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- #!/usr/bin/python
- # -*- coding: utf-8 -*-
-
- # __author__="Danqing Wang"
-
- #
- # 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.
- # ==============================================================================
-
- import torch
- import numpy as np
-
- def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
- ''' Sinusoid position encoding table '''
-
- def cal_angle(position, hid_idx):
- return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)
-
- def get_posi_angle_vec(position):
- return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
-
- sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])
-
- sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
- sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
-
- if padding_idx is not None:
- # zero vector for padding dimension
- sinusoid_table[padding_idx] = 0.
-
- return torch.FloatTensor(sinusoid_table)
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