Finalize DaViT, some formatting and modelling simplifications (separate PatchEmbed to Stem + Downsample, weights on HF hub.

tests/test_models.py

@@ -29,7 +29,6 @@ NON_STD_FILTERS = [
     'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
     'eva_*', 'flexivit*'
 ]
-#'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', '
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/davit.py

@@ -11,9 +11,10 @@ DaViT model defs and weights adapted from https://github.com/dingmyu/davit, orig
 # Copyright (c) 2022 Mingyu Ding
 # All rights reserved.
 # This source code is licensed under the MIT license
-
-from collections import OrderedDict
 import itertools
+from collections import OrderedDict
+from functools import partial
+from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -21,9 +22,8 @@ import torch.nn.functional as F
 from torch import Tensor
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, Mlp # ClassifierHead
+from timm.layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, Mlp, LayerNorm2d, get_norm_layer
 from ._builder import build_model_with_cfg
-from ._features import FeatureInfo
 from ._features_fx import register_notrace_function
 from ._manipulate import checkpoint_seq
 from ._pretrained import generate_default_cfgs
@@ -33,89 +33,83 @@ __all__ = ['DaViT']
 
 
 class ConvPosEnc(nn.Module):
-    def __init__(self, dim : int, k : int=3, act : bool=False, normtype : str='none'):
+    def __init__(self, dim: int, k: int = 3, act: bool = False):
-
         super(ConvPosEnc, self).__init__()
-        self.proj = nn.Conv2d(dim,
-                              dim,
-                              to_2tuple(k),
-                              to_2tuple(1),
-                              to_2tuple(k // 2),
-                              groups=dim)
-        self.normtype = normtype
-        self.norm = nn.Identity()
-        if self.normtype == 'batch':
-            self.norm = nn.BatchNorm2d(dim)
-        elif self.normtype == 'layer':
-            self.norm = nn.LayerNorm(dim)
-        self.activation = nn.GELU() if act else nn.Identity()
 
-    def forward(self, x : Tensor):
+        self.proj = nn.Conv2d(dim, dim, k, 1, k // 2, groups=dim)
-        B, C, H, W = x.shape
+        self.act = nn.GELU() if act else nn.Identity()
 
-        #feat = x.transpose(1, 2).view(B, C, H, W)
+    def forward(self, x: Tensor):
         feat = self.proj(x)
-        if self.normtype == 'batch':
+        x = x + self.act(feat)
-            feat = self.norm(feat).flatten(2).transpose(1, 2)
-        elif self.normtype == 'layer':
-            feat = self.norm(feat.flatten(2).transpose(1, 2))
-        else:
-            feat = feat.flatten(2).transpose(1, 2)
-        x = x + self.activation(feat).transpose(1, 2).view(B, C, H, W)
         return x
 
 
-class PatchEmbed(nn.Module):
+class Stem(nn.Module):
     """ Size-agnostic implementation of 2D image to patch embedding,
         allowing input size to be adjusted during model forward operation
     """
 
     def __init__(
             self,
-            patch_size=4,
+            in_chs=3,
-            in_chans=3,
+            out_chs=96,
-            embed_dim=96,
+            stride=4,
-            overlapped=False):
+            norm_layer=LayerNorm2d,
+    ):
         super().__init__()
-        patch_size = to_2tuple(patch_size)
+        stride = to_2tuple(stride)
-        self.patch_size = patch_size
+        self.stride = stride
-        self.in_chans = in_chans
+        self.in_chs = in_chs
-        self.embed_dim = embed_dim
+        self.out_chs = out_chs
-
+        assert stride[0] == 4  # only setup for stride==4
-        if patch_size[0] == 4:
+        self.conv = nn.Conv2d(
-            self.proj = nn.Conv2d(
+            in_chs,
-                in_chans,
+            out_chs,
-                embed_dim,
+            kernel_size=7,
-                kernel_size=(7, 7),
+            stride=stride,
-                stride=patch_size,
+            padding=3,
-                padding=(3, 3))
+        )
-            self.norm = nn.LayerNorm(embed_dim)
+        self.norm = norm_layer(out_chs)
-        if patch_size[0] == 2:
-            kernel = 3 if overlapped else 2
-            pad = 1 if overlapped else 0
-            self.proj = nn.Conv2d(
-                in_chans,
-                embed_dim,
-                kernel_size=to_2tuple(kernel),
-                stride=patch_size,
-                padding=to_2tuple(pad))
-            self.norm = nn.LayerNorm(in_chans)
-
 
     def forward(self, x: Tensor):
         B, C, H, W = x.shape
-        if self.norm.normalized_shape[0] == self.in_chans:
+        x = F.pad(x, (0, (self.stride[1] - W % self.stride[1]) % self.stride[1]))
-            x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        x = F.pad(x, (0, 0, 0, (self.stride[0] - H % self.stride[0]) % self.stride[0]))
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
 
-        x = F.pad(x, (0, (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]))
-        x = F.pad(x, (0, 0, 0, (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]))
 
-        x = self.proj(x)
+class Downsample(nn.Module):
+    def __init__(
+            self,
+            in_chs,
+            out_chs,
+            norm_layer=LayerNorm2d,
+    ):
+        super().__init__()
+        self.in_chs = in_chs
+        self.out_chs = out_chs
 
-        if self.norm.normalized_shape[0] == self.embed_dim:
+        self.norm = norm_layer(in_chs)
-            x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        self.conv = nn.Conv2d(
+            in_chs,
+            out_chs,
+            kernel_size=2,
+            stride=2,
+            padding=0,
+        )
+
+    def forward(self, x: Tensor):
+        B, C, H, W = x.shape
+        x = self.norm(x)
+        x = F.pad(x, (0, (2 - W % 2) % 2))
+        x = F.pad(x, (0, 0, 0, (2 - H % 2) % 2))
+        x = self.conv(x)
         return x
 
+
 class ChannelAttention(nn.Module):
 
     def __init__(self, dim, num_heads=8, qkv_bias=False):
@@ -131,7 +125,7 @@ class ChannelAttention(nn.Module):
         B, N, C = x.shape
 
         qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
+        q, k, v = qkv.unbind(0)
 
         k = k * self.scale
         attention = k.transpose(-1, -2) @ v
@@ -144,46 +138,60 @@ class ChannelAttention(nn.Module):
 
 class ChannelBlock(nn.Module):
 
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,
+    def __init__(
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+            self,
-                 ffn=True, cpe_act=False):
+            dim,
+            num_heads,
+            mlp_ratio=4.,
+            qkv_bias=False,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+            ffn=True,
+            cpe_act=False,
+    ):
         super().__init__()
 
         self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
         self.ffn = ffn
         self.norm1 = norm_layer(dim)
         self.attn = ChannelAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
         self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
 
         if self.ffn:
             self.norm2 = norm_layer(dim)
-            mlp_hidden_dim = int(dim * mlp_ratio)
             self.mlp = Mlp(
                 in_features=dim,
-                hidden_features=mlp_hidden_dim,
+                hidden_features=int(dim * mlp_ratio),
-                act_layer=act_layer)
+                act_layer=act_layer,
-
+            )
+            self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        else:
+            self.norm2 = None
+            self.mlp = None
+            self.drop_path2 = None
 
     def forward(self, x: Tensor):
-
         B, C, H, W = x.shape
 
         x = self.cpe1(x).flatten(2).transpose(1, 2)
 
         cur = self.norm1(x)
         cur = self.attn(cur)
-        x = x + self.drop_path(cur)
+        x = x + self.drop_path1(cur)
 
-        x = self.cpe2(x.transpose(1, 2).view(B, C, H, W)).flatten(2).transpose(1, 2)
+        x = self.cpe2(x.transpose(1, 2).view(B, C, H, W))
-        if self.ffn:
-            x = x + self.drop_path(self.mlp(self.norm2(x)))
 
+        if self.mlp is not None:
+            x = x.flatten(2).transpose(1, 2)
+            x = x + self.drop_path2(self.mlp(self.norm2(x)))
             x = x.transpose(1, 2).view(B, C, H, W)
 
         return x
 
-def window_partition(x : Tensor, window_size: int):
+
+def window_partition(x: Tensor, window_size: Tuple[int, int]):
     """
     Args:
         x: (B, H, W, C)
@@ -192,12 +200,13 @@ def window_partition(x : Tensor, window_size: int):
         windows: (num_windows*B, window_size, window_size, C)
     """
     B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
     return windows
 
+
 @register_notrace_function  # reason: int argument is a Proxy
-def window_reverse(windows : Tensor, window_size: int, H: int, W: int):
+def window_reverse(windows: Tensor, window_size: Tuple[int, int], H: int, W: int):
     """
     Args:
         windows: (num_windows*B, window_size, window_size, C)
@@ -207,9 +216,8 @@ def window_reverse(windows : Tensor, window_size: int, H: int, W: int):
     Returns:
         x: (B, H, W, C)
     """
-    
+    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
     x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
     return x
 
