Pytorch数据加载器批处理周期中的错误

Question

通过Pytorch DataLoader Batch Cycles迭代时，我一直在SageMaker中遇到此错误：

Traceback (most recent call last):
  File "main.py", line 371, in <module>
    g_scaler=g_scaler, d_scaler=d_scaler, runtime_log_folder=runtime_log_folder, runtime_log_file_name=runtime_log_file_name)
  File "main.py", line 78, in train_fn
    for idx, (x, y) in enumerate(loop):
  File "/opt/conda/lib/python3.6/site-packages/tqdm/std.py", line 1171, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 525, in __next__
    (data, worker_id) = self._next_data()
  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1252, in _next_data
    return (self._process_data(data), w_id)
  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1299, in _process_data
    data.reraise()
  File "/opt/conda/lib/python3.6/site-packages/torch/_utils.py", line 429, in reraise
    raise self.exc_type(msg)
  File "/opt/conda/lib/python3.6/site-packages/botocore/exceptions.py", line 84, in __init__
    super(HTTPClientError, self).__init__(**kwargs)
  File "/opt/conda/lib/python3.6/site-packages/botocore/exceptions.py", line 40, in __init__
    msg = self.fmt.format(**kwargs)
KeyError: 'error'

---------------------------------------------------------------------------
UnexpectedStatusException                 Traceback (most recent call last)
<ipython-input-1-81655136a841> in <module>
     58                             py_version='py3')
     59 
---> 60 pytorch_estimator.fit({'train': Runtime.dataset_path}, job_name=Runtime.job_name)
     61 
     62 #print(pytorch_estimator.latest_job_tensorboard_artifacts_path())

~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
    955         self.jobs.append(self.latest_training_job)
    956         if wait:
--> 957             self.latest_training_job.wait(logs=logs)
    958 
    959     def _compilation_job_name(self):

~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/sagemaker/estimator.py in wait(self, logs)
   1954         # If logs are requested, call logs_for_jobs.
   1955         if logs != "None":
-> 1956             self.sagemaker_session.logs_for_job(self.job_name, wait=True, log_type=logs)
   1957         else:
   1958             self.sagemaker_session.wait_for_job(self.job_name)

~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/sagemaker/session.py in logs_for_job(self, job_name, wait, poll, log_type)
   3751 
   3752         if wait:
-> 3753             self._check_job_status(job_name, description, "TrainingJobStatus")
   3754             if dot:
   3755                 print()

~/anaconda3/envs/pytorch_latest_p36/lib/python3.6/site-packages/sagemaker/session.py in _check_job_status(self, job, desc, status_key_name)
   3304                 ),
   3305                 allowed_statuses=["Completed", "Stopped"],
-> 3306                 actual_status=status,
   3307             )
   3308 

UnexpectedStatusException: Error for Training job 2022-06-03-05-16-49-pix2pix-U12239-2022-05-09-14-39-18-training: Failed. Reason: AlgorithmError: ExecuteUserScriptError:
Command "/opt/conda/bin/python3.6 main.py --runtime_var dataset_name=U12239-2022-05-09-14-39-18,job_name=2022-06-03-05-16-49-pix2pix-U12239-2022-05-09-14-39-18-training,model_name=pix2pix"

  0%|          | 0/248 [00:00<?, ?it/s]
  0%|          | 1/248 [00:30<2:07:28, 30.97s/it]
  0%|          | 1/248 [00:30<2:07:28, 30.97s/it]
Traceback (most recent call last):
  File "main.py", line 371, in <module>
    g_scaler=g_scaler, d_scaler=d_scaler, runtime_log_folder=runtime_log_folder, runtime_log_file_name=runtime_log_file_name)
  File "main.py", line 78, in train_fn
    for idx, (x, y) in enumerate(loop):
  File "/opt/conda/lib/python3.6/site-packages/tqdm/std.py", line 1171, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 525, in __next__
    (data, worker_id) = self._next_data()
  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1252, in _next_data
    return (self

这是导致错误的代码：

def train_fn(disc, gen, loader, opt_disc, opt_gen, l1, bce, g_scaler, d_scaler,runtime_log_folder,runtime_log_file_name):

    total_output=''
    
    loop = tqdm(loader, leave=True)
    device = "cuda" if torch.cuda.is_available() else "cpu"

    print("Loop")
    print(loop)
    print("Length loop")
    print(len(loop))
    for idx, (x, y) in enumerate(loop): #<--error happens here
        print("Loop index")
        print(idx)
        print("Loop item")
        print(x,y)
        x = x.to(device)
        y = y.to(device)
        
        # train discriminator
        with torch.cuda.amp.autocast():
            y_fake = gen(x)

            D_real = disc(x, y)
            D_fake = disc(x, y_fake.detach())
            # use detach so as to avoid breaking computational graph when do optimizer.step on discriminator
            # can use detach, or when do loss.backward put loss.backward(retain_graph = True)

            D_real_loss = bce(D_real, torch.ones_like(D_real))
            D_fake_loss = bce(D_fake, torch.ones_like(D_fake))

