├── .dockerignore ├── requirements.txt ├── .gitignore ├── .gitmodules ├── src ├── __init__.py ├── models │ ├── __init__.py │ ├── embeddings.py │ ├── attention.py │ └── msgat.py ├── metrics.py ├── main.py ├── loss.py ├── data_loader.py └── engine.py ├── Dockerfile ├── Pipfile ├── data └── meta.yaml ├── README.md └── LICENSE /.dockerignore: -------------------------------------------------------------------------------- 1 | .git 2 | .vscode 3 | 4 | checkpoints 5 | paper 6 | 7 | __pycache__ 8 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch>=1.12.1 2 | numpy>=1.23.2 3 | click>=8.1.3 4 | pyyaml>=6.0 5 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .vscode 2 | 3 | checkpoints 4 | data/pems* 5 | 6 | __pycache__ 7 | 8 | Pipfile.lock 9 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "ms-gat-paper"] 2 | path = paper 3 | url = https://github.com/luokn/ms-gat-paper 4 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : __init__.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM luokn/pytorch-runtime 2 | 3 | LABEL maintainer="luokun485@gmail.com" 4 | 5 | WORKDIR /ms-gat 6 | 7 | COPY ./ /ms-gat/ 8 | 9 | ENTRYPOINT ["python3", "src/main.py"] 10 | -------------------------------------------------------------------------------- /src/models/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : __init__.py 4 | # @Data : 2022/09/07 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | from .msgat import msgat48, msgat72, msgat96 9 | -------------------------------------------------------------------------------- /Pipfile: -------------------------------------------------------------------------------- 1 | [[source]] 2 | url = "https://pypi.org/simple" 3 | verify_ssl = true 4 | name = "pypi" 5 | 6 | [[source]] 7 | url = "https://download.pytorch.org/whl/cu113" 8 | verify_ssl = true 9 | name = "pytorch" 10 | 11 | [packages] 12 | click = "==8.1.3" 13 | numpy = "==1.23.2" 14 | pyyaml = "==6.0" 15 | torch = {version = "==1.12.1+cu113", index = "pytorch"} 16 | 17 | [dev-packages] 18 | black = "*" 19 | ipykernel = "*" 20 | 21 | [requires] 22 | python_version = "3.8" 23 | -------------------------------------------------------------------------------- /data/meta.yaml: -------------------------------------------------------------------------------- 1 | pemsd3: 2 | adj-file: data/pemsd3.csv 3 | data-file: data/pemsd3.npz 4 | num-nodes: 358 5 | num-channels: 1 6 | timesteps-per-hour: 12 7 | 8 | pemsd4: 9 | adj-file: data/pemsd4.csv 10 | data-file: data/pemsd4.npz 11 | num-nodes: 307 12 | num-channels: 3 13 | timesteps-per-hour: 12 14 | 15 | pemsd7: 16 | adj-file: data/pemsd7.csv 17 | data-file: data/pemsd7.npz 18 | num-nodes: 883 19 | num-channels: 1 20 | timesteps-per-hour: 12 21 | 22 | pemsd8: 23 | adj-file: data/pemsd8.csv 24 | data-file: data/pemsd8.npz 25 | num-nodes: 170 26 | num-channels: 3 27 | timesteps-per-hour: 12 28 | 29 | pemsd-bay: 30 | adj-file: data/pemsd-bay.csv 31 | data-file: data/pemsd-bay.npz 32 | num-nodes: 325 33 | num-channels: 1 34 | timesteps-per-hour: 12 35 | -------------------------------------------------------------------------------- /src/metrics.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : metrics.py 4 | # @Data : 2022/05/24 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | import torch 9 | 10 | 11 | class Metrics: 12 | """ 13 | Calculate ``MAE/MAPE/RMSE``. 14 | """ 15 | 16 | def __init__(self, mask_value=0.0): 17 | self.n, self.mask_value = 0, mask_value 18 | self.AE, self.APE, self.SE, self.MAE, self.MAPE, self.RMSE = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 19 | 20 | def update(self, y_pred, y_true): 21 | self.n += y_true.nelement() 22 | 23 | # MAE 24 | self.AE += torch.abs(y_pred - y_true).sum().item() 25 | self.MAE = self.AE / self.n 26 | 27 | # MAPE 28 | mask = y_true > self.mask_value 29 | masked_pred, masked_true = y_pred[mask], y_true[mask] 30 | self.APE += 100 * torch.abs((masked_pred - masked_true) / masked_true).sum().item() 31 | self.MAPE = self.APE / self.n 32 | 33 | # RMSE 34 | self.SE += torch.square(y_pred - y_true).sum().item() 35 | self.RMSE = (self.SE / self.n)**0.5 36 | 37 | def todict(self): 38 | return {"MAE": self.MAE, "MAPE": self.MAPE, "RMSE": self.RMSE} 39 | -------------------------------------------------------------------------------- /src/models/embeddings.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : embeddings.py 4 | # @Data : 2021/07/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | import torch 9 | from torch import nn 10 | 11 | 12 | class TimeEmbedding(nn.Module): 13 | """ 14 | Time Embedding. 