Rename valid_ to val_

README.md

@@ -129,7 +129,7 @@ download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'da
 # 2. Load the data
 datamodule = ImageClassificationData.from_folders(
     train_folder="data/hymenoptera_data/train/",
-    valid_folder="data/hymenoptera_data/val/",
+    val_folder="data/hymenoptera_data/val/",
     test_folder="data/hymenoptera_data/test/",
 )
 
@@ -205,11 +205,11 @@ download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
 
 # 2. Load the data
 datamodule = SummarizationData.from_files(
-  train_file="data/xsum/train.csv",
-  valid_file="data/xsum/valid.csv",
-  test_file="data/xsum/test.csv",
-  input="input",
-  target="target"
+    train_file="data/xsum/train.csv",
+    val_file="data/xsum/valid.csv",
+    test_file="data/xsum/test.csv",
+    input="input",
+    target="target"
 )
 
 # 3. Build the model

flash/data/data_module.py

@@ -32,7 +32,7 @@ class DataModule(pl.LightningDataModule):
 
     Args:
         train_dataset: Dataset for training. Defaults to None.
-        valid_dataset: Dataset for validating model performance during training. Defaults to None.
+        val_dataset: Dataset for validating model performance during training. Defaults to None.
         test_dataset: Dataset to test model performance. Defaults to None.
         predict_dataset: Dataset to predict model performance. Defaults to None.
         num_workers: The number of workers to use for parallelized loading. Defaults to None.
@@ -49,7 +49,7 @@ class DataModule(pl.LightningDataModule):
     def __init__(
         self,
         train_dataset: Optional[Dataset] = None,
-        valid_dataset: Optional[Dataset] = None,
+        val_dataset: Optional[Dataset] = None,
         test_dataset: Optional[Dataset] = None,
         predict_dataset: Optional[Dataset] = None,
         batch_size: int = 1,
@@ -58,14 +58,14 @@ def __init__(
 
         super().__init__()
         self._train_ds = train_dataset
-        self._valid_ds = valid_dataset
+        self._val_ds = val_dataset
         self._test_ds = test_dataset
         self._predict_ds = predict_dataset
 
         if self._train_ds:
             self.train_dataloader = self._train_dataloader
 
-        if self._valid_ds:
+        if self._val_ds:
             self.val_dataloader = self._val_dataloader
 
         if self._test_ds:
@@ -104,8 +104,8 @@ def set_running_stages(self):
         if self._train_ds:
             self.set_dataset_attribute(self._train_ds, 'running_stage', RunningStage.TRAINING)
 
-        if self._valid_ds:
-            self.set_dataset_attribute(self._valid_ds, 'running_stage', RunningStage.VALIDATING)
+        if self._val_ds:
+            self.set_dataset_attribute(self._val_ds, 'running_stage', RunningStage.VALIDATING)
 
         if self._test_ds:
             self.set_dataset_attribute(self._test_ds, 'running_stage', RunningStage.TESTING)
@@ -130,13 +130,13 @@ def _train_dataloader(self) -> DataLoader:
         )
 
     def _val_dataloader(self) -> DataLoader:
-        valid_ds: Dataset = self._valid_ds() if isinstance(self._valid_ds, Callable) else self._valid_ds
+        val_ds: Dataset = self._val_ds() if isinstance(self._val_ds, Callable) else self._val_ds
         return DataLoader(
-            valid_ds,
+            val_ds,
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             pin_memory=True,
-            collate_fn=self._resolve_collate_fn(valid_ds, RunningStage.VALIDATING)
+            collate_fn=self._resolve_collate_fn(val_ds, RunningStage.VALIDATING)
         )
 
     def _test_dataloader(self) -> DataLoader:
@@ -214,10 +214,10 @@ def autogenerate_dataset(
         return AutoDataset(data, whole_data_load_fn, per_sample_load_fn, data_pipeline, running_stage=running_stage)
 
     @staticmethod
-    def train_valid_test_split(
+    def train_val_test_split(
         dataset: torch.utils.data.Dataset,
         train_split: Optional[Union[float, int]] = None,
-        valid_split: Optional[Union[float, int]] = None,
+        val_split: Optional[Union[float, int]] = None,
         test_split: Optional[Union[float, int]] = None,
         seed: Optional[int] = 1234,
     ) -> Tuple[Dataset, Optional[Dataset], Optional[Dataset]]:
@@ -227,11 +227,11 @@ def train_valid_test_split(
             dataset: Dataset to be split.
             train_split: If Float, ratio of data to be contained within the train dataset. If Int,
                 number of samples to be contained within train dataset.
-            valid_split: If Float, ratio of data to be contained within the validation dataset. If Int,
+            val_split: If Float, ratio of data to be contained within the validation dataset. If Int,
                 number of samples to be contained within test dataset.
             test_split: If Float, ratio of data to be contained within the test dataset. If Int,
                 number of samples to be contained within test dataset.
-            seed: Used for the train/val splits when valid_split is not None.
+            seed: Used for the train/val splits when val_split is not None.
 
