text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import math
  import paddle
  import paddle.nn as nn
  import paddle.nn.functional as F
  from paddle import ParamAttr
  
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from .position_encoding import PositionEmbedding
  from .utils import _get_clones, deformable_attention_core_func
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_
  
  __all__ = ['DeformableTransformer']
  
  
  class MSDeformableAttention(nn.Layer):
      def __init__(self,
                   embed_dim=256,
                   num_heads=8,
                   num_levels=4,
                   num_points=4,
                   lr_mult=0.1):
          """
          Multi-Scale Deformable Attention Module
          """
          super(MSDeformableAttention, self).__init__()
          self.embed_dim = embed_dim
          self.num_heads = num_heads
          self.num_levels = num_levels
          self.num_points = num_points
          self.total_points = num_heads * num_levels * num_points
  
          self.head_dim = embed_dim // num_heads
          assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
  
          self.sampling_offsets = nn.Linear(
              embed_dim,
              self.total_points * 2,
              weight_attr=ParamAttr(learning_rate=lr_mult),
              bias_attr=ParamAttr(learning_rate=lr_mult))
  
          self.attention_weights = nn.Linear(embed_dim, self.total_points)
          self.value_proj = nn.Linear(embed_dim, embed_dim)
          self.output_proj = nn.Linear(embed_dim, embed_dim)
  
          self._reset_parameters()
  
      def _reset_parameters(self):
          # sampling_offsets
          constant_(self.sampling_offsets.weight)
          thetas = paddle.arange(
              self.num_heads,
              dtype=paddle.float32) * (2.0 * math.pi / self.num_heads)
          grid_init = paddle.stack([thetas.cos(), thetas.sin()], -1)
          grid_init = grid_init / grid_init.abs().max(-1, keepdim=True)
          grid_init = grid_init.reshape([self.num_heads, 1, 1, 2]).tile(
              [1, self.num_levels, self.num_points, 1])
          scaling = paddle.arange(
              1, self.num_points + 1,
              dtype=paddle.float32).reshape([1, 1, -1, 1])
          grid_init *= scaling
          self.sampling_offsets.bias.set_value(grid_init.flatten())
          # attention_weights
          constant_(self.attention_weights.weight)
          constant_(self.attention_weights.bias)
          # proj
          xavier_uniform_(self.value_proj.weight)
          constant_(self.value_proj.bias)
          xavier_uniform_(self.output_proj.weight)
          constant_(self.output_proj.bias)
  
      def forward(self,
                  query,
                  reference_points,
                  value,
                  value_spatial_shapes,
                  value_mask=None):
          """
          Args:
              query (Tensor): [bs, query_length, C]
              reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
                  bottom-right (1, 1), including padding area
              value (Tensor): [bs, value_length, C]
              value_spatial_shapes (Tensor): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
              value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements
  
          Returns:
              output (Tensor): [bs, Length_{query}, C]
          """
          bs, Len_q = query.shape[:2]
          Len_v = value.shape[1]
          assert int(value_spatial_shapes.prod(1).sum()) == Len_v
  
          value = self.value_proj(value)
          if value_mask is not None:
              value_mask = value_mask.astype(value.dtype).unsqueeze(-1)
              value *= value_mask
          value = value.reshape([bs, Len_v, self.num_heads, self.head_dim])
  
          sampling_offsets = self.sampling_offsets(query).reshape(
              [bs, Len_q, self.num_heads, self.num_levels, self.num_points, 2])
          attention_weights = self.attention_weights(query).reshape(
              [bs, Len_q, self.num_heads, self.num_levels * self.num_points])
          attention_weights = F.softmax(attention_weights, -1).reshape(
              [bs, Len_q, self.num_heads, self.num_levels, self.num_points])
  
          offset_normalizer = value_spatial_shapes.flip([1]).reshape(
              [1, 1, 1, self.num_levels, 1, 2])
          sampling_locations = reference_points.reshape([
              bs, Len_q, 1, self.num_levels, 1, 2
          ]) + sampling_offsets / offset_normalizer
  
          output = deformable_attention_core_func(
              value, value_spatial_shapes, sampling_locations, attention_weights)
          output = self.output_proj(output)
  
