Add expected output to the sample code for ViTMSNForImageClassification (#19183)

* chore: add expected output to the sample code.

* add: imagenet-1k labels to the model config.

* chore: apply code formatting.

* chore: change the expected output.

src/transformers/models/vit_msn/convert_msn_to_pytorch.py

@@ -15,11 +15,13 @@
 """Convert ViT MSN checkpoints from the original repository: https://github.com/facebookresearch/msn"""
 
 import argparse
+import json
 
 import torch
 from PIL import Image
 
 import requests
+from huggingface_hub import hf_hub_download
 from transformers import ViTFeatureExtractor, ViTMSNConfig, ViTMSNModel
 from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
@@ -147,6 +149,13 @@ def convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path):
     config = ViTMSNConfig()
     config.num_labels = 1000
 
+    repo_id = "datasets/huggingface/label-files"
+    filename = "imagenet-1k-id2label.json"
+    id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
+    id2label = {int(k): v for k, v in id2label.items()}
+    config.id2label = id2label
+    config.label2id = {v: k for k, v in id2label.items()}
+
     if "s16" in checkpoint_url:
         config.hidden_size = 384
         config.intermediate_size = 1536

src/transformers/models/vit_msn/modeling_vit_msn.py

@@ -632,6 +632,8 @@ class ViTMSNForImageClassification(ViTMSNPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
+        >>> torch.manual_seed(2)
+
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
@@ -644,6 +646,7 @@ class ViTMSNForImageClassification(ViTMSNPreTrainedModel):
         >>> # model predicts one of the 1000 ImageNet classes
         >>> predicted_label = logits.argmax(-1).item()
         >>> print(model.config.id2label[predicted_label])
+        Kerry blue terrier
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict