2 |
3 |
2 |
3 |
12 |
13 | 
32 |
33 | This repository contains only model code.
34 |
35 | ## Installation
36 | This project recommends Python 3.7 or higher.
37 | We recommend creating a new virtual environment for this project (using virtual env or conda).
38 |
39 | ### Prerequisites
40 |
42 |
43 | ### Install from source
44 |
50 |
51 | ## Usage
52 |
53 |
83 |
84 | ## Troubleshoots and Contributing
85 |
86 | If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/tuanio/nextformer/issues) on github or
87 | contacts nvatuan3@gmail.com please.
88 |
89 | I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.
90 |
91 | ## Code Style
92 |
93 | I follow [Black](https://black.readthedocs.io/en/stable/) for code style. Especially the style of docstrings is important to generate documentation.
94 |
95 | ## Reference
96 |
97 | - [Nextformer: A ConvNeXt Augmented Conformer For End-To-End Speech Recognition](https://arxiv.org/abs/2206.14747)
98 | - [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
99 | - [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/pdf/2005.08100.pdf)
100 | - [sooftware/conformer](https://github.com/sooftware/conformer)
101 | - [facebookresearch/ConvNeXt](https://github.com/facebookresearch/ConvNeXt)
102 |
103 | ## Author
104 |
105 | - Nguyen Van Anh Tuan [@tuanio](https://github.com/tuanio)
106 | - Contacts: nvatuan3@gmail.com
107 |
--------------------------------------------------------------------------------
/nextformer/convolution.py:
--------------------------------------------------------------------------------
1 | # Copyright 2022 Nguyen Van Anh Tuan
2 |
3 | # Licensed under the Apache License, Version 2.0 (the "License");
4 | # you may not use this file except in compliance with the License.
5 | # You may obtain a copy of the License at
6 |
7 | # http://www.apache.org/licenses/LICENSE-2.0
8 |
9 | # Unless required by applicable law or agreed to in writing, software
10 | # distributed under the License is distributed on an "AS IS" BASIS,
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 | # See the License for the specific language governing permissions and
13 | # limitations under the License.
14 |
15 |
16 | from .utils import Transpose
17 |
18 |
19 | class DepthwiseConv1d(nn.Module):
20 | """
21 | When groups == in_channels and out_channels == K * in_channels, where K is a positive integer,
22 | this operation is termed in literature as depthwise convolution.
23 | Args:
24 | in_channels (int): Number of channels in the input
25 | out_channels (int): Number of channels produced by the convolution
26 | kernel_size (int or tuple): Size of the convolving kernel
27 | stride (int, optional): Stride of the convolution. Default: 1
28 | padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
29 | bias (bool, optional): If True, adds a learnable bias to the output. Default: True
30 | Inputs: inputs
31 | - **inputs** (batch, in_channels, time): Tensor containing input vector
32 | Returns: outputs
33 | - **outputs** (batch, out_channels, time): Tensor produces by depthwise 1-D convolution.
34 | """
35 |
36 | def __init__(
37 | self,
38 | in_channels: int,
39 | out_channels: int,
40 | kernel_size: int,
41 | stride: int = 1,
42 | padding: int = 0,
43 | bias: bool = False,
44 | ) -> None:
45 | super(DepthwiseConv1d, self).__init__()
46 | assert (
47 | out_channels % in_channels == 0
48 | ), "out_channels should be constant multiple of in_channels"
49 | self.conv = nn.Conv1d(
50 | in_channels=in_channels,
51 | out_channels=out_channels,
52 | kernel_size=kernel_size,
53 | groups=in_channels,
54 | stride=stride,
55 | padding=padding,
56 | bias=bias,
57 | )
58 |
59 | def forward(self, inputs: Tensor) -> Tensor:
60 | return self.conv(inputs)
61 |
62 |
63 | class PointwiseConv1d(nn.Module):
64 | """
65 | When kernel size == 1 conv1d, this operation is termed in literature as pointwise convolution.
66 | This operation often used to match dimensions.
67 | Args:
68 | in_channels (int): Number of channels in the input
69 | out_channels (int): Number of channels produced by the convolution
70 | stride (int, optional): Stride of the convolution. Default: 1
71 | padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
72 | bias (bool, optional): If True, adds a learnable bias to the output. Default: True
73 | Inputs: inputs
74 | - **inputs** (batch, in_channels, time): Tensor containing input vector
75 | Returns: outputs
76 | - **outputs** (batch, out_channels, time): Tensor produces by pointwise 1-D convolution.
77 | """
78 |
79 | def __init__(
80 | self,
81 | in_channels: int,
82 | out_channels: int,
83 | stride: int = 1,
84 | padding: int = 0,
85 | bias: bool = True,
86 | ) -> None:
87 | super(PointwiseConv1d, self).__init__()
88 | self.conv = nn.Conv1d(
89 | in_channels=in_channels,
90 | out_channels=out_channels,
91 | kernel_size=1,
92 | stride=stride,
93 | padding=padding,
94 | bias=bias,
95 | )
96 |
97 | def forward(self, inputs: Tensor) -> Tensor:
98 | return self.conv(inputs)
99 |
100 |
101 | class ConvolutionModule(nn.Module):
102 | """
103 | Conformer convolution module starts with a pointwise convolution and a gated linear unit (GLU).
104 | This is followed by a single 1-D depthwise convolution layer. Batchnorm is deployed just after the convolution
105 | to aid training deep models.
106 | Args:
107 | in_channels (int): Number of channels in the input
108 | kernel_size (int or tuple, optional): Size of the convolving kernel Default: 31
109 | dropout_p (float, optional): probability of dropout
110 | Inputs: inputs
111 | inputs (batch, time, dim): Tensor contains input sequences
112 | Outputs: outputs
113 | outputs (batch, time, dim): Tensor produces by conformer convolution module.
114 | """
115 |
116 | def __init__(
117 | self,
118 | in_channels: int,
119 | kernel_size: int = 31,
120 | expansion_factor: int = 2,
121 | dropout_p: float = 0.1,
122 | ) -> None:
123 | super(ConformerConvModule, self).__init__()
124 | assert (
125 | kernel_size - 1
126 | ) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
127 | assert expansion_factor == 2, "Currently, Only Supports expansion_factor 2"
128 |
129 | self.sequential = nn.Sequential(
130 | nn.LayerNorm(in_channels),
131 | Transpose(shape=(1, 2)),
132 | PointwiseConv1d(
133 | in_channels,
134 | in_channels * expansion_factor,
135 | stride=1,
136 | padding=0,
137 | bias=True,
138 | ),
139 | nn.GLU(dim=1),
140 | DepthwiseConv1d(
141 | in_channels,
142 | in_channels,
143 | kernel_size,
144 | stride=1,
145 | padding=(kernel_size - 1) // 2,
146 | ),
147 | nn.BatchNorm1d(in_channels),
148 | nn.SiLU(),
149 | PointwiseConv1d(in_channels, in_channels, stride=1, padding=0, bias=True),
150 | nn.Dropout(p=dropout_p),
151 | )
152 |
153 | def forward(self, inputs: Tensor) -> Tensor:
154 | return self.sequential(inputs).transpose(1, 2)
155 |
--------------------------------------------------------------------------------
/nextformer/attention.py:
--------------------------------------------------------------------------------
1 | # Copyright 2022 Nguyen Van Anh Tuan
2 |
3 | # Licensed under the Apache License, Version 2.0 (the "License");
4 | # you may not use this file except in compliance with the License.
5 | # You may obtain a copy of the License at
6 |
7 | # http://www.apache.org/licenses/LICENSE-2.0
8 |
9 | # Unless required by applicable law or agreed to in writing, software
10 | # distributed under the License is distributed on an "AS IS" BASIS,
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 | # See the License for the specific language governing permissions and
13 | # limitations under the License.
14 |
15 | import math
16 | import torch
17 | import torch.nn as nn
18 | from torch import Tensor
19 |
20 |
21 | class PositionalEncoding(nn.Module):
22 | """
23 | Positional Encoding proposed in "Attention Is All You Need".
24 | Since transformer contains no recurrence and no convolution, in order for the model to make
25 | use of the order of the sequence, we must add some positional information.
26 | "Attention Is All You Need" use sine and cosine functions of different frequencies:
27 | PE_(pos, 2i) = sin(pos / power(10000, 2i / d_model))
28 | PE_(pos, 2i+1) = cos(pos / power(10000, 2i / d_model))
29 | """
30 |
31 | def __init__(self, d_model: int = 512, max_len: int = 10000) -> None:
32 | super(PositionalEncoding, self).__init__()
33 | pe = torch.zeros(max_len, d_model, requires_grad=False)
34 | position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
35 | div_term = torch.exp(
36 | torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
37 | )
38 | pe[:, 0::2] = torch.sin(position * div_term)
39 | pe[:, 1::2] = torch.cos(position * div_term)
40 | pe = pe.unsqueeze(0)
41 | self.register_buffer("pe", pe)
42 |
43 | def forward(self, length: int) -> Tensor:
44 | return self.pe[:, :length]
45 |
46 |
47 | class RelativeMultiHeadAttention(nn.Module):
48 | """
49 | Multi-head attention with relative positional encoding.
50 | This concept was proposed in the "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
51 | Args:
52 | d_model (int): The dimension of model
53 | num_heads (int): The number of attention heads.
54 | dropout_p (float): probability of dropout
55 | Inputs: query, key, value, pos_embedding, mask
56 | - **query** (batch, time, dim): Tensor containing query vector
57 | - **key** (batch, time, dim): Tensor containing key vector
58 | - **value** (batch, time, dim): Tensor containing value vector
59 | - **pos_embedding** (batch, time, dim): Positional embedding tensor
60 | - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
61 | Returns:
62 | - **outputs**: Tensor produces by relative multi head attention module.
63 | """
64 |
65 | def __init__(
66 | self, d_model: int = 512, num_heads: int = 16, dropout_p: float = 0.1,
67 | ):
68 | super(RelativeMultiHeadAttention, self).__init__()
69 | assert d_model % num_heads == 0, "d_model % num_heads should be zero."
70 | self.d_model = d_model
71 | self.d_head = int(d_model / num_heads)
72 | self.num_heads = num_heads
73 | self.sqrt_dim = math.sqrt(d_model)
74 |
75 | self.query_proj = Linear(d_model, d_model)
76 | self.key_proj = Linear(d_model, d_model)
77 | self.value_proj = Linear(d_model, d_model)
78 | self.pos_proj = Linear(d_model, d_model, bias=False)
79 |
80 | self.dropout = nn.Dropout(p=dropout_p)
81 | self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
82 | self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
83 | torch.nn.init.xavier_uniform_(self.u_bias)
84 | torch.nn.init.xavier_uniform_(self.v_bias)
85 |
86 | self.out_proj = Linear(d_model, d_model)
87 |
88 | def forward(
89 | self,
90 | query: Tensor,
91 | key: Tensor,
92 | value: Tensor,
93 | pos_embedding: Tensor,
94 | mask: Optional[Tensor] = None,
95 | ) -> Tensor:
96 | batch_size = value.size(0)
97 |
98 | query = self.query_proj(query).view(batch_size, -1, self.num_heads, self.d_head)
99 | key = (
100 | self.key_proj(key)
101 | .view(batch_size, -1, self.num_heads, self.d_head)
102 | .permute(0, 2, 1, 3)
103 | )
104 | value = (
105 | self.value_proj(value)
106 | .view(batch_size, -1, self.num_heads, self.d_head)
107 | .permute(0, 2, 1, 3)
108 | )
109 | pos_embedding = self.pos_proj(pos_embedding).view(
110 | batch_size, -1, self.num_heads, self.d_head
111 | )
112 |
113 | content_score = torch.matmul(
114 | (query + self.u_bias).transpose(1, 2), key.transpose(2, 3)
115 | )
116 | pos_score = torch.matmul(
117 | (query + self.v_bias).transpose(1, 2), pos_embedding.permute(0, 2, 3, 1)
118 | )
119 | pos_score = self._relative_shift(pos_score)
120 |
121 | score = (content_score + pos_score) / self.sqrt_dim
122 |
123 | if mask is not None:
124 | mask = mask.unsqueeze(1)
125 | score.masked_fill_(mask, -1e9)
126 |
127 | attn = F.softmax(score, -1)
128 | attn = self.dropout(attn)
129 |
130 | context = torch.matmul(attn, value).transpose(1, 2)
131 | context = context.contiguous().view(batch_size, -1, self.d_model)
132 |
133 | return self.out_proj(context)
134 |
135 | def _relative_shift(self, pos_score: Tensor) -> Tensor:
136 | batch_size, num_heads, seq_length1, seq_length2 = pos_score.size()
137 | zeros = pos_score.new_zeros(batch_size, num_heads, seq_length1, 1)
138 | padded_pos_score = torch.cat([zeros, pos_score], dim=-1)
139 |
140 | padded_pos_score = padded_pos_score.view(
141 | batch_size, num_heads, seq_length2 + 1, seq_length1
142 | )
143 | pos_score = padded_pos_score[:, :, 1:].view_as(pos_score)
144 |
145 | return pos_score
146 |
147 |
148 | class MultiHeadedSelfAttentionModule(nn.Module):
149 | """
150 | Conformer employ multi-headed self-attention (MHSA) while integrating an important technique from Transformer-XL,
151 | the relative sinusoidal positional encoding scheme. The relative positional encoding allows the self-attention
152 | module to generalize better on different input length and the resulting encoder is more robust to the variance of
153 | the utterance length. Conformer use prenorm residual units with dropout which helps training
154 | and regularizing deeper models.
155 | Args:
156 | d_model (int): The dimension of model
157 | num_heads (int): The number of attention heads.
158 | dropout_p (float): probability of dropout
159 | Inputs: inputs, mask
160 | - **inputs** (batch, time, dim): Tensor containing input vector
161 | - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
162 | Returns:
163 | - **outputs** (batch, time, dim): Tensor produces by relative multi headed self attention module.
164 | """
165 |
166 | def __init__(self, d_model: int, num_heads: int, dropout_p: float = 0.1):
167 | super(MultiHeadedSelfAttentionModule, self).__init__()
168 | self.positional_encoding = PositionalEncoding(d_model)
169 | self.layer_norm = nn.LayerNorm(d_model)
170 | self.attention = RelativeMultiHeadAttention(d_model, num_heads, dropout_p)
171 | self.dropout = nn.Dropout(p=dropout_p)
172 |
173 | def forward(self, inputs: Tensor, mask: Optional[Tensor] = None):
174 | batch_size, seq_length, _ = inputs.size()
175 | pos_embedding = self.positional_encoding(seq_length)
176 | pos_embedding = pos_embedding.repeat(batch_size, 1, 1)
177 |
178 | inputs = self.layer_norm(inputs)
179 | outputs = self.attention(
180 | inputs, inputs, inputs, pos_embedding=pos_embedding, mask=mask
181 | )
182 |
183 | return self.dropout(outputs)
184 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 |
2 | Apache License
3 | Version 2.0, January 2004
4 | http://www.apache.org/licenses/
5 |
6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
7 |
8 | 1. Definitions.
9 |
10 | "License" shall mean the terms and conditions for use, reproduction,
11 | and distribution as defined by Sections 1 through 9 of this document.
12 |
13 | "Licensor" shall mean the copyright owner or entity authorized by
14 | the copyright owner that is granting the License.
15 |
16 | "Legal Entity" shall mean the union of the acting entity and all
17 | other entities that control, are controlled by, or are under common
18 | control with that entity. For the purposes of this definition,
19 | "control" means (i) the power, direct or indirect, to cause the
20 | direction or management of such entity, whether by contract or
21 | otherwise, or (ii) ownership of fifty percent (50%) or more of the
22 | outstanding shares, or (iii) beneficial ownership of such entity.
23 |
24 | "You" (or "Your") shall mean an individual or Legal Entity
25 | exercising permissions granted by this License.
26 |
27 | "Source" form shall mean the preferred form for making modifications,
28 | including but not limited to software source code, documentation
29 | source, and configuration files.
30 |
31 | "Object" form shall mean any form resulting from mechanical
32 | transformation or translation of a Source form, including but
33 | not limited to compiled object code, generated documentation,
34 | and conversions to other media types.