          return output
  
  
  class DeformableTransformerEncoderLayer(nn.Layer):
      def __init__(self,
                   d_model=256,
                   n_head=8,
                   dim_feedforward=1024,
                   dropout=0.1,
                   activation="relu",
                   n_levels=4,
                   n_points=4,
                   weight_attr=None,
                   bias_attr=None):
          super(DeformableTransformerEncoderLayer, self).__init__()
          # self attention
          self.self_attn = MSDeformableAttention(d_model, n_head, n_levels,
                                                 n_points)
          self.dropout1 = nn.Dropout(dropout)
          self.norm1 = nn.LayerNorm(d_model)
          # ffn
          self.linear1 = nn.Linear(d_model, dim_feedforward, weight_attr,
                                   bias_attr)
          self.activation = getattr(F, activation)
          self.dropout2 = nn.Dropout(dropout)
          self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr,
                                   bias_attr)
          self.dropout3 = nn.Dropout(dropout)
          self.norm2 = nn.LayerNorm(d_model)
          self._reset_parameters()
  
      def _reset_parameters(self):
          linear_init_(self.linear1)
          linear_init_(self.linear2)
          xavier_uniform_(self.linear1.weight)
          xavier_uniform_(self.linear2.weight)
  
      def with_pos_embed(self, tensor, pos):
          return tensor if pos is None else tensor + pos
  
      def forward_ffn(self, src):
          src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
          src = src + self.dropout3(src2)
          src = self.norm2(src)
          return src
  
      def forward(self,
                  src,
                  reference_points,
                  spatial_shapes,
                  src_mask=None,
                  pos_embed=None):
          # self attention
          src2 = self.self_attn(
              self.with_pos_embed(src, pos_embed), reference_points, src,
              spatial_shapes, src_mask)
          src = src + self.dropout1(src2)
          src = self.norm1(src)
          # ffn
          src = self.forward_ffn(src)
  
          return src
  
  
  class DeformableTransformerEncoder(nn.Layer):
      def __init__(self, encoder_layer, num_layers):
          super(DeformableTransformerEncoder, self).__init__()
          self.layers = _get_clones(encoder_layer, num_layers)
          self.num_layers = num_layers
  
      @staticmethod
      def get_reference_points(spatial_shapes, valid_ratios):
          valid_ratios = valid_ratios.unsqueeze(1)
          reference_points = []
          for i, (H, W) in enumerate(spatial_shapes.tolist()):
              ref_y, ref_x = paddle.meshgrid(
                  paddle.linspace(0.5, H - 0.5, H),
                  paddle.linspace(0.5, W - 0.5, W))
              ref_y = ref_y.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 1] *
                                                      H)
              ref_x = ref_x.flatten().unsqueeze(0) / (valid_ratios[:, :, i, 0] *
                                                      W)
              reference_points.append(paddle.stack((ref_x, ref_y), axis=-1))
          reference_points = paddle.concat(reference_points, 1).unsqueeze(2)
          reference_points = reference_points * valid_ratios
          return reference_points
  
      def forward(self,
                  src,
                  spatial_shapes,
                  src_mask=None,
                  pos_embed=None,
                  valid_ratios=None):
          output = src
          if valid_ratios is None:
              valid_ratios = paddle.ones(
                  [src.shape[0], spatial_shapes.shape[0], 2])
          reference_points = self.get_reference_points(spatial_shapes,
                                                       valid_ratios)
          for layer in self.layers:
              output = layer(output, reference_points, spatial_shapes, src_mask,
                             pos_embed)
  
          return output
  
  
  class DeformableTransformerDecoderLayer(nn.Layer):
      def __init__(self,
                   d_model=256,
                   n_head=8,
                   dim_feedforward=1024,
                   dropout=0.1,
                   activation="relu",
                   n_levels=4,
                   n_points=4,
                   weight_attr=None,
                   bias_attr=None):
          super(DeformableTransformerDecoderLayer, self).__init__()
  
          # self attention
          self.self_attn = MultiHeadAttention(d_model, n_head, dropout=dropout)
          self.dropout1 = nn.Dropout(dropout)
          self.norm1 = nn.LayerNorm(d_model)
  
          # cross attention
          self.cross_attn = MSDeformableAttention(d_model, n_head, n_levels,
                                                  n_points)
          self.dropout2 = nn.Dropout(dropout)
          self.norm2 = nn.LayerNorm(d_model)
  