@@ -225,7 +233,6 @@ class WindowAttention(nn.Module):
     """
 
     def __init__(self, dim, window_size, num_heads, qkv_bias=True):
-
         super().__init__()
         self.dim = dim
         self.window_size = window_size
@@ -242,7 +249,7 @@ class WindowAttention(nn.Module):
         B_, N, C = x.shape
 
         qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
+        q, k, v = qkv.unbind(0)
 
         q = q * self.scale
         attn = (q @ k.transpose(-2, -1))
@@ -266,74 +273,86 @@ class SpatialBlock(nn.Module):
         norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
     """
 
-    def __init__(self, dim, num_heads, window_size=7,
+    def __init__(
-                 mlp_ratio=4., qkv_bias=True, drop_path=0.,
+            self,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+            dim,
-                 ffn=True, cpe_act=False):
+            num_heads,
+            window_size=7,
+            mlp_ratio=4.,
+            qkv_bias=True,
+            drop_path=0.,
+            act_layer=nn.GELU,
+            norm_layer=nn.LayerNorm,
+            ffn=True,
+            cpe_act=False,
+    ):
         super().__init__()
         self.dim = dim
         self.ffn = ffn
         self.num_heads = num_heads
-        self.window_size = window_size
+        self.window_size = to_2tuple(window_size)
         self.mlp_ratio = mlp_ratio
 
         self.cpe1 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
         self.norm1 = norm_layer(dim)
         self.attn = WindowAttention(
             dim,
-            window_size=to_2tuple(self.window_size),
+            self.window_size,
             num_heads=num_heads,
-            qkv_bias=qkv_bias)
+            qkv_bias=qkv_bias,
+        )
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
         self.cpe2 = ConvPosEnc(dim=dim, k=3, act=cpe_act)
- 
         if self.ffn:
             self.norm2 = norm_layer(dim)
             mlp_hidden_dim = int(dim * mlp_ratio)
             self.mlp = Mlp(
                 in_features=dim,
                 hidden_features=mlp_hidden_dim,
-                act_layer=act_layer)
+                act_layer=act_layer,
-
+            )
+            self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        else:
+            self.norm2 = None
+            self.mlp = None
+            self.drop_path1 = None
 
     def forward(self, x: Tensor):
         B, C, H, W = x.shape
 
-
         shortcut = self.cpe1(x).flatten(2).transpose(1, 2)
+
         x = self.norm1(shortcut)
         x = x.view(B, H, W, C)
 
         pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_r = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        pad_b = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
         x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
         _, Hp, Wp, _ = x.shape
 
         x_windows = window_partition(x, self.window_size)
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
+        x_windows = x_windows.view(-1, self.window_size[0] * self.window_size[1], C)
 
         # W-MSA/SW-MSA
         attn_windows = self.attn(x_windows)
 
         # merge windows
-        attn_windows = attn_windows.view(-1,
+        attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
-                                         self.window_size,
-                                         self.window_size,
-                                         C)
         x = window_reverse(attn_windows, self.window_size, Hp, Wp)
 
         # if pad_r > 0 or pad_b > 0:
         x = x[:, :H, :W, :].contiguous()
 
         x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(x)
+        x = shortcut + self.drop_path1(x)
 
-        x = self.cpe2(x.transpose(1, 2).view(B, C, H, W)).flatten(2).transpose(1, 2)
+        x = self.cpe2(x.transpose(1, 2).view(B, C, H, W))
-        if self.ffn:
-            x = x + self.drop_path(self.mlp(self.norm2(x)))
 
+        if self.mlp is not None:
+            x = x.flatten(2).transpose(1, 2)
+            x = x + self.drop_path2(self.mlp(self.norm2(x)))
             x = x.transpose(1, 2).view(B, C, H, W)
 
         return x
@@ -345,15 +364,15 @@ class DaViTStage(nn.Module):
             in_chs,
             out_chs,
             depth=1,
-        patch_size = 4,
+            downsample=True,
-        overlapped_patch = False,
+            attn_types=('spatial', 'channel'),
-        attention_types = ('spatial', 'channel'),
             num_heads=3,
             window_size=7,
             mlp_ratio=4,
             qkv_bias=True,
             drop_path_rates=(0, 0),
-        norm_layer = nn.LayerNorm,
+            norm_layer=LayerNorm2d,
+            norm_layer_cl=nn.LayerNorm,
             ffn=True,
             cpe_act=False
     ):
@@ -361,13 +380,12 @@ class DaViTStage(nn.Module):
 
