Only include resnet-rs changes

Author: amaarora

timm/models/resnet.py

@@ -1,8 +1,6 @@
 """PyTorch ResNet
-
 This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
 additional dropout and dynamic global avg/max pool.
-
 ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
 Copyright 2020 Ross Wightman
 """
@@ -442,7 +440,7 @@ 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., first_conv_stride=1, **kwargs):
+        down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs):
     stages = []
     feature_info = []
     net_num_blocks = sum(block_repeats)
@@ -451,7 +449,7 @@ 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 = first_conv_stride if stage_idx == 0 else 2
+        stride = 1 if stage_idx == 0 else 2
         if net_stride >= output_stride:
             dilation *= stride
             stride = 1
@@ -494,7 +492,7 @@ class ResNet(nn.Module):
     This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s
     variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
     'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
-
+    
     ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
       * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
       * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
@@ -503,18 +501,18 @@ class ResNet(nn.Module):
       * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
       * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
       * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
-
+    
     ResNeXt
       * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
       * same c,d, e, s variants as ResNet can be enabled
-
+    
     SE-ResNeXt
       * normal - 7x7 stem, stem_width = 64
       * same c, d, e, s variants as ResNet can be enabled
-
+    
     SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
         reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
-
+    
     Parameters
     ----------
     block : Block
@@ -558,12 +556,12 @@ def __init__(self, block, layers, num_classes=1000, in_chans=3,
                  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, skip_stem_max_pool=False):
+                 drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None, replace_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
+        self.replace_stem_max_pool = replace_stem_max_pool
         super(ResNet, self).__init__()
 
         # Stem
@@ -588,25 +586,27 @@ def __init__(self, block, layers, num_classes=1000, in_chans=3,
         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 not self.replace_stem_max_pool:
             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
+            self.maxpool = nn.Sequential(*[
+                nn.Conv2d(inplanes, inplanes, 3, stride=2, padding=1), 
+                nn.BatchNorm2d(inplanes), 
+                nn.ReLU()
+            ])
 
         # Feature Blocks
         channels = [64, 128, 256, 512]
         stage_modules, stage_feature_info = make_blocks(
             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, first_conv_stride=first_conv_stride, **block_args)
+            drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args)
         for stage in stage_modules:
             self.add_module(*stage)  # layer1, layer2, etc
         self.feature_info.extend(stage_feature_info)
@@ -1078,39 +1078,39 @@ def ecaresnet50d(pretrained=False, **kwargs):
 @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,
+        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_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,
+        block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_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,
+        block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_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,
+        block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_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,
+        block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
         avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
     return _create_resnet('resnetrs270', pretrained, **model_args)
 
@@ -1119,15 +1119,15 @@ def resnetrs270(pretrained=False, **kwargs):
 @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,
+        block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_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,
+        block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True,
         avg_down=True,  block_args=dict(attn_layer='se'), **kwargs)
     return _create_resnet('resnetrs420', pretrained, **model_args)
 
@@ -1373,4 +1373,4 @@ def senet154(pretrained=False, **kwargs):
     model_args = dict(
         block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
         down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'), **kwargs)
-    return _create_resnet('senet154', pretrained, **model_args)
+    return _create_resnet('senet154', pretrained, **model_args)