15 | 16 | Args: 17 | n_components (int): Number of TPC. 18 | n_nodes (int): Number of nodes in the graph. 19 | n_timesteps (int): Number of output timesteps. 20 | 21 | Shape: 22 | H: ``[batch_size]`` 23 | D: ``[batch_size]`` 24 | output: ``[batch_size, n_components, n_nodes, n_timesteps]`` 25 | """ 26 | 27 | def __init__(self, n_components: int, n_nodes: int, n_timesteps: int): 28 | super(TimeEmbedding, self).__init__() 29 | self.n_components, self.n_nodes, self.n_timesteps = n_components, n_nodes, n_timesteps 30 | self.d_ebd = nn.Embedding(7, n_components * n_nodes * n_timesteps) 31 | self.h_ebd = nn.Embedding(24, n_components * n_nodes * n_timesteps) 32 | 33 | def forward(self, H: torch.Tensor, D: torch.Tensor) -> torch.Tensor: 34 | G = self.h_ebd(H) + self.d_ebd(D) # -> [(batch_size * n_components * n_nodes * n_timesteps)] 35 | # -> [batch_size, n_components, n_nodes, n_timesteps] 36 | return G.view(len(G), self.n_components, self.n_nodes, self.n_timesteps) 37 | 38 | def extra_repr(self) -> str: 39 | return f"n_components={self.n_components}, n_nodes={self.n_nodes}, n_timesteps={self.n_timesteps}" 40 | -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : main.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | import os 9 | 10 | import click 11 | from torch import cuda, nn 12 | 13 | from data_loader import DataLoaderForMSGAT 14 | from engine import Evaluator, Trainer 15 | from models import msgat48, msgat72, msgat96 16 | 17 | models = {"ms-gat": msgat72, "ms-gat48": msgat48, "ms-gat72": msgat72, "ms-gat96": msgat96} 18 | 19 | 20 | def tolist(ctx, param, value): 21 | return [int(i) for i in value.split(",")] 22 | 23 | 24 | @click.command() 25 | @click.option("-d", "--data", type=str, help="Dataset name.", required=True) 26 | @click.option("-c", "--ckpt", type=str, help="Checkpoint file.", default=None) 27 | @click.option("-o", "--out-dir", type=str, help="Output directory.", default="checkpoints") 28 | @click.option("-i", "--in-hours", type=str, callback=tolist, help="Input hours.", default="1,2,3,24,168") 29 | @click.option("-b", "--batch-size", type=int, help="Batch size.", default=64) 30 | @click.option("-w", "--num-workers", type=int, help="Number of 'DataLoader' workers.", default=0) 31 | @click.option("--model", type=str, help="Model name.", default="ms-gat") 32 | @click.option("--delta", type=float, help="Delta of 'HuberLoss'.", default=50) 33 | @click.option("--gpu-ids", type=str, help="GPUs.", default="0") 34 | @click.option("--out-timesteps", type=int, help="Length of output timesteps.", default=12) 35 | @click.option("--no-te", type=bool, is_flag=True, help="Disable 'TE'.", default=False) 36 | @click.option("--eval", type=bool, is_flag=True, help="Evaluate only.", default=False) 37 | def main(data, ckpt, out_dir, in_hours, batch_size, num_workers, model, delta, gpu_ids, out_timesteps, no_te, eval): 38 | # load data. 39 | data = DataLoaderForMSGAT(data, in_hours, out_timesteps, batch_size, num_workers) 40 | 41 | # create model. 42 | model = models[model]( 43 | n_components=len(in_hours), 44 | in_channels=data.num_channels, 45 | in_timesteps=data.timesteps_per_hour, 46 | out_timesteps=out_timesteps, 47 | use_te=not no_te, 48 | adj=data.adj, 49 | ) 50 | 51 | # enable cuda. 52 | os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids 53 | if cuda.device_count() > 1: 54 | model = nn.DataParallel(model) 55 | model.cuda() 56 | 57 | # train or eval. 58 | if not eval: 59 | # train. 60 | trainer = Trainer(model, delta, out_dir) 61 | if ckpt is not None: 62 | trainer.load(ckpt) 63 | trainer.fit((data.training, data.validation)) 64 | click.echo("Training completed!") 65 | 66 | # evaluate. 67 | evaluator = Evaluator(model, delta, out_dir, ckpt if eval else trainer.best["ckpt"]) 68 | evaluator.eval(data.evaluation) 69 | 70 | 71 | if __name__ == "__main__": 72 | main() 73 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction

2 | 3 | **Data** 4 | 5 | 1. _Download from:_ [](https://drive.google.com/file/d/1oXSKwV71olfoeyt4dgoVXSdIN_S17hsL/view?usp=sharing) [](https://1drv.ms/u/s!AufZP2YDvxUDlg5G8bGu7Ay7vzhX?e=X0asLx) 6 | 2. _Unzip and move to_ `./data` 7 | 8 | **Usage** 9 | 10 | 1. _Train_ 11 | 12 | - Docker container (recommended) 13 | 14 | ```sh 15 | # PEMSD3 16 | docker run -it --rm --gpus=all --shm-size=512m -v /path/to/checkpoints:/ms-gat/checkpoints luokn/ms-gat -d pemsd3 -i 1,2,3,24 -w 8 17 | # PEMSD4 18 | docker run -it --rm --gpus=all --shm-size=512m -v /path/to/checkpoints:/ms-gat/checkpoints luokn/ms-gat -d pemsd4 -w 8 19 | # PEMSD7 20 | docker run -it --rm --gpus=all --shm-size=512m -v /path/to/checkpoints:/ms-gat/checkpoints