Migrate metric to Evaluate in Pytorch examples (#18369)

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -21,7 +21,6 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
-import datasets
 import numpy as np
 import torch
 from datasets import load_dataset
@@ -30,6 +29,7 @@ from torch import nn
 from torchvision import transforms
 from torchvision.transforms import functional
 
+import evaluate
 import transformers
 from huggingface_hub import hf_hub_download
 from transformers import (
@@ -337,7 +337,7 @@ def main():
     label2id = {v: str(k) for k, v in id2label.items()}
 
     # Load the mean IoU metric from the datasets package
-    metric = datasets.load_metric("mean_iou")
+    metric = evaluate.load("mean_iou")
 
     # 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/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -24,13 +24,14 @@ from pathlib import Path
 import datasets
 import numpy as np
 import torch
-from datasets import load_dataset, load_metric
+from datasets import load_dataset
 from PIL import Image
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from torchvision.transforms import functional
 from tqdm.auto import tqdm
 
+import evaluate
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
@@ -500,7 +501,7 @@ def main():
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
     # Instantiate metric
-    metric = load_metric("mean_iou")
+    metric = evaluate.load("mean_iou")
 
     # We need to initialize the trackers we use, and also store our configuration.
     # We initialize the trackers only on main process because `accelerator.log`