text: # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import paddle
  import paddle.nn as nn
  import paddle.nn.functional as F
  import numpy as np
  
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
  
  
  def to_2tuple(x):
      return tuple([x] * 2)
  
  
  def drop_path(x, drop_prob=0., training=False):
      """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
      the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
      See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
      """
      if drop_prob == 0. or not training:
          return x
      keep_prob = paddle.to_tensor(1 - drop_prob)
      shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
      random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
      random_tensor = paddle.floor(random_tensor)  # binarize
      output = x.divide(keep_prob) * random_tensor
      return output
  
  
  class DropPath(nn.Layer):
      """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)
  
  
  class Identity(nn.Layer):
      def __init__(self):
          super(Identity, self).__init__()
  
      def forward(self, input):
          return input
  
  
  class Mlp(nn.Layer):
      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.Layer):
      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_attr=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):
          x_shape = paddle.shape(x)
          N, C = x_shape[1], x_shape[2]
          qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads,
                                     C // self.num_heads)).transpose((2, 0, 3, 1,
                                                                      4))
          q, k, v = qkv[0], qkv[1], qkv[2]
  
          attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
          attn = nn.functional.softmax(attn, axis=-1)
          attn = self.attn_drop(attn)
  
          x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, N, C))
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class Block(nn.Layer):
      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',
                   epsilon=1e-5):
          super().__init__()
          self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
          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 Identity()
          self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
          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.Layer):
      """ Image to Patch Embedding
      """
  
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
          super().__init__()
          self.img_size = to_2tuple(img_size)
          self.patch_size = to_2tuple(patch_size)
  
          self.proj = nn.Conv2D(
              in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
  
      @property
      def num_patches_in_h(self):
          return self.img_size[1] // self.patch_size[1]
  
      @property
      def num_patches_in_w(self):
          return self.img_size[0] // self.patch_size[0]
  
      def forward(self, x):
          x = self.proj(x)
          return x
  
  
  class VisionTransformer(nn.Layer):
      """ Vision Transformer with support for patch input
      """
  
      def __init__(self,
                   img_size=224,
                   patch_size=16,
                   in_chans=3,
                   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',
                   epsilon=1e-5,
                   pretrained=None,
                   **args):
          super().__init__()
          self.depth = depth
          self.embed_dim = embed_dim
  
          self.patch_embed = PatchEmbed(
              img_size=img_size,
              patch_size=patch_size,
              in_chans=in_chans,
              embed_dim=embed_dim)
          self.pos_w = self.patch_embed.num_patches_in_w
          self.pos_h = self.patch_embed.num_patches_in_h
  
          self.pos_embed = self.create_parameter(
              shape=(1, self.pos_w * self.pos_h + 1, embed_dim),
              default_initializer=paddle.nn.initializer.Constant(value=0.))
          self.add_parameter("pos_embed", self.pos_embed)
          self.cls_token = self.create_parameter(
              shape=(1, 1, embed_dim),
              default_initializer=paddle.nn.initializer.Constant(value=0.))
          self.add_parameter("cls_token", self.cls_token)
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          dpr = np.linspace(0, drop_path_rate, depth)
  
          self.blocks = nn.LayerList([
              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,
                  epsilon=epsilon) for i in range(depth)
          ])
  
          self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
          self.pretrained = pretrained
          self.init_weight()
  
      def init_weight(self):
          utils.load_pretrained_model(self, self.pretrained)
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          x_shape = paddle.shape(x)
          pos_embed = self.pos_embed[:, 1:, :]
          cls_pos_embed = self.pos_embed[:, :1, :]
          cls_tokens = self.cls_token.expand((x_shape[0], -1, -1))
  
          pos_embed = pos_embed.transpose([0, 2, 1])
          pos_embed = pos_embed.reshape([1, -1, self.pos_h, self.pos_w])
          pos_embed = F.interpolate(
              pos_embed, x_shape[2:], mode='bilinear', align_corners=False)
  
          pos_embed = pos_embed.flatten(2).transpose([0, 2, 1])
          pos_embed = paddle.concat([cls_pos_embed, pos_embed], axis=1)
          x = x.flatten(2).transpose([0, 2, 1])
          x = paddle.concat([cls_tokens, x], axis=1)
          x = x + pos_embed
  
          x = self.pos_drop(x)
          res = []
          for idx, blk in enumerate(self.blocks):
              x = blk(x)
              res.append(x[:, 1:, :])
          return res, x_shape
  
      def forward(self, x):
          return self.forward_features(x)
  
  
  @manager.BACKBONES.add_component
  def ViT_small_patch16_224(**kwargs):
      model = VisionTransformer(
          patch_size=16,
          embed_dim=768,
          depth=8,
          num_heads=8,
          mlp_ratio=3,
          qk_scale=768**-0.5,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_base_patch16_224(**kwargs):
      model = VisionTransformer(
          patch_size=16,
          embed_dim=768,
          depth=12,
          num_heads=12,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_base_patch16_384(**kwargs):
      model = VisionTransformer(
          img_size=384,
          patch_size=16,
          embed_dim=768,
          depth=12,
          num_heads=12,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_base_patch32_384(**kwargs):
      model = VisionTransformer(
          img_size=384,
          patch_size=32,
          embed_dim=768,
          depth=12,
          num_heads=12,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_large_patch16_224(**kwargs):
      model = VisionTransformer(
          patch_size=16,
          embed_dim=1024,
          depth=24,
          num_heads=16,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_large_patch16_384(**kwargs):
      model = VisionTransformer(
          img_size=384,
          patch_size=16,
          embed_dim=1024,
          depth=24,
          num_heads=16,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_large_patch32_384(**kwargs):
      model = VisionTransformer(
          img_size=384,
          patch_size=32,
          embed_dim=1024,
          depth=24,
          num_heads=16,
          mlp_ratio=4,
          qkv_bias=True,
          epsilon=1e-6,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_huge_patch16_224(**kwargs):
      model = VisionTransformer(
          patch_size=16,
          embed_dim=1280,
          depth=32,
          num_heads=16,
          mlp_ratio=4,
          **kwargs)
      return model
  
  
  @manager.BACKBONES.add_component
  def ViT_huge_patch32_384(**kwargs):
      model = VisionTransformer(
          img_size=384,
          patch_size=32,
          embed_dim=1280,
          depth=32,
          num_heads=16,
          mlp_ratio=4,
          **kwargs)
      return model
  