luokn/ms-gat -d pemsd7 -b 32 -w 8 21 | # PEMSD8 22 | docker run -it --rm --gpus=all --shm-size=512m -v /path/to/checkpoints:/ms-gat/checkpoints luokn/ms-gat -d pemsd8 -w 8 23 | ``` 24 | 25 | - Physical machine: 26 | 27 | ```sh 28 | # PEMSD3 29 | python3 src/main.py -d pemsd3 -o checkpoints/pemsd3 -i 1,2,3,24 -w 8 30 | # PEMSD4 31 | python3 src/main.py -d pemsd4 -o checkpoints/pemsd4 -w 8 32 | # PEMSD7 33 | python3 src/main.py -d pemsd7 -o checkpoints/pemsd7 -b 32 -w 8 34 | # PEMSD8 35 | python3 src/main.py -d pemsd8 -o checkpoints/pemsd8 -w 8 36 | ``` 37 | 38 | 2. _Evaluate_ 39 | 40 | ```sh 41 | python3 src/main.py --eval -d pemsd4 -o checkpoints/pemsd4 -c checkpoints/pemsd4/xx_xxx.xx.pkl 42 | ``` 43 | 44 | **Checkpoints** 45 | 46 | - PEMSD3: [_MAE = 15.60 MAPE = 16.36% RMSE = 26.36_](https://drive.google.com/file/d/16bUCaI4p23vTGdMOXRRT45TNqci7VLCi/view?usp=sharing) 47 | - PEMSD4: [_MAE = 19.59 MAPE = 13.34% RMSE = 31.58_](https://drive.google.com/file/d/1i3H6GuqBvCOZ_DdPRReKECwb14zvQzY3/view?usp=sharing) 48 | - PEMSD7: [_MAE = 20.44 MAPE = 8.85% RMSE = 34.11_](https://drive.google.com/file/d/1a9VdvFOaMGU9-JyeRlDUDlzjHdrsEKSr/view?usp=sharing) 49 | - PEMSD8: [_MAE = 14.58 MAPE = 10.10% RMSE = 23.94_](https://drive.google.com/file/d/18_mJtL0G6KQZF8QxSLQu9THFg-h_46q-/view?usp=sharing) 50 | 51 | **Citation** 52 | 53 | ```tex 54 | @ARTICLE{9780244, 55 | author ={Huang, Jing and Luo, Kun and Cao, Longbing and Wen, Yuanqiao and Zhong, Shuyuan}, 56 | journal ={IEEE Transactions on Intelligent Transportation Systems}, 57 | title ={Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction}, 58 | year ={2022}, 59 | volume ={}, 60 | number ={}, 61 | pages ={1-15}, 62 | doi ={10.1109/TITS.2022.3173689} 63 | } 64 | ``` 65 | -------------------------------------------------------------------------------- /src/loss.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : loss.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | import torch 9 | from torch import nn 10 | 11 | 12 | class HuberLoss(nn.Module): 13 | """ 14 | Pytorch Implement of huber loss. 15 | 16 | Args: 17 | delta (float, optional): Defaults to 1.0. 18 | """ 19 | 20 | def __init__(self, delta=1.0): 21 | super(HuberLoss, self).__init__() 22 | self.delta = delta 23 | 24 | def forward(self, output: torch.Tensor, target: torch.Tensor): 25 | return huber_loss(output, target, self.delta) 26 | 27 | 28 | def huber_loss(output: torch.Tensor, target: torch.Tensor, delta=1.0) -> torch.Tensor: 29 | r""" 30 | Huber loss function. 31 | 32 | .. math:: 33 | \begin{equation} 34 | \mathcal{L}_{\delta}(\mathcal{Y}, \hat{\mathcal{Y}}) = 35 | \begin{aligned} 36 | \begin{cases} 37 | \frac{1}{2} \left ( \mathcal{Y} - \hat{\mathcal{Y}} \right ) ^2 & if \ \left | \mathcal{Y} - \hat{\mathcal{Y}} \right | \leq \delta \\ 38 | \delta \left | \mathcal{Y} - \hat{\mathcal{Y}} \right | - \frac{1}{2} \delta ^2 & otherwise 39 | \end{cases} 40 | \end{aligned} 41 | \end{equation} 42 | 43 | Args: 44 | output (torch.Tensor): Network output. 45 | target (torch.Tensor): Ground truth. 46 | delta (float, optional): Defaults to 1.0. 47 | 48 | Returns: 49 | torch.Tensor: loss 50 | """ 51 | l1, l2 = delta * torch.abs(output - target) - delta**2 / 2, (output - target)**2 / 2 52 | return torch.where(torch.abs(output - target) <= delta, l2, l1).mean() 53 | 54 | 55 | class GaussLoss(nn.Module): 56 | """ 57 | Pytorch Implement of gauss loss. 58 | 59 | Args: 60 | sigma (float, optional): Defaults to 1.0. 61 | delta (float, optional): Defaults to 5e-2. 62 | """ 63 | 64 | def __init__(self, sigma=1.0, delta=5e-2): 65 | super(GaussLoss, self).__init__() 66 | self.sigma, self.delta = sigma, delta 67 | 68 | def forward(self, output: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 69 | return gauss_loss(output, target, self.sigma, self.delta) 70 | 71 | def extra_repr(self) -> str: 72 | return f"sigma={self.sigma}, delta={self.delta}" 73 | 74 | 75 | def gauss_loss(output: torch.Tensor, target: torch.Tensor, sigma=1.0, delta=5e-2) -> torch.Tensor: 76 | r""" 77 | Gauss loss function. 78 | 79 | .. math:: 80 | \begin{equation} 81 | \mathcal{L}_{\sigma,\delta}(\mathcal{Y}, \hat{\mathcal{Y}}) = 82 | \sigma^2 \left( 1 - \exp \{-\frac{(\mathcal{Y} - \hat{\mathcal{Y}}) ^2}{2 \sigma^2} \} \right) + \delta \left | \mathcal{Y} - \hat{\mathcal{Y}} \right | 83 | \end{equation} 84 | 85 | Args: 86 | output (torch.Tensor): Network output. 87 | target (torch.Tensor): Ground truth. 88 | sigma (float, optional): Defaults to 1.0. 89 | delta (float, optional): Defaults to 5e-2. 