content: from functools import partial
  import numpy as np
  from typing import Tuple
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from dataclasses import dataclass
  from alphaction.modeling.poolers import make_3d_pooler
  import math
  import pdb
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 400, 'input_size': (3, 224, 224), 'pool_size': None,
          'crop_pct': .9, 'interpolation': 'bicubic',
          'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
          **kwargs
      }
  
  
  class HR2O_NL(nn.Module):
      def __init__(self, hidden_dim=512, kernel_size=3, mlp_1x1=False):
          super(HR2O_NL, self).__init__()
  
          self.hidden_dim = hidden_dim
  
          padding = kernel_size // 2
          self.conv_q = nn.Conv2d(hidden_dim, hidden_dim, kernel_size, padding=padding, bias=False)
          self.conv_k = nn.Conv2d(hidden_dim, hidden_dim, kernel_size, padding=padding, bias=False)
          self.conv_v = nn.Conv2d(hidden_dim, hidden_dim, kernel_size, padding=padding, bias=False)
  
          self.conv = nn.Conv2d(
              hidden_dim, hidden_dim,
              1 if mlp_1x1 else kernel_size,
              padding=0 if mlp_1x1 else padding,
              bias=False
          )
          self.norm = nn.GroupNorm(1, hidden_dim, affine=True)
          self.dp = nn.Dropout(0.2)
  
      def forward(self, x):
          query = self.conv_q(x).unsqueeze(1)
          key = self.conv_k(x).unsqueeze(0)
          att = (query * key).sum(2) / (self.hidden_dim ** 0.5)
          att = nn.Softmax(dim=1)(att)
          value = self.conv_v(x)
          virt_feats = (att.unsqueeze(2) * value).sum(1)
  
          virt_feats = self.norm(virt_feats)
          virt_feats = nn.functional.relu(virt_feats)
          virt_feats = self.conv(virt_feats)
          virt_feats = self.dp(virt_feats)
  
          x = x + virt_feats
          return x
  
  
  class ACARHead(nn.Module):
      def __init__(self, num_classes=60, dropout=0., bias=False,
                   reduce_dim=1024, hidden_dim=512, downsample='max2x2', depth=2,
                   kernel_size=3, mlp_1x1=False):
          super(ACARHead, self).__init__()
  
          # actor-context feature encoder
          self.conv1 = nn.Conv2d(reduce_dim * 2, hidden_dim, 1, bias=False)
          self.conv2 = nn.Conv2d(hidden_dim, hidden_dim, 3, bias=False)
  
          # down-sampling before HR2O
          assert downsample in ['none', 'max2x2']
          if downsample == 'none':
              self.downsample = nn.Identity()
          elif downsample == 'max2x2':
              self.downsample = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          # high-order relation reasoning operator (HR2O_NL)
          layers = []
          for _ in range(depth):
              layers.append(HR2O_NL(hidden_dim, kernel_size, mlp_1x1))
          self.hr2o = nn.Sequential(*layers)
  
          # classification
          self.gap = nn.AdaptiveAvgPool2d(1)
          self.fc1 = nn.Linear(reduce_dim, hidden_dim, bias=False)
          self.fc2 = nn.Linear(hidden_dim * 2, num_classes, bias=bias)
  
          if dropout > 0:
              self.dp = nn.Dropout(dropout)
          else:
              self.dp = None
  
      # data: features, rois, num_rois, roi_ids, sizes_before_padding
      # returns: outputs
      def forward(self, roi_feats, roi_ids, img_features):
          """
          roi_feats: [num_rois, emb_dim]
          roi_ids: [num_rois]
          img_features: [bs,emb_dim,t,h,w]
          """
          high_order_feats = []
          cur_roi_id = 0
          # pdb.set_trace()
          for idx in range(img_features.shape[0]):  # iterate over mini-batch
              n_rois = roi_ids[idx]
              if n_rois == 0:
                  continue
  
              # eff_h, eff_w = math.ceil(h * sizes_before_padding[idx][1]), math.ceil(w * sizes_before_padding[idx][0])
              bg_feats = img_features[idx]
              bg_feats = bg_feats.unsqueeze(0).repeat((n_rois, 1, 1, 1))
              actor_feats = roi_feats[cur_roi_id:cur_roi_id+roi_ids[idx]]
              cur_roi_id += n_rois
              tiled_actor_feats = actor_feats.unsqueeze(2).unsqueeze(2).expand_as(bg_feats)
              interact_feats = torch.cat([bg_feats, tiled_actor_feats], dim=1)
  
              interact_feats = self.conv1(interact_feats)
              interact_feats = nn.functional.relu(interact_feats)
              interact_feats = self.conv2(interact_feats)
              interact_feats = nn.functional.relu(interact_feats)
  
              interact_feats = self.downsample(interact_feats)
  
              interact_feats = self.hr2o(interact_feats)
              interact_feats = self.gap(interact_feats)
              high_order_feats.append(interact_feats)
  
          high_order_feats = torch.cat(high_order_feats, dim=0).view(np.sum(np.array(roi_ids)), -1)
  
          outputs = self.fc1(roi_feats)
          outputs = nn.functional.relu(outputs)
          outputs = torch.cat([outputs, high_order_feats], dim=1)
  
          if self.dp is not None:
              outputs = self.dp(outputs)
          outputs = self.fc2(outputs)
  
          return outputs
  
  
  
  class DropPath(nn.Module):
      """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
      """
  
      def __init__(self, drop_prob=None):
          super(DropPath, self).__init__()
          self.drop_prob = drop_prob
  
      def forward(self, x):
          return drop_path(x, self.drop_prob, self.training)
  
      def extra_repr(self) -> str:
          return 'p={}'.format(self.drop_prob)
  
