text: # Copyright (c) OpenMMLab. All rights reserved.
  import torch
  import torch.nn as nn
  from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
  from mmcv.cnn.bricks import DropPath
  from mmcv.cnn.bricks.transformer import PatchEmbed
  from mmcv.runner import BaseModule, ModuleList
  from mmcv.utils.parrots_wrapper import _BatchNorm
  
  from ..builder import BACKBONES
  from .base_backbone import BaseBackbone
  
  
  class MixFFN(BaseModule):
      """An implementation of MixFFN of VAN. Refer to
      mmdetection/mmdet/models/backbones/pvt.py.
  
      The differences between MixFFN & FFN:
          1. Use 1X1 Conv to replace Linear layer.
          2. Introduce 3X3 Depth-wise Conv to encode positional information.
  
      Args:
          embed_dims (int): The feature dimension. Same as
              `MultiheadAttention`.
          feedforward_channels (int): The hidden dimension of FFNs.
          act_cfg (dict, optional): The activation config for FFNs.
              Default: dict(type='GELU').
          ffn_drop (float, optional): Probability of an element to be
              zeroed in FFN. Default 0.0.
          init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
              Default: None.
      """
  
      def __init__(self,
                   embed_dims,
                   feedforward_channels,
                   act_cfg=dict(type='GELU'),
                   ffn_drop=0.,
                   init_cfg=None):
          super(MixFFN, self).__init__(init_cfg=init_cfg)
  
          self.embed_dims = embed_dims
          self.feedforward_channels = feedforward_channels
          self.act_cfg = act_cfg
  
          self.fc1 = Conv2d(
              in_channels=embed_dims,
              out_channels=feedforward_channels,
              kernel_size=1)
          self.dwconv = Conv2d(
              in_channels=feedforward_channels,
              out_channels=feedforward_channels,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=True,
              groups=feedforward_channels)
          self.act = build_activation_layer(act_cfg)
          self.fc2 = Conv2d(
              in_channels=feedforward_channels,
              out_channels=embed_dims,
              kernel_size=1)
          self.drop = nn.Dropout(ffn_drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.dwconv(x)
          x = self.act(x)
          x = self.drop(x)
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  class LKA(BaseModule):
      """Large Kernel Attention(LKA) of VAN.
  
      .. code:: text
              DW_conv (depth-wise convolution)
                              |
                              |
          DW_D_conv (depth-wise dilation convolution)
                              |
                              |
          Transition Convolution (1×1 convolution)
  
      Args:
          embed_dims (int): Number of input channels.
          init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
              Default: None.
      """
  
      def __init__(self, embed_dims, init_cfg=None):
          super(LKA, self).__init__(init_cfg=init_cfg)
  
          # a spatial local convolution (depth-wise convolution)
          self.DW_conv = Conv2d(
              in_channels=embed_dims,
              out_channels=embed_dims,
              kernel_size=5,
              padding=2,
              groups=embed_dims)
  
          # a spatial long-range convolution (depth-wise dilation convolution)
          self.DW_D_conv = Conv2d(
              in_channels=embed_dims,
              out_channels=embed_dims,
              kernel_size=7,
              stride=1,
              padding=9,
              groups=embed_dims,
              dilation=3)
  
          self.conv1 = Conv2d(
              in_channels=embed_dims, out_channels=embed_dims, kernel_size=1)
  
      def forward(self, x):
          u = x.clone()
          attn = self.DW_conv(x)
          attn = self.DW_D_conv(attn)
          attn = self.conv1(attn)
  
          return u * attn
  
  
  class SpatialAttention(BaseModule):
      """Basic attention module in VANBloack.
  
      Args:
          embed_dims (int): Number of input channels.
          act_cfg (dict, optional): The activation config for FFNs.
              Default: dict(type='GELU').
          init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
              Default: None.
      """
  
      def __init__(self, embed_dims, act_cfg=dict(type='GELU'), init_cfg=None):
          super(SpatialAttention, self).__init__(init_cfg=init_cfg)
  
          self.proj_1 = Conv2d(
              in_channels=embed_dims, out_channels=embed_dims, kernel_size=1)
          self.activation = build_activation_layer(act_cfg)
          self.spatial_gating_unit = LKA(embed_dims)
          self.proj_2 = Conv2d(
              in_channels=embed_dims, out_channels=embed_dims, kernel_size=1)
  
      def forward(self, x):
          shorcut = x.clone()
          x = self.proj_1(x)
          x = self.activation(x)
          x = self.spatial_gating_unit(x)
          x = self.proj_2(x)
          x = x + shorcut
          return x
  
  
  class VANBlock(BaseModule):
      """A block of VAN.
  
