content: from math import ceil, sqrt
  from collections import OrderedDict
  import torch
  import torch.nn as nn
  import torch.utils.checkpoint as checkpoint
  from functools import partial
  from timm.models.vision_transformer import _cfg
  from timm.models.layers import trunc_normal_, DropPath, to_2tuple
  from .build import MODEL_REGISTRY
  import os
  
  import slowfast.utils.logging as logging
  
  logger = logging.get_logger(__name__)
  
  model_path = '/mnt/lustre/share_data/likunchang.vendor/model'
  model_path = {
      'uniformer_small_k400_16x4': os.path.join(model_path, 'uniformer_small_k400_16x4.pth'),
      'uniformer_small_k600_16x4': os.path.join(model_path, 'uniformer_small_k600_16x4.pth'),
      'uniformer_base_k400_16x4': os.path.join(model_path, 'uniformer_base_k400_16x4.pth'),
      'uniformer_base_k600_16x4': os.path.join(model_path, 'uniformer_base_k600_16x4.pth'),
      'uniformer_base_k400_32x4': os.path.join(model_path, 'uniformer_base_k400_32x4.pth'),
      'uniformer_base_k600_32x4': os.path.join(model_path, 'uniformer_base_k600_32x4.pth'),
  }
  
  
  def conv_3xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
      return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (2, stride, stride), (1, 0, 0), groups=groups)
      
  def conv_1xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
      return nn.Conv3d(inp, oup, (1, kernel_size, kernel_size), (1, stride, stride), (0, 0, 0), groups=groups)
  
  def conv_3xnxn_std(inp, oup, kernel_size=3, stride=3, groups=1):
      return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (1, stride, stride), (1, 0, 0), groups=groups)
  
  def conv_1x1x1(inp, oup, groups=1):
      return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
  
  def conv_3x3x3(inp, oup, groups=1):
      return nn.Conv3d(inp, oup, (3, 3, 3), (1, 1, 1), (1, 1, 1), groups=groups)
  
  def conv_5x5x5(inp, oup, groups=1):
      return nn.Conv3d(inp, oup, (5, 5, 5), (1, 1, 1), (2, 2, 2), groups=groups)
  
  def bn_3d(dim):
      return nn.BatchNorm3d(dim)
  
  
  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 CMlp(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 = conv_1x1x1(in_features, hidden_features)
          self.act = act_layer()
          self.fc2 = conv_1x1x1(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 CBlock(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.pos_embed = conv_3x3x3(dim, dim, groups=dim)
          self.norm1 = bn_3d(dim)
          self.conv1 = conv_1x1x1(dim, dim, 1)
          self.conv2 = conv_1x1x1(dim, dim, 1)
          self.attn = conv_5x5x5(dim, dim, groups=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 = bn_3d(dim)
          mlp_hidden_dim = int(dim * mlp_ratio)
          self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
  
      def forward(self, x):
          x = x + self.pos_embed(x)
          x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
          x = x + self.drop_path(self.mlp(self.norm2(x)))
          return x   
  
  
  class SABlock(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.pos_embed = conv_3x3x3(dim, dim, groups=dim)
          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.pos_embed(x)
          B, C, T, H, W = x.shape
          x = x.flatten(2).transpose(1, 2)
          x = x + self.drop_path(self.attn(self.norm1(x)))
          x = x + self.drop_path(self.mlp(self.norm2(x)))
          x = x.transpose(1, 2).reshape(B, C, T, H, W)
          return x    
  
  
  class SplitSABlock(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.pos_embed = conv_3x3x3(dim, dim, groups=dim)
          self.t_norm = norm_layer(dim)
          self.t_attn = Attention(
              dim,
              num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
              attn_drop=attn_drop, proj_drop=drop)
          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.pos_embed(x)
          B, C, T, H, W = x.shape
          attn = x.view(B, C, T, H * W).permute(0, 3, 2, 1).contiguous()
          attn = attn.view(B * H * W, T, C)
          attn = attn + self.drop_path(self.t_attn(self.t_norm(attn)))
          attn = attn.view(B, H * W, T, C).permute(0, 2, 1, 3).contiguous()
          attn = attn.view(B * T, H * W, C)
          residual = x.view(B, C, T, H * W).permute(0, 2, 3, 1).contiguous()
          residual = residual.view(B * T, H * W, C)
          attn = residual + self.drop_path(self.attn(self.norm1(attn)))
          attn = attn.view(B, T * H * W, C)
          out = attn + self.drop_path(self.mlp(self.norm2(attn)))
          out = out.transpose(1, 2).reshape(B, C, T, H, W)
          return out
  
  
  class SpeicalPatchEmbed(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.norm = nn.LayerNorm(embed_dim)
          self.proj = conv_3xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
  
      def forward(self, x):
          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)
          B, C, T, H, W = x.shape
          x = x.flatten(2).transpose(1, 2)
          x = self.norm(x)
          x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
          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, std=False):
          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.norm = nn.LayerNorm(embed_dim)
          if std:
              self.proj = conv_3xnxn_std(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
          else:
              self.proj = conv_1xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
  
      def forward(self, x):
          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)
          B, C, T, H, W = x.shape
          x = x.flatten(2).transpose(1, 2)
          x = self.norm(x)
          x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
          return x
  
  
  @MODEL_REGISTRY.register()
  class Uniformer(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
      """
      def __init__(self, cfg):
          super().__init__()
  
          depth = cfg.UNIFORMER.DEPTH
          num_classes = cfg.MODEL.NUM_CLASSES 
          img_size = cfg.DATA.TRAIN_CROP_SIZE
          in_chans = cfg.DATA.INPUT_CHANNEL_NUM[0]
          embed_dim = cfg.UNIFORMER.EMBED_DIM
          head_dim = cfg.UNIFORMER.HEAD_DIM
          mlp_ratio = cfg.UNIFORMER.MLP_RATIO
          qkv_bias = cfg.UNIFORMER.QKV_BIAS
          qk_scale = cfg.UNIFORMER.QKV_SCALE
          representation_size = cfg.UNIFORMER.REPRESENTATION_SIZE
          drop_rate = cfg.UNIFORMER.DROPOUT_RATE
          attn_drop_rate = cfg.UNIFORMER.ATTENTION_DROPOUT_RATE
          drop_path_rate = cfg.UNIFORMER.DROP_DEPTH_RATE
          split = cfg.UNIFORMER.SPLIT
          std = cfg.UNIFORMER.STD
          self.use_checkpoint = cfg.MODEL.USE_CHECKPOINT
          self.checkpoint_num = cfg.MODEL.CHECKPOINT_NUM
  
          logger.info(f'Use checkpoint: {self.use_checkpoint}')
          logger.info(f'Checkpoint number: {self.checkpoint_num}')
  
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          norm_layer = partial(nn.LayerNorm, eps=1e-6) 
          
          self.patch_embed1 = SpeicalPatchEmbed(
              img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
          self.patch_embed2 = PatchEmbed(
              img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1], std=std)
          self.patch_embed3 = PatchEmbed(
              img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2], std=std)
          self.patch_embed4 = PatchEmbed(
              img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3], std=std)
  
          self.pos_drop = nn.Dropout(p=drop_rate)
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))]  # stochastic depth decay rule
          num_heads = [dim // head_dim for dim in embed_dim]
          self.blocks1 = nn.ModuleList([
              CBlock(
                  dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio[0], 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[0])])
          self.blocks2 = nn.ModuleList([
              CBlock(
                  dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]], norm_layer=norm_layer)
              for i in range(depth[1])])
          if split:
              self.blocks3 = nn.ModuleList([
                  SplitSABlock(
                      dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
                      drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
                  for i in range(depth[2])])
              self.blocks4 = nn.ModuleList([
                  SplitSABlock(
                      dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
                      drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
              for i in range(depth[3])])
          else:
              self.blocks3 = nn.ModuleList([
                  SABlock(
                      dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
                      drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
                  for i in range(depth[2])])
              self.blocks4 = nn.ModuleList([
                  SABlock(
                      dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
                      drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
              for i in range(depth[3])])
          self.norm = bn_3d(embed_dim[-1])
          
          # Representation layer
          if representation_size:
              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()
          
          # Classifier head
          self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
          
          self.apply(self._init_weights)
  
          for name, p in self.named_parameters():
              # fill proj weight with 1 here to improve training dynamics. Otherwise temporal attention inputs
              # are multiplied by 0*0, which is hard for the model to move out of.
              if 't_attn.qkv.weight' in name:
                  nn.init.constant_(p, 0)
              if 't_attn.qkv.bias' in name:
                  nn.init.constant_(p, 0)
              if 't_attn.proj.weight' in name:
                  nn.init.constant_(p, 1)
              if 't_attn.proj.bias' in name:
                  nn.init.constant_(p, 0)
  
      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 inflate_weight(self, weight_2d, time_dim, center=False):
          if center:
              weight_3d = torch.zeros(*weight_2d.shape)
              weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
              middle_idx = time_dim // 2
              weight_3d[:, :, middle_idx, :, :] = weight_2d
          else:
              weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
              weight_3d = weight_3d / time_dim
          return weight_3d
  
      def get_pretrained_model(self, cfg):
          if cfg.UNIFORMER.PRETRAIN_NAME:
              checkpoint = torch.load(model_path[cfg.UNIFORMER.PRETRAIN_NAME], map_location='cpu')
  
              state_dict_3d = self.state_dict()
              for k in checkpoint.keys():
                  if checkpoint[k].shape != state_dict_3d[k].shape:
                      if len(state_dict_3d[k].shape) <= 2:
                          logger.info(f'Ignore: {k}')
                          continue
                      logger.info(f'Inflate: {k}, {checkpoint[k].shape} => {state_dict_3d[k].shape}')
                      time_dim = state_dict_3d[k].shape[2]
                      checkpoint[k] = self.inflate_weight(checkpoint[k], time_dim)
  
              if self.num_classes != checkpoint['head.weight'].shape[0]:
                  del checkpoint['head.weight'] 
                  del checkpoint['head.bias'] 
              return checkpoint
          else:
              return None
              
      def forward_features(self, x):
          x = self.patch_embed1(x)
          x = self.pos_drop(x)
          for i, blk in enumerate(self.blocks1):
              if self.use_checkpoint and i < self.checkpoint_num[0]:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          x = self.patch_embed2(x)
          for i, blk in enumerate(self.blocks2):
              if self.use_checkpoint and i < self.checkpoint_num[1]:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          x = self.patch_embed3(x)
          for i, blk in enumerate(self.blocks3):
              if self.use_checkpoint and i < self.checkpoint_num[2]:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          x = self.patch_embed4(x)
          for i, blk in enumerate(self.blocks4):
              if self.use_checkpoint and i < self.checkpoint_num[3]:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          x = self.norm(x)
          x = self.pre_logits(x)
          return x
  
      def forward(self, x):
          x = x[0]
          x = self.forward_features(x)
          x = x.flatten(2).mean(-1)
          x = self.head(x)
          return x
  