90 | 91 | Returns: 92 | torch.Tensor: loss 93 | """ 94 | abs = torch.abs(output - target) 95 | return sigma**2 * torch.mean(1 - torch.exp(-(abs**2) / (2 * sigma**2))) + delta * torch.mean(abs) 96 | -------------------------------------------------------------------------------- /src/models/attention.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : attention.py 4 | # @Data : 2021/07/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | import torch 9 | from torch import nn 10 | 11 | 12 | class GraphAttention(nn.Module): 13 | """ 14 | Graph Attention. 15 | 16 | Args: 17 | n_channels (int): Number of channels. 18 | n_timesteps (int): Number of timesteps. 19 | 20 | Shape: 21 | - signals: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 22 | - adjacency: ``[... , n_nodes, n_nodes]`` 23 | - output: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 24 | """ 25 | 26 | def __init__(self, n_channels: int, n_timesteps: int): 27 | super(GraphAttention, self).__init__() 28 | self.n_channels, self.n_timesteps = n_channels, n_timesteps 29 | self.Wg = nn.Parameter(torch.Tensor(n_timesteps, n_timesteps)) 30 | self.alpha = nn.Parameter(torch.Tensor(n_channels)) 31 | 32 | def forward(self, signals: torch.Tensor, adjacency: torch.Tensor) -> torch.Tensor: 33 | k = q = torch.einsum("bint,i->bnt", signals, self.alpha) # -> [batch_size, n_nodes, n_timesteps] 34 | att = torch.softmax(k @ self.Wg @ q.transpose(1, 2), dim=-1) # -> [batch_size, n_nodes, n_nodes] 35 | # -> [batch_size, in_channels, n_nodes, n_timesteps] 36 | return torch.einsum("bni,bcit->bcnt", att * adjacency, signals) 37 | 38 | def extra_repr(self) -> str: 39 | return f"n_channels={self.n_channels}, n_timesteps={self.n_timesteps}" 40 | 41 | 42 | class TemporalAttention(nn.Module): 43 | """ 44 | Temporal Attention. 45 | 46 | Args: 47 | n_channels (int): Number of channels. 48 | n_nodes (int): Number of nodes. 49 | 50 | Shape: 51 | - signals: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 52 | - output: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 53 | """ 54 | 55 | def __init__(self, n_channels: int, n_nodes: int): 56 | super(TemporalAttention, self).__init__() 57 | self.n_channels, self.n_nodes = n_channels, n_nodes 58 | self.Wt1 = nn.Parameter(torch.Tensor(10, n_nodes)) 59 | self.Wt2 = nn.Parameter(torch.Tensor(10, n_nodes)) 60 | self.alpha = nn.Parameter(torch.Tensor(n_channels)) 61 | 62 | def forward(self, signals: torch.Tensor) -> torch.Tensor: 63 | k = q = torch.einsum("bint,i->btn", signals, self.alpha) # -> [batch_size, n_timesteps, n_nodes] 64 | # -> [batch_size, n_timesteps, n_timesteps] 65 | att = torch.softmax((k @ self.Wt1.T) @ (q @ self.Wt2.T).transpose(1, 2), dim=-1) 66 | return torch.einsum("bti,bcni->bcnt", att, signals) # -> [batch_size, n_channels, n_nodes, n_timesteps] 67 | 68 | def extra_repr(self) -> str: 69 | return f"n_channels={self.n_channels}, n_nodes={self.n_nodes}" 70 | 71 | 72 | class ChannelAttention(nn.Module): 73 | """ 74 | Channel Attention. 75 | 76 | Args: 77 | n_nodes (int): Number of nodes. 78 | n_timesteps (int): Number of timesteps. 79 | 80 | Shape: 81 | - signals: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 82 | - output: ``[batch_size, n_channels, n_nodes, n_timesteps]`` 83 | """ 84 | 85 | def __init__(self, n_nodes, n_timesteps): 86 | super(ChannelAttention, self).__init__() 87 | self.n_nodes, self.n_timesteps = n_nodes, n_timesteps 88 | self.Wc = nn.Parameter(torch.Tensor(n_timesteps, n_timesteps)) 89 | self.alpha = nn.Parameter(torch.Tensor(n_nodes)) 90 | 91 | def forward(self, signals: torch.Tensor) -> torch.Tensor: 92 | k = q = torch.einsum("bcit,i->bct", signals, self.alpha) # -> [batch_size, n_channels, n_timesteps] 93 | att = torch.softmax(k @ self.Wc @ q.transpose(1, 2), dim=-1) # -> [batch_size, n_channels, n_channels] 94 | return torch.einsum("bci,bint->bcnt", att, signals) # -> [batch_size, n_channels, n_nodes, n_timesteps] 95 | 96 | def extra_repr(self) -> str: 97 | return f"n_nodes={self.n_nodes}, n_timesteps={self.n_timesteps}" 98 | -------------------------------------------------------------------------------- /src/data_loader.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : data_loader.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | from typing import List, Tuple 9 | 10 | import numpy as np 11 | import torch 12 | import yaml 13 | from torch.utils.data import DataLoader, Dataset 14 | 15 | 16 | class DataLoaderForMSGAT: 17 | """ 18 | Adjacency matrix and Training, validation, evaluation data loader for MS-GAT. 19 | 20 | Args: 21 | name (str): Dataset name. 22 | in_hours (List[int]): Number of input hours. 