code: # Copyright (c) [2012]-[2021] Shanghai Yitu Technology Co., Ltd.
  #
  # This source code is licensed under the Clear BSD License
  # LICENSE file in the root directory of this file
  # All rights reserved.
  """
  Borrow from timm(https://github.com/rwightman/pytorch-image-models)
  """
  import torch
  import torch.nn as nn
  import numpy as np
  from timm.models.layers import DropPath
  
  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 = original_shape = x.shape
          C = int(C)
          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(*original_shape)
          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)
          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
  
  
  def get_sinusoid_encoding(n_position, d_hid):
      ''' Sinusoid position encoding table '''
  
      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)
  
  
  def get_sinusoid_encoding_pt(n_position, d_hid):
      ''' Sinusoid position encoding table '''
      pos = torch.arange(n_position).reshape(-1, 1).float()
      dh = torch.pow(10000, 2 * (torch.arange(d_hid) // 2) / d_hid).reshape(1, -1).float()
      sinusoid_table = pos / dh
      shape = sinusoid_table.shape
      sinusoid_table = sinusoid_table.reshape(-1, 2)
      sinusoid_table = torch.stack([torch.sin(sinusoid_table[..., 0]), torch.cos(sinusoid_table[..., 1])], dim=1)
      sinusoid_table = sinusoid_table.reshape(1, *shape)
      # sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2])  # dim 2i
      # sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2])  # dim 2i+1
      return sinusoid_table
  
apis: torch.FloatTensor torch.nn.Linear torch.cos numpy.cos torch.sin numpy.power torch.arange torch.nn.Identity numpy.sin torch.nn.Dropout