Add support to ConvNextBlock for downsample and channel expansion to improve stand alone use. Fix #1699

Author: rwightman

timm/models/convnext.py

@@ -45,7 +45,7 @@
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
-from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, Mlp, GlobalResponseNormMlp, \
+from timm.layers import trunc_normal_, AvgPool2dSame, DropPath, Mlp, GlobalResponseNormMlp, \
     LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple
 from timm.layers import NormMlpClassifierHead, ClassifierHead
 from ._builder import build_model_with_cfg
@@ -56,6 +56,28 @@
 __all__ = ['ConvNeXt']  # model_registry will add each entrypoint fn to this
 
 
+class Downsample(nn.Module):
+
+    def __init__(self, in_chs, out_chs, stride=1, dilation=1):
+        super().__init__()
+        avg_stride = stride if dilation == 1 else 1
+        if stride > 1 or dilation > 1:
+            avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
+            self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
+        else:
+            self.pool = nn.Identity()
+
+        if in_chs != out_chs:
+            self.conv = create_conv2d(in_chs, out_chs, 1, stride=1)
+        else:
+            self.conv = nn.Identity()
+
+    def forward(self, x):
+        x = self.pool(x)
+        x = self.conv(x)
+        return x
+
+
 class ConvNeXtBlock(nn.Module):
     """ ConvNeXt Block
     There are two equivalent implementations:
@@ -65,41 +87,65 @@ class ConvNeXtBlock(nn.Module):
     Unlike the official impl, this one allows choice of 1 or 2, 1x1 conv can be faster with appropriate
     choice of LayerNorm impl, however as model size increases the tradeoffs appear to change and nn.Linear
     is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
-
-    Args:
-        in_chs (int): Number of input channels.
-        drop_path (float): Stochastic depth rate. Default: 0.0
-        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
     """
 
     def __init__(
             self,
-            in_chs,
-            out_chs=None,
-            kernel_size=7,
-            stride=1,
-            dilation=1,
-            mlp_ratio=4,
-            conv_mlp=False,
-            conv_bias=True,
-            use_grn=False,
-            ls_init_value=1e-6,
-            act_layer='gelu',
-            norm_layer=None,
-            drop_path=0.,
+            in_chs: int,
+            out_chs: Optional[int] = None,
+            kernel_size: int = 7,
+            stride: int = 1,
+            dilation: Union[int, Tuple[int, int]] = (1, 1),
+            mlp_ratio: float = 4,
+            conv_mlp: bool = False,
+            conv_bias: bool = True,
+            use_grn: bool = False,
+            ls_init_value: Optional[float] = 1e-6,
+            act_layer: Union[str, Callable] = 'gelu',
+            norm_layer: Optional[Callable] = None,
+            drop_path: float = 0.,
     ):
+        """
+
+        Args:
+            in_chs: Block input channels.
+            out_chs: Block output channels (same as in_chs if None).
+            kernel_size: Depthwise convolution kernel size.
+            stride: Stride of depthwise convolution.
+            dilation: Tuple specifying input and output dilation of block.
+            mlp_ratio: MLP expansion ratio.
+            conv_mlp: Use 1x1 convolutions for MLP and a NCHW compatible norm layer if True.
+            conv_bias: Apply bias for all convolution (linear) layers.
+            use_grn: Use GlobalResponseNorm in MLP (from ConvNeXt-V2)
+            ls_init_value: Layer-scale init values, layer-scale applied if not None.
+            act_layer: Activation layer.
+            norm_layer: Normalization layer (defaults to LN if not specified).
+            drop_path: Stochastic depth probability.
+        """
         super().__init__()
         out_chs = out_chs or in_chs
+        dilation = to_ntuple(2)(dilation)
         act_layer = get_act_layer(act_layer)
         if not norm_layer:
             norm_layer = LayerNorm2d if conv_mlp else LayerNorm
         mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp)
         self.use_conv_mlp = conv_mlp
         self.conv_dw = create_conv2d(
-            in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
+            in_chs,
+            out_chs,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation[0],
+            depthwise=True,
+            bias=conv_bias,
+        )
         self.norm = norm_layer(out_chs)
         self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer)
         self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None
+        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
+            self.shortcut = Downsample(in_chs, out_chs, stride=stride, dilation=dilation[0])
+        else:
+            self.shortcut = nn.Identity()
         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
     def forward(self, x):
@@ -116,7 +162,7 @@ def forward(self, x):
         if self.gamma is not None:
             x = x.mul(self.gamma.reshape(1, -1, 1, 1))
 
-        x = self.drop_path(x) + shortcut
+        x = self.drop_path(x) + self.shortcut(shortcut)
         return x
 
 
@@ -148,8 +194,14 @@ def __init__(
             self.downsample = nn.Sequential(
                 norm_layer(in_chs),
                 create_conv2d(
-                    in_chs, out_chs, kernel_size=ds_ks, stride=stride,
-                    dilation=dilation[0], padding=pad, bias=conv_bias),
+                    in_chs,
+                    out_chs,
+                    kernel_size=ds_ks,
+                    stride=stride,
+                    dilation=dilation[0],
+                    padding=pad,
+                    bias=conv_bias,
+                ),
             )
             in_chs = out_chs
         else: