text: """ ConViT Model
  
  @article{d2021convit,
    title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
    author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
    journal={arXiv preprint arXiv:2103.10697},
    year={2021}
  }
  
  Paper link: https://arxiv.org/abs/2103.10697
  Original code: https://github.com/facebookresearch/convit, original copyright below
  """
  # Copyright (c) 2015-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the CC-by-NC license found in the
  # LICENSE file in the root directory of this source tree.
  #
  '''These modules are adapted from those of timm, see
  https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
  '''
  
  import torch
  import torch.nn as nn
  from functools import partial
  import torch.nn.functional as F
  
  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_, PatchEmbed, Mlp
  from .registry import register_model
  from .vision_transformer_hybrid import HybridEmbed
  
  import torch
  import torch.nn as nn
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), '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 = {
      # ConViT
      'convit_tiny': _cfg(
          url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
      'convit_small': _cfg(
          url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
      'convit_base': _cfg(
          url="https://dl.fbaipublicfiles.com/convit/convit_base.pth")
  }
  
  
  class GPSA(nn.Module):
      def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,
                   locality_strength=1.):
          super().__init__()
          self.num_heads = num_heads
          self.dim = dim
          head_dim = dim // num_heads
          self.scale = head_dim ** -0.5
          self.locality_strength = locality_strength
  
          self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
          self.v = nn.Linear(dim, dim, bias=qkv_bias)
  
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(dim, dim)
          self.pos_proj = nn.Linear(3, num_heads)
          self.proj_drop = nn.Dropout(proj_drop)
          self.gating_param = nn.Parameter(torch.ones(self.num_heads))
          self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3)  # silly torchscript hack, won't work with None
  
      def forward(self, x):
          B, N, C = x.shape
          if self.rel_indices is None or self.rel_indices.shape[1] != N:
              self.rel_indices = self.get_rel_indices(N)
          attn = self.get_attention(x)
          v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
          x = (attn @ v).transpose(1, 2).reshape(B, N, C)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
      def get_attention(self, x):
          B, N, C = x.shape
          qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
          q, k = qk[0], qk[1]
          pos_score = self.rel_indices.expand(B, -1, -1, -1)
          pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
          patch_score = (q @ k.transpose(-2, -1)) * self.scale
          patch_score = patch_score.softmax(dim=-1)
          pos_score = pos_score.softmax(dim=-1)
  
          gating = self.gating_param.view(1, -1, 1, 1)
          attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
          attn /= attn.sum(dim=-1).unsqueeze(-1)
          attn = self.attn_drop(attn)
          return attn
  
      def get_attention_map(self, x, return_map=False):
          attn_map = self.get_attention(x).mean(0)  # average over batch
          distances = self.rel_indices.squeeze()[:, :, -1] ** .5
          dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
          if return_map:
              return dist, attn_map
          else:
              return dist
  
      def local_init(self):
          self.v.weight.data.copy_(torch.eye(self.dim))
          locality_distance = 1  # max(1,1/locality_strength**.5)
  
          kernel_size = int(self.num_heads ** .5)
          center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
          for h1 in range(kernel_size):
              for h2 in range(kernel_size):
                  position = h1 + kernel_size * h2
                  self.pos_proj.weight.data[position, 2] = -1
                  self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
                  self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
          self.pos_proj.weight.data *= self.locality_strength
  
      def get_rel_indices(self, num_patches: int) -> torch.Tensor:
          img_size = int(num_patches ** .5)
          rel_indices = torch.zeros(1, num_patches, num_patches, 3)
          ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
          indx = ind.repeat(img_size, img_size)
          indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
          indd = indx ** 2 + indy ** 2
          rel_indices[:, :, :, 2] = indd.unsqueeze(0)
          rel_indices[:, :, :, 1] = indy.unsqueeze(0)
          rel_indices[:, :, :, 0] = indx.unsqueeze(0)
          device = self.qk.weight.device
          return rel_indices.to(device)
  
  
  class MHSA(nn.Module):
      def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
          super().__init__()
          self.num_heads = num_heads
          head_dim = dim // num_heads
          self.scale = 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 get_attention_map(self, x, return_map=False):
          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]
          attn_map = (q @ k.transpose(-2, -1)) * self.scale
          attn_map = attn_map.softmax(dim=-1).mean(0)
  
          img_size = int(N ** .5)
          ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
          indx = ind.repeat(img_size, img_size)
          indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
          indd = indx ** 2 + indy ** 2
          distances = indd ** .5
          distances = distances.to('cuda')
  
          dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
          if return_map:
              return dist, attn_map
          else:
              return dist
  
      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]
  
          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, drop=0., attn_drop=0.,
                   drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs):
          super().__init__()
          self.norm1 = norm_layer(dim)
          self.use_gpsa = use_gpsa
          if self.use_gpsa:
              self.attn = GPSA(
                  dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, **kwargs)
          else:
              self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, 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)
          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 ConViT(nn.Module):
      """ Vision Transformer with support for patch or hybrid CNN input stage
      """
  
      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, drop_rate=0., attn_drop_rate=0.,
                   drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
                   local_up_to_layer=3, locality_strength=1., use_pos_embed=True):
          super().__init__()
          embed_dim *= num_heads
          self.num_classes = num_classes
          self.local_up_to_layer = local_up_to_layer
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          self.locality_strength = locality_strength
          self.use_pos_embed = use_pos_embed
  
          if hybrid_backbone is not None:
              self.patch_embed = HybridEmbed(
                  hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
          else:
              self.patch_embed = PatchEmbed(
                  img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
          num_patches = self.patch_embed.num_patches
          self.num_patches = num_patches
  
          self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          if self.use_pos_embed:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
              trunc_normal_(self.pos_embed, std=.02)
  
          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,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                  use_gpsa=True,
                  locality_strength=locality_strength)
              if i < local_up_to_layer else
              Block(
                  dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                  use_gpsa=False)
              for i in range(depth)])
          self.norm = norm_layer(embed_dim)
  
          # Classifier head
          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.cls_token, std=.02)
          self.apply(self._init_weights)
          for n, m in self.named_modules():
              if hasattr(m, 'local_init'):
                  m.local_init()
  
      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 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 = x.shape[0]
          x = self.patch_embed(x)
  
          cls_tokens = self.cls_token.expand(B, -1, -1)
  
          if self.use_pos_embed:
              x = x + self.pos_embed
          x = self.pos_drop(x)
  
          for u, blk in enumerate(self.blocks):
              if u == self.local_up_to_layer:
                  x = torch.cat((cls_tokens, x), dim=1)
              x = blk(x)
  
          x = self.norm(x)
          return x[:, 0]
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.head(x)
          return x
  
  
  def _create_convit(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(
          ConViT, variant, pretrained,
          default_cfg=default_cfgs[variant],
          **kwargs)
  
  
  @register_model
  def convit_tiny(pretrained=False, **kwargs):
      model_args = dict(
          local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
          num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
      return model
  
  
  @register_model
  def convit_small(pretrained=False, **kwargs):
      model_args = dict(
          local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
          num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
      return model
  
  
  @register_model
  def convit_base(pretrained=False, **kwargs):
      model_args = dict(
          local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
          num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
      return model
  