# Tutorial WRITING CUSTOM DATASETS, DATALOADERS AND TRANSFORMS tutorial

**URL:** <https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788>\
**Category:** Uncategorized\
**Created:** [June 22, 2022, 12:47pm UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788 "2022-06-22T12:47:37Z")\
**Posts on this page:** 10\
**Page:** 1

<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 22, 2022, 12:47pm UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/1 "2022-06-22T12:47:37Z")

</div>

Good afternoon!  
I have questions about the following tutorial:  
[https://pytorch.org/tutorials/beginner/data\_loading\_tutorial.html](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html)

I have a similar dataset (images + landmarks). I’ve built the custom dataloader following the tutorial and checked the types of dataloader components (torch.float64 for both images and landmarks).

Then I applied the dataloader to the classification model with this training class:

```python
class Trainer():
  
    def __init__ (self,criterion = None,optimizer = None,schedular = None):
      
        self.criterion = criterion
        self.optimizer = optimizer
        self.schedular = schedular
  
    def train_batch_loop(self,model,train_dataloader):    
        train_loss = 0.0
        train_acc = 0.0     
        for images,landmarks, labels in train_dataloader: 
                images = images.to(device)
                landmarks = landmarks.to(device)
                labels = labels.to(device)        
                 self.optimizer.zero_grad()

```

I won’t be elaborating further because the training crushes at **images = images.to(device)** with the following error: **AttributeError: ‘str’ object has no attribute 'to’**

I don’t understand where this string is coming from if all the dataloader components are torch.float64.  
I went back to check the initial data: in the tutorial, the landmarks are summarized in a pandas dataframe with landmark values as int64 and image name as “object”.  
In my summary dataframe image name is an “object” as well and landmarks are numpy.float64. Again, no strings anywhere…

I will share the code for creating a summary datatable in the first comment.  
Appreciate any ideas!

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<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 22, 2022, 12:55pm UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/2 "2022-06-22T12:55:54Z")

</div>

```python
def load_landmarks(path, header):
    filename = path.split("\\")[-1].replace("txt", "png")
    instance = np.swapaxes(pd.read_csv(path, delimiter ="\t", index_col=False).values, 0,1)
    data_list = list(zip(instance[1], instance[2]))
    flattened = [item for sublist in data_list for item in sublist]
    flattened.insert(0, filename)
    frame = pd.DataFrame(np.array(flattened).reshape(-1,len(flattened)), columns = header)
    return frame

```

```python
def data_summary(path, num_landmarks):
    header = ['image_name']
    for i in range(num_landmarks):
        header += ['landmark_{}_x'.format(i), 'landmark_{}_y'.format(i)]
    data_table = pd.DataFrame(columns = header)
    labels_lst = []
    for label in os.listdir(path):
        for roots, dirs, filenames in os.walk(os.path.join(path, label)):
            for file in filenames:
                filename = os.path.splitext(file)
                if filename[1] == ".png":
                    label = label
                    cat_number = file.split("_")[1]
                    labels_lst.append(tuple((int(label), cat_number)))
                    shutil.copy(os.path.join(roots, file), images_path)
                if filename[1] == ".txt":
                    temp_path = os.path.abspath(os.path.join(path, label, file))
                    landmark = load_landmarks(temp_path, header)
                    data_table = data_table.append(landmark)
    labels = [i[0] for i in labels_lst]
    cat_numbers = [i[1] for i in labels_lst]
    
    data_table.insert(0, 'cat_number', cat_numbers)
    data_table.insert(1, 'label', labels)
    data_table['cat_number'] = data_table['cat_number'].astype('int64')
    return data_table

```

```python
class FaceLandmarksDataset(Dataset):
    def __init__ (self, data_frame, root_dir, transform=None):
        self.data_frame = data_frame
        self.root_dir = root_dir
        self.transform = transform

    def __len__ (self):
        return len(self.data_frame)

    def __getitem__ (self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        img_name = os.path.join(self.root_dir, self.data_frame.iloc[idx, 2])
        image = io.imread(img_name)
        landmarks = self.data_frame.iloc[idx, 3:]
        landmarks = np.array([landmarks])
        landmarks = landmarks.astype('float').reshape(-1, 2)
        labels = self.data_frame.iloc[idx, 1].reshape(1)
        sample = {'image': image, 'landmarks': landmarks, 'labels': labels}

        if self.transform:
            sample = self.transform(sample)

        return sample

```

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [June 23, 2022, 7:41am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/3 "2022-06-23T07:41:44Z")

</div>

Print the `dtypes` of the `sample` in the ` __getitem__ ` before and after applying `self.transform` on them and check if some of them are strings.

