Add ResNet-RS models

timm/models/resnet.py

@@ -233,7 +233,23 @@ default_cfgs = {
         interpolation='bicubic'),
     'resnetblur50': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth',
-        interpolation='bicubic')
+        interpolation='bicubic'),
+
+    # ResNet-RS models
+    'resnetrs50': _cfg(
+        interpolation='bicubic'),
+    'resnetrs101': _cfg(
+        interpolation='bicubic'),
+    'resnetrs152': _cfg(
+        interpolation='bicubic'),
+    'resnetrs200': _cfg(
+        interpolation='bicubic'),
+    'resnetrs270': _cfg(
+        interpolation='bicubic'),
+    'resnetrs350': _cfg(
+        interpolation='bicubic'),
+    'resnetrs420': _cfg(
+        interpolation='bicubic'),
 }
 
 
@@ -426,7 +442,7 @@ def drop_blocks(drop_block_rate=0.):
 
 def make_blocks(
         block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32,
-        down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs):
+        down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., first_conv_stride=1, **kwargs):
     stages = []
     feature_info = []
     net_num_blocks = sum(block_repeats)
@@ -435,7 +451,7 @@ def make_blocks(
     dilation = prev_dilation = 1
     for stage_idx, (planes, num_blocks, db) in enumerate(zip(channels, block_repeats, drop_blocks(drop_block_rate))):
         stage_name = f'layer{stage_idx + 1}'  # never liked this name, but weight compat requires it
-        stride = 1 if stage_idx == 0 else 2
+        stride = first_conv_stride if stage_idx == 0 else 2
         if net_stride >= output_stride:
             dilation *= stride
             stride = 1
@@ -542,11 +558,12 @@ class ResNet(nn.Module):
                  cardinality=1, base_width=64, stem_width=64, stem_type='',
                  output_stride=32, block_reduce_first=1, down_kernel_size=1, avg_down=False,
                  act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0.0, drop_path_rate=0.,
-                 drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
+                 drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None, skip_stem_max_pool=False):
         block_args = block_args or dict()
         assert output_stride in (8, 16, 32)
         self.num_classes = num_classes
         self.drop_rate = drop_rate
+        self.skip_stem_max_pool = skip_stem_max_pool
         super(ResNet, self).__init__()
 
         # Stem
@@ -571,12 +588,17 @@ class ResNet(nn.Module):
         self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
 
         # Stem Pooling
+        if not self.skip_stem_max_pool:
+            first_conv_stride = 1
             if aa_layer is not None:
                 self.maxpool = nn.Sequential(*[
                     nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
                     aa_layer(channels=inplanes, stride=2)])
             else:
                 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        else:
+            self.maxpool = nn.Identity()
+            first_conv_stride = 2
 
         # Feature Blocks
         channels = [64, 128, 256, 512]
@@ -584,7 +606,7 @@ class ResNet(nn.Module):
             block, channels, layers, inplanes, cardinality=cardinality, base_width=base_width,
             output_stride=output_stride, reduce_first=block_reduce_first, avg_down=avg_down,
             down_kernel_size=down_kernel_size, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer,
-            drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args)
+            drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, first_conv_stride=first_conv_stride, **block_args)
         for stage in stage_modules:
             self.add_module(*stage)  # layer1, layer2, etc
         self.feature_info.extend(stage_feature_info)
@@ -1053,6 +1075,63 @@ def ecaresnet50d(pretrained=False, **kwargs):
     return _create_resnet('ecaresnet50d', pretrained, **model_args)
 
 
+@register_model
+def resnetrs50(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs50', pretrained, **model_args)
+
+
+@register_model
+def resnetrs101(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs101', pretrained, **model_args)
+
+
+@register_model
+def resnetrs152(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs152', pretrained, **model_args)
+
+
+@register_model
+def resnetrs200(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs200', pretrained, **model_args)
+
+
+@register_model
+def resnetrs270(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs270', pretrained, **model_args)
+
+
+
+@register_model
+def resnetrs350(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs350', pretrained, **model_args)
+
+
+@register_model
+def resnetrs420(pretrained=False, **kwargs):
+    model_args = dict(
+        block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True,
+        avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('resnetrs420', pretrained, **model_args)
+
+
 @register_model
 def ecaresnet50d_pruned(pretrained=False, **kwargs):
     """Constructs a ResNet-50-D model pruned with eca.