add crossvit

tests/test_models.py

@@ -17,7 +17,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
 # transformer models don't support many of the spatial / feature based model functionalities
 NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
-    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*']
+    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/__init__.py

@@ -1,6 +1,7 @@
 from .byoanet import *
 from .byobnet import *
 from .cait import *
+from .crossvit import *
 from .coat import *
 from .convit import *
 from .cspnet import *

timm/models/crossvit.py

@@ -0,0 +1,443 @@
+""" CrossViT Model
+
+@inproceedings{
+    chen2021crossvit,
+    title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
+    author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
+    booktitle={International Conference on Computer Vision (ICCV)},
+    year={2021}
+}
+
+Paper link: https://arxiv.org/abs/2103.14899
+Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
+"""
+
+# Copyright IBM All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+
+"""
+Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.hub
+from functools import partial
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .helpers import build_model_with_cfg
+from .layers import DropPath, to_2tuple, trunc_normal_
+from .registry import register_model
+from .vision_transformer import Mlp, Block
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None,
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
+        # 'first_conv': 'patch_embed.proj',
+        'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    'crossvit_15_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'),
+    'crossvit_15_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth'),
+    'crossvit_15_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth'),
+    'crossvit_18_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
+    'crossvit_18_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth'),
+    'crossvit_18_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth'),
+    'crossvit_9_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
+    'crossvit_9_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth'),
+    'crossvit_base_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'),
+    'crossvit_small_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'),
+    'crossvit_tiny_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_tiny_224.pth'),
+}
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, multi_conv=False):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+        if multi_conv:
+            if patch_size[0] == 12:
+                self.proj = nn.Sequential(
+                    nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
+                )
+            elif patch_size[0] == 16:
+                self.proj = nn.Sequential(
+                    nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1),
+                )
+        else:
+            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
+        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+
+        B, N, C = x.shape
+        q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # B1C -> B1H(C/H) -> BH1(C/H)
+        k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
+        v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, 1, C)   # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class CrossAttentionBlock(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, has_mlp=True):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = CrossAttention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.has_mlp = has_mlp
+        if has_mlp:
+            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, drop=drop)
+
+    def forward(self, x):
+        x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
+        if self.has_mlp:
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class MultiScaleBlock(nn.Module):
+
+    def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+
+        num_branches = len(dim)
+        self.num_branches = num_branches
+        # different branch could have different embedding size, the first one is the base
+        self.blocks = nn.ModuleList()
+        for d in range(num_branches):
+            tmp = []
+            for i in range(depth[d]):
+                tmp.append(
+                    Block(dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, 
+                          drop=drop, attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer))
+            if len(tmp) != 0:
+                self.blocks.append(nn.Sequential(*tmp))
+
+        if len(self.blocks) == 0:
+            self.blocks = None
+
+        self.projs = nn.ModuleList()
+        for d in range(num_branches):
+            if dim[d] == dim[(d+1) % num_branches] and False:
+                tmp = [nn.Identity()]
+            else:
+                tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d+1) % num_branches])]
+            self.projs.append(nn.Sequential(*tmp))
+
+        self.fusion = nn.ModuleList()
+        for d in range(num_branches):
+            d_ = (d+1) % num_branches
+            nh = num_heads[d_]
+            if depth[-1] == 0:  # backward capability:
+                self.fusion.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                                       drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
+                                                       has_mlp=False))
+            else:
+                tmp = []
+                for _ in range(depth[-1]):
+                    tmp.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                                   drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer,
+                                                   has_mlp=False))
+                self.fusion.append(nn.Sequential(*tmp))
+
+        self.revert_projs = nn.ModuleList()
+        for d in range(num_branches):
+            if dim[(d+1) % num_branches] == dim[d] and False:
+                tmp = [nn.Identity()]
+            else:
+                tmp = [norm_layer(dim[(d+1) % num_branches]), act_layer(), nn.Linear(dim[(d+1) % num_branches], dim[d])]
+            self.revert_projs.append(nn.Sequential(*tmp))
+
+    def forward(self, x):
+        outs_b = [block(x_) for x_, block in zip(x, self.blocks)]
+        # only take the cls token out
+        proj_cls_token = [proj(x[:, 0:1]) for x, proj in zip(outs_b, self.projs)]
+        # cross attention
+        outs = []
+        for i in range(self.num_branches):
+            tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
+            tmp = self.fusion[i](tmp)
+            reverted_proj_cls_token = self.revert_projs[i](tmp[:, 0:1, ...])
+            tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1)
+            outs.append(tmp)
+        return outs
+
+
+def _compute_num_patches(img_size, patches):
+    return [i // p * i // p for i, p in zip(img_size,patches)]
+
+
+class CrossViT(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+    def __init__(self, img_size=(224, 224), patch_size=(8, 16), in_chans=3, num_classes=1000, embed_dim=(192, 384), depth=([1, 3, 1], [1, 3, 1], [1, 3, 1]),
