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108 lines
4.0 KiB
108 lines
4.0 KiB
# Copyright (c) 2022 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 paddlers.models.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=None, |
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embed_type='sine', |
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num_embeddings=50, |
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offset=0.): |
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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 = 1e-6 |
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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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if scale is not None and normalize is False: |
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raise ValueError("normalize should be True if scale is passed") |
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if scale is None: |
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scale = 2 * math.pi |
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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"not supported {self.embed_type}") |
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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, C, H, W] |
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""" |
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assert mask.dtype == paddle.bool |
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if self.embed_type == 'sine': |
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mask = mask.astype('float32') |
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y_embed = mask.cumsum(1, dtype='float32') |
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x_embed = mask.cumsum(2, dtype='float32') |
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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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pos = paddle.concat((pos_y, pos_x), axis=3).transpose([0, 3, 1, 2]) |
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return pos |
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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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pos = paddle.concat( |
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[ |
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x_emb.unsqueeze(0).repeat(h, 1, 1), |
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y_emb.unsqueeze(1).repeat(1, w, 1), |
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], |
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axis=-1).transpose([2, 0, 1]).unsqueeze(0).tile(mask.shape[0], |
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1, 1, 1) |
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return pos |
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else: |
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raise ValueError(f"not supported {self.embed_type}")
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