text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_, lecun_normal_
  from functools import partial
  from collections import OrderedDict
  import math
  # from mmcv_custom import load_checkpoint
  # from mmseg.utils import get_root_logger
  # from ..builder import BACKBONES
  
  class Mlp(nn.Module):
      def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
          super().__init__()
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          self.fc1 = nn.Linear(in_features, hidden_features)
          self.act = act_layer()
          self.fc2 = nn.Linear(hidden_features, out_features)
          self.drop = nn.Dropout(drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act(x)
          x = self.drop(x)
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  class Attention(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
          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]   # make torchscript happy (cannot use tensor as tuple)
  
          attn = (q @ k.transpose(-2, -1)) * self.scale
          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):
  
      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):
          super().__init__()
          self.norm1 = norm_layer(dim)
          self.attn = Attention(
              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(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 PatchEmbed(nn.Module):
      """ Image to Patch Embedding
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          self.img_size = img_size
          self.patch_size = patch_size
          self.patch_grid = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
          self.num_patches = self.patch_grid[0] * self.patch_grid[1]
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
          self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
  
      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)
          x = self.norm(x)
          return x
  
  
  def _init_vit_weights(m, n: str = '', head_bias: float = 0., jax_impl: bool = False):
      """ ViT weight initialization
      * When called without n, head_bias, jax_impl args it will behave exactly the same
        as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
      * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
      """
      if isinstance(m, nn.Linear):
          if n.startswith('head'):
              nn.init.zeros_(m.weight)
              nn.init.constant_(m.bias, head_bias)
          elif n.startswith('pre_logits'):
              lecun_normal_(m.weight)
              nn.init.zeros_(m.bias)
          else:
              if jax_impl:
                  nn.init.xavier_uniform_(m.weight)
                  if m.bias is not None:
                      if 'mlp' in n:
                          nn.init.normal_(m.bias, std=1e-6)
                      else:
                          nn.init.zeros_(m.bias)
              else:
                  trunc_normal_(m.weight, std=.02)
                  if m.bias is not None:
                      nn.init.zeros_(m.bias)
      elif jax_impl and isinstance(m, nn.Conv2d):
          # NOTE conv was left to pytorch default in my original init
          lecun_normal_(m.weight)
          if m.bias is not None:
              nn.init.zeros_(m.bias)
      elif isinstance(m, nn.LayerNorm):
          nn.init.zeros_(m.bias)
          nn.init.ones_(m.weight)
  
  
  class VisionTransformer(nn.Module):
      """ Vision Transformer
  
      A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
          - https://arxiv.org/abs/2010.11929
  
      Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
          - https://arxiv.org/abs/2012.12877
      """
  
      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=True, qk_scale=None, representation_size=None, distilled=False,
                   drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
                   act_layer=None, weight_init=''):
          """
          Args:
              img_size (int, tuple): input image size
              patch_size (int, tuple): patch size
              in_chans (int): number of input channels
              num_classes (int): number of classes for classification head
              embed_dim (int): embedding dimension
              depth (int): depth of transformer
              num_heads (int): number of attention heads
              mlp_ratio (int): ratio of mlp hidden dim to embedding dim
              qkv_bias (bool): enable bias for qkv if True
              qk_scale (float): override default qk scale of head_dim ** -0.5 if set
              representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
              distilled (bool): model includes a distillation token and head as in DeiT models
              drop_rate (float): dropout rate
              attn_drop_rate (float): attention dropout rate
              drop_path_rate (float): stochastic depth rate
              embed_layer (nn.Module): patch embedding layer
              norm_layer: (nn.Module): normalization layer
              weight_init: (str): weight init scheme
          """
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
          act_layer = act_layer or nn.GELU
          self.depth = depth
          self.patch_embed = embed_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.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
          self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
          self.blocks = nn.ModuleList([
              Block(
                  dim=embed_dim, 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[i], norm_layer=norm_layer, act_layer=act_layer)
              for i in range(depth)])
          self.norm = norm_layer(embed_dim)
  
          # Representation layer
          if representation_size and not distilled:
              self.num_features = representation_size
              self.pre_logits = nn.Sequential(OrderedDict([
                  ('fc', nn.Linear(embed_dim, representation_size)),
                  ('act', nn.Tanh())
              ]))
          else:
              self.pre_logits = nn.Identity()
  
  
          # Weight init
          assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
          head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
          trunc_normal_(self.pos_embed, std=.02)
          if self.dist_token is not None:
              trunc_normal_(self.dist_token, std=.02)
          if weight_init.startswith('jax'):
              # leave cls token as zeros to match jax impl
              for n, m in self.named_modules():
                  _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
          else:
              trunc_normal_(self.cls_token, std=.02)
              self.apply(_init_vit_weights)
  
      def _init_weights(self, m):
          # this fn left here for compat with downstream users
          _init_vit_weights(m)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token', 'dist_token'}
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          x = self.pos_drop(x + self.pos_embed)
          outs = []
          for i in range(self.depth):
              x = self.blocks[i](x)
              x = self.norm(x)
              if i+1 in [2,4,10,12]:
                  hw = int(self.patch_embed.num_patches ** 0.5)
                  out = x.view(-1, hw, hw, self.embed_dim).permute(0, 3, 1, 2).contiguous()
                  outs.append(out)
          return tuple(outs)
  
      def forward(self, x):
          x = self.forward_features(x)
  
          return x
  
  model = VisionTransformer(img_size=512, patch_size=16, embed_dim=384, depth=12, num_heads=6)
  
  model = model.cuda()
  
  o = model(torch.ones((1,3,512,512)).cuda())