      Args:
          embed_dims (int): Number of input channels.
          ffn_ratio (float): The expansion ratio of feedforward network hidden
              layer channels. Defaults to 4.
          drop_rate (float): Dropout rate after embedding. Defaults to 0.
          drop_path_rate (float): Stochastic depth rate. Defaults to 0.1.
          act_cfg (dict, optional): The activation config for FFNs.
              Default: dict(type='GELU').
          layer_scale_init_value (float): Init value for Layer Scale.
              Defaults to 1e-2.
          init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
              Default: None.
      """
  
      def __init__(self,
                   embed_dims,
                   ffn_ratio=4.,
                   drop_rate=0.,
                   drop_path_rate=0.,
                   act_cfg=dict(type='GELU'),
                   norm_cfg=dict(type='BN', eps=1e-5),
                   layer_scale_init_value=1e-2,
                   init_cfg=None):
          super(VANBlock, self).__init__(init_cfg=init_cfg)
          self.out_channels = embed_dims
  
          self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
          self.attn = SpatialAttention(embed_dims, act_cfg=act_cfg)
          self.drop_path = DropPath(
              drop_path_rate) if drop_path_rate > 0. else nn.Identity()
  
          self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
          mlp_hidden_dim = int(embed_dims * ffn_ratio)
          self.mlp = MixFFN(
              embed_dims=embed_dims,
              feedforward_channels=mlp_hidden_dim,
              act_cfg=act_cfg,
              ffn_drop=drop_rate)
          self.layer_scale_1 = nn.Parameter(
              layer_scale_init_value * torch.ones((embed_dims)),
              requires_grad=True) if layer_scale_init_value > 0 else None
          self.layer_scale_2 = nn.Parameter(
              layer_scale_init_value * torch.ones((embed_dims)),
              requires_grad=True) if layer_scale_init_value > 0 else None
  
      def forward(self, x):
          identity = x
          x = self.norm1(x)
          x = self.attn(x)
          if self.layer_scale_1 is not None:
              x = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * x
          x = identity + self.drop_path(x)
  
          identity = x
          x = self.norm2(x)
          x = self.mlp(x)
          if self.layer_scale_2 is not None:
              x = self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * x
          x = identity + self.drop_path(x)
  
          return x
  
  
  class VANPatchEmbed(PatchEmbed):
      """Image to Patch Embedding of VAN.
  
      The differences between VANPatchEmbed & PatchEmbed:
          1. Use BN.
          2. Do not use 'flatten' and 'transpose'.
      """
  
      def __init__(self, *args, norm_cfg=dict(type='BN'), **kwargs):
          super(VANPatchEmbed, self).__init__(*args, norm_cfg=norm_cfg, **kwargs)
  
      def forward(self, x):
          """
          Args:
              x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
          Returns:
              tuple: Contains merged results and its spatial shape.
              - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
              - out_size (tuple[int]): Spatial shape of x, arrange as
                (out_h, out_w).
          """
  
          if self.adaptive_padding:
              x = self.adaptive_padding(x)
  
          x = self.projection(x)
          out_size = (x.shape[2], x.shape[3])
          if self.norm is not None:
              x = self.norm(x)
          return x, out_size
  
  
  @BACKBONES.register_module()
  class VAN(BaseBackbone):
      """Visual Attention Network.
  
      A PyTorch implement of : `Visual Attention Network
      <https://arxiv.org/pdf/2202.09741v2.pdf>`_
  
      Inspiration from
      https://github.com/Visual-Attention-Network/VAN-Classification
  
      Args:
          arch (str | dict): Visual Attention Network architecture.
              If use string, choose from 'tiny', 'small', 'base' and 'large'.
              If use dict, it should have below keys:
  
              - **embed_dims** (List[int]): The dimensions of embedding.
              - **depths** (List[int]): The number of blocks in each stage.
              - **ffn_ratios** (List[int]): The number of expansion ratio of
                feedforward network hidden layer channels.
  
              Defaults to 'tiny'.
          patch_sizes (List[int | tuple]): The patch size in patch embeddings.
              Defaults to [7, 3, 3, 3].
          in_channels (int): The num of input channels. Defaults to 3.
          drop_rate (float): Dropout rate after embedding. Defaults to 0.
          drop_path_rate (float): Stochastic depth rate. Defaults to 0.1.
          out_indices (Sequence[int]): Output from which stages.
              Default: ``(3, )``.
          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
              -1 means not freezing any parameters. Defaults to -1.
          norm_eval (bool): Whether to set norm layers to eval mode, namely,
              freeze running stats (mean and var). Note: Effect on Batch Norm
              and its variants only. Defaults to False.
          norm_cfg (dict): Config dict for normalization layer for all output
              features. Defaults to ``dict(type='LN')``
          block_cfgs (Sequence[dict] | dict): The extra config of each block.
              Defaults to empty dicts.
          init_cfg (dict, optional): The Config for initialization.
              Defaults to None.
  
