✨ update image classification example (#13824)

* ✨ update image classification example

* 📌 update reqs

examples/pytorch/image-classification/requirements.txt

-torch>=1.9.0
-torchvision>=0.10.0
\ No newline at end of file
+torch>=1.5.0
+torchvision>=0.6.0
+datasets>=1.8.0
\ No newline at end of file

examples/pytorch/image-classification/run_image_classification.py

@@ -56,7 +56,7 @@ logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
 check_min_version("4.12.0.dev0")
 
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@@ -102,7 +102,6 @@ class DataTrainingArguments:
             "value if set."
         },
     )
-    image_size: Optional[int] = field(default=224, metadata={"help": " The size (resolution) of each image."})
 
     def __post_init__(self):
         data_files = dict()
@@ -210,35 +209,6 @@ def main():
         task="image-classification",
     )
 
-    # Define torchvision transforms to be applied to each image.
-    normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-    _train_transforms = Compose(
-        [
-            RandomResizedCrop(data_args.image_size),
-            RandomHorizontalFlip(),
-            ToTensor(),
-            normalize,
-        ]
-    )
-    _val_transforms = Compose(
-        [
-            Resize(data_args.image_size),
-            CenterCrop(data_args.image_size),
-            ToTensor(),
-            normalize,
-        ]
-    )
-
-    def train_transforms(example_batch):
-        """Apply _train_transforms across a batch."""
-        example_batch["pixel_values"] = [_train_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
-        return example_batch
-
-    def val_transforms(example_batch):
-        """Apply _val_transforms across a batch."""
-        example_batch["pixel_values"] = [_val_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
-        return example_batch
-
     # 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 ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
@@ -281,20 +251,42 @@ def main():
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
     )
-    # NOTE - We aren't directly using this feature extractor since we defined custom transforms above.
-    # We initialize this instance below and pass it to Trainer to ensure that the feature extraction
-    # config, preprocessor_config.json, is included in output directories.
-    # This way if we push a model to the hub, the inference widget will work.
     feature_extractor = AutoFeatureExtractor.from_pretrained(
         model_args.feature_extractor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
-        size=data_args.image_size,
-        image_mean=normalize.mean,
-        image_std=normalize.std,
     )
 
+    # Define torchvision transforms to be applied to each image.
+    normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+    _train_transforms = Compose(
+        [
+            RandomResizedCrop(feature_extractor.size),
+            RandomHorizontalFlip(),
+            ToTensor(),
+            normalize,
+        ]
+    )
+    _val_transforms = Compose(
+        [
+            Resize(feature_extractor.size),
+            CenterCrop(feature_extractor.size),
+            ToTensor(),
+            normalize,
+        ]
+    )
+
+    def train_transforms(example_batch):
+        """Apply _train_transforms across a batch."""
+        example_batch["pixel_values"] = [_train_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
+        return example_batch
+
+    def val_transforms(example_batch):
+        """Apply _val_transforms across a batch."""
+        example_batch["pixel_values"] = [_val_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
+        return example_batch
+
     if training_args.do_train:
         if "train" not in ds:
             raise ValueError("--do_train requires a train dataset")