  
  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)
          # commit this for the orignal BERT implement 
          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., attn_head_dim=None):
          super().__init__()
          self.num_heads = num_heads
          head_dim = dim // num_heads
          if attn_head_dim is not None:
              head_dim = attn_head_dim
          all_head_dim = head_dim * self.num_heads
          self.scale = qk_scale or head_dim ** -0.5
  
          self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
          if qkv_bias:
              self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
              self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
          else:
              self.q_bias = None
              self.v_bias = None
  
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(all_head_dim, dim)
          self.proj_drop = nn.Dropout(proj_drop)
  
      def forward(self, x):
          B, N, C = x.shape
          qkv_bias = None
          if self.q_bias is not None:
              qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
          # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
          qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
          qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
          q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
  
          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, -1)
          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., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                   attn_head_dim=None):
          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, attn_head_dim=attn_head_dim)
          # 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)
  
          if init_values > 0:
              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)
          else:
              self.gamma_1, self.gamma_2 = None, None
  
      def forward(self, x):
          if self.gamma_1 is None:
              x = x + self.drop_path(self.attn(self.norm1(x)))
              x = x + self.drop_path(self.mlp(self.norm2(x)))
          else:
              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 PatchEmbed(nn.Module):
      """ Image to Patch Embedding
      """
  
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, num_frames=16, tubelet_size=2):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          self.tubelet_size = int(tubelet_size)
          num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (
                      num_frames // self.tubelet_size)
          self.img_size = img_size
          self.patch_size = patch_size
          self.num_patches = num_patches
          self.proj = nn.Conv3d(in_channels=in_chans, out_channels=embed_dim,
                                kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]),
                                stride=(self.tubelet_size, patch_size[0], patch_size[1]))
  
      def forward(self, x, **kwargs):
          # B, C, T, 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.proj(x)
          return x
  
  
  # sin-cos position encoding
  # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
  def get_sinusoid_encoding_table(n_position, d_hid):
      ''' Sinusoid position encoding table '''
  
      # TODO: make it with torch instead of numpy 
      def get_position_angle_vec(position):
          return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
  
      sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
      sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
      sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
  
      return torch.FloatTensor(sinusoid_table).unsqueeze(0)
  
  
  @dataclass
  class ROIPoolingCfg:
      POOLER_RESOLUTION: int = 7
      POOLER_SCALE: float = 0.0625
      POOLER_SAMPLING_RATIO: int = 0
      POOLER_TYPE: str = 'align3d'
      MEAN_BEFORE_POOLER: bool = True
  
  
  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=80,
                   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,
                   init_values=0.,
                   use_learnable_pos_emb=False,
                   init_scale=0.,
                   all_frames=16,
                   tubelet_size=2,
                   head_type='linear',
                   use_mean_pooling=True):
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          self.tubelet_size = tubelet_size
          self.patch_embed = PatchEmbed(
              img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, num_frames=all_frames,
              tubelet_size=self.tubelet_size)
          num_patches = self.patch_embed.num_patches  # 8x14x14
          self.grid_size = [img_size//patch_size, img_size//patch_size]  # [14,14]
          self.head_type = head_type
          if use_learnable_pos_emb:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
          else:
              # sine-cosine positional embeddings is on the way
              self.pos_embed = get_sinusoid_encoding_table(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,
                  init_values=init_values)
              for i in range(depth)])
  
          # self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
          # self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
          self.norm = norm_layer(embed_dim)  # 这一项是预训练权重中没有的
          self.fc_norm = None
          if head_type == 'acar':
              self.head = ACARHead(num_classes=num_classes, hidden_dim=embed_dim, reduce_dim=embed_dim) if num_classes > 0 else nn.Identity()
              trunc_normal_(self.head.fc2.weight, std=.02)
              self.head.fc2.weight.data.mul_(init_scale)
              # self.head.fc2.bias.data.mul_(init_scale)
          else:
              self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
              trunc_normal_(self.head.weight, std=.02)
              self.head.weight.data.mul_(init_scale)
              self.head.bias.data.mul_(init_scale)
  
          # rois setting
          self.head_cfg = ROIPoolingCfg()
          self.pooler = make_3d_pooler(self.head_cfg)
          resolution = self.head_cfg.POOLER_RESOLUTION
          self.max_pooler = nn.MaxPool2d((resolution, resolution))
  
          self.test_ext = (0.1, 0.05)
          self.proposal_per_clip = 100
  
          if use_learnable_pos_emb:
              trunc_normal_(self.pos_embed, 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)
  
      def get_num_layers(self):
          return len(self.blocks)
  
      @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, proposals):
          x = self.patch_embed(x)
          B, width, t, h, w = x.size()
          x = x.flatten(2).transpose(1, 2)
  
          # B, _, _ = x.size()
          if self.pos_embed is not None:  # 预测阶段插值
              # positional_embedding=[1 8*14*14 768]->[1 8*16*29 768]
              pos_embed = self.pos_embed.reshape(t, -1, width)
              pos_embed = interpolate_pos_embed_online(
                  pos_embed, self.grid_size, [h, w], 0).reshape(1, -1, width)
              x = x + pos_embed.expand(B, -1, -1).type_as(x).to(x.device).clone().detach()
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              x = blk(x)
  
          x = self.norm(x)  # [b thw=8x14x14 c=768]
          # if self.fc_norm is not None:  # default
          #     return self.fc_norm(x.mean(1))
          # else:  # cls_token
          #     return x[:, 0]
          # rois
          x = x.reshape(B, t, h, w, -1).permute(0, 4, 1, 2, 3)  # [b c t h w]
          x = x.mean(dim=2, keepdim=False)  # [b c h w]
          rois = self.pooler(x, proposals)  # [n c 7 7]
          rois = self.max_pooler(rois).view(rois.size(0), -1)  # [n c]
          return rois, x
  
      def sample_box(self, boxes):
          proposals = []
          num_proposals = self.proposal_per_clip
          for boxes_per_image in boxes:
              num_boxes = len(boxes_per_image)
  
              if num_boxes > num_proposals:
                  choice_inds = torch.randperm(num_boxes)[:num_proposals]
                  proposals_per_image = boxes_per_image[choice_inds]
              else:
                  proposals_per_image = boxes_per_image
              proposals_per_image = proposals_per_image.random_aug(0.2, 0.1, 0.1, 0.05)
              proposals.append(proposals_per_image)
          return proposals
  
  
      def forward(self, x, boxes):
          if self.training:
              proposals = self.sample_box(boxes)  # 暂不考虑训练时限制action数量
          else:
              proposals = [box.extend(self.test_ext) for box in boxes]
          rois, x = self.forward_features(x, proposals)
          if self.head_type == 'acar':
              roi_ids = [len(i) for i in proposals]
              rois = self.head(rois,roi_ids,x)
          else:
              rois = self.head(rois)
          return rois
  
  
  @register_model
  def vit_small_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          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), **kwargs)
      model.default_cfg = _cfg()
      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 = _cfg()
      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 = _cfg()
      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 = _cfg()
      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 = _cfg()
      return model
  
  
  @register_model
  def vit_large_patch16_512(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=512, 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 = _cfg()
      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, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  @register_model
  def vit_giant_patch14_224(pretrained=False, **kwargs):
      model = VisionTransformer(patch_size=14,
                                embed_dim=1408,
                                depth=40,
                                num_heads=16,
                                mlp_ratio=48 / 11,
                                qkv_bias=True,
                                norm_layer=partial(nn.LayerNorm, eps=1e-6),
                                **kwargs)
      model.default_cfg = _cfg()
      return model
  
  
  def interpolate_pos_embed_online(
      pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int
  ):
      extra_tokens = pos_embed[:, :num_extra_tokens]
      pos_tokens = pos_embed[:, num_extra_tokens:]
      embedding_size = pos_tokens.shape[-1]
      pos_tokens = pos_tokens.reshape(
          -1, orig_size[0], orig_size[1], embedding_size
      ).permute(0, 3, 1, 2)
      pos_tokens = torch.nn.functional.interpolate(
          pos_tokens, size=new_size, mode="bicubic", align_corners=False,
      )
      pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
      new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
      return new_pos_embed
  
  
  if __name__ == '__main__':
      # test forward
      # create proposal
      from alphaction.structures.bounding_box import BoxList
  
      im_w, im_h = 464, 256
      n = 2
      xy = torch.zeros([n, 2])
      w = torch.rand([n, 1]) * 464
      h = torch.rand([n, 1]) * 256
      boxes = torch.cat([xy, w, h], dim=1)
      boxes_tensor = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)  # guard against no boxes
  
      boxes = BoxList(boxes_tensor, (im_w, im_h), mode="xywh").convert("xyxy")
      # print(boxes.bbox)
  
      bs = 2
      t = 16
      proposals = [boxes, boxes]  # bs=2
      x = torch.rand([bs, 3, t, im_h, im_w])
  
      visual_transformer = vit_base_patch16_224(head_type='acar')
      print(visual_transformer)
  
      rois = visual_transformer(x, proposals)
      print(rois.shape)  # [4,num_classes]
  
      # pos_embed = get_sinusoid_encoding_table(8*14*14, 384)
      # print(pos_embed.shape)
  