# 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. # # Modified from DETR (https://github.com/facebookresearch/detr) # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import paddle import paddle.nn as nn from ppdet.core.workspace import register, serializable @register @serializable class PositionEmbedding(nn.Layer): def __init__(self, num_pos_feats=128, temperature=10000, normalize=True, scale=2 * math.pi, embed_type='sine', num_embeddings=50, offset=0., eps=1e-6): super(PositionEmbedding, self).__init__() assert embed_type in ['sine', 'learned'] self.embed_type = embed_type self.offset = offset self.eps = eps if self.embed_type == 'sine': self.num_pos_feats = num_pos_feats self.temperature = temperature self.normalize = normalize self.scale = scale elif self.embed_type == 'learned': self.row_embed = nn.Embedding(num_embeddings, num_pos_feats) self.col_embed = nn.Embedding(num_embeddings, num_pos_feats) else: raise ValueError(f"{self.embed_type} is not supported.") def forward(self, mask): """ Args: mask (Tensor): [B, H, W] Returns: pos (Tensor): [B, H, W, C] """ if self.embed_type == 'sine': y_embed = mask.cumsum(1) x_embed = mask.cumsum(2) if self.normalize: y_embed = (y_embed + self.offset) / ( y_embed[:, -1:, :] + self.eps) * self.scale x_embed = (x_embed + self.offset) / ( x_embed[:, :, -1:] + self.eps) * self.scale dim_t = 2 * (paddle.arange(self.num_pos_feats) // 2).astype('float32') dim_t = self.temperature**(dim_t / self.num_pos_feats) pos_x = x_embed.unsqueeze(-1) / dim_t pos_y = y_embed.unsqueeze(-1) / dim_t pos_x = paddle.stack( (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), axis=4).flatten(3) pos_y = paddle.stack( (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), axis=4).flatten(3) return paddle.concat((pos_y, pos_x), axis=3) elif self.embed_type == 'learned': h, w = mask.shape[-2:] i = paddle.arange(w) j = paddle.arange(h) x_emb = self.col_embed(i) y_emb = self.row_embed(j) return paddle.concat( [ x_emb.unsqueeze(0).tile([h, 1, 1]), y_emb.unsqueeze(1).tile([1, w, 1]), ], axis=-1).unsqueeze(0) else: raise ValueError(f"not supported {self.embed_type}")