) model defs and weights. Update halonet50ts weights w/ slightly better variant in1k val, more robust to test sets.

timm/models/byoanet.py

@@ -36,6 +36,9 @@ default_cfgs = {
     'botnet26t_256': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/botnet26t_c1_256-167a0e9f.pth',
         fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
+    'sebotnet33ts_256': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sebotnet33ts_a1h2_256-957e3c3e.pth',
+        fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.94),
     'botnet50ts_256': _cfg(
         url='',
         fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
@@ -51,7 +54,7 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sehalonet33ts_256-87e053f9.pth',
         input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
     'halonet50ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet50ts_a1h_256-c6d7ff15.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet50ts_a1h2_256-f3a3daee.pth',
         input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
     'eca_halonext26ts': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_halonext26ts_c_256-06906299.pth',
@@ -97,6 +100,22 @@ model_cfgs = dict(
         self_attn_layer='bottleneck',
         self_attn_kwargs=dict()
     ),
+    sebotnet33ts=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=512, s=2, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), every=[2], d=3, c=1024, s=2, gs=0, br=0.25),
+            ByoBlockCfg('self_attn', d=2, c=1536, s=2, gs=0, br=0.333),
+        ),
+        stem_chs=64,
+        stem_type='tiered',
+        stem_pool='',
+        act_layer='silu',
+        num_features=1280,
+        attn_layer='se',
+        self_attn_layer='bottleneck',
+        self_attn_kwargs=dict()
+    ),
     botnet50ts=ByoModelCfg(
         blocks=(
             ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
@@ -322,6 +341,13 @@ def botnet26t_256(pretrained=False, **kwargs):
     return _create_byoanet('botnet26t_256', 'botnet26t', pretrained=pretrained, **kwargs)
 
 
+@register_model
+def sebotnet33ts_256(pretrained=False, **kwargs):
+    """ Bottleneck Transformer w/ a ResNet33-t backbone, SE attn for non Halo blocks, SiLU,
+    """
+    return _create_byoanet('sebotnet33ts_256', 'sebotnet33ts', pretrained=pretrained, **kwargs)
+
+
 @register_model
 def botnet50ts_256(pretrained=False, **kwargs):
     """ Bottleneck Transformer w/ ResNet50-T backbone, silu act.

timm/models/layers/norm.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 
 
 class GroupNorm(nn.GroupNorm):
-    def __init__(self, num_channels, num_groups, eps=1e-5, affine=True):
+    def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True):
         # NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
         super().__init__(num_groups, num_channels, eps=eps, affine=affine)
 

timm/models/resnet.py

@@ -15,7 +15,7 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg
-from .layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, create_attn, get_attn, create_classifier
+from .layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, create_classifier
 from .registry import register_model
 
 __all__ = ['ResNet', 'BasicBlock', 'Bottleneck']  # model_registry will add each entrypoint fn to this
@@ -89,6 +89,11 @@ default_cfgs = {
         interpolation='bicubic'),
     'wide_resnet101_2': _cfg(url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth'),
 
+    # ResNets w/ alternative norm layers
+    'resnet50_gn': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_gn_a1h2-8fe6c4d0.pth',
+        crop_pct=0.94, interpolation='bicubic'),
+
     # ResNeXt
     'resnext50_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1h-0146ab0a.pth',
@@ -881,6 +886,14 @@ def wide_resnet101_2(pretrained=False, **kwargs):
     return _create_resnet('wide_resnet101_2', pretrained, **model_args)
 
 
+@register_model
+def resnet50_gn(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model w/ GroupNorm
+    """
+    model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3],  **kwargs)
+    return _create_resnet('resnet50_gn', pretrained, norm_layer=GroupNorm, **model_args)
+
+
 @register_model
 def resnext50_32x4d(pretrained=False, **kwargs):
     """Constructs a ResNeXt50-32x4d model.