          # ffn
          self.linear1 = nn.Linear(d_model, dim_feedforward, weight_attr,
                                   bias_attr)
          self.activation = getattr(F, activation)
          self.dropout3 = nn.Dropout(dropout)
          self.linear2 = nn.Linear(dim_feedforward, d_model, weight_attr,
                                   bias_attr)
          self.dropout4 = nn.Dropout(dropout)
          self.norm3 = nn.LayerNorm(d_model)
          self._reset_parameters()
  
      def _reset_parameters(self):
          linear_init_(self.linear1)
          linear_init_(self.linear2)
          xavier_uniform_(self.linear1.weight)
          xavier_uniform_(self.linear2.weight)
  
      def with_pos_embed(self, tensor, pos):
          return tensor if pos is None else tensor + pos
  
      def forward_ffn(self, tgt):
          tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
          tgt = tgt + self.dropout4(tgt2)
          tgt = self.norm3(tgt)
          return tgt
  
      def forward(self,
                  tgt,
                  reference_points,
                  memory,
                  memory_spatial_shapes,
                  memory_mask=None,
                  query_pos_embed=None):
          # self attention
          q = k = self.with_pos_embed(tgt, query_pos_embed)
          tgt2 = self.self_attn(q, k, value=tgt)
          tgt = tgt + self.dropout1(tgt2)
          tgt = self.norm1(tgt)
  
          # cross attention
          tgt2 = self.cross_attn(
              self.with_pos_embed(tgt, query_pos_embed), reference_points, memory,
              memory_spatial_shapes, memory_mask)
          tgt = tgt + self.dropout2(tgt2)
          tgt = self.norm2(tgt)
  
          # ffn
          tgt = self.forward_ffn(tgt)
  
          return tgt
  
  
  class DeformableTransformerDecoder(nn.Layer):
      def __init__(self, decoder_layer, num_layers, return_intermediate=False):
          super(DeformableTransformerDecoder, self).__init__()
          self.layers = _get_clones(decoder_layer, num_layers)
          self.num_layers = num_layers
          self.return_intermediate = return_intermediate
  
      def forward(self,
                  tgt,
                  reference_points,
                  memory,
                  memory_spatial_shapes,
                  memory_mask=None,
                  query_pos_embed=None):
          output = tgt
          intermediate = []
          for lid, layer in enumerate(self.layers):
              output = layer(output, reference_points, memory,
                             memory_spatial_shapes, memory_mask, query_pos_embed)
  
              if self.return_intermediate:
                  intermediate.append(output)
  
          if self.return_intermediate:
              return paddle.stack(intermediate)
  
          return output.unsqueeze(0)
  
  
  @register
  class DeformableTransformer(nn.Layer):
      __shared__ = ['hidden_dim']
  
      def __init__(self,
                   num_queries=300,
                   position_embed_type='sine',
                   return_intermediate_dec=True,
                   backbone_num_channels=[512, 1024, 2048],
                   num_feature_levels=4,
                   num_encoder_points=4,
                   num_decoder_points=4,
                   hidden_dim=256,
                   nhead=8,
                   num_encoder_layers=6,
                   num_decoder_layers=6,
                   dim_feedforward=1024,
                   dropout=0.1,
                   activation="relu",
                   lr_mult=0.1,
                   weight_attr=None,
                   bias_attr=None):
          super(DeformableTransformer, self).__init__()
          assert position_embed_type in ['sine', 'learned'], \
              f'ValueError: position_embed_type not supported {position_embed_type}!'
          assert len(backbone_num_channels) <= num_feature_levels
  
          self.hidden_dim = hidden_dim
          self.nhead = nhead
          self.num_feature_levels = num_feature_levels
  
          encoder_layer = DeformableTransformerEncoderLayer(
              hidden_dim, nhead, dim_feedforward, dropout, activation,
              num_feature_levels, num_encoder_points, weight_attr, bias_attr)
          self.encoder = DeformableTransformerEncoder(encoder_layer,
                                                      num_encoder_layers)
  
          decoder_layer = DeformableTransformerDecoderLayer(
              hidden_dim, nhead, dim_feedforward, dropout, activation,
              num_feature_levels, num_decoder_points, weight_attr, bias_attr)
          self.decoder = DeformableTransformerDecoder(
              decoder_layer, num_decoder_layers, return_intermediate_dec)
  
          self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
          self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
          self.query_pos_embed = nn.Embedding(num_queries, hidden_dim)
  
          self.reference_points = nn.Linear(
              hidden_dim,
              2,
              weight_attr=ParamAttr(learning_rate=lr_mult),
              bias_attr=ParamAttr(learning_rate=lr_mult))
  
