101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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import paddle.nn as nn
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from ppdet.core.workspace import register, serializable
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@register
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@serializable
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class PositionEmbedding(nn.Layer):
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def __init__(self,
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num_pos_feats=128,
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temperature=10000,
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normalize=True,
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scale=2 * math.pi,
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embed_type='sine',
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num_embeddings=50,
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offset=0.,
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eps=1e-6):
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super(PositionEmbedding, self).__init__()
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assert embed_type in ['sine', 'learned']
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self.embed_type = embed_type
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self.offset = offset
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self.eps = eps
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if self.embed_type == 'sine':
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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self.scale = scale
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elif self.embed_type == 'learned':
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self.row_embed = nn.Embedding(num_embeddings, num_pos_feats)
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self.col_embed = nn.Embedding(num_embeddings, num_pos_feats)
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else:
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raise ValueError(f"{self.embed_type} is not supported.")
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def forward(self, mask):
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"""
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Args:
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mask (Tensor): [B, H, W]
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Returns:
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pos (Tensor): [B, H, W, C]
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"""
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if self.embed_type == 'sine':
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y_embed = mask.cumsum(1)
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x_embed = mask.cumsum(2)
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if self.normalize:
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y_embed = (y_embed + self.offset) / (
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y_embed[:, -1:, :] + self.eps) * self.scale
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x_embed = (x_embed + self.offset) / (
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x_embed[:, :, -1:] + self.eps) * self.scale
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dim_t = 2 * (paddle.arange(self.num_pos_feats) //
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2).astype('float32')
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dim_t = self.temperature**(dim_t / self.num_pos_feats)
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pos_x = x_embed.unsqueeze(-1) / dim_t
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pos_y = y_embed.unsqueeze(-1) / dim_t
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pos_x = paddle.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
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axis=4).flatten(3)
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pos_y = paddle.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
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axis=4).flatten(3)
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return paddle.concat((pos_y, pos_x), axis=3)
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elif self.embed_type == 'learned':
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h, w = mask.shape[-2:]
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i = paddle.arange(w)
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j = paddle.arange(h)
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x_emb = self.col_embed(i)
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y_emb = self.row_embed(j)
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return paddle.concat(
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[
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x_emb.unsqueeze(0).tile([h, 1, 1]),
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y_emb.unsqueeze(1).tile([1, w, 1]),
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],
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axis=-1).unsqueeze(0)
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else:
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raise ValueError(f"not supported {self.embed_type}")
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