AttributeError：无法获取属性“video_dataset”<module '__main__' (built-in)></module>

Question

我有一个已预处理的数据集，并且此错误继续发生。 我已经尝试过来自多个社区的 if__name___==' main _': 方法，但是如果我使用不正确，我会不断收到错误消息。

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)>

这就是它的定义方式。

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

这是最终错误发生的地方。

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"]))

原始代码在这里https://github.com/sanchit2843/Videoclassification/blob/master/train.ipynb

Answer 1

这也发生在我身上一次。

我试图在一个新脚本中调用一个数据集，这个数据集是使用class自定义并保存在磁盘上的。

我修复了它，将我用于创建数据集的class添加到他们调用数据集的脚本中。

在你的情况下，我建议只复制 class：

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]

然后将其添加到发生此错误的脚本中。

总帐