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<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 23, 2022, 7:55am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/4 "2022-06-23T07:55:59Z")

</div>

Thanks for your response.  
I’ve checked that - maybe I did something wrong?

Before transformation:

```auto
face_dataset = FaceLandmarksDataset(full_data_table, images_path)
for i in range(len(face_dataset)):
    sample = face_dataset[i]
    print(i, sample['image'].shape, sample['landmarks'].shape, sample['labels'].shape)
    print(i, sample['image'].dtype, sample['landmarks'].dtype, sample['labels'].dtype)

    # image - (1000, 1000, 4), uint8 
    # landmarks - (48, 2), float64
    # label - (1,), int64

```

After transformation:

```auto
scale = Rescale(224)
transformed_dataset = FaceLandmarksDataset(full_data_table, images_path,
                                           transform=transforms.Compose([scale, ToTensor()]))

for i in range(len(transformed_dataset)):
    sample = transformed_dataset[i]

    print(i, sample['image'].size(), sample['landmarks'].size(), sample['labels'].size())
    print(i, sample['image'].dtype, sample['landmarks'].dtype, sample['labels'].dtype)

    # image - [4, 224, 224]), torch.float64 
    # landmarks - torch.Size([48, 2]), torch.float64
    # label - torch.Size([1]), torch.int64

```

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [June 23, 2022, 8:53am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/5 "2022-06-23T08:53:19Z")

</div>

The `dtypes` look correct. Are you seeing the error in a specific iteration or directly in the first one?  
In the former case, you could keep the print statements, start the training, and check if the `dtype` changes in one iteration. I would recommend not to shuffle the dataset so that you could check which index was used to create the data.

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<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 23, 2022, 9:04am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/6 "2022-06-23T09:04:30Z")

</div>

The error shows up directly in the first iteration and I don’t shuffle the data at Dataloader stage. the dataset, fed into dataloader already comes shuffled.

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [June 23, 2022, 9:08am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/7 "2022-06-23T09:08:06Z")

</div>

OK, that’s interesting as it seems that iterating `transformed_dataset` creates the expected tensors while iterating the `DataLoader` returns a string and causes the error.  
Could you use a batch size of 1 and iterate the `DataLoader` again with the same print statements?

---

<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 23, 2022, 9:17am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/8 "2022-06-23T09:17:02Z")

</div>

Tried - same results. Still the strings are not found.

Is it possible that somewhere during data processing dataloader gets an image name instead of an actual image? Where can it be (maybe data\_summary function?)

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [June 23, 2022, 9:23am UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/9 "2022-06-23T09:23:04Z")

</div>

If the strings are not found anymore, `images = images.to(device)` wouldn’t be failing with `AttributeError: ‘str’ object has no attribute 'to’`, would it?

In case it’s still failing you, it seems you are hitting these issues now:

- the data has definitely the correct `dtype` in the ` __getitem__ ` before the `return` statement
- inside the `DataLoader` loop the `images` are strings and the `to` call is failing

If so, then the place to look next into would be the `collate_fn` and I assume you might be using a custom one. By default this method stacks the samples returned by `Dataset. __getitem__ ` into a batch and does not manipulate the data.  
However, I don’t know if a custom `collate_fn` is used which might not be interacting nicely with the returned `dict`.

Another idea: are you indexing the `dict` with the right keys or are you iterating the returned `dict` and are trying to use the `keys` as the data?  
Remove the `dict` usage for the sake of debugging and just return the samples:

```python
        sample = {'image': image, 'landmarks': landmarks, 'labels': labels}

        if self.transform:
            sample = self.transform(sample)

        return sample['image'], sample['landmarks'], sample['labels']

```

---

<div class="post-metadata">

**Author:** ![Anna\_Inberg](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/anna_inberg/32/44661_2.png) [@Anna\_Inberg](https://discuss.pytorch.org/u/Anna_Inberg)\
**Post date:** [June 26, 2022, 12:19pm UTC](https://discuss.pytorch.org/t/tutorial-writing-custom-datasets-dataloaders-and-transforms-tutorial/154788/10 "2022-06-26T12:19:02Z")

</div>

Thanks for your response. I tried the dict manipulation you suggested, dtypes are still torch floats.

I’ve just found the string. The for-loop in Trainer class “for images,landmarks, labels in train\_dataloader: …” is iterating incorrectly over the dataloder.  
Dataoader dtypes are fine, but this for-loop returns strings instead of dataloader items.

![image](https://discuss.pytorch.org/uploads/default/original/3X/9/0/90dc3f149e72da54057aa1e3176a56ba11181b75.png)

the printout shows “str”

Now I need to figure out the way to fix it 🙂