Remove experimental downsample in block support in ConvNeXt. Experiment further before keeping it in.

timm/models/convnext.py

@@ -17,7 +17,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .fx_features import register_notrace_module
 from .helpers import named_apply, build_model_with_cfg, checkpoint_seq
 from .layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, create_conv2d
 from .registry import register_model
@@ -124,7 +123,6 @@ class ConvNeXtBlock(nn.Module):
             norm_layer = partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
         mlp_layer = ConvMlp if conv_mlp else Mlp
         self.use_conv_mlp = conv_mlp
-        self.shortcut_after_dw = stride > 1
 
         self.conv_dw = create_conv2d(dim, dim_out, kernel_size=7, stride=stride, depthwise=True, bias=conv_bias)
         self.norm = norm_layer(dim_out)
@@ -135,9 +133,6 @@ class ConvNeXtBlock(nn.Module):
     def forward(self, x):
         shortcut = x
         x = self.conv_dw(x)
-        if self.shortcut_after_dw:
-            shortcut = x
-
         if self.use_conv_mlp:
             x = self.norm(x)
             x = self.mlp(x)
@@ -150,7 +145,6 @@ class ConvNeXtBlock(nn.Module):
             x = x.mul(self.gamma.reshape(1, -1, 1, 1))
 
         x = self.drop_path(x) + shortcut
-        #print('b', x.shape)
         return x
 
 
@@ -164,7 +158,6 @@ class ConvNeXtStage(nn.Module):
             depth=2,
             drop_path_rates=None,
             ls_init_value=1.0,
-            downsample_block=False,
             conv_mlp=False,
             conv_bias=True,
             norm_layer=None,
@@ -173,14 +166,14 @@ class ConvNeXtStage(nn.Module):
         super().__init__()
         self.grad_checkpointing = False
 
-        if downsample_block or (in_chs == out_chs and stride == 1):
+        if in_chs != out_chs or stride > 1:
-            self.downsample = nn.Identity()
-        else:
             self.downsample = nn.Sequential(
                 norm_layer(in_chs),
                 nn.Conv2d(in_chs, out_chs, kernel_size=stride, stride=stride, bias=conv_bias),
             )
             in_chs = out_chs
+        else:
+            self.downsample = nn.Identity()
 
         drop_path_rates = drop_path_rates or [0.] * depth
         stage_blocks = []
@@ -188,7 +181,6 @@ class ConvNeXtStage(nn.Module):
             stage_blocks.append(ConvNeXtBlock(
                 dim=in_chs,
                 dim_out=out_chs,
-                stride=stride if downsample_block and i == 0 else 1,
                 drop_path=drop_path_rates[i],
                 ls_init_value=ls_init_value,
                 conv_mlp=conv_mlp,
@@ -236,7 +228,6 @@ class ConvNeXt(nn.Module):
             stem_stride=4,
             head_init_scale=1.,
             head_norm_first=False,
-            downsample_block=False,
             conv_mlp=False,
             conv_bias=True,
             norm_layer=None,
@@ -291,7 +282,6 @@ class ConvNeXt(nn.Module):
                 depth=depths[i],
                 drop_path_rates=dp_rates[i],
                 ls_init_value=ls_init_value,
-                downsample_block=downsample_block,
                 conv_mlp=conv_mlp,
                 conv_bias=conv_bias,
                 norm_layer=norm_layer,
@@ -418,7 +408,7 @@ def convnext_nano_hnf(pretrained=False, **kwargs):
 @register_model
 def convnext_nano_ols(pretrained=False, **kwargs):
     model_args = dict(
-        depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), downsample_block=True,
+        depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True,
         conv_bias=False, stem_type='overlap', stem_kernel_size=9, **kwargs)
     model = _create_convnext('convnext_nano_ols', pretrained=pretrained, **model_args)
     return model
@@ -426,7 +416,8 @@ def convnext_nano_ols(pretrained=False, **kwargs):
 
 @register_model
 def convnext_tiny_hnf(pretrained=False, **kwargs):
-    model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
+    model_args = dict(
+        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
     model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
     return model