AttributeError: Can't get attribute 'video_dataset' on <module '__main__' (built-in)>

Question

I have a dataset that has been preprocessed, and this error continues to occur. I've tried the if__name___=='main_': method from several communities, but I keep getting errors if I used it incorrectly.

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "D:\anaconda\envs\mj\lib\multiprocessing\spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "D:\anaconda\envs\mj\lib\multiprocessing\spawn.py", line 126, in _main
    self = reduction.pickle.load(from_parent)
AttributeError: Can't get attribute 'video_dataset' on <module '__main__' (built-in)>

This is how it is defined.

class video_dataset(Dataset):
    def __init__(self,frame_list,sequence_length = 16,transform = None):
        self.frame_list = frame_list
        self.transform = transform
        self.sequence_length = sequence_length
    def __len__(self):
        return len(self.frame_list)
    def __getitem__(self,idx):
        label,path = self.frame_list[idx]
        img = cv2.imread(path)
        seq_img = list()
        for i in range(16):
          img1 = img[:,128*i:128*(i+1),:]
          if(self.transform):
            img1 = self.transform(img1)
          seq_img.append(img1)
        seq_image = torch.stack(seq_img)
        seq_image = seq_image.reshape(3,16,im_size,im_size)
        return seq_image,decoder[label]

import torchvision
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.models as models
import torch.optim as optim
import copy
import os
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
from torchvision import transforms
from torch.utils.data import DataLoader
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
import cv2
import sys
import import_ipynb

This is where the final error occurs.

from torch.autograd import Variable
iteration = 0
acc_all = list()
loss_all = list()
    
for epoch in range(num_epochs):
    print('')
    print(f"--- Epoch {epoch} ---")
    phase1 = dataloaders.keys()
    for phase in phase1:
        print('')
        print(f"--- Phase {phase} ---")
        epoch_metrics = {"loss": [], "acc": []}
        for batch_i, (X, y) in enumerate(dataloaders[phase]):
            #iteration = iteration+1
            image_sequences = Variable(X.to(device), requires_grad=True)
            labels = Variable(y.to(device), requires_grad=False)
            optimizer.zero_grad()
            #model.lstm.reset_hidden_state()
            predictions = model(image_sequences)
            loss = cls_criterion(predictions, labels)
            acc = 100 * (predictions.detach().argmax(1) == labels).cpu().numpy().mean()
            loss.backward()
            optimizer.step()
            epoch_metrics["loss"].append(loss.item())
            epoch_metrics["acc"].append(acc)
            if(phase=='train'):
                lr,mom = onecyc.calc()
                update_lr(optimizer, lr)
                update_mom(optimizer, mom)
            batches_done = epoch * len(dataloaders[phase]) + batch_i
            batches_left = num_epochs * len(dataloaders[phase]) - batches_done
            sys.stdout.write(
                    "\r[Epoch %d/%d] [Batch %d/%d] [Loss: %f (%f), Acc: %.2f%% (%.2f%%)]"
                    % (
                        epoch,
                        num_epochs,
                        batch_i,
                        len(dataloaders[phase]),
                        loss.item(),
                        np.mean(epoch_metrics["loss"]),
                        acc,
                        np.mean(epoch_metrics["acc"]),
                    )
                )

                # Empty cache
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            
        print('')
        print('{} , acc: {}'.format(phase,np.mean(epoch_metrics["acc"])))
        torch.save(model.state_dict(),'weights_crime/c3d_{}.h5'.format(epoch))
        if(phase=='train'):
          acc_all.append(np.mean(epoch_metrics["acc"]))
          loss_all.append(np.mean(epoch_metrics["loss"]))

original code is here https://github.com/sanchit2843/Videoclassification/blob/master/train.ipynb

Answer 1

I got the same issue of AttributeError: Can't get attribute XYZ on <module 'main' (built-in)>` and it seems like sometimes it's not about the class that you wrote, but the way you import it. For some reason it does not work with objects not defined in an imported module, especially if you are using notebook. So you have to write your function into a different file and import the module.

Doing so fix the issue for me instantly, hope it helps anyone who stumbles into similar issue.