refactor: use fewer triplet models

timm/models/layers/create_attn.py

@@ -6,7 +6,7 @@ import torch
 from .se import SEModule, EffectiveSEModule
 from .eca import EcaModule, CecaModule
 from .cbam import CbamModule, LightCbamModule
-from .triplet import TripletModule
+from .triplet import TripletAttention
 
 
 def create_attn(attn_type, channels, **kwargs):
@@ -27,7 +27,7 @@ def create_attn(attn_type, channels, **kwargs):
             elif attn_type == 'lcbam':
                 module_cls = LightCbamModule
             elif attn_type == 'triplet':
-                module_cls = TripletModule
+                module_cls = TripletAttention
             else:
                 assert False, "Invalid attn module (%s)" % attn_type
         elif isinstance(attn_type, bool):

timm/models/layers/triplet.py

@@ -48,9 +48,9 @@ class AttentionGate(nn.Module):
         scale = torch.sigmoid_(x_out) 
         return x * scale
 
-class TripletModule(nn.Module):
+class TripletAttention(nn.Module):
     def __init__(self, no_spatial=False):
-        super(TripletModule, self).__init__()
+        super(TripletAttention, self).__init__()
         self.cw = AttentionGate()
         self.hc = AttentionGate()
         self.no_spatial=no_spatial

timm/models/resnet.py

@@ -227,57 +227,18 @@ default_cfgs = {
         interpolation='bicubic'),
 
     # Triplet Attention ResNets
-    'triplet_resnet18': _cfg(
+    'triplet_resnet18d': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnet34': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnet50': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnet50tn': _cfg(
         url='',
         interpolation='bicubic',
         first_conv='conv1.0'),
-    'triplet_resnet101': _cfg(
+    'triplet_resnet50d': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnet152': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnet152d': _cfg(
-        url='',
-        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
-    'triplet_resnet152d_320': _cfg(
-        url='',
-        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
-
-    # Triplet Atention ResNeXts
-    'triplet_resnext26_32x4d': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnext26d_32x4d': _cfg(
-        url='',
-        interpolation='bicubic',
-        first_conv='conv1.0'),
-    'triplet_resnext26t_32x4d': _cfg(
         url='',
         interpolation='bicubic',
         first_conv='conv1.0'),
-    'triplet_resnext26tn_32x4d': _cfg(
+    'triplet_resnet101d': _cfg(
         url='',
         interpolation='bicubic',
         first_conv='conv1.0'),
-    'triplet_resnext50_32x4d': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnext101_32x4d': _cfg(
-        url='',
-        interpolation='bicubic'),
-    'triplet_resnext101_32x8d': _cfg(
-        url='',
-        interpolation='bicubic'),
     
     # ResNets with anti-aliasing blur pool
     'resnetblur18': _cfg(
@@ -1335,122 +1296,22 @@ def senet154(pretrained=False, **kwargs):
     return _create_resnet('senet154', pretrained, **model_args)
 
 @register_model
-def triplet_resnet18(pretrained=False, **kwargs):
+def triplet_resnet18d(pretrained=False, **kwargs):
     model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='triplet'), **kwargs)
     return _create_resnet('triplet_resnet18', pretrained, **model_args)
 
 
 @register_model
-def triplet_resnet34(pretrained=False, **kwargs):
+def triplet_resnet50d(pretrained=False, **kwargs):
-    model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet34', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet50(pretrained=False, **kwargs):
-    model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet50', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet50tn(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[3, 4, 6, 3],  stem_width=32, stem_type='deep_tiered_narrow', avg_down=True,
-        block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet50tn', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet101(pretrained=False, **kwargs):
-    model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet101', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet152(pretrained=False, **kwargs):
-    model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet152', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet152d(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
-        block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet152d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnet152d_320(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
-        block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnet152d_320', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext26_32x4d(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4,
-        block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext26_32x4d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext26d_32x4d(pretrained=False, **kwargs):
-    """Constructs a ResNeXt-26-D (with Triplet Attention) model.`
-    This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
-    combination of deep stem and avg_pool in downsample.
-    """
-    model_args = dict(
-        block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
-        stem_type='deep', avg_down=True, block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext26d_32x4d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext26t_32x4d(pretrained=False, **kwargs):
-    """Constructs a ResNet-26-T (with Triplet Attention) model.
-    This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels
-    in the deep stem.
-    """
     model_args = dict(
-        block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
+        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
-        stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext26t_32x4d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext26tn_32x4d(pretrained=False, **kwargs):
-    """Constructs a ResNeXt-26-TN (with Triplet Attention) model.
-    This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
-    in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.
-    """
-    model_args = dict(
-        block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
-        stem_type='deep_tiered_narrow', avg_down=True, block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext26tn_32x4d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext50_32x4d(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
-        block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext50_32x4d', pretrained, **model_args)
-
-
-@register_model
-def triplet_resnext101_32x4d(pretrained=False, **kwargs):
-    model_args = dict(
-        block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
         block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext101_32x4d', pretrained, **model_args)
+    return _create_resnet('triplet_resnet50', pretrained, **model_args)
 
 
 @register_model
-def triplet_resnext101_32x8d(pretrained=False, **kwargs):
+def triplet_resnet101d(pretrained=False, **kwargs):
     model_args = dict(
-        block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
+        block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
         block_args=dict(attn_layer='triplet'), **kwargs)
-    return _create_resnet('triplet_resnext101_32x8d', pretrained, **model_args)
+    return _create_resnet('triplet_resnet101', pretrained, **model_args)