35 |
36 | "Work" shall mean the work of authorship, whether in Source or
37 | Object form, made available under the License, as indicated by a
38 | copyright notice that is included in or attached to the work
39 | (an example is provided in the Appendix below).
40 |
41 | "Derivative Works" shall mean any work, whether in Source or Object
42 | form, that is based on (or derived from) the Work and for which the
43 | editorial revisions, annotations, elaborations, or other modifications
44 | represent, as a whole, an original work of authorship. For the purposes
45 | of this License, Derivative Works shall not include works that remain
46 | separable from, or merely link (or bind by name) to the interfaces of,
47 | the Work and Derivative Works thereof.
48 |
49 | "Contribution" shall mean any work of authorship, including
50 | the original version of the Work and any modifications or additions
51 | to that Work or Derivative Works thereof, that is intentionally
52 | submitted to Licensor for inclusion in the Work by the copyright owner
53 | or by an individual or Legal Entity authorized to submit on behalf of
54 | the copyright owner. For the purposes of this definition, "submitted"
55 | means any form of electronic, verbal, or written communication sent
56 | to the Licensor or its representatives, including but not limited to
57 | communication on electronic mailing lists, source code control systems,
58 | and issue tracking systems that are managed by, or on behalf of, the
59 | Licensor for the purpose of discussing and improving the Work, but
60 | excluding communication that is conspicuously marked or otherwise
61 | designated in writing by the copyright owner as "Not a Contribution."
62 |
63 | "Contributor" shall mean Licensor and any individual or Legal Entity
64 | on behalf of whom a Contribution has been received by Licensor and
65 | subsequently incorporated within the Work.
66 |
67 | 2. Grant of Copyright License. Subject to the terms and conditions of
68 | this License, each Contributor hereby grants to You a perpetual,
69 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable
70 | copyright license to reproduce, prepare Derivative Works of,
71 | publicly display, publicly perform, sublicense, and distribute the
72 | Work and such Derivative Works in Source or Object form.
73 |
74 | 3. Grant of Patent License. Subject to the terms and conditions of
75 | this License, each Contributor hereby grants to You a perpetual,
76 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable
77 | (except as stated in this section) patent license to make, have made,
78 | use, offer to sell, sell, import, and otherwise transfer the Work,
79 | where such license applies only to those patent claims licensable
80 | by such Contributor that are necessarily infringed by their
81 | Contribution(s) alone or by combination of their Contribution(s)
82 | with the Work to which such Contribution(s) was submitted. If You
83 | institute patent litigation against any entity (including a
84 | cross-claim or counterclaim in a lawsuit) alleging that the Work
85 | or a Contribution incorporated within the Work constitutes direct
86 | or contributory patent infringement, then any patent licenses
87 | granted to You under this License for that Work shall terminate
88 | as of the date such litigation is filed.
89 |
90 | 4. Redistribution. You may reproduce and distribute copies of the
91 | Work or Derivative Works thereof in any medium, with or without
92 | modifications, and in Source or Object form, provided that You
93 | meet the following conditions:
94 |
95 | (a) You must give any other recipients of the Work or
96 | Derivative Works a copy of this License; and
97 |
98 | (b) You must cause any modified files to carry prominent notices
99 | stating that You changed the files; and
100 |
101 | (c) You must retain, in the Source form of any Derivative Works
102 | that You distribute, all copyright, patent, trademark, and
103 | attribution notices from the Source form of the Work,
104 | excluding those notices that do not pertain to any part of
105 | the Derivative Works; and
106 |
107 | (d) If the Work includes a "NOTICE" text file as part of its
108 | distribution, then any Derivative Works that You distribute must
109 | include a readable copy of the attribution notices contained
110 | within such NOTICE file, excluding those notices that do not
111 | pertain to any part of the Derivative Works, in at least one
112 | of the following places: within a NOTICE text file distributed
113 | as part of the Derivative Works; within the Source form or
114 | documentation, if provided along with the Derivative Works; or,
115 | within a display generated by the Derivative Works, if and
116 | wherever such third-party notices normally appear. The contents
117 | of the NOTICE file are for informational purposes only and
118 | do not modify the License. You may add Your own attribution
119 | notices within Derivative Works that You distribute, alongside
120 | or as an addendum to the NOTICE text from the Work, provided
121 | that such additional attribution notices cannot be construed
122 | as modifying the License.