            D_loss = (D_real_loss + D_fake_loss) / 2
            
            # log tensorboard
            
        disc.zero_grad()
        d_scaler.scale(D_loss).backward()
        d_scaler.step(opt_disc)
        d_scaler.update()
        
        # train generator
        with torch.cuda.amp.autocast():
            
            D_fake = disc(x, y_fake)

            # compute fake loss
            # trick discriminator to believe these are real, hence send in torch.oneslikedfake
            G_fake_loss = bce(D_fake, torch.ones_like(D_fake))

            # compute L1 loss
            L1 = l1(y_fake, y) * args.l1_lambda

            G_loss = G_fake_loss + L1
            
            # log tensorboard
           
        opt_gen.zero_grad()
        g_scaler.scale(G_loss).backward()
        g_scaler.step(opt_gen)
        g_scaler.update()
        
        # print epoch, generator loss, discriminator loss
        print(f'[Epoch {epoch}/{args.num_epochs} (b: {idx})] [D loss: {D_loss}, D real loss: {D_real_loss}, D fake loss: {D_fake_loss}] [G loss: ##{G_loss}, G fake loss: {G_fake_loss}, L1 loss: {L1}]')
        output = f'[Epoch {epoch}/{args.num_epochs} (b: {idx})] [D loss: {D_loss}, D real loss: {D_real_loss}, D fake loss: {D_fake_loss}] [G loss: ##{G_loss}, G fake loss: {G_fake_loss}, L1 loss: {L1}]\n'
        total_output+=output



    runtime_log = get_json_file_from_s3(runtime_log_folder, runtime_log_file_name)
    runtime_log += total_output
    upload_json_file_to_s3(runtime_log_folder,runtime_log_file_name,json.dumps(runtime_log))



def __getitem__(self, index):
    print("Index ",index)
    pair_key = self.list_files[index]
    print("Pair key ",pair_key)
    pair = Boto.s3_client.list_objects(Bucket=Boto.bucket_name, Prefix=pair_key, Delimiter='/')

    input_image_key = pair.get('Contents')[1].get('Key')
    input_image_path = f's3://{Boto.bucket_name}/{input_image_key}'
    print("Input image path ",input_image_path)
    input_image_s3_source = get_file_from_filepath(input_image_path)
    input_image = np.array(Image.open(input_image_s3_source))

    target_image_key = pair.get('Contents')[0].get('Key')
    target_image_path = f's3://{Boto.bucket_name}/{target_image_key}'
    print("Target image path ",target_image_path)
    target_image_s3_source = get_file_from_filepath(target_image_path)
    target_image = np.array(Image.open(target_image_s3_source))

    augmentations = config.both_transform(image=input_image, image0=target_image)

    # get input image and target image by doing augmentations of images
    input_image, target_image = augmentations['image'], augmentations['image0']

    input_image = config.transform_only_input(image=input_image)['image']
    target_image = config.transform_only_mask(image=target_image)['image']
    
    print("Input image size ",input_image.size())
    print("Target image size ",target_image.size())
    
    return input_image, target_image

我进行了多次运行，这是我的批次大小为248的失败点的痕迹

i) 2022-06-03-05-00-04-pix2pix-U12239-2022-05-09-14-39-18-training
No index shown
[Epoch 0/100 (b: 0)]

ii) 2022-06-03-05-16-49-pix2pix-U12239-2022-05-09-14-39-18-training
Index  160
[Epoch 0/100 (b: 0)]

iii) 2022-06-03-05-44-46-pix2pix-U12239-2022-05-09-14-39-18-training
Index  160
[Epoch 0/100 (b: 0)]

iv) 2022-06-03-06-08-33-pix2pix-U12239-2022-05-09-14-39-18-training
Index  160
[Epoch 1/100 (b: 0)]

v) 2022-06-15-02-49-20-pix2pix-U12239-2022-05-09-14-39-18-training
Index  160
Pair key  datasets/training-data/testing/2022-05-09-14-39-18/match-raws-finals/U12239/P423712/Pair_71/
[Epoch 0/100 (b: 0)

vi) 2022-06-15-02-59-43-pix2pix-U12239-2022-05-09-14-39-18-training
Index  64
Pair key  datasets/training-data/testing/2022-05-09-14-39-18/match-raws-finals/U12239/P425642/Pair_27/
[Epoch 0/100 (b: 247)]

vii) 2022-06-15-04-49-33-pix2pix-U12239-2022-05-09-14-39-18-training
Index  64
Pair key  datasets/training-data/testing/2022-05-09-14-39-18/match-raws-finals/U12239/P415414/Pair_124/
No specific epoch 

，因此您可以看到它似乎在批处理（0）的开始或末尾（247）失败。另外，GET项目中有一些常见的索引似乎导致其失败，即索引64和索引160。但是，数据集中似乎没有一个常见的数据点会导致其失败，如可见的那样从配对密钥中，数据集中的所有3个数据点都不同。

有人知道为什么会发生此错误吗？

Answer 1

尝试在Sagemaker培训工作之外运行相同的培训脚本并查看会发生什么。
如果该错误未发生在独立脚本上，请尝试以本地射手制造商培训工作，因此您可以在几秒钟内而不是几分钟而不是几分钟来复制它，并可能使用调试器来找出什么是问题。