einsum() operands do not broadcast with remapped shapes

[immusferr]

Hi, Gerald.
I am trying to running your code on my own dataset. But I got some problems here:

RuntimeError: einsum() operands do not broadcast with remapped shapes [original->remapped]: [32, 37, 320]->[32, 37, 1, 320] [101, 320, 160]->[1, 101, 160, 320]

[gorold]

Hey, thanks for your interest in our work, could you provide more information on this error (the full error log) and the dimensions of the data you are working with? I can't really tell whats wrong from just this error message.

[yyyujintang]

I met the same problem.
`
import torch

from torch import nn

import torchinfo

from torchinfo import summary

net = CoSTEncoder(
input_dims=12, output_dims=3,
kernels=[1, 2, 4, 8, 16, 32, 64, 128],
length=3000,
hidden_dims=64, depth=10,
)

summary(net,input_size=(1,121,12),col_names=["kernel_size","output_size","num_params","mult_adds"])
`
And I get the error einsum(): operands do not broadcast with remapped shapes [original->remapped]: [1, 61, 3]->[1, 61, 1, 3] [1501, 3, 1]->[1, 1501, 1, 3]

[yyyujintang]

I figure out the problem, the length in CoSTEncoder must match your dataset.
For example, my length is 121, then change it in the Enocder part.
`
import torch

from torch import nn

import torchinfo

from torchinfo import summary

net = CoSTEncoder(
input_dims=12, output_dims=3,
kernels=[1, 2, 4, 8, 16, 32, 64, 128],
length=121,
hidden_dims=64, depth=10,
)
`
summary(net,input_size=(1,121,12),col_names=["kernel_size","output_size","num_params","mult_adds"])

[17103023]

My dataset is similar to yours, with a total of 8 columns and 1681 rows, and the runtime reports such an error

Traceback (most recent call last):
File "train.py", line 109, in
out, eval_res = tasks.eval_forecasting(model, data, train_slice, valid_slice, test_slice, scaler, pred_lens, n_covariate_cols, args.max_train_length-1)
File "/userdata/lwy/CoST-main/tasks/forecasting.py", line 55, in eval_forecasting
lr = eval_protocols.fit_ridge(train_features, train_labels, valid_features, valid_labels)
File "/userdata/lwy/CoST-main/tasks/_eval_protocols.py", line 25, in fit_ridge
lr = Ridge(alpha=alpha).fit(train_features, train_y)
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py", line 762, in fit
return super().fit(X, y, sample_weight=sample_weight)
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py", line 542, in fit
X, y = self._validate_data(X, y,
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 814, in check_X_y
X = check_array(X, accept_sparse=accept_sparse,
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/userdata/lwy/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 669, in check_array
raise ValueError("Found array with %d sample(s) (shape=%s) while a"
ValueError: Found array with 0 sample(s) (shape=(0, 320)) while a minimum of 1 is required.

[luojike2333]

I figure out the problem, the length in CoSTEncoder must match your dataset.我弄清楚了问题，CoSTEncoder 中的长度必须与你的数据集相匹配。 For example, my length is 121, then change it in the Enocder part.例如，我的长度是 121，然后在 Enocder 部分更改它。 ` import torch Import Torch （导入火炬）

from torch import nn 从 Torch import nn

import torchinfo 导入 torchinfo

from torchinfo import summary从 torchinfo 导入摘要

net = CoSTEncoder( net = CoSTEncoder（ input_dims=12, output_dims=3,input_dims=12， output_dims=3， kernels=[1, 2, 4, 8, 16, 32, 64, 128],内核=[1， 2， 4， 8， 16， 32， 64， 128]， length=121, length=121， hidden_dims=64, depth=10,hidden_dims=64，深度=10， ) ` summary(net,input_size=(1,121,12),col_names=["kernel_size","output_size","num_params","mult_adds"])摘要（net，input_size=（1,121,12），col_names=[“kernel_size”，“output_size”，“num_params”，“mult_adds”]）

That is right！