Add timm ConvNeXt 'atto' weights, change test resolution for FB ConvNeXt 224x224 weights, add support for different dw kernel_size

Author: rwightman

README.md

@@ -20,6 +20,12 @@ Thanks to the following for hardware support:
 And a big thanks to all GitHub sponsors who helped with some of my costs before I joined Hugging Face.
 
 ## What's New
+
+### Aug 15, 2022
+* ConvNeXt atto weights added
+  * `convnext_atto` - 75.7 @ 224, 77.0 @ 288
+  * `convnext_atto_ols` - 75.9  @ 224, 77.2 @ 288
+
 ### Aug 5, 2022
 * More custom ConvNeXt smaller model defs with weights 
   * `convnext_femto` - 77.5 @ 224, 78.7 @ 288

timm/models/convnext.py

@@ -16,12 +16,11 @@
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import named_apply, build_model_with_cfg, checkpoint_seq
 from .layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d,\
-    create_conv2d, make_divisible
+    create_conv2d, get_act_layer, make_divisible, to_ntuple
 from .registry import register_model
 
 
@@ -40,14 +39,13 @@ def _cfg(url='', **kwargs):
 
 
 default_cfgs = dict(
-    convnext_tiny=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"),
-    convnext_small=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"),
-    convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
-    convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
-
     # timm specific variants
-    convnext_atto=_cfg(url=''),
-    convnext_atto_ols=_cfg(url=''),
+    convnext_atto=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_d2-01bb0f51.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=0.95),
+    convnext_atto_ols=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_ols_a2-78d1c8f3.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=0.95),
     convnext_femto=_cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_d1-d71d5b4c.pth',
         test_input_size=(3, 288, 288), test_crop_pct=0.95),
@@ -70,16 +68,34 @@ def _cfg(url='', **kwargs):
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
         crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
 
+    convnext_tiny=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
+    convnext_small=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
+    convnext_base=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
+    convnext_large=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
+
     convnext_tiny_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth'),
+        url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
     convnext_small_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth'),
+        url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
     convnext_base_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth'),
+        url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
     convnext_large_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth'),
+        url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
     convnext_xlarge_in22ft1k=_cfg(
-        url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth'),
+        url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0),
 
     convnext_tiny_384_in22ft1k=_cfg(
         url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth',
@@ -121,37 +137,39 @@ class ConvNeXtBlock(nn.Module):
     is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
 
     Args:
-        dim (int): Number of input channels.
+        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,
-            dim,
-            dim_out=None,
+            in_chs,
+            out_chs=None,
+            kernel_size=7,
             stride=1,
             dilation=1,
             mlp_ratio=4,
             conv_mlp=False,
             conv_bias=True,
             ls_init_value=1e-6,
+            act_layer='gelu',
             norm_layer=None,
-            act_layer=nn.GELU,
             drop_path=0.,
     ):
         super().__init__()
-        dim_out = dim_out or dim
+        out_chs = out_chs or in_chs
+        act_layer = get_act_layer(act_layer)
         if not norm_layer:
             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.conv_dw = create_conv2d(
-            dim, dim_out, kernel_size=7, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
-        self.norm = norm_layer(dim_out)
-        self.mlp = mlp_layer(dim_out, int(mlp_ratio * dim_out), act_layer=act_layer)
-        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim_out)) if ls_init_value > 0 else None
+            in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, 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 > 0 else None
         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
     def forward(self, x):
@@ -178,13 +196,15 @@ def __init__(
             self,
             in_chs,
             out_chs,
+            kernel_size=7,
             stride=2,
             depth=2,
             dilation=(1, 1),
             drop_path_rates=None,
             ls_init_value=1.0,
             conv_mlp=False,
             conv_bias=True,
+            act_layer='gelu',
             norm_layer=None,
             norm_layer_cl=None
     ):
@@ -208,13 +228,15 @@ def __init__(
         stage_blocks = []
         for i in range(depth):
             stage_blocks.append(ConvNeXtBlock(
-                dim=in_chs,
-                dim_out=out_chs,
+                in_chs=in_chs,
+                out_chs=out_chs,
+                kernel_size=kernel_size,
                 dilation=dilation[1],
                 drop_path=drop_path_rates[i],
                 ls_init_value=ls_init_value,
                 conv_mlp=conv_mlp,
                 conv_bias=conv_bias,
+                act_layer=act_layer,
                 norm_layer=norm_layer if conv_mlp else norm_layer_cl
             ))
             in_chs = out_chs
@@ -252,19 +274,22 @@ def __init__(
             output_stride=32,
             depths=(3, 3, 9, 3),
             dims=(96, 192, 384, 768),
+            kernel_sizes=7,
             ls_init_value=1e-6,
             stem_type='patch',
             patch_size=4,
             head_init_scale=1.,
             head_norm_first=False,
             conv_mlp=False,
             conv_bias=True,
+            act_layer='gelu',
             norm_layer=None,
             drop_rate=0.,
             drop_path_rate=0.,
     ):
         super().__init__()
         assert output_stride in (8, 16, 32)
+        kernel_sizes = to_ntuple(4)(kernel_sizes)
         if norm_layer is None:
             norm_layer = partial(LayerNorm2d, eps=1e-6)
             norm_layer_cl = norm_layer if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
@@ -312,13 +337,15 @@ def __init__(
             stages.append(ConvNeXtStage(
                 prev_chs,
                 out_chs,
+                kernel_size=kernel_sizes[i],
                 stride=stride,
                 dilation=(first_dilation, dilation),
                 depth=depths[i],
                 drop_path_rates=dp_rates[i],
                 ls_init_value=ls_init_value,
                 conv_mlp=conv_mlp,
                 conv_bias=conv_bias,
+                act_layer=act_layer,
                 norm_layer=norm_layer,
                 norm_layer_cl=norm_layer_cl
             ))

timm/version.py

@@ -1 +1 @@
-__version__ = '0.6.8'
+__version__ = '0.6.9'