23 | timesteps_per_hour (int): Timesteps per hour. 24 | out_timesteps (int): Number of output timesteps. 25 | batch_size (int): Batch size. 26 | num_workers (int): Number of workers. Defaults to 0. 27 | """ 28 | 29 | def __init__( 30 | self, 31 | name: str, 32 | in_hours: List[int], 33 | out_timesteps: int, 34 | batch_size: int, 35 | num_workers: int, 36 | ): 37 | with open("data/meta.yaml", "r") as f: 38 | metadata = yaml.safe_load(f)[name] 39 | self.adj_file = metadata["adj-file"] 40 | self.data_file = metadata["data-file"] 41 | self.num_nodes = metadata["num-nodes"] 42 | self.num_channels = metadata["num-channels"] 43 | self.timesteps_per_hour = metadata["timesteps-per-hour"] 44 | self.in_hours, self.out_timesteps = in_hours, out_timesteps 45 | self.batch_size, self.num_workers = batch_size, num_workers 46 | self.adj = self.__load_adj() 47 | self.training, self.validation, self.evaluation = self.__load_data() 48 | 49 | def __load_adj(self) -> torch.Tensor: 50 | r""" 51 | Load adjacency matrix from file. 52 | 53 | .. math:: 54 | \tilde A = \tilde{D}^{-1/2} (A + I_n) \tilde{D}^{-1/2} 55 | 56 | Returns: 57 | torch.Tensor: Adjacency matrix. 58 | """ 59 | A = torch.eye(self.num_nodes) 60 | for line in open(self.adj_file, "r").readlines()[1:]: 61 | src, dst, _ = line.split(",") 62 | src, dst = int(src), int(dst) 63 | A[src, dst] = A[dst, src] = 1 64 | 65 | D_rsqrt = A.sum(dim=1).rsqrt().diag() 66 | return D_rsqrt @ A @ D_rsqrt 67 | 68 | def __load_data(self) -> List[DataLoader]: 69 | in_timesteps = self.timesteps_per_hour * max(self.in_hours) 70 | # -> [n_channels, n_nodes, n_timesteps] 71 | data = torch.from_numpy(np.load(self.data_file)["data"]).float().transpose(0, -1) 72 | length = data.size(-1) - in_timesteps - self.out_timesteps + 1 73 | split1, split2 = int(0.6 * length), int(0.8 * length) 74 | intervals = [ 75 | [in_timesteps, in_timesteps + split1], # training. 76 | [in_timesteps + split1, in_timesteps + split2], # validation. 77 | [in_timesteps + split2, in_timesteps + length], # evaluation. 78 | ] 79 | normalized_data = normalize(data, split=in_timesteps + split1) 80 | return [ 81 | DataLoader( 82 | TimeSeriesSlice(normalized_data, data[0], interval, self.in_hours, self.out_timesteps, 83 | self.timesteps_per_hour), 84 | self.batch_size, 85 | shuffle=i == 0, 86 | pin_memory=True, 87 | num_workers=self.num_workers, 88 | ) for i, interval in enumerate(intervals) 89 | ] 90 | 91 | 92 | class TimeSeriesSlice(Dataset): 93 | 94 | def __init__( 95 | self, 96 | inputs: torch.Tensor, 97 | target: torch.Tensor, 98 | interval: Tuple[int, int], 99 | hours: List[int], 100 | out_timesteps: int, 101 | timesteps_per_hour: int, 102 | ): 103 | self.inputs, self.target = inputs, target 104 | self.interval, self.hours, self.q, self.tau = interval, hours, out_timesteps, timesteps_per_hour 105 | 106 | def __getitem__(self, i: int): 107 | t = torch.tensor(i + self.interval[0], dtype=torch.long) 108 | h = torch.div(t, self.tau, rounding_mode="trunc") 109 | d = torch.div(h, 24, rounding_mode="trunc") 110 | x = torch.stack([self.inputs[..., (t - h * self.tau):(t - h * self.tau + self.tau)] for h in self.hours]) 111 | y = self.target[..., t:(t + self.q)] 112 | return x, h % 24, d % 7, y 113 | 114 | def __len__(self): 115 | return self.interval[1] - self.interval[0] 116 | 117 | 118 | def normalize(tensor: torch.Tensor, split: int) -> torch.Tensor: 119 | std, mean = torch.std_mean(tensor[..., :split], dim=-1, keepdim=True) 120 | return (tensor - mean) / std 121 | -------------------------------------------------------------------------------- /src/engine.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : engine.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | from pathlib import Path 9 | from time import localtime, strftime 10 | from typing import Tuple 11 | 12 | import click 13 | import torch 14 | from torch import nn, optim 15 | from torch.cuda import amp 16 | from torch.optim import lr_scheduler 17 | from torch.utils.data import DataLoader 18 | 19 | from loss import HuberLoss 20 | from metrics import Metrics 21 | 22 | 23 | class Engine: 24 | 25 | __labels__ = { 26 | "train": "[Train ]", 27 | "validate": "[Validate]", 28 | "evaluate": "[Evaluate]", 29 | } 30 | 31 | def __init__(self, model: nn.Module, loss_delta: float, out_dir: str): 32 | self.model = model 33 | self.loss_fn, self.out_dir = HuberLoss(loss_delta), Path(out_dir) 34 | 35 | # make sure the output directory exists. 