text: """ Vision Transformer (ViT) in PyTorch
  
  A PyTorch implement of Vision Transformers as described in
  'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
  
  The official jax code is released and available at https://github.com/google-research/vision_transformer
  
  Status/TODO:
  * Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
  * Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
  * Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
  * Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
  
  Acknowledgments:
  * The paper authors for releasing code and weights, thanks!
  * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
  for some einops/einsum fun
  * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
  * Bert reference code checks against Huggingface Transformers and Tensorflow Bert
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import torch
  import torch.nn as nn
  from functools import partial
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from ..utils.helpers import load_pretrained
  from ..utils.layers import DropPath, to_2tuple, trunc_normal_
  from .resnet import resnet26d, resnet50d
  from .registry import register_model
  
  # pylint: disable=no-member
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
          'crop_pct': .9, 'interpolation': 'bicubic',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'patch_embed.proj', 'classifier': 'head',
          **kwargs
      }
  
  
  default_cfgs = {
      # patch models
      'vit_small_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
      ),
      'vit_base_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
      ),
      'vit_base_patch16_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_base_patch32_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_large_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_large_patch16_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_large_patch32_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_huge_patch16_224': _cfg(),
      'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
      # hybrid models
      'vit_small_resnet26d_224': _cfg(),
      'vit_small_resnet50d_s3_224': _cfg(),
      'vit_base_resnet26d_224': _cfg(),
      'vit_base_resnet50d_224': _cfg(),
  }
  
  
  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
          # 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.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):
          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 = 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 HybridEmbed(nn.Module):
      """ CNN Feature Map Embedding
      Extract feature map from CNN, flatten, project to embedding dim.
      """
      def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
          super().__init__()
          assert isinstance(backbone, nn.Module)
          img_size = to_2tuple(img_size)
          self.img_size = img_size
          self.backbone = backbone
          if feature_size is None:
              with torch.no_grad():
                  # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
                  # map for all networks, the feature metadata has reliable channel and stride info, but using
                  # stride to calc feature dim requires info about padding of each stage that isn't captured.
                  training = backbone.training
                  if training:
                      backbone.eval()
                  o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
                  feature_size = o.shape[-2:]
                  feature_dim = o.shape[1]
                  backbone.train(training)
          else:
              feature_size = to_2tuple(feature_size)
              feature_dim = self.backbone.feature_info.channels()[-1]
          self.num_patches = feature_size[0] * feature_size[1]
          self.proj = nn.Linear(feature_dim, embed_dim)
  
      def forward(self, x):
          x = self.backbone(x)[-1]
          x = x.flatten(2).transpose(1, 2)
          x = self.proj(x)
          return x
  
  
  class VisionTransformer(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, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                   drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm):
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
  
          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.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
          self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 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)
              for i in range(depth)])
          self.norm = norm_layer(embed_dim)
  
          # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
          #self.repr = nn.Linear(embed_dim, representation_size)
          #self.repr_act = nn.Tanh()
  
          # Classifier 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 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)  # stole cls_tokens impl from Phil Wang, thanks
          x = torch.cat((cls_tokens, x), dim=1)
          x = x + self.pos_embed
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              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 _conv_filter(state_dict, patch_size=16):
      """ convert patch embedding weight from manual patchify + linear proj to conv"""
      out_dict = {}
      for k, v in state_dict.items():
          if 'patch_embed.proj.weight' in k:
              v = v.reshape((v.shape[0], 3, patch_size, patch_size))
          out_dict[k] = v
      return out_dict
  
  
  @register_model
  def vit_small_patch16_224(pretrained=False, **kwargs):
      if pretrained:
          # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
          kwargs.setdefault('qk_scale', 768 ** -0.5)
      model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
      model.default_cfg = default_cfgs['vit_small_patch16_224']
      if pretrained:
          load_pretrained(
              model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
      return model
  
  
  @register_model
  def vit_base_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          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), **kwargs)
      model.default_cfg = default_cfgs['vit_base_patch16_224']
      if pretrained:
          load_pretrained(
              model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
      return model
  
  
  @register_model
  def vit_base_patch16_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, 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), **kwargs)
      model.default_cfg = default_cfgs['vit_base_patch16_384']
      if pretrained:
          load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
      return model
  
  
  @register_model
  def vit_base_patch32_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = default_cfgs['vit_base_patch32_384']
      if pretrained:
          load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
      return model
  
  
  @register_model
  def vit_large_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          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), **kwargs)
      model.default_cfg = default_cfgs['vit_large_patch16_224']
      if pretrained:
          load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
      return model
  
  
  @register_model
  def vit_large_patch16_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, 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), **kwargs)
      model.default_cfg = default_cfgs['vit_large_patch16_384']
      if pretrained:
          load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
      return model
  
  
  @register_model
  def vit_large_patch32_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,  qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = default_cfgs['vit_large_patch32_384']
      if pretrained:
          load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
      return model
  
  
  @register_model
  def vit_huge_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
      model.default_cfg = default_cfgs['vit_huge_patch16_224']
      return model
  
  
  @register_model
  def vit_huge_patch32_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
      model.default_cfg = default_cfgs['vit_huge_patch32_384']
      return model
  
  
  @register_model
  def vit_small_resnet26d_224(pretrained=False, **kwargs):
      pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
      backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
      model = VisionTransformer(
          img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
      model.default_cfg = default_cfgs['vit_small_resnet26d_224']
      return model
  
  
  @register_model
  def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
      pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
      backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[3])
      model = VisionTransformer(
          img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
      model.default_cfg = default_cfgs['vit_small_resnet50d_s3_224']
      return model
  
  
  @register_model
  def vit_base_resnet26d_224(pretrained=False, **kwargs):
      pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
      backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
      model = VisionTransformer(
          img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
      model.default_cfg = default_cfgs['vit_base_resnet26d_224']
      return model
  
  
  @register_model
  def vit_base_resnet50d_224(pretrained=False, **kwargs):
      pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
      backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
      model = VisionTransformer(
          img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
      model.default_cfg = default_cfgs['vit_base_resnet50d_224']
      return model
  