Migrate metric to Evaluate in Pytorch examples (#18369)

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

examples/pytorch/image-classification/run_image_classification.py

@@ -19,7 +19,6 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
-import datasets
 import numpy as np
 import torch
 from datasets import load_dataset
@@ -34,6 +33,7 @@ from torchvision.transforms import (
     ToTensor,
 )
 
+import evaluate
 import transformers
 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@@ -252,7 +252,7 @@ def main():
         id2label[str(i)] = label
 
     # Load the accuracy metric from the datasets package
-    metric = datasets.load_metric("accuracy")
+    metric = evaluate.load("accuracy")
 
     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -22,7 +22,7 @@ from pathlib import Path
 
 import datasets
 import torch
-from datasets import load_dataset, load_metric
+from datasets import load_dataset
 from torch.utils.data import DataLoader
 from torchvision.transforms import (
     CenterCrop,
@@ -35,6 +35,7 @@ from torchvision.transforms import (
 )
 from tqdm.auto import tqdm
 
+import evaluate
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
@@ -415,7 +416,7 @@ def main():
             accelerator.init_trackers("image_classification_no_trainer", experiment_config)
 
     # Get the metric function
-    metric = load_metric("accuracy")
+    metric = evaluate.load("accuracy")
 
     # Train!
     total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps