Defaul lambda r=7. Define '26t' stage 4/5 256x256 variants for all of bot/halo/lambda nets for experiment. Add resnet50t for exp. Fix a few comments.

timm/models/byoanet.py

@@ -45,15 +45,16 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = {
     # GPU-Efficient (ResNet) weights
+    'botnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256)),
     'botnet50t_224': _cfg(url='', fixed_input_size=True),
     '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=''),
+    'halonet26t': _cfg(url='', input_size=(3, 256, 256)),
     'halonet50t': _cfg(url=''),
 
-    'lambda_resnet26t': _cfg(url='', min_input_size=(3, 128, 128)),
+    'lambda_resnet26t': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256)),
     'lambda_resnet50t': _cfg(url='', min_input_size=(3, 128, 128)),
 }
 
@@ -92,6 +93,21 @@ def interleave_attn(
 
 model_cfgs = dict(
 
+    botnet26t=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),
+            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=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()
+    ),
     botnet50t=ByoaCfg(
         blocks=(
             ByoaBlocksCfg(type='bottle', d=3, c=256, s=2, gs=0, br=0.25),
@@ -161,7 +177,7 @@ model_cfgs = dict(
         blocks=(
             ByoaBlocksCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
             ByoaBlocksCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
-            ByoaBlocksCfg(type='bottle', d=2, c=1024, 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,
@@ -169,7 +185,7 @@ model_cfgs = dict(
         stem_pool='maxpool',
         num_features=0,
         self_attn_layer='halo',
-        self_attn_kwargs=dict(block_size=7, halo_size=2)
+        self_attn_kwargs=dict(block_size=8, halo_size=2)  # intended for 256x256 res
     ),
     halonet50t=ByoaCfg(
         blocks=(
@@ -370,6 +386,14 @@ def _create_byoanet(variant, cfg_variant=None, pretrained=False, **kwargs):
         **kwargs)
 
 
+@register_model
+def botnet26t_256(pretrained=False, **kwargs):
+    """ Bottleneck Transformer w/ ResNet26-T backbone. Bottleneck attn in final stage.
+    """
+    kwargs.setdefault('img_size', 256)
+    return _create_byoanet('botnet26t_256', 'botnet26t', pretrained=pretrained, **kwargs)
+
+
 @register_model
 def botnet50t_224(pretrained=False, **kwargs):
     """ Bottleneck Transformer w/ ResNet50-T backbone. Bottleneck attn in final stage.

timm/models/layers/halo_attn.py

@@ -115,7 +115,7 @@ class HaloAttn(nn.Module):
         self.win_size = block_size + halo_size * 2  # neighbourhood window size
         self.scale = self.dim_head ** -0.5
 
-        # FIXME not clear if this stride behaviour is what the paper intended, not really clear
+        # FIXME not clear if this stride behaviour is what the paper intended
         # Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
         # data in unfolded block form. I haven't wrapped my head around how that'd look.
         self.q = nn.Conv2d(dim, self.dim_qk, 1, stride=self.stride, bias=qkv_bias)
@@ -139,10 +139,10 @@ class HaloAttn(nn.Module):
 
         kv = self.kv(x)
         # FIXME I 'think' this unfold does what I want it to, but I should investigate
-        k = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
+        kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
-        k = k.reshape(
+        kv = kv.reshape(
             B * self.num_heads, self.dim_head + (self.dim_v // self.num_heads), -1, num_blocks).transpose(1, 3)
-        k, v = torch.split(k, [self.dim_head, self.dim_v // self.num_heads], dim=-1)
+        k, v = torch.split(kv, [self.dim_head, self.dim_v // self.num_heads], dim=-1)
 
         attn_logits = (q @ k.transpose(-1, -2)) * self.scale  # FIXME should usual attn scale be applied?
         attn_logits = attn_logits + self.pos_embed(q)  # B * num_heads, block_size ** 2, win_size ** 2

timm/models/layers/lambda_layer.py

@@ -34,7 +34,7 @@ class LambdaLayer(nn.Module):
     """
     def __init__(
             self,
-            dim, dim_out=None, stride=1, num_heads=4, dim_head=16, r=5, qkv_bias=False):
+            dim, dim_out=None, stride=1, num_heads=4, dim_head=16, r=7, qkv_bias=False):
         super().__init__()
         self.dim_out = dim_out or dim
         self.dim_k = dim_head  # query depth 'k'

timm/models/resnet.py

@@ -54,6 +54,9 @@ default_cfgs = {
     'resnet50d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
         interpolation='bicubic', first_conv='conv1.0'),
+    'resnet50t': _cfg(
+        url='',
+        interpolation='bicubic', first_conv='conv1.0'),
     'resnet101': _cfg(url='', interpolation='bicubic'),
     'resnet101d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
@@ -706,6 +709,15 @@ def resnet50d(pretrained=False, **kwargs):
     return _create_resnet('resnet50d', pretrained, **model_args)
 
 
+@register_model
+def resnet50t(pretrained=False, **kwargs):
+    """Constructs a ResNet-50-T model.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs)
+    return _create_resnet('resnet50t', pretrained, **model_args)
+
+
 @register_model
 def resnet101(pretrained=False, **kwargs):
     """Constructs a ResNet-101 model.