Source code for towhee.models.layers.position_encoding

# Copyright 2022 Zilliz. 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# 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.
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# limitations under the License.
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# Original code from https://github.com/jhcho99/CoFormer.
#
# Modified by Zilliz.

"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn

from towhee.models.coformer.utils import NestedTensor


[docs]class PositionEmbedding(nn.Module): """ Standard positional encoding """
[docs] def __init__(self, d_model, max_len=5000, dropout=0.1): super().__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer('pe', pe)
[docs] def forward(self, x): if isinstance(x, NestedTensor): x = x.tensors x = x + self.pe[:x.size(0), :] x = self.dropout(x) return x
[docs]class PositionEmbeddingLearned(nn.Module): """ Absolute pos embedding, learned. """
[docs] def __init__(self, num_pos_feats=256): super().__init__() self.row_embed = nn.Embedding(50, num_pos_feats) self.col_embed = nn.Embedding(50, num_pos_feats) self.reset_parameters()
def reset_parameters(self): nn.init.uniform_(self.row_embed.weight) nn.init.uniform_(self.col_embed.weight)
[docs] def forward(self, x): if isinstance(x, NestedTensor): x = x.tensors h, w = x.shape[-2:] i = torch.arange(w, device=x.device) j = torch.arange(h, device=x.device) x_emb = self.col_embed(i) y_emb = self.row_embed(j) pos = torch.cat([ x_emb.unsqueeze(0).repeat(h, 1, 1), y_emb.unsqueeze(1).repeat(1, w, 1), ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) return pos
[docs]def build_position_encoding( hidden_dim=512, position_embedding='sine', max_len=5000, dropout=0. ): n_steps = hidden_dim // 2 if position_embedding in 'sine': position_embedding = PositionEmbedding(n_steps, max_len=max_len, dropout=dropout) elif position_embedding in 'learned': position_embedding = PositionEmbeddingLearned(n_steps) else: raise ValueError(f'not supported {position_embedding}') return position_embedding