36 | if not self.out_dir.exists(): 37 | self.out_dir.mkdir(parents=True) 38 | self.log_file = self.out_dir / "run.log" 39 | 40 | def run_epoch(self, data: DataLoader, gpu_id=None, epoch=None, mode="train"): 41 | self.model.train(mode == "train") 42 | with torch.set_grad_enabled(mode == "train"): 43 | loss_acc, loss_ave, metrics = 0.0, 0.0, Metrics() 44 | 45 | with click.progressbar(length=len(data), 46 | label=self.__labels__[mode], 47 | item_show_func=self.__show_item, 48 | width=25) as pbar: 49 | for batch_idx, batch_data in enumerate(data): 50 | batch_data = [tensor.cuda(gpu_id) for tensor in batch_data] 51 | inputs, truth = batch_data[:-1], batch_data[-1] 52 | 53 | # forward. 54 | with amp.autocast(): 55 | pred = self.model(*inputs) 56 | loss = self.loss_fn(pred, truth) 57 | 58 | # backward. 59 | if mode == "train": 60 | self.optimizer.zero_grad() 61 | self.grad_scaler.scale(loss).backward() 62 | self.grad_scaler.step(self.optimizer) 63 | self.grad_scaler.update() 64 | 65 | # loss. 66 | loss_acc += loss.item() 67 | loss_ave = loss_acc / (batch_idx + 1) 68 | 69 | # metrics. 70 | metrics.update(pred, truth) 71 | 72 | # progress bar. 73 | pbar.update(n_steps=1, current_item=(loss_ave, metrics)) 74 | 75 | stats = {"loss": loss_ave, "MAE": metrics.MAE, "MAPE": metrics.MAPE, "RMSE": metrics.RMSE} 76 | 77 | # log to file. 78 | if mode == "evaluate": 79 | self.log_to_file(self.__labels__[mode], **stats) 80 | else: 81 | self.log_to_file(self.__labels__[mode], epoch=epoch, **stats) 82 | 83 | return loss_ave 84 | 85 | def log_to_file(self, *args, **kwargs): 86 | with open(self.log_file, "a") as f: 87 | f.write(strftime("%Y/%m/%d %H:%M:%S", localtime())) 88 | f.write(" - ") 89 | f.write(" - ".join([f"{i}" for i in args])) 90 | f.write(" - ") 91 | f.write(",".join([f"{k}={v}" for k, v in kwargs.items()])) 92 | f.write("\n") 93 | 94 | @staticmethod 95 | def __show_item(current_item): 96 | if current_item is None: 97 | return "" 98 | loss, metrics = current_item 99 | return f"loss={loss:.2f} MAE={metrics.MAE:.2f} MAPE={metrics.MAPE:.2f}% RMSE={metrics.RMSE:.2f}" 100 | 101 | 102 | class Trainer(Engine): 103 | 104 | def __init__(self, model: nn.Module, loss_delta: float, out_dir: str): 105 | super(Trainer, self).__init__(model, loss_delta=loss_delta, out_dir=out_dir) 106 | self.optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4) 107 | self.scheduler = lr_scheduler.StepLR(self.optimizer, step_size=30, gamma=0.1) 108 | self.grad_scaler = amp.GradScaler() 109 | 110 | self.best = {"epoch": 0, "loss": float("inf"), "ckpt": ""} 111 | self.epoch = 1 112 | self.patience, self.min_delta = 20, 1e-4 113 | self.max_epochs, self.min_epochs = 100, 20 114 | 115 | def fit(self, data_loaders: Tuple[DataLoader, DataLoader], gpu_id=None): 116 | while self.epoch <= self.max_epochs: 117 | click.echo(f"Epoch {self.epoch}") 118 | 119 | self.run_epoch(data_loaders[0], gpu_id=gpu_id, epoch=self.epoch, mode="train") 120 | loss = self.run_epoch(data_loaders[1], gpu_id=gpu_id, epoch=self.epoch, mode="validate") 121 | 122 | self.scheduler.step() 123 | 124 | if self.epoch > self.min_epochs: 125 | if loss < (1 - self.min_delta) * self.best["loss"]: 126 | # save model. 127 | self.best = dict(epoch=self.epoch, loss=loss, ckpt=self.out_dir / f"{self.epoch}_{loss:.2f}.pkl") 128 | self.save(ckpt=self.best["ckpt"]) 129 | 130 | elif self.epoch > self.best["epoch"] + self.patience: 131 | # early stop. 132 | break 133 | self.epoch += 1 134 | 135 | def save(self, ckpt): 136 | states = dict( 137 | best=self.best, 138 | epoch=self.epoch, 139 | model=self.model.state_dict(), 140 | optimizer=self.optimizer.state_dict(), 141 | scheduler=self.scheduler.state_dict(), 142 | grad_scaler=self.grad_scaler.state_dict(), 143 | ) 144 | torch.save(states, ckpt) 145 | 146 | click.echo(f"• Save checkpoint {ckpt}") 147 | 148 | def load(self, ckpt): 149 | states = torch.load(ckpt) 150 | self.best = states["best"] 151 | self.epoch = states["epoch"] + 1 152 | self.model.load_state_dict(states["model"]) 153 | self.optimizer.load_state_dict(states["optimizer"]) 154 | self.scheduler.load_state_dict(states["scheduler"]) 155 | self.grad_scaler.load_state_dict(states["grad_scaler"]) 156 | 157 | click.echo(f"• Load checkpoint {ckpt}") 158 | 159 | 160 | class Evaluator(Engine): 161 | 162 | def __init__(self, model: nn.Module, delta: float, out_dir: str, ckpt: str): 163 | super(Evaluator, self).__init__(model, loss_delta=delta, out_dir=out_dir) 164 | states = torch.load(ckpt) 165 | model.load_state_dict(states["model"]) 166 | 167 | def eval(self, data_loader: DataLoader, gpu_id=None): 168 | self.run_epoch(data_loader, gpu_id=gpu_id, mode="evaluate") 169 | -------------------------------------------------------------------------------- /src/models/msgat.