AttributeError: 'list' object has no attribute 'dim' when predicting in pytorch

Question

I'm currently loading in a model and 11 input values. Then I'm sending those 11 values into a tensor and attempting to predict outputs. Here is my code:

# coding: utf-8

# In[5]:


import torch
import torchvision
from torchvision import transforms, datasets
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as utils
import numpy as np

data_np = np.loadtxt('input_preds.csv', delimiter=',')




train_ds = utils.TensorDataset(torch.tensor(data_np, dtype=torch.float32).view(-1,11))

trainset = torch.utils.data.DataLoader(train_ds, batch_size=1, shuffle=True)



# setting device on GPU if available, else CPU, replace .cuda() with .to(device)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        #self.bn = nn.BatchNorm2d(11)
        self.fc1 = nn.Linear(11, 22)
        self.fc2 = nn.Linear(22, 44)
        self.fc3 = nn.Linear(44, 22)
        self.fc4 = nn.Linear(22, 11)

    def forward(self, x):
        #x = x.view(-1, 11)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        #return F.log_softmax(x, dim=1)
        return x
model1 = torch.load('./1e-2')
model2 = torch.load('./1e-3')

for data in trainset:   
        X = data  
        X = X

        output = model1(X).to(device)
        print(output)

However, I get this error

Traceback (most recent call last):
  File "inference.py", line 53, in <module>
    output = model1(X).to(device)
  File "C:\Users\Happy\Miniconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "inference.py", line 40, in forward
    x = F.relu(self.fc1(x))
  File "C:\Users\Happy\Miniconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\Users\Happy\Miniconda3\envs\torch\lib\site-packages\torch\nn\modules\linear.py", line 55, in forward
    return F.linear(input, self.weight, self.bias)
  File "C:\Users\Happy\Miniconda3\envs\torch\lib\site-packages\torch\nn\functional.py", line 1022, in linear
    if input.dim() == 2 and bias is not None:
AttributeError: 'list' object has no attribute 'dim'

I've tried to convert the batch to a numpy array but that didn't help. How do I resolve this error? Thank you for your help.

Solution

It looks like your X (data) is a list of tensors, while a PyTorch tensor is expected. Try X = torch.stack(X).to(device) before sending to the model.

Comments

[Admin]

Thank you for responding! I tried implementing your suggestion, and I got this error: Traceback (most recent call last): File "inference.py", line 45, in <module> X = torch.Tensor(X) ValueError: only one element tensors can be converted to Python scalars

[Admin]

I get a new error. RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'mat2'

[Sergii Dymchenko]

Looks like the mode is on GPU, need to put data on GPU also: X = torch.stack(X).to(device)

[Admin]

Sorry about that silly mistake. I'm used to the tensorflow programming, where you don't need to send data to the GPU.