+                 num_heads=(6, 12), mlp_ratio=(2., 2., 4.), qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer=nn.LayerNorm, multi_conv=False):
+        super().__init__()
+
+        self.num_classes = num_classes
+        if not isinstance(img_size, list):
+            img_size = to_2tuple(img_size)
+        self.img_size = img_size
+
+        num_patches = _compute_num_patches(img_size, patch_size)
+        self.num_branches = len(patch_size)
+
+        self.patch_embed = nn.ModuleList()
+        self.pos_embed = nn.ParameterList([nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i])) for i in range(self.num_branches)])
+        for im_s, p, d in zip(img_size, patch_size, embed_dim):
+            self.patch_embed.append(PatchEmbed(img_size=im_s, patch_size=p, in_chans=in_chans, embed_dim=d, multi_conv=multi_conv))
+
+        self.cls_token = nn.ParameterList([nn.Parameter(torch.zeros(1, 1, embed_dim[i])) for i in range(self.num_branches)])
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        total_depth = sum([sum(x[-2:]) for x in depth])
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)]  # stochastic depth decay rule
+        dpr_ptr = 0
+        self.blocks = nn.ModuleList()
+        for idx, block_cfg in enumerate(depth):
+            curr_depth = max(block_cfg[:-1]) + block_cfg[-1]
+            dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth]
+            blk = MultiScaleBlock(embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio,
+                                  qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr_,
+                                  norm_layer=norm_layer)
+            dpr_ptr += curr_depth
+            self.blocks.append(blk)
+
+        self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)])
+        self.head = nn.ModuleList([nn.Linear(embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in range(self.num_branches)])
+
+        for i in range(self.num_branches):
+            if self.pos_embed[i].requires_grad:
+                trunc_normal_(self.pos_embed[i], std=.02)
+            trunc_normal_(self.cls_token[i], std=.02)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            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 no_weight_decay(self):
+        out = {'cls_token'}
+        if self.pos_embed[0].requires_grad:
+            out.add('pos_embed')
+        return out
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        B, C, H, W = x.shape
+        xs = []
+        for i in range(self.num_branches):
+            x_ = torch.nn.functional.interpolate(x, size=(self.img_size[i], self.img_size[i]), mode='bicubic') if H != self.img_size[i] else x
+            tmp = self.patch_embed[i](x_)
+            cls_tokens = self.cls_token[i].expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+            tmp = torch.cat((cls_tokens, tmp), dim=1)
+            tmp = tmp + self.pos_embed[i]
+            tmp = self.pos_drop(tmp)
+            xs.append(tmp)
+
+        for blk in self.blocks:
+            xs = blk(xs)
+
+        # NOTE: was before branch token section, move to here to assure all branch token are before layer norm
+        xs = [self.norm[i](x) for i, x in enumerate(xs)]
+        out = [x[:, 0] for x in xs]
+
+        return out
+
+    def forward(self, x):
+        xs = self.forward_features(x)
+        ce_logits = [self.head[i](x) for i, x in enumerate(xs)]
+        ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
+        return ce_logits
+
+
+def _create_crossvit(variant, pretrained=False, **kwargs):
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Vision Transformer models.')
+
+    return build_model_with_cfg(
+        CrossViT, variant, pretrained,
+        default_cfg=default_cfgs[variant],
+        **kwargs)
+
+
+@register_model
+def crossvit_tiny_224(pretrained=False, **kwargs):
+    model_args = dict(
+        img_size=[240, 224], patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
+        num_heads=[3, 3], mlp_ratio=[4, 4, 1], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_tiny_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_small_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
+                              num_heads=[6, 6], mlp_ratio=[4, 4, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_small_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_base_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[384, 768], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
+                              num_heads=[12, 12], mlp_ratio=[4, 4, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_base_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_9_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
+                              num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_9_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_15_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
+                              num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_15_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_18_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
+                              num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model = _create_crossvit(variant='crossvit_18_224', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def crossvit_9_dagger_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
+                              num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
+    model = _create_crossvit(variant='crossvit_9_dagger_224', pretrained=pretrained, **model_args)
+    return model
+
+@register_model
+def crossvit_15_dagger_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
+                              num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
+    model = _create_crossvit(variant='crossvit_15_dagger_224', pretrained=pretrained, **model_args)
+    return model
+
+@register_model
+def crossvit_15_dagger_384(pretrained=False, **kwargs):
+    model_args = dict(img_size=[408, 384],
+                              patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
+                              num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
+    model = _create_crossvit(variant='crossvit_15_dagger_384', pretrained=pretrained, **model_args)
+    return model
+
+@register_model
+def crossvit_18_dagger_224(pretrained=False, **kwargs):
+    model_args = dict(img_size=[240, 224],
+                              patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
+                              num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
+    model = _create_crossvit(variant='crossvit_18_dagger_224', pretrained=pretrained, **model_args)
+    return model
+
+@register_model
+def crossvit_18_dagger_384(pretrained=False, **kwargs):
+    model_args = dict(img_size=[408, 384],
+                              patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
+                              num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
+                              norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
+    model = _create_crossvit(variant='crossvit_18_dagger_384', pretrained=pretrained, **model_args)
+    return model