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | # -*- coding: utf-8 -*- 3 | # @File : msgat.py 4 | # @Data : 2021/06/02 5 | # @Author : Luo Kun 6 | # @Contact: luokun485@gmail.com 7 | 8 | from typing import List 9 | 10 | import torch 11 | from torch import nn 12 | 13 | from .attention import ChannelAttention, GraphAttention, TemporalAttention 14 | from .embeddings import TimeEmbedding 15 | 16 | 17 | class GACN(nn.Module): 18 | 19 | def __init__(self, in_channels: int, out_channels: int, n_timesteps: int): 20 | super(GACN, self).__init__() 21 | self.in_channels, self.out_channels, self.n_timesteps = in_channels, out_channels, n_timesteps 22 | self.gatt = GraphAttention(n_channels=in_channels, n_timesteps=n_timesteps) 23 | self.W = nn.Parameter(torch.Tensor(out_channels, in_channels), requires_grad=True) 24 | 25 | def forward(self, signals: torch.Tensor, adjacency: torch.Tensor) -> torch.Tensor: 26 | output = self.gatt(signals, adjacency) # -> [batch_size, in_channels, n_nodes, n_timesteps] 27 | output = output.transpose(1, -1) @ self.W.T # -> [batch_size, n_timesteps, n_nodes, out_channels] 28 | return output.transpose(1, -1) # -> [batch_size, out_channels, n_nodes, n_timesteps] 29 | 30 | def extra_repr(self) -> str: 31 | return f"in_channels={self.in_channels}, out_channels={self.out_channels}, n_timesteps={self.n_timesteps}" 32 | 33 | 34 | class Chomp(nn.Module): 35 | """ 36 | Crop a fixed length on the last dimension. 37 | 38 | Args: 39 | chomp_size (int): Length of cropping. 40 | 41 | Shape: 42 | - input: ``[..., n_features]`` 43 | - output: ``[..., n_features - chomp_size] 44 | """ 45 | 46 | def __init__(self, chomp_size: int): 47 | super(Chomp, self).__init__() 48 | self.chomp_size = chomp_size 49 | 50 | def forward(self, input: torch.Tensor) -> torch.Tensor: 51 | return input[..., :-self.chomp_size] 52 | 53 | def extra_repr(self) -> str: 54 | return f"chomp_size={self.chomp_size}" 55 | 56 | 57 | class TACN(nn.Module): 58 | 59 | def __init__(self, in_channels: int, out_channels: int, n_nodes: int, dilations: List[int]): 60 | super(TACN, self).__init__() 61 | self.in_channels, self.out_channels, self.n_nodes, self.dilations = ( 62 | in_channels, 63 | out_channels, 64 | n_nodes, 65 | dilations, 66 | ) 67 | channels = [in_channels] + [out_channels] * len(dilations) 68 | seq = [TemporalAttention(n_channels=in_channels, n_nodes=n_nodes)] 69 | for i, dilation in enumerate(dilations): 70 | seq += [ 71 | nn.Conv2d(channels[i], channels[i + 1], [1, 2], padding=[0, dilation], dilation=[1, dilation]), 72 | Chomp(chomp_size=dilation), 73 | ] 74 | self.seq = nn.Sequential(*seq) 75 | 76 | def forward(self, signals: torch.Tensor) -> torch.Tensor: 77 | return self.seq(signals) # -> [batch_size, out_channels, n_nodes, n_timesteps] 78 | 79 | def extra_repr(self) -> str: 80 | return f"in_channels={self.in_channels}, out_channels={self.out_channels}, n_nodes={self.n_nodes}, dilations={self.dilations}" 81 | 82 | 83 | class CACN(nn.Module): 84 | 85 | def __init__(self, in_channels: int, out_channels: int, n_nodes: int, n_timesteps: int): 86 | super(CACN, self).__init__() 87 | self.in_channels, self.out_channels, self.n_nodes, self.n_timesteps = ( 88 | in_channels, 89 | out_channels, 90 | n_nodes, 91 | n_timesteps, 92 | ) 93 | self.seq = nn.Sequential(ChannelAttention(n_nodes=n_nodes, n_timesteps=n_timesteps), 94 | nn.Conv2d(in_channels, out_channels, 1)) 95 | 96 | def forward(self, signals: torch.Tensor) -> torch.Tensor: 97 | return self.seq(signals) # -> [batch_size, out_channels, n_nodes, n_timesteps] 98 | 99 | def extra_repr(self) -> str: 100 | return f"in_channels={self.in_channels}, out_channels={self.out_channels}, n_nodes={self.n_nodes}, n_timesteps={self.n_timesteps}" 101 | 102 | 103 | class MEAM(nn.Module): 104 | 105 | def __init__(self, in_channels: int, out_channels: int, n_nodes: int, n_timesteps: int, dilations: List[int]): 106 | assert out_channels % 3 == 0 107 | super(MEAM, self).