123 |
124 | You may add Your own copyright statement to Your modifications and
125 | may provide additional or different license terms and conditions
126 | for use, reproduction, or distribution of Your modifications, or
127 | for any such Derivative Works as a whole, provided Your use,
128 | reproduction, and distribution of the Work otherwise complies with
129 | the conditions stated in this License.
130 |
131 | 5. Submission of Contributions. Unless You explicitly state otherwise,
132 | any Contribution intentionally submitted for inclusion in the Work
133 | by You to the Licensor shall be under the terms and conditions of
134 | this License, without any additional terms or conditions.
135 | Notwithstanding the above, nothing herein shall supersede or modify
136 | the terms of any separate license agreement you may have executed
137 | with Licensor regarding such Contributions.
138 |
139 | 6. Trademarks. This License does not grant permission to use the trade
140 | names, trademarks, service marks, or product names of the Licensor,
141 | except as required for reasonable and customary use in describing the
142 | origin of the Work and reproducing the content of the NOTICE file.
143 |
144 | 7. Disclaimer of Warranty. Unless required by applicable law or
145 | agreed to in writing, Licensor provides the Work (and each
146 | Contributor provides its Contributions) on an "AS IS" BASIS,
147 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
148 | implied, including, without limitation, any warranties or conditions
149 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
150 | PARTICULAR PURPOSE. You are solely responsible for determining the
151 | appropriateness of using or redistributing the Work and assume any
152 | risks associated with Your exercise of permissions under this License.
153 |
154 | 8. Limitation of Liability. In no event and under no legal theory,
155 | whether in tort (including negligence), contract, or otherwise,
156 | unless required by applicable law (such as deliberate and grossly
157 | negligent acts) or agreed to in writing, shall any Contributor be
158 | liable to You for damages, including any direct, indirect, special,
159 | incidental, or consequential damages of any character arising as a
160 | result of this License or out of the use or inability to use the
161 | Work (including but not limited to damages for loss of goodwill,
162 | work stoppage, computer failure or malfunction, or any and all
163 | other commercial damages or losses), even if such Contributor
164 | has been advised of the possibility of such damages.
165 |
166 | 9. Accepting Warranty or Additional Liability. While redistributing
167 | the Work or Derivative Works thereof, You may choose to offer,
168 | and charge a fee for, acceptance of support, warranty, indemnity,
169 | or other liability obligations and/or rights consistent with this
170 | License. However, in accepting such obligations, You may act only
171 | on Your own behalf and on Your sole responsibility, not on behalf
172 | of any other Contributor, and only if You agree to indemnify,
173 | defend, and hold each Contributor harmless for any liability
174 | incurred by, or claims asserted against, such Contributor by reason
175 | of your accepting any such warranty or additional liability.
176 |
177 | END OF TERMS AND CONDITIONS
178 |
179 | APPENDIX: How to apply the Apache License to your work.
180 |
181 | To apply the Apache License to your work, attach the following
182 | boilerplate notice, with the fields enclosed by brackets "[]"
183 | replaced with your own identifying information. (Don't include
184 | the brackets!) The text should be enclosed in the appropriate
185 | comment syntax for the file format. We also recommend that a
186 | file or class name and description of purpose be included on the
187 | same "printed page" as the copyright notice for easier
188 | identification within third-party archives.
189 |
190 | Copyright [yyyy] [name of copyright owner]
191 |
192 | Licensed under the Apache License, Version 2.0 (the "License");
193 | you may not use this file except in compliance with the License.
194 | You may obtain a copy of the License at
195 |
196 | http://www.apache.org/licenses/LICENSE-2.0
197 |
198 | Unless required by applicable law or agreed to in writing, software
199 | distributed under the License is distributed on an "AS IS" BASIS,
200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
201 | See the License for the specific language governing permissions and
202 | limitations under the License.
203 |
--------------------------------------------------------------------------------