          self.input_proj = nn.LayerList()
          for in_channels in backbone_num_channels:
              self.input_proj.append(
                  nn.Sequential(
                      nn.Conv2D(
                          in_channels,
                          hidden_dim,
                          kernel_size=1,
                          weight_attr=weight_attr,
                          bias_attr=bias_attr),
                      nn.GroupNorm(32, hidden_dim)))
          in_channels = backbone_num_channels[-1]
          for _ in range(num_feature_levels - len(backbone_num_channels)):
              self.input_proj.append(
                  nn.Sequential(
                      nn.Conv2D(
                          in_channels,
                          hidden_dim,
                          kernel_size=3,
                          stride=2,
                          padding=1,
                          weight_attr=weight_attr,
                          bias_attr=bias_attr),
                      nn.GroupNorm(32, hidden_dim)))
              in_channels = hidden_dim
  
          self.position_embedding = PositionEmbedding(
              hidden_dim // 2,
              normalize=True if position_embed_type == 'sine' else False,
              embed_type=position_embed_type,
              offset=-0.5)
  
          self._reset_parameters()
  
      def _reset_parameters(self):
          normal_(self.level_embed.weight)
          normal_(self.tgt_embed.weight)
          normal_(self.query_pos_embed.weight)
          xavier_uniform_(self.reference_points.weight)
          constant_(self.reference_points.bias)
          for l in self.input_proj:
              xavier_uniform_(l[0].weight)
              constant_(l[0].bias)
  
      @classmethod
      def from_config(cls, cfg, input_shape):
          return {'backbone_num_channels': [i.channels for i in input_shape], }
  
      def _get_valid_ratio(self, mask):
          mask = mask.astype(paddle.float32)
          _, H, W = mask.shape
          valid_ratio_h = paddle.sum(mask[:, :, 0], 1) / H
          valid_ratio_w = paddle.sum(mask[:, 0, :], 1) / W
          valid_ratio = paddle.stack([valid_ratio_w, valid_ratio_h], -1)
          return valid_ratio
  
      def forward(self, src_feats, src_mask=None):
          srcs = []
          for i in range(len(src_feats)):
              srcs.append(self.input_proj[i](src_feats[i]))
          if self.num_feature_levels > len(srcs):
              len_srcs = len(srcs)
              for i in range(len_srcs, self.num_feature_levels):
                  if i == len_srcs:
                      srcs.append(self.input_proj[i](src_feats[-1]))
                  else:
                      srcs.append(self.input_proj[i](srcs[-1]))
          src_flatten = []
          mask_flatten = []
          lvl_pos_embed_flatten = []
          spatial_shapes = []
          valid_ratios = []
          for level, src in enumerate(srcs):
              bs, c, h, w = src.shape
              spatial_shapes.append([h, w])
              src = src.flatten(2).transpose([0, 2, 1])
              src_flatten.append(src)
              if src_mask is not None:
                  mask = F.interpolate(
                      src_mask.unsqueeze(0).astype(src.dtype),
                      size=(h, w))[0].astype('bool')
              else:
                  mask = paddle.ones([bs, h, w], dtype='bool')
              valid_ratios.append(self._get_valid_ratio(mask))
              pos_embed = self.position_embedding(mask).flatten(2).transpose(
                  [0, 2, 1])
              lvl_pos_embed = pos_embed + self.level_embed.weight[level].reshape(
                  [1, 1, -1])
              lvl_pos_embed_flatten.append(lvl_pos_embed)
              mask = mask.astype(src.dtype).flatten(1)
              mask_flatten.append(mask)
          src_flatten = paddle.concat(src_flatten, 1)
          mask_flatten = paddle.concat(mask_flatten, 1)
          lvl_pos_embed_flatten = paddle.concat(lvl_pos_embed_flatten, 1)
          # [l, 2]
          spatial_shapes = paddle.to_tensor(spatial_shapes, dtype='int64')
          # [b, l, 2]
          valid_ratios = paddle.stack(valid_ratios, 1)
  
          # encoder
          memory = self.encoder(src_flatten, spatial_shapes, mask_flatten,
                                lvl_pos_embed_flatten, valid_ratios)
  
          # prepare input for decoder
          bs, _, c = memory.shape
          query_embed = self.query_pos_embed.weight.unsqueeze(0).tile([bs, 1, 1])
          tgt = self.tgt_embed.weight.unsqueeze(0).tile([bs, 1, 1])
          reference_points = F.sigmoid(self.reference_points(query_embed))
          reference_points_input = reference_points.unsqueeze(
              2) * valid_ratios.unsqueeze(1)
  
          # decoder
          hs = self.decoder(tgt, reference_points_input, memory, spatial_shapes,
                            mask_flatten, query_embed)
  
          return (hs, memory, reference_points)
  