__init__() 108 | self.in_channels, self.out_channels, self.n_nodes, self.n_timesteps, self.dilations = ( 109 | in_channels, 110 | out_channels, 111 | n_nodes, 112 | n_timesteps, 113 | dilations, 114 | ) 115 | self.ln = nn.LayerNorm([n_timesteps]) 116 | self.res = nn.Conv2d(in_channels, out_channels, kernel_size=1) 117 | self.cacn = CACN(in_channels, out_channels // 3, n_nodes=n_nodes, n_timesteps=n_timesteps) 118 | self.tacn = TACN(in_channels, out_channels // 3, n_nodes=n_nodes, dilations=dilations) 119 | self.gacn = GACN(in_channels, out_channels // 3, n_timesteps=n_timesteps) 120 | 121 | def forward(self, signals: torch.Tensor, adjacency: torch.Tensor) -> torch.Tensor: 122 | output = self.ln(signals) # -> [batch_size, in_channels, n_nodes, n_timesteps] 123 | output = torch.cat( 124 | [ 125 | self.cacn(output), # channel dimension 126 | self.tacn(output), # temporal dimension 127 | self.gacn(output, adjacency), # spatial dimension 128 | ], 129 | dim=1, 130 | ) # -> [batch_size, out_channels, n_nodes, n_timesteps] 131 | return torch.relu(output + self.res(signals)) # -> [batch_size, out_channels, n_nodes, n_timesteps] 132 | 133 | def extra_repr(self) -> str: 134 | return f"in_channels={self.in_channels}, out_channels={self.out_channels}, n_nodes={self.n_nodes}, n_timesteps={self.n_timesteps}, dilations={self.dilations}" 135 | 136 | 137 | class TPC(nn.Module): 138 | 139 | def __init__(self, channels: List[int], n_nodes: int, in_timesteps: int, out_timesteps: int, dilations: List[int]): 140 | super(TPC, self).__init__() 141 | self.channels, self.n_nodes, self.in_timesteps, self.out_timesteps, self.dilations = ( 142 | channels, 143 | n_nodes, 144 | in_timesteps, 145 | out_timesteps, 146 | dilations, 147 | ) 148 | self.tgacns = nn.ModuleList([ 149 | MEAM(channels[i], channels[i + 1], n_nodes=n_nodes, n_timesteps=in_timesteps, dilations=d) 150 | for i, d in enumerate(dilations) 151 | ]) 152 | self.ln = nn.LayerNorm([in_timesteps]) 153 | self.fc = nn.Conv2d(in_timesteps, out_timesteps, kernel_size=[1, channels[-1]]) 154 | 155 | def forward(self, signals: torch.Tensor, adjacency: torch.Tensor) -> torch.Tensor: 156 | for tgacn in self.tgacns: 157 | signals = tgacn(signals, adjacency) 158 | output = self.ln(signals) # -> [batch_size, out_channels, n_nodes, in_timesteps] 159 | output = self.fc(output.transpose(1, 3)) # -> [batch_size, out_timesteps, n_nodes, 1] 160 | return output[..., 0].transpose(1, 2) # -> [batch_size, n_nodes, out_timesteps] 161 | 162 | def extra_repr(self) -> str: 163 | return f"channels={self.channels}, n_nodes={self.n_nodes}, in_timesteps={self.in_timesteps}, out_timesteps={self.out_timesteps}, dilations={self.dilations}" 164 | 165 | 166 | class MSGAT(nn.Module): 167 | """ 168 | The MS-GAT Model. 169 | 170 | Args: 171 | components (list): Configurations for the components. 172 | in_timesteps (int): Number of input timesteps. 173 | out_timesteps (int): Number of outpuy timesteps. 174 | use_te (bool, optional): Use TE. Defaults to True. 175 | adj (torch.Tensor): Adjacency matrix. 176 | 177 | Shape: 178 | X: ``[batch_size, n_channels, n_nodes, in_timesteps]`` 179 | H: ``[batch_size]`` 180 | D: ``[batch_size]`` 181 | output: ``[batch_size, n_nodes, out_timesteps]`` 182 | """ 183 | 184 | def __init__(self, components: List[dict], in_timesteps: int, out_timesteps: int, use_te: bool, adj: torch.Tensor): 185 | super(MSGAT, self).__init__() 186 | if use_te: 187 | self.te = TimeEmbedding(len(components), len(adj), out_timesteps) 188 | else: 189 | self.W = nn.Parameter(torch.Tensor(len(components), len(adj), out_timesteps), requires_grad=True) 190 | self.adj = nn.Parameter(adj, requires_grad=False) 191 | self.tpcs = nn.ModuleList([ 192 | TPC( 193 | channels=component["channels"], 194 | n_nodes=len(adj), 195 | in_timesteps=in_timesteps, 196 | out_timesteps=out_timesteps, 197 | dilations=component["dilations"], 198 | ) for component in components 199 | ]) 200 | self.reset_parameters() 201 | 202 | def forward(self, X: torch.Tensor, H: torch.Tensor, D: torch.Tensor) -> torch.Tensor: 203 | G = self.te(H, D).unbind(1) if self.te is not None else self.W.unbind() 204 | return sum((tpc(x, self.adj) * g for tpc, x, g in zip(self.tpcs, X.unbind(1), G))) 205 | 206 | def reset_parameters(self): 207 | """ 208 | Use ``xavier_normal_`` or ``uniform_`` to initialize the parameters of the network 209 | """ 210 | for param in self.parameters(): 211 | if not param.requires_grad: 212 | continue 213 | if param.ndim >= 2: 214 | nn.init.xavier_normal_(param) 215 | else: 216 | f_out = param.size(0) 217 | nn.init.uniform_(param, -(f_out**-0.5), f_out**-0.5) 218 | 219 | 220 | def msgat48(n_components: int, in_channels: int, **kwargs): 221 | return MSGAT([{"channels": [in_channels, 48, 48], "dilations": [[1, 2], [2, 4]]}] * n_components, **kwargs) 222 | 223 | 224 | def msgat72(n_components: int, in_channels: int, **kwargs): 225 | return MSGAT([{"channels": [in_channels, 72, 72], "dilations": [[1, 2], [2, 4]]}] * n_components, **kwargs) 226 | 227 | 228 | def msgat96(n_components: int, in_channels: int, **kwargs): 229 | return MSGAT([{"channels": [in_channels, 96, 96], "dilations": [[1, 1, 2, 2], [4, 4]]}] * n_components, **kwargs) 230 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------