nnzhan / graph-wavenet Goto Github PK

RuntimeError maybe due to torch version can anyone send me help pls?

test: https://colab.research.google.com/drive/1VltsQGVO81P6lc9RKGnlxjXFp6VkY4YK?usp=sharing

Hi,
I notice that in generate_training_data.py, it first uses a sliding window to reshape the training data into (num_samples - seq_len * 2 + 1, seq_len, num_nodes, num_feature), then splits the training data with num_train = round(num_samples * 0.7), but this would cause data breach problem. Maybe it's better to split the dataset and then using a sliding window.

你好，我想向您请教一个代码里的细节问题，谢谢
为何在实现gated TCN中一个用的是Conv-2d，一个用的是Conv-1d，是出于什么考虑呢？谢谢

为什么table2里的结果要比table3里的低很多，我的疑问是：table3在做消融实验，table3中加粗的最好的结果不应该放在table2 里面吗？为什么现在不是这样的呢？请各位大神看到后相助 ，十分感谢

Please help me，I don’t know how to solve it，the loation of the error code is as below

with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f)

I ran the Forward Backward Adaptive Command:

python train.py --device cuda:0 --gcn_bool --adjtype doubletransition --addaptadj  --randomadj  --epoch 100 $ep --expid $expid

and got what I think are slightly worse results than Table 2 and 3 of the paper.

Table 3/METR-LA:

MAE, RMSE, MAPE = 3.04, 6.09, 8.23%
My results: 3.0737, 6.1674, 8.30%

Does that sound like a normal amount of error, wrong command, or bug?

Training finished
The valid loss on best model is 2.7565
Evaluate best model on test data for horizon 1, Test MAE: 2.2372, Test MAPE
: 0.0533, Test RMSE: 3.8697
Evaluate best model on test data for horizon 2, Test MAE: 2.5196, Test MAPE
: 0.0626, Test RMSE: 4.6753
Evaluate best model on test data for horizon 3, Test MAE: 2.7171, Test MAPE
: 0.0695, Test RMSE: 5.2287
Evaluate best model on test data for horizon 4, Test MAE: 2.8760, Test MAPE
: 0.0754, Test RMSE: 5.6681
Evaluate best model on test data for horizon 5, Test MAE: 3.0037, Test MAPE
: 0.0803, Test RMSE: 6.0149
Evaluate best model on test data for horizon 6, Test MAE: 3.1157, Test MAPE
: 0.0844, Test RMSE: 6.3154
Evaluate best model on test data for horizon 7, Test MAE: 3.2154, Test MAPE
: 0.0882, Test RMSE: 6.5706
Evaluate best model on test data for horizon 8, Test MAE: 3.3002, Test MAPE: 0.0913, Test RMSE: 6.7903
Evaluate best model on test data for horizon 9, Test MAE: 3.3777, Test MAP$: 0.0941, Test RMSE: 6.9856
Evaluate best model on test data for horizon 10, Test MAE: 3.4449, Test MA$E: 0.0965, Test RMSE: 7.1507
Evaluate best model on test data for horizon 11, Test MAE: 3.5081, Test MA$E: 0.0989, Test RMSE: 7.2993
Evaluate best model on test data for horizon 12, Test MAE: 3.5691, Test MA$E: 0.1011, Test RMSE: 7.4404

On average over 12 horizons, Test MAE: 3.0737, Test MAPE: 0.0830, Test RMS$: 6.1674
Total time spent: 4299.2252

The code metrics = engine.train(trainx, trainy[:, 0, :, :]) in line84 of train.py seems only predict one dimension of total D dimension.
But the paper wrote the output dimension is D.

你好，想请教一下各位大神，table2中，是不是 将output length =12 一次性输出整个12个值，然后把第3，6，12处的值填进table2中就可以了？
还是output length =12 只是60mins时刻，我们需要将output length 分别设为 3 和 6 才能得到表格中的 15mins的值和30mins的值？
英语怕说不清楚， 写的中文。 希望能解答我的疑惑，万分谢谢！！！

Hello,
Thank you for sharing the code of Graph WaveNet.

I have some confusion about the conv1d function using in your code.
Why use conv1d in gate_convs and how to understand that the kernel_size is a tuple (1, 2)?
Thanks

WaveNet is a very good paper for ST prediction, recently I take some experiments about the adaptive adj matrix. However, I found the adaptive matrix learned by the GWN model is not like the paper.

the adaptive adj listed in the paper is like this

but i get the results like this

I load the model from a performance-good .pth file.

I cannot find why for this problem, any experts can explain this error, thank you very very much!

Hello,

First, thank you for sharing the code!

Where can we find the adj_mx.pkl for METR-LA?

Thanks.

Hi,I have a question. When I train, my loss is getting bigger and bigger, and I don't know what's causing it

Namespace(addaptadj=True, adjdata='data/sensor_graph/adj_mx.pkl', adjtype='doubletransition', aptonly=False, batch_size=64, data='data/METR-LA', device='cuda:0', dropout=0.3, epochs=100, expid=1, gcn_bool=True, in_dim=2, learning_rate=0.001, nhid=32, num_nodes=207, print_every=50, randomadj=True, save='./garage/metr', seq_length=12, weight_decay=0.0001)
start training...
Iter: 000, Train Loss: 11.5344, Train MAPE: 0.3075, Train RMSE: 13.9952
Iter: 050, Train Loss: 377.5500, Train MAPE: 7.0916, Train RMSE: 630.7411
Iter: 100, Train Loss: 352.2685, Train MAPE: 6.7022, Train RMSE: 619.5583
Iter: 150, Train Loss: 376.2829, Train MAPE: 7.7274, Train RMSE: 651.6628
Iter: 200, Train Loss: 415.7277, Train MAPE: 8.6364, Train RMSE: 692.0403
Iter: 250, Train Loss: 427.4500, Train MAPE: 8.3187, Train RMSE: 706.2341
Iter: 300, Train Loss: 403.1333, Train MAPE: 7.4553, Train RMSE: 686.9592
Iter: 350, Train Loss: 421.0964, Train MAPE: 8.3744, Train RMSE: 704.3835
Epoch: 001, Inference Time: 4.2194 secs
Epoch: 001, Train Loss: 388.0823, Train MAPE: 7.5629, Train RMSE: 660.2726, Valid Loss: 409.1663, Valid MAPE: 8.0573, Valid RMSE: 683.0002, Training Time: 1063.4692/epoch
Iter: 000, Train Loss: 383.0825, Train MAPE: 7.3515, Train RMSE: 672.6279
Iter: 050, Train Loss: 436.3956, Train MAPE: 7.9620, Train RMSE: 721.0291
Iter: 100, Train Loss: 423.3630, Train MAPE: 7.6969, Train RMSE: 715.8647
Iter: 150, Train Loss: 414.0448, Train MAPE: 9.1630, Train RMSE: 713.6893
Iter: 200, Train Loss: 396.0155, Train MAPE: 7.4647, Train RMSE: 699.6281
Iter: 250, Train Loss: 433.5888, Train MAPE: 8.1940, Train RMSE: 735.1317
Iter: 300, Train Loss: 445.8810, Train MAPE: 8.4676, Train RMSE: 747.1703
Iter: 350, Train Loss: 430.3223, Train MAPE: 8.1599, Train RMSE: 734.9741
Epoch: 002, Inference Time: 3.6677 secs
Epoch: 002, Train Loss: 423.8275, Train MAPE: 8.3188, Train RMSE: 721.4823, Valid Loss: 434.8144, Valid MAPE: 8.5525, Valid RMSE: 726.0127, Training Time: 122.6467/epoch
Iter: 000, Train Loss: 429.0443, Train MAPE: 7.9226, Train RMSE: 733.3237
Iter: 050, Train Loss: 417.4486, Train MAPE: 8.1126, Train RMSE: 723.7980
Iter: 100, Train Loss: 428.7359, Train MAPE: 8.0147, Train RMSE: 734.3259
Iter: 150, Train Loss: 443.9696, Train MAPE: 8.9476, Train RMSE: 747.8458
Iter: 200, Train Loss: 431.4466, Train MAPE: 8.3825, Train RMSE: 737.4367
Iter: 250, Train Loss: 418.8755, Train MAPE: 7.8954, Train RMSE: 726.3869
Iter: 300, Train Loss: 438.4908, Train MAPE: 8.5767, Train RMSE: 744.3538
Iter: 350, Train Loss: 421.3578, Train MAPE: 8.0956, Train RMSE: 728.9158
Epoch: 003, Inference Time: 4.0353 secs
Epoch: 003, Train Loss: 433.9845, Train MAPE: 8.4935, Train RMSE: 739.3757, Valid Loss: 437.7613, Valid MAPE: 8.6092, Valid RMSE: 730.9322, Training Time: 120.3957/epoch

Thanks for your wonderful code! I am confused that why you use nn.functional.pad() on training and eval input but not on test input. Can you explain the reason?

Hi, I have a question: we utilze GCN with AXW, but in your model.py, I find it become WXA, Why?

Hi,

Currently I'm working on the Graph-WaveNet code with my own dataset (predicting the amount of patients occupying a bed in a hospital).

There is one thing I don't fully understand from the code.

In this line of code (test.py line 100-104):

y12 = realy[:,99,11].cpu().detach().numpy()
yhat12 = scaler.inverse_transform(yhat[:,99,11]).cpu().detach().numpy()

y3 = realy[:,99,2].cpu().detach().numpy()
yhat3 = scaler.inverse_transform(yhat[:,99,2]).cpu().detach().numpy()

Can anyone explain to me what they are doing in this piece of code? And what the difference is between y12 and y3?

I really hope someone can help me! :)

Thanks in advance!

Thanks for your coding!
I run the train.py and found this error.
Who can help me ?
Thanks

感谢文章和代码！

请问一下，如果预测60分钟的交通流，数据首先输入t1-t12的，然后预测t13-t24时刻的交通流（即预测一个小时）；然后第二步是的输入是t25-t36，然后预测t37-t48吗？以此类推，请问我这样理解的对吗？

Such as pytorch_geometric's MessagePassing. Thx!

Traceback (most recent call last):
File "test.py", line 111, in
main()
File "test.py", line 50, in main
model.load_state_dict(torch.load(args.checkpoint))
File "/public/home/hpc0919170203/anaconda3/envs/GWN2/lib/python3.7/site-packages/torch/serialization.py", line 386, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/public/home/hpc0919170203/anaconda3/envs/GWN2/lib/python3.7/site-packages/torch/serialization.py", line 548, in _load
_check_seekable(f)
File "/public/home/hpc0919170203/anaconda3/envs/GWN2/lib/python3.7/site-packages/torch/serialization.py", line 194, in _check_seekable
raise_err_msg(["seek", "tell"], e)
File "/public/home/hpc0919170203/anaconda3/envs/GWN2/lib/python3.7/site-packages/torch/serialization.py", line 187, in raise_err_msg
raise type(e)(msg)
AttributeError: 'NoneType' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.

Traceback (most recent call last):
File "C:\Users\Administrator\Desktop\Graph-WaveNet-master\train.py", line 177, in
main()
File "C:\Users\Administrator\Desktop\Graph-WaveNet-master\train.py", line 87, in main
metrics = engine.train(trainx, trainy[:,0,:,:])
File "C:\Users\Administrator\Desktop\Graph-WaveNet-master\engine.py", line 17, in train
output = self.model(input)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\Administrator\Desktop\Graph-WaveNet-master\model.py", line 175, in forward
gate = self.gate_convsi
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\conv.py", line 307, in forward
return self._conv_forward(input, self.weight, self.bias)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\conv.py", line 303, in _conv_forward
return F.conv1d(input, weight, bias, self.stride,

RuntimeError: Expected 2D (unbatched) or 3D (batched) input to conv1d, but got input of size: [64, 32, 207, 13]

Process finished with exit code 1

how to solve this question?
AttributeError: 'gwnet' object has no attribute 'nodevec1'
Thanks a lot!!!!

Hi, I have run your code on some datasets that I constructed, and I found an problem in computing the MAPE metrics.

For the ground truth data, you normalize them into a small scale, and do the inserver normalize when evaluating the model. That will change some value like 0 to a small value like 1e-5. The computation of MAPE will take that small value into account and generate a very large MAPE value because mask only filter the value equals zero instead of these small value.

AttributeError: 'NoneType' object has no attribute 'seek'.You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.

How to solve this problem!Please!!!!!For helping !!!!!!

Hi , Thanks for sharing the code. Could you please tell me how to get the road network distance in PeMS ? I'm confused for a week.

I don't konw how to solve this problem,><,谢谢大家了
Traceback (most recent call last):
File "train.py", line 177, in
main()
File "train.py", line 88, in main
metrics = engine.train(trainx, trainy[:,0,:,:])
File "/home/mist/Graph-WaveNet-master/engine.py", line 17, in train
output = self.model(input)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/mist/Graph-WaveNet-master/model.py", line 192, in forward
x = self.gconv[i](x, new_supports)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/mist/Graph-WaveNet-master/model.py", line 36, in forward
x1 = self.nconv(x,a)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/mist/Graph-WaveNet-master/model.py", line 13, in forward
x = torch.einsum('ncvl,vw->ncwl',(x,A))
File "/usr/local/lib/python3.6/dist-packages/torch/functional.py", line 342, in einsum
return einsum(equation, *_operands)
File "/usr/local/lib/python3.6/dist-packages/torch/functional.py", line 344, in einsum
return _VF.einsum(equation, operands) # type: ignore
RuntimeError: size of dimension does not match previous size, operand 1, dim 0

The arg.save parameter will not been created successfully. Please open a branch so that I could fix it. Thanks