Update examples

examples/contrastive-image-text/run_bridgetower.py

@@ -617,7 +617,11 @@ def transform_images(examples):
         trainer.save_metrics("test", metrics)
 
     # 12. Write Training Stats and push to hub.
-    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"}
+    finetuned_from = model_args.model_name_or_path
+    # If from a local directory, don't set `finetuned_from` as this is required to be a valid repo. id on the Hub.
+    if os.path.isdir(finetuned_from):
+        finetuned_from = None
+    kwargs = {"finetuned_from": finetuned_from, "tasks": "contrastive-image-text-modeling"}
     if data_args.dataset_name is not None:
         kwargs["dataset_tags"] = data_args.dataset_name
         if data_args.dataset_config_name is not None:

examples/contrastive-image-text/run_clip.py

@@ -603,7 +603,11 @@ def filter_corrupt_images(examples):
         trainer.save_metrics("eval", metrics)
 
     # 11. Write Training Stats and push to hub.
-    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"}
+    finetuned_from = model_args.model_name_or_path
+    # If from a local directory, don't set `finetuned_from` as this is required to be a valid repo. id on the Hub.
+    if os.path.isdir(finetuned_from):
+        finetuned_from = None
+    kwargs = {"finetuned_from": finetuned_from, "tasks": "contrastive-image-text-modeling"}
     if data_args.dataset_name is not None:
         kwargs["dataset_tags"] = data_args.dataset_name
         if data_args.dataset_config_name is not None:

tests/example_diff/run_image_classification.txt

@@ -30,84 +30,30 @@
 > # Will error if the minimal version of Transformers and Optimum Habana are not installed. Remove at your own risks.
 > check_min_version("4.34.0")
 > check_optimum_habana_min_version("1.8.1")
-113a122,129
->     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'."},
->     )
-178,183d193
-< 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}
-< 
-< 
-189c199
+191c199
 <     parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
 ---
 >     parser = HfArgumentParser((ModelArguments, DataTrainingArguments, GaudiTrainingArguments))
-226a237,243
+228a237,243
 >     gaudi_config = GaudiConfig.from_pretrained(
 >         training_args.gaudi_config_name,
 >         cache_dir=model_args.cache_dir,
 >         revision=model_args.model_revision,
 >         use_auth_token=True if model_args.use_auth_token else None,
 >     )
 > 
-227a245
+229a245
 >     mixed_precision = training_args.bf16 or gaudi_config.use_torch_autocast
-229,230c247,249
+231,232c247,249
 <         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
 <         + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
 ---
 >         f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
 >         + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, "
 >         + f"mixed-precision training: {mixed_precision}"
-258d276
-<             task="image-classification",
-271d288
-<             task="image-classification",
-273a291,309
->     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}
-> 
-283c319
-<     labels = dataset["train"].features["labels"].names
----
->     labels = dataset["train"].features[data_args.label_column_name].names
-357c393
-<             _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]
-363c399,401
-<         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]
->         ]
-387c425
+408c425
 <     trainer = Trainer(
 ---
 >     trainer = GaudiTrainer(
-388a427
+409a427
 >         gaudi_config=gaudi_config,