Remove task arg in load_dataset in image-classification example (#…

…28408)

* Remove `task` arg in `load_dataset` in image-classification example

* Manage case where "train" is not in dataset

* Add new args to manage image and label column names

* Similar to audio-classification example

* Fix README

* Update tests

examples/pytorch/image-classification/README.md

@@ -41,6 +41,7 @@ python run_image_classification.py \
     --dataset_name beans \
     --output_dir ./beans_outputs/ \
     --remove_unused_columns False \
+    --label_column_name labels \
     --do_train \
     --do_eval \
     --push_to_hub \
@@ -197,7 +198,7 @@ accelerate test
 that will check everything is ready for training. Finally, you can launch training with
 
 ```bash
-accelerate launch run_image_classification_trainer.py
+accelerate launch run_image_classification_no_trainer.py --image_column_name img
 ```
 
 This command is the same and will work for:

examples/pytorch/image-classification/run_image_classification.py

@@ -111,6 +111,14 @@ class DataTrainingArguments:
             )
         },
     )
+    image_column_name: str = field(
+        default="image",
+        metadata={"help": "The name of the dataset column containing the image data. Defaults to 'image'."},
+    )
+    label_column_name: str = field(
+        default="label",
+        metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'."},
+    )
 
     def __post_init__(self):
         if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
@@ -175,12 +183,6 @@ class ModelArguments:
     )
 
 
-def collate_fn(examples):
-    pixel_values = torch.stack([example["pixel_values"] for example in examples])
-    labels = torch.tensor([example["labels"] for example in examples])
-    return {"pixel_values": pixel_values, "labels": labels}
-
-
 def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
@@ -255,7 +257,6 @@ def main():
             data_args.dataset_name,
             data_args.dataset_config_name,
             cache_dir=model_args.cache_dir,
-            task="image-classification",
             token=model_args.token,
         )
     else:
@@ -268,9 +269,27 @@ def main():
             "imagefolder",
             data_files=data_files,
             cache_dir=model_args.cache_dir,
-            task="image-classification",
         )
 
+    dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
+    if data_args.image_column_name not in dataset_column_names:
+        raise ValueError(
+            f"--image_column_name {data_args.image_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--image_column_name` to the correct audio column - one of "
+            f"{', '.join(dataset_column_names)}."
+        )
+    if data_args.label_column_name not in dataset_column_names:
+        raise ValueError(
+            f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--label_column_name` to the correct text column - one of "
+            f"{', '.join(dataset_column_names)}."
+        )
+
+    def collate_fn(examples):
+        pixel_values = torch.stack([example["pixel_values"] for example in examples])
+        labels = torch.tensor([example[data_args.label_column_name] for example in examples])
+        return {"pixel_values": pixel_values, "labels": labels}
+
     # If we don't have a validation split, split off a percentage of train as validation.
     data_args.train_val_split = None if "validation" in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
@@ -280,7 +299,7 @@ def main():
 
     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
-    labels = dataset["train"].features["labels"].names
+    labels = dataset["train"].features[data_args.label_column_name].names
     label2id, id2label = {}, {}
     for i, label in enumerate(labels):
         label2id[label] = str(i)
@@ -354,13 +373,15 @@ def compute_metrics(p):
     def train_transforms(example_batch):
         """Apply _train_transforms across a batch."""
         example_batch["pixel_values"] = [
-            _train_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]
+            _train_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name]
         ]
         return example_batch
 
     def val_transforms(example_batch):
         """Apply _val_transforms across a batch."""
-        example_batch["pixel_values"] = [_val_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]]
+        example_batch["pixel_values"] = [
+            _val_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name]
+        ]
         return example_batch
 
     if training_args.do_train:

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -189,6 +189,18 @@ def parse_args():
         action="store_true",
         help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
     )
+    parser.add_argument(
+        "--image_column_name",
+        type=str,
+        default="image",
+        help="The name of the dataset column containing the image data. Defaults to 'image'.",
+    )
+    parser.add_argument(
+        "--label_column_name",
+        type=str,
+        default="label",
+        help="The name of the dataset column containing the labels. Defaults to 'label'.",
+    )
     args = parser.parse_args()
 
     # Sanity checks
@@ -272,7 +284,7 @@ def main():
     # download the dataset.
     if args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(args.dataset_name, task="image-classification")
+        dataset = load_dataset(args.dataset_name)
     else:
         data_files = {}
         if args.train_dir is not None:
@@ -282,11 +294,24 @@ def main():
         dataset = load_dataset(
             "imagefolder",
             data_files=data_files,
-            task="image-classification",
         )
         # See more about loading custom images at
         # https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder.
 
+    dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
+    if args.image_column_name not in dataset_column_names:
+        raise ValueError(
+            f"--image_column_name {args.image_column_name} not found in dataset '{args.dataset_name}'. "
+            "Make sure to set `--image_column_name` to the correct audio column - one of "
+            f"{', '.join(dataset_column_names)}."
+        )
+    if args.label_column_name not in dataset_column_names:
+        raise ValueError(
+            f"--label_column_name {args.label_column_name} not found in dataset '{args.dataset_name}'. "
+            "Make sure to set `--label_column_name` to the correct text column - one of "
+            f"{', '.join(dataset_column_names)}."
+        )
+
     # If we don't have a validation split, split off a percentage of train as validation.
     args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
     if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
@@ -296,7 +321,7 @@ def main():
 
     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
-    labels = dataset["train"].features["labels"].names
+    labels = dataset["train"].features[args.label_column_name].names
     label2id = {label: str(i) for i, label in enumerate(labels)}
     id2label = {str(i): label for i, label in enumerate(labels)}
 
@@ -355,12 +380,16 @@ def main():
 
     def preprocess_train(example_batch):
         """Apply _train_transforms across a batch."""
-        example_batch["pixel_values"] = [train_transforms(image.convert("RGB")) for image in example_batch["image"]]
+        example_batch["pixel_values"] = [
+            train_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
+        ]
         return example_batch
 
     def preprocess_val(example_batch):
         """Apply _val_transforms across a batch."""
-        example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]]
+        example_batch["pixel_values"] = [
+            val_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
+        ]
         return example_batch
 
     with accelerator.main_process_first():
@@ -376,7 +405,7 @@ def preprocess_val(example_batch):
     # DataLoaders creation:
     def collate_fn(examples):
         pixel_values = torch.stack([example["pixel_values"] for example in examples])
-        labels = torch.tensor([example["labels"] for example in examples])
+        labels = torch.tensor([example[args.label_column_name] for example in examples])
         return {"pixel_values": pixel_values, "labels": labels}
 
     train_dataloader = DataLoader(

examples/pytorch/test_accelerate_examples.py

@@ -322,6 +322,7 @@ def test_run_image_classification_no_trainer(self):
             --output_dir {tmp_dir}
             --with_tracking
             --checkpointing_steps 1
+            --label_column_name labels
         """.split()
 
         run_command(self._launch_args + testargs)

examples/pytorch/test_pytorch_examples.py

@@ -398,6 +398,7 @@ def test_run_image_classification(self):
             --max_steps 10
             --train_val_split 0.1
             --seed 42
+            --label_column_name labels
         """.split()
 
         if is_torch_fp16_available_on_device(torch_device):