      Examples:
          >>> from mmcls.models import VAN
          >>> import torch
          >>> cfg = dict(arch='tiny')
          >>> model = VAN(**cfg)
          >>> inputs = torch.rand(1, 3, 224, 224)
          >>> outputs = model(inputs)
          >>> for out in outputs:
          >>>     print(out.size())
          (1, 256, 7, 7)
      """
      arch_zoo = {
          **dict.fromkeys(['t', 'tiny'],
                          {'embed_dims': [32, 64, 160, 256],
                           'depths': [3, 3, 5, 2],
                           'ffn_ratios': [8, 8, 4, 4]}),
          **dict.fromkeys(['s', 'small'],
                          {'embed_dims': [64, 128, 320, 512],
                           'depths': [2, 2, 4, 2],
                           'ffn_ratios': [8, 8, 4, 4]}),
          **dict.fromkeys(['b', 'base'],
                          {'embed_dims': [64, 128, 320, 512],
                           'depths': [3, 3, 12, 3],
                           'ffn_ratios': [8, 8, 4, 4]}),
          **dict.fromkeys(['l', 'large'],
                          {'embed_dims': [64, 128, 320, 512],
                           'depths': [3, 5, 27, 3],
                           'ffn_ratios': [8, 8, 4, 4]}),
      }  # yapf: disable
  
      def __init__(self,
                   arch='tiny',
                   patch_sizes=[7, 3, 3, 3],
                   in_channels=3,
                   drop_rate=0.,
                   drop_path_rate=0.,
                   out_indices=(3, ),
                   frozen_stages=-1,
                   norm_eval=False,
                   norm_cfg=dict(type='LN'),
                   block_cfgs=dict(),
                   init_cfg=None):
          super(VAN, self).__init__(init_cfg=init_cfg)
  
          if isinstance(arch, str):
              arch = arch.lower()
              assert arch in set(self.arch_zoo), \
                  f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
              self.arch_settings = self.arch_zoo[arch]
          else:
              essential_keys = {'embed_dims', 'depths', 'ffn_ratios'}
              assert isinstance(arch, dict) and set(arch) == essential_keys, \
                  f'Custom arch needs a dict with keys {essential_keys}'
              self.arch_settings = arch
  
          self.embed_dims = self.arch_settings['embed_dims']
          self.depths = self.arch_settings['depths']
          self.ffn_ratios = self.arch_settings['ffn_ratios']
          self.num_stages = len(self.depths)
          self.out_indices = out_indices
          self.frozen_stages = frozen_stages
          self.norm_eval = norm_eval
  
          total_depth = sum(self.depths)
          dpr = [
              x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
          ]  # stochastic depth decay rule
  
          cur_block_idx = 0
          for i, depth in enumerate(self.depths):
              patch_embed = VANPatchEmbed(
                  in_channels=in_channels if i == 0 else self.embed_dims[i - 1],
                  input_size=None,
                  embed_dims=self.embed_dims[i],
                  kernel_size=patch_sizes[i],
                  stride=patch_sizes[i] // 2 + 1,
                  padding=(patch_sizes[i] // 2, patch_sizes[i] // 2),
                  norm_cfg=dict(type='BN'))
  
              blocks = ModuleList([
                  VANBlock(
                      embed_dims=self.embed_dims[i],
                      ffn_ratio=self.ffn_ratios[i],
                      drop_rate=drop_rate,
                      drop_path_rate=dpr[cur_block_idx + j],
                      **block_cfgs) for j in range(depth)
              ])
              cur_block_idx += depth
              norm = build_norm_layer(norm_cfg, self.embed_dims[i])[1]
  
              self.add_module(f'patch_embed{i + 1}', patch_embed)
              self.add_module(f'blocks{i + 1}', blocks)
              self.add_module(f'norm{i + 1}', norm)
  
      def train(self, mode=True):
          super(VAN, self).train(mode)
          self._freeze_stages()
          if mode and self.norm_eval:
              for m in self.modules():
                  # trick: eval have effect on BatchNorm only
                  if isinstance(m, _BatchNorm):
                      m.eval()
  
      def _freeze_stages(self):
          for i in range(0, self.frozen_stages + 1):
              # freeze patch embed
              m = getattr(self, f'patch_embed{i + 1}')
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
              # freeze blocks
              m = getattr(self, f'blocks{i + 1}')
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
              # freeze norm
              m = getattr(self, f'norm{i + 1}')
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
      def forward(self, x):
          outs = []
          for i in range(self.num_stages):
              patch_embed = getattr(self, f'patch_embed{i + 1}')
              blocks = getattr(self, f'blocks{i + 1}')
              norm = getattr(self, f'norm{i + 1}')
              x, hw_shape = patch_embed(x)
              for block in blocks:
                  x = block(x)
              x = x.flatten(2).transpose(1, 2)
              x = norm(x)
              x = x.reshape(-1, *hw_shape,
                            block.out_channels).permute(0, 3, 1, 2).contiguous()
              if i in self.out_indices:
                  outs.append(x)
  
          return tuple(outs)
  