         self.grad_checkpointing = False
 
-        # patch embedding layer at the beginning of each stage
+        # downsample embedding layer at the beginning of each stage
-        self.patch_embed = PatchEmbed(
+        if downsample:
-            patch_size=patch_size,
+            self.downsample = Downsample(in_chs, out_chs, norm_layer=norm_layer)
-            in_chans=in_chs,
+        else:
-            embed_dim=out_chs,
+            self.downsample = nn.Identity()
-            overlapped=overlapped_patch
+
-        )
         '''
          repeating alternating attention blocks in each stage
          default: (spatial -> channel) x depth
@@ -377,36 +395,32 @@ class DaViTStage(nn.Module):
         '''
         stage_blocks = []
         for block_idx in range(depth):
-        
             dual_attention_block = []
-            
+            for attn_idx, attn_type in enumerate(attn_types):
-            for attention_id, attention_type in enumerate(attention_types):
+                if attn_type == 'spatial':
-                if attention_type == 'spatial':
                     dual_attention_block.append(SpatialBlock(
                         dim=out_chs,
                         num_heads=num_heads,
                         mlp_ratio=mlp_ratio,
                         qkv_bias=qkv_bias,
-                        drop_path=drop_path_rates[len(attention_types) * block_idx + attention_id],
+                        drop_path=drop_path_rates[block_idx],
-                        norm_layer=norm_layer,
+                        norm_layer=norm_layer_cl,
                         ffn=ffn,
                         cpe_act=cpe_act,
                         window_size=window_size,
                     ))
-                elif attention_type == 'channel':
+                elif attn_type == 'channel':
                     dual_attention_block.append(ChannelBlock(
                         dim=out_chs,
                         num_heads=num_heads,
                         mlp_ratio=mlp_ratio,
                         qkv_bias=qkv_bias,
-                        drop_path=drop_path_rates[len(attention_types) * block_idx + attention_id],
+                        drop_path=drop_path_rates[block_idx],
-                        norm_layer=norm_layer,
+                        norm_layer=norm_layer_cl,
                         ffn=ffn,
                         cpe_act=cpe_act
                     ))
-            
             stage_blocks.append(nn.Sequential(*dual_attention_block))
-            
         self.blocks = nn.Sequential(*stage_blocks)
 
     @torch.jit.ignore
@@ -414,7 +428,7 @@ class DaViTStage(nn.Module):
         self.grad_checkpointing = enable
 
     def forward(self, x: Tensor):
-        x = self.patch_embed(x)
+        x = self.downsample(x)
         if self.grad_checkpointing and not torch.jit.is_scripting():
             x = checkpoint_seq(self.blocks, x)
         else:
@@ -431,7 +445,6 @@ class DaViT(nn.Module):
         in_chans (int): Number of input image channels. Default: 3
         num_classes (int): Number of classes for classification head. Default: 1000
         depths (tuple(int)): Number of blocks in each stage. Default: (1, 1, 3, 1)
-        patch_size (int | tuple(int)): Patch size. Default: 4
         embed_dims (tuple(int)): Patch embedding dimension. Default: (96, 192, 384, 768)
         num_heads (tuple(int)): Number of attention heads in different layers. Default: (3, 6, 12, 24)
         window_size (int): Window size. Default: 7
@@ -445,69 +458,61 @@ class DaViT(nn.Module):
             self,
             in_chans=3,
             depths=(1, 1, 3, 1),
-        patch_size=4,
             embed_dims=(96, 192, 384, 768),
             num_heads=(3, 6, 12, 24),
             window_size=7,
-        mlp_ratio=4.,
+            mlp_ratio=4,
             qkv_bias=True,
-        drop_path_rate=0.1,
+            norm_layer='layernorm2d',
-        norm_layer=nn.LayerNorm,
+            norm_layer_cl='layernorm',
-        attention_types=('spatial', 'channel'),
+            norm_eps=1e-5,
+            attn_types=('spatial', 'channel'),
             ffn=True,
-        overlapped_patch=False,
             cpe_act=False,
             drop_rate=0.,
             attn_drop_rate=0.,
+            drop_path_rate=0.,
             num_classes=1000,
             global_pool='avg',
             head_norm_first=False,
     ):
         super().__init__()
-
+        num_stages = len(embed_dims)
-        architecture = [[index] * item for index, item in enumerate(depths)]
+        assert num_stages == len(num_heads) == len(depths)
-        self.architecture = architecture
+        norm_layer = partial(get_norm_layer(norm_layer), eps=norm_eps)
-        self.embed_dims = embed_dims
+        norm_layer_cl = partial(get_norm_layer(norm_layer_cl), eps=norm_eps)
-        self.num_heads = num_heads
-        self.num_stages = len(self.embed_dims)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, len(attention_types) * len(list(itertools.chain(*self.architecture))))]
-        assert self.num_stages == len(self.num_heads) == (sorted(list(itertools.chain(*self.architecture)))[-1] + 1)
-        
         self.num_classes = num_classes
         self.num_features = embed_dims[-1]
         self.drop_rate = drop_rate
         self.grad_checkpointing = False
         self.feature_info = []
 
-        self.patch_embed = None
+        self.stem = Stem(in_chans, embed_dims[0], norm_layer=norm_layer)
-        stages = []
+        in_chs = embed_dims[0]
-        
-        for stage_id in range(self.num_stages):
-            stage_drop_rates = dpr[len(attention_types) * sum(depths[:stage_id]):len(attention_types) * sum(depths[:stage_id + 1])]
 
+        dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        stages = []
+        for stage_idx in range(num_stages):
+            out_chs = embed_dims[stage_idx]
             stage = DaViTStage(
-                in_chans if stage_id == 0 else embed_dims[stage_id - 1],
+                in_chs,
-                embed_dims[stage_id],
+                out_chs,
-                depth = depths[stage_id],
+                depth=depths[stage_idx],
-                patch_size = patch_size if stage_id == 0 else 2,
+                downsample=stage_idx > 0,
-                overlapped_patch = overlapped_patch,
+                attn_types=attn_types,
-                attention_types = attention_types,
+                num_heads=num_heads[stage_idx],
-                num_heads = num_heads[stage_id],
                 window_size=window_size,
                 mlp_ratio=mlp_ratio,
                 qkv_bias=qkv_bias,
-                drop_path_rates = stage_drop_rates,
+                drop_path_rates=dpr[stage_idx],
-                norm_layer = nn.LayerNorm,
+                norm_layer=norm_layer,
+                norm_layer_cl=norm_layer_cl,
                 ffn=ffn,
-                cpe_act = cpe_act
+                cpe_act=cpe_act,
             )
-            
+            in_chs = out_chs
-            if stage_id == 0:
-                self.patch_embed = stage.patch_embed
-                stage.patch_embed = nn.Identity()
-            
             stages.append(stage)
-            self.feature_info += [dict(num_chs=self.embed_dims[stage_id], reduction=2, module=f'stages.{stage_id}')]
+            self.feature_info += [dict(num_chs=out_chs, reduction=2, module=f'stages.{stage_idx}')]
 
         self.stages = nn.Sequential(*stages)
 
@@ -529,9 +534,6 @@ class DaViT(nn.Module):
             trunc_normal_(m.weight, std=.02)
             if isinstance(m, nn.Linear) and m.bias is not None:
                 nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
 
     @torch.jit.ignore
     def set_grad_checkpointing(self, enable=True):
@@ -550,17 +552,17 @@ class DaViT(nn.Module):
         self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
-        x = self.patch_embed(x)
+        x = self.stem(x)
         if self.grad_checkpointing and not torch.jit.is_scripting():
             x = checkpoint_seq(self.stages, x)
         else:
             x = self.stages(x)
-        x = self.norm_pre(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        x = self.norm_pre(x)
         return x
 
     def forward_head(self, x, pre_logits: bool = False):
         x = self.head.global_pool(x)
-        x = self.head.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        x = self.head.norm(x)
         x = self.head.flatten(x)
         x = self.head.drop(x)
         return x if pre_logits else self.head.fc(x)
@@ -573,7 +575,7 @@ class DaViT(nn.Module):
 
 def checkpoint_filter_fn(state_dict, model):
     """ Remap MSFT checkpoints -> timm """
-    if 'head' in state_dict:
+    if 'head.fc.weight' in state_dict:
         return state_dict  # non-MSFT checkpoint
 
     if 'state_dict' in state_dict:
@@ -582,10 +584,10 @@ def checkpoint_filter_fn(state_dict, model):
     import re
     out_dict = {}
     for k, v in state_dict.items():
-        
+        k = re.sub(r'patch_embeds.([0-9]+)', r'stages.\1.downsample', k)
-        k = re.sub(r'patch_embeds.([0-9]+)', r'stages.\1.patch_embed', k)
         k = re.sub(r'main_blocks.([0-9]+)', r'stages.\1.blocks', k)
-        k = k.replace('stages.0.patch_embed', 'patch_embed')
+        k = k.replace('downsample.proj', 'downsample.conv')
+        k = k.replace('stages.0.downsample', 'stem')
         k = k.replace('head.', 'head.fc.')
         k = k.replace('norms.', 'head.norm.')
         k = k.replace('cpe.0', 'cpe1')
@@ -595,7 +597,6 @@ def checkpoint_filter_fn(state_dict, model):
 
 
 def _create_davit(variant, pretrained=False, **kwargs):
-
     default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
     out_indices = kwargs.pop('out_indices', default_out_indices)
 
@@ -610,67 +611,69 @@ def _create_davit(variant, pretrained=False, **kwargs):
     return model
 
 
-
 def _cfg(url='', **kwargs):
     return {
         'url': url,
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 0.850, 'interpolation': 'bicubic',
+        'crop_pct': 0.95, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
+        'first_conv': 'stem.conv', 'classifier': 'head.fc',
         **kwargs
     }
 
 
-
 # TODO contact authors to get larger pretrained models
 default_cfgs = generate_default_cfgs({
     # official microsoft weights from https://github.com/dingmyu/davit
     'davit_tiny.msft_in1k': _cfg(
-        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_tiny_ed28dd55.pth.tar"),
+        hf_hub_id='timm/'),
     'davit_small.msft_in1k': _cfg(
-        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_small_d1ecf281.pth.tar"),
+        hf_hub_id='timm/'),
     'davit_base.msft_in1k': _cfg(
-        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/checkpoint/davit_base_67d9ac26.pth.tar"),
+        hf_hub_id='timm/'),
     'davit_large': _cfg(),
     'davit_huge': _cfg(),
     'davit_giant': _cfg(),
 })
 
 
-
 @register_model
 def davit_tiny(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 3, 1), embed_dims=(96, 192, 384, 768),
+    model_kwargs = dict(
-    num_heads=(3, 6, 12, 24), **kwargs)
+        depths=(1, 1, 3, 1), embed_dims=(96, 192, 384, 768), num_heads=(3, 6, 12, 24), **kwargs)
     return _create_davit('davit_tiny', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
 def davit_small(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(96, 192, 384, 768),
+    model_kwargs = dict(
-    num_heads=(3, 6, 12, 24), **kwargs)
+        depths=(1, 1, 9, 1), embed_dims=(96, 192, 384, 768), num_heads=(3, 6, 12, 24), **kwargs)
     return _create_davit('davit_small', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
 def davit_base(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(128, 256, 512, 1024),
+    model_kwargs = dict(
-    num_heads=(4, 8, 16, 32), **kwargs)
+        depths=(1, 1, 9, 1), embed_dims=(128, 256, 512, 1024), num_heads=(4, 8, 16, 32), **kwargs)
     return _create_davit('davit_base', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
 def davit_large(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(192, 384, 768, 1536),
+    model_kwargs = dict(
-    num_heads=(6, 12, 24, 48), **kwargs)
+        depths=(1, 1, 9, 1), embed_dims=(192, 384, 768, 1536), num_heads=(6, 12, 24, 48), **kwargs)
     return _create_davit('davit_large', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
 def davit_huge(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 9, 1), embed_dims=(256, 512, 1024, 2048),
+    model_kwargs = dict(
-    num_heads=(8, 16, 32, 64), **kwargs)
+        depths=(1, 1, 9, 1), embed_dims=(256, 512, 1024, 2048), num_heads=(8, 16, 32, 64), **kwargs)
     return _create_davit('davit_huge', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
 def davit_giant(pretrained=False, **kwargs):
-    model_kwargs = dict(depths=(1, 1, 12, 3), embed_dims=(384, 768, 1536, 3072),
+    model_kwargs = dict(
-    num_heads=(12, 24, 48, 96), **kwargs)
+        depths=(1, 1, 12, 3), embed_dims=(384, 768, 1536, 3072), num_heads=(12, 24, 48, 96), **kwargs)
     return _create_davit('davit_giant', pretrained=pretrained, **model_kwargs)