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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import os.path
import torch
import torch.nn as nn
from functools import partial
from timm.models.vision_transformer import Mlp, PatchEmbed , _cfg
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
# from xformers.ops import memory_efficient_attention
class Attention(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, 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
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]
# x = memory_efficient_attention(q, k, v).transpose(1, 2).reshape(B, N, C)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention_block(
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.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class Layer_scale_init_Block(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
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,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention_block(
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.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class Layer_scale_init_Block_paralx2(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
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,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.norm11 = norm_layer(dim)
self.attn = Attention_block(
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.attn1 = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.norm21 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.mlp1 = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_1_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.gamma_1*self.attn(self.norm1(x))) + self.drop_path(self.gamma_1_1 * self.attn1(self.norm11(x)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + self.drop_path(self.gamma_2_1 * self.mlp1(self.norm21(x)))
return x
class Block_paralx2(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
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,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.norm11 = norm_layer(dim)
self.attn = Attention_block(
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.attn1 = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.norm21 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.mlp1 = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x))) + self.drop_path(self.attn1(self.norm11(x)))
x = x + self.drop_path(self.mlp(self.norm2(x))) + self.drop_path(self.mlp1(self.norm21(x)))
return x
class hMLP_stem(nn.Module):
""" hMLP_stem: https://arxiv.org/pdf/2203.09795.pdf
taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
with slight modifications
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768,norm_layer=nn.SyncBatchNorm):
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
self.proj = torch.nn.Sequential(*[nn.Conv2d(in_chans, embed_dim//4, kernel_size=4, stride=4),
norm_layer(embed_dim//4),
nn.GELU(),
nn.Conv2d(embed_dim//4, embed_dim//4, kernel_size=2, stride=2),
norm_layer(embed_dim//4),
nn.GELU(),
nn.Conv2d(embed_dim//4, embed_dim, kernel_size=2, stride=2),
norm_layer(embed_dim),
])
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class vit_models(nn.Module):
""" Vision Transformer with LayerScale (https://arxiv.org/abs/2103.17239) support
taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
with slight modifications
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None,
block_layers = Block,
Patch_layer=PatchEmbed,act_layer=nn.GELU,
Attention_block = Attention, Mlp_block=Mlp,
dpr_constant=True,init_scale=1e-4,
mlp_ratio_clstk = 4.0):
super().__init__()
self.dropout_rate = drop_rate
self.depth = depth
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.patch_embed = Patch_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
block_layers(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=0.0, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
act_layer=act_layer,Attention_block=Attention_block,Mlp_block=Mlp_block,init_values=init_scale)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, 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):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def get_num_layers(self):
return len(self.blocks)
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 extract_block_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = x + self.pos_embed
x = torch.cat((cls_tokens, x), dim=1)
outs = {}
for i, blk in enumerate(self.blocks):
x = blk(x)
outs[i] = x.detach()
return outs
def selective_forward(self, x, begin, end):
for i, blk in enumerate(self.blocks):
if i < begin:
continue
if i > end:
break
x = blk(x)
return x
def forward_until(self, x, blk_id):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = x + self.pos_embed
x = torch.cat((cls_tokens, x), dim=1)
for i, blk in enumerate(self.blocks):
x = blk(x)
if i == blk_id:
break
return x
def forward_from(self, x, blk_id):
for i, blk in enumerate(self.blocks):
if i < blk_id:
continue
x = blk(x)
x = self.norm(x)
x = self.head(x[:, 0])
return x
def forward_patch_embed(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = x + self.pos_embed
x = torch.cat((cls_tokens, x), dim=1)
return x
def forward_norm_head(self, x):
x = self.norm(x)
x = self.head(x[:, 0])
return x
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = x + self.pos_embed
x = torch.cat((cls_tokens, x), dim=1)
for i , blk in enumerate(self.blocks):
x = blk(x)
x = self.norm(x)
return x[:, 0]
def forward(self, x):
x = self.forward_features(x)
if self.dropout_rate:
x = F.dropout(x, p=float(self.dropout_rate), training=self.training)
x = self.head(x)
return x
# DeiT III: Revenge of the ViT (https://arxiv.org/abs/2204.07118)
@register_model
def deit_tiny_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg_overlay=None, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
return model
@register_model
def deit_small_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
model.default_cfg = _cfg()
if pretrained:
# name = 'https://dl.fbaipublicfiles.com/deit/deit_3_small_'+str(img_size)+'_'
# if pretrained_21k:
# name+='21k.pth'
# else:
# name+='1k.pth'
# checkpoint = torch.hub.load_state_dict_from_url(
# url=name,
# map_location="cpu", check_hash=True
# )
checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_small_224_21k.pth'))
model.load_state_dict(checkpoint["model"])
return model
@register_model
def deit_medium_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
patch_size=16, embed_dim=512, depth=12, num_heads=8, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs)
model.default_cfg = _cfg()
if pretrained:
name = 'https://dl.fbaipublicfiles.com/deit/deit_3_medium_'+str(img_size)+'_'
if pretrained_21k:
name+='21k.pth'
else:
name+='1k.pth'
checkpoint = torch.hub.load_state_dict_from_url(
url=name,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def deit_base_patch16_LS(pretrained=False, pretrained_cfg=None, img_size=224, pretrained_21k = False, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
if pretrained:
# name = 'https://dl.fbaipublicfiles.com/deit/deit_3_small_'+str(img_size)+'_'
# if pretrained_21k:
# name+='21k.pth'
# else:
# name+='1k.pth'
# checkpoint = torch.hub.load_state_dict_from_url(
# url=name,
# map_location="cpu", check_hash=True
# )
checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_base_224_21k.pth'))
model.load_state_dict(checkpoint["model"])
return model
@register_model
def deit_large_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
if pretrained:
# name = 'https://dl.fbaipublicfiles.com/deit/deit_3_large_'+str(img_size)+'_'
# if pretrained_21k:
# name+='21k.pth'
# else:
# name+='1k.pth'
#
# checkpoint = torch.hub.load_state_dict_from_url(
# url=name,
# map_location="cpu", check_hash=True
# )
checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_large_224_21k.pth'))
model.load_state_dict(checkpoint["model"])
return model
@register_model
def deit_huge_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs)
if pretrained:
name = 'https://dl.fbaipublicfiles.com/deit/deit_3_huge_'+str(img_size)+'_'
if pretrained_21k:
name+='21k_v1.pth'
else:
name+='1k_v1.pth'
checkpoint = torch.hub.load_state_dict_from_url(
url=name,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def deit_huge_patch14_52_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1280, depth=52, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs)
return model
@register_model
def deit_huge_patch14_26x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1280, depth=26, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block_paralx2, **kwargs)
return model
@register_model
def deit_Giant_48x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs)
return model
@register_model
def deit_giant_40x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs)
return model
@register_model
def deit_Giant_48_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs)
return model
@register_model
def deit_giant_40_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs)
#model.default_cfg = _cfg()
return model
# Models from Three things everyone should know about Vision Transformers (https://arxiv.org/pdf/2203.09795.pdf)
@register_model
def deit_small_patch16_36_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
return model
@register_model
def deit_small_patch16_36(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
@register_model
def deit_small_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs)
return model
@register_model
def deit_small_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs)
return model
@register_model
def deit_base_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs)
return model
@register_model
def deit_base_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs)
return model
@register_model
def deit_base_patch16_36x1_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs)
return model
@register_model
def deit_base_patch16_36x1(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
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