Remove unused test_dataset (huggingface#34516)

examples/pytorch/contrastive-image-text/run_clip.py

@@ -141,10 +141,6 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
     )
-    test_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input testing data file (a jsonlines file)."},
-    )
     max_seq_length: Optional[int] = field(
         default=128,
         metadata={
@@ -190,9 +186,6 @@ def __post_init__(self):
             if self.validation_file is not None:
                 extension = self.validation_file.split(".")[-1]
                 assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-            if self.test_file is not None:
-                extension = self.test_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
 
 
 dataset_name_mapping = {
@@ -315,9 +308,6 @@ def main():
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
             extension = data_args.validation_file.split(".")[-1]
-        if data_args.test_file is not None:
-            data_files["test"] = data_args.test_file
-            extension = data_args.test_file.split(".")[-1]
         dataset = load_dataset(
             extension,
             data_files=data_files,
@@ -387,8 +377,6 @@ def _freeze_params(module):
         column_names = dataset["train"].column_names
     elif training_args.do_eval:
         column_names = dataset["validation"].column_names
-    elif training_args.do_predict:
-        column_names = dataset["test"].column_names
     else:
         logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
         return
@@ -490,29 +478,6 @@ def filter_corrupt_images(examples):
         # Transform images on the fly as doing it on the whole dataset takes too much time.
         eval_dataset.set_transform(transform_images)
 
-    if training_args.do_predict:
-        if "test" not in dataset:
-            raise ValueError("--do_predict requires a test dataset")
-        test_dataset = dataset["test"]
-        if data_args.max_eval_samples is not None:
-            max_eval_samples = min(len(test_dataset), data_args.max_eval_samples)
-            test_dataset = test_dataset.select(range(max_eval_samples))
-
-        test_dataset = test_dataset.filter(
-            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
-        )
-        test_dataset = test_dataset.map(
-            function=tokenize_captions,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=[col for col in column_names if col != image_column],
-            load_from_cache_file=not data_args.overwrite_cache,
-            desc="Running tokenizer on test dataset",
-        )
-
-        # Transform images on the fly as doing it on the whole dataset takes too much time.
-        test_dataset.set_transform(transform_images)
-
     # 8. Initialize our trainer
     trainer = Trainer(
         model=model,