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rtdetr_paddle/ppdet/modeling/transformers/position_encoding.py

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+# 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}")