# Loading div2k from super-image into Pytorch **URL:** **Author:** ![lihengc6](https://avatars.discourse-cdn.com/v4/letter/l/8e8cbc/32.png) [@lihengc6](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Flihengc6)%5C **Post date:** [September 14, 2021, 2:58am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floading-div2k-from-super-image-into-pytorch%2F9961%2F1 "2021-09-14T02:58:55Z") I am following [this page](https://github.com/eugenesiow/super-image) to load div2k from super-image, and I want to convert it so that I can train my model built in PyTorch. To do that I am following [Using a Dataset with PyTorch/Tensorflow — datasets 1.11.0 documentation](https://huggingface.co/docs/datasets/torch_tensorflow.html). However, I got an error ```python TypeError: new(): invalid data type 'numpy.str_' ``` My code is ```python from datasets import load_dataset from super_image.data import EvalDataset, TrainDataset, augment_five_crop import torch augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader augmented_dataset.set_format(type='torch', columns= ["lr", "hr"]) augmented_dataset. __getitem__ (0) ``` and the full error ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in 12 13 augmented_dataset.set_format(type='torch', columns= ["lr", "hr"]) ---> 14 augmented_dataset. __getitem__ (0) ~\anaconda3\lib\site-packages\datasets\arrow_dataset.py in __getitem__ (self, key) 1515 def __getitem__ (self, key: Union[int, slice, str]) -> Union[Dict, List]: 1516 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1517 return self._getitem( 1518 key, 1519 format_type=self._format_type, ~\anaconda3\lib\site-packages\datasets\arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs) 1508 formatter = get_formatter(format_type, **format_kwargs) 1509 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1510 formatted_output = format_table( 1511 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1512 ) ~\anaconda3\lib\site-packages\datasets\formatting\formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 420 else: 421 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 422 formatted_output = formatter(pa_table_to_format, query_type=query_type) 423 if output_all_columns: 424 if isinstance(formatted_output, MutableMapping): ~\anaconda3\lib\site-packages\datasets\formatting\formatting.py in __call__ (self, pa_table, query_type) 192 def __call__ (self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 193 if query_type == "row": --> 194 return self.format_row(pa_table) 195 elif query_type == "column": 196 return self.format_column(pa_table) ~\anaconda3\lib\site-packages\datasets\formatting\torch_formatter.py in format_row(self, pa_table) 57 def format_row(self, pa_table: pa.Table) -> dict: 58 row = self.numpy_arrow_extractor().extract_row(pa_table) ---> 59 return self.recursive_tensorize(row) 60 61 def format_column(self, pa_table: pa.Table) -> "torch.Tensor": ~\anaconda3\lib\site-packages\datasets\formatting\torch_formatter.py in recursive_tensorize(self, data_struct) 53 54 def recursive_tensorize(self, data_struct: dict): ---> 55 return map_nested(self._recursive_tensorize, data_struct, map_list=False) 56 57 def format_row(self, pa_table: pa.Table) -> dict: ~\anaconda3\lib\site-packages\datasets\utils\py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types) 202 num_proc = 1 203 if num_proc <= 1 or len(iterable) <= num_proc: --> 204 mapped = [ 205 _single_map_nested((function, obj, types, None, True)) 206 for obj in utils.tqdm(iterable, disable=disable_tqdm) ~\anaconda3\lib\site-packages\datasets\utils\py_utils.py in (.0) 203 if num_proc <= 1 or len(iterable) <= num_proc: 204 mapped = [ --> 205 _single_map_nested((function, obj, types, None, True)) 206 for obj in utils.tqdm(iterable, disable=disable_tqdm) 207 ] ~\anaconda3\lib\site-packages\datasets\utils\py_utils.py in _single_map_nested(args) 141 # Singleton first to spare some computation 142 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 143 return function(data_struct) 144 145 # Reduce logging to keep things readable in multiprocessing with tqdm ~\anaconda3\lib\site-packages\datasets\formatting\torch_formatter.py in _recursive_tensorize(self, data_struct) 50 if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects 51 return [self.recursive_tensorize(substruct) for substruct in data_struct] ---> 52 return self._tensorize(data_struct) 53 54 def recursive_tensorize(self, data_struct: dict): ~\anaconda3\lib\site-packages\datasets\formatting\torch_formatter.py in _tensorize(self, value) 42 default_dtype = {"dtype": torch.float32} 43 ---> 44 return torch.tensor(value, **{** default_dtype, **self.torch_tensor_kwargs}) 45 46 def _recursive_tensorize(self, data_struct: dict): TypeError: new(): invalid data type 'numpy.str_' ``` The same error is thrown if I use PyTorch DataLoader ```python my_ldr = torch.utils.data.DataLoader(augmented_dataset, batch_size=1, shuffle=False) next(iter(my_ldr)) ``` ---
**Author:** ![lhoestq](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lhoestq/32/64813_2.png) [@lhoestq](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Flhoestq)%5C **Post date:** [September 14, 2021, 3:13pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floading-div2k-from-super-image-into-pytorch%2F9961%2F2 "2021-09-14T15:13:31Z")
Hi ! What are the feature types of your dataset ? ```auto print(augmented_dataset.features) ``` The ‘torch’ formats only work for columns of numeric data, since strings can’t be converted to pytorch tensors. Could it be that one of “lr” and “hr” has text data ? ---
**Author:** ![lihengc6](https://avatars.discourse-cdn.com/v4/letter/l/8e8cbc/32.png) [@lihengc6](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Flihengc6)%5C **Post date:** [September 15, 2021, 1:31am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floading-div2k-from-super-image-into-pytorch%2F9961%2F3 "2021-09-15T01:31:09Z")
Hi @lhoestq, Yes, the features are “lr” and “hr”. `augmented_dataset` comes in as the dataset.Dataset class once I load them. This is what augmented\_dataset looks like ```python Dataset({ features: ['hr', 'lr'], num_rows: 4000 }) ``` If I try using numbers instead, ```python augmented_dataset.set_format(type='torch', columns= [0,1]) ``` I get this errorr ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 augmented_dataset.set_format(type='torch', columns= [0,1]) ~\anaconda3\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~\anaconda3\lib\site-packages\datasets\arrow_dataset.py in set_format(self, type, columns, output_all_columns, **format_kwargs) 1349 columns = [columns] 1350 if columns is not None and any(col not in self._data.column_names for col in columns): -> 1351 raise ValueError( 1352 "Columns {} not in the dataset. Current columns in the dataset: {}".format( 1353 list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names ValueError: Columns [0, 1] not in the dataset. Current columns in the dataset: ['hr', 'lr'] ``` I’ve reached the point where I should set `columns=['hr', 'lr']` but also I can’t use string type to represent them. This seems a bit contradicting. ---
**Author:** ![lhoestq](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lhoestq/32/64813_2.png) [@lhoestq](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Flhoestq)%5C **Post date:** [September 15, 2021, 9:59am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Floading-div2k-from-super-image-into-pytorch%2F9961%2F4 "2021-09-15T09:59:51Z")
As you said you have to pass the column names as strings in `set_format`: `columns=['hr', 'lr']`. However this is the feature type of the data itself of each column that may actually cause the issue. When I was talking about string type, I was referring to the type of the data inside each column (for example the type of `augmented_dataset[0]["hr"]`) You can check if the feature type of each column with ```auto print(augmented_dataset.features) ``` or you can directly check the first row `augmented_dataset[0]`