         """
         n = len(dataset)
@@ -243,12 +243,12 @@ def train_valid_test_split(
         else:
             _test_length = test_split
 
-        if valid_split is None:
+        if val_split is None:
             _val_length = 0
-        elif isinstance(valid_split, float):
-            _val_length = int(n * valid_split)
+        elif isinstance(val_split, float):
+            _val_length = int(n * val_split)
         else:
-            _val_length = valid_split
+            _val_length = val_split
 
         if train_split is None:
             _train_length = n - _val_length - _test_length
@@ -265,7 +265,7 @@ def train_valid_test_split(
         train_ds, val_ds, test_ds = torch.utils.data.random_split(
             dataset, [_train_length, _val_length, _test_length], generator
         )
-        if valid_split is None:
+        if val_split is None:
             val_ds = None
         if test_split is None:
             test_ds = None
@@ -293,7 +293,7 @@ def _generate_dataset_if_possible(
     def from_load_data_inputs(
         cls,
         train_load_data_input: Optional[Any] = None,
-        valid_load_data_input: Optional[Any] = None,
+        val_load_data_input: Optional[Any] = None,
         test_load_data_input: Optional[Any] = None,
         predict_load_data_input: Optional[Any] = None,
         preprocess: Optional[Preprocess] = None,
@@ -306,7 +306,7 @@ def from_load_data_inputs(
         Args:
             cls: ``DataModule`` subclass
             train_load_data_input: Data to be received by the ``train_load_data`` function from this ``Preprocess``
-            valid_load_data_input: Data to be received by the ``val_load_data`` function from this ``Preprocess``
+            val_load_data_input: Data to be received by the ``val_load_data`` function from this ``Preprocess``
             test_load_data_input: Data to be received by the ``test_load_data`` function from this ``Preprocess``
             predict_load_data_input: Data to be received by the ``predict_load_data`` function from this ``Preprocess``
             kwargs: Any extra arguments to instantiate the provided ``DataModule``
@@ -322,8 +322,8 @@ def from_load_data_inputs(
         train_dataset = cls._generate_dataset_if_possible(
             train_load_data_input, running_stage=RunningStage.TRAINING, data_pipeline=data_pipeline
         )
-        valid_dataset = cls._generate_dataset_if_possible(
-            valid_load_data_input, running_stage=RunningStage.VALIDATING, data_pipeline=data_pipeline
+        val_dataset = cls._generate_dataset_if_possible(
+            val_load_data_input, running_stage=RunningStage.VALIDATING, data_pipeline=data_pipeline
         )
         test_dataset = cls._generate_dataset_if_possible(
             test_load_data_input, running_stage=RunningStage.TESTING, data_pipeline=data_pipeline
@@ -333,7 +333,7 @@ def from_load_data_inputs(
         )
         datamodule = cls(
             train_dataset=train_dataset,
-            valid_dataset=valid_dataset,
+            val_dataset=val_dataset,
             test_dataset=test_dataset,
             predict_dataset=predict_dataset,
             **kwargs

flash/data/process.py

@@ -87,13 +87,13 @@ class Preprocess(Properties, torch.nn.Module):
     def __init__(
         self,
         train_transform: Optional[Union[Callable, Module, Dict[str, Callable]]] = None,
-        valid_transform: Optional[Union[Callable, Module, Dict[str, Callable]]] = None,
+        val_transform: Optional[Union[Callable, Module, Dict[str, Callable]]] = None,
         test_transform: Optional[Union[Callable, Module, Dict[str, Callable]]] = None,
         predict_transform: Optional[Union[Callable, Module, Dict[str, Callable]]] = None,
     ):
         super().__init__()
         self.train_transform = convert_to_modules(train_transform)
-        self.valid_transform = convert_to_modules(valid_transform)
+        self.val_transform = convert_to_modules(val_transform)
         self.test_transform = convert_to_modules(test_transform)
         self.predict_transform = convert_to_modules(predict_transform)
 

flash/tabular/classification/data/data.py

@@ -83,7 +83,7 @@ def state(self) -> TabularState:
     @staticmethod
     def generate_state(
         train_df: DataFrame,
-        valid_df: Optional[DataFrame],
+        val_df: Optional[DataFrame],
         test_df: Optional[DataFrame],
         predict_df: Optional[DataFrame],
         target_col: str,
@@ -100,8 +100,8 @@ def generate_state(
 
         dfs = [train_df]
 
-        if valid_df is not None:
-            dfs += [valid_df]
+        if val_df is not None:
+            dfs += [val_df]
 
         if test_df is not None:
             dfs += [test_df]
@@ -197,7 +197,7 @@ def from_csv(
         train_csv: Optional[str] = None,
         categorical_cols: Optional[List] = None,
         numerical_cols: Optional[List] = None,
-        valid_csv: Optional[str] = None,
+        val_csv: Optional[str] = None,
         test_csv: Optional[str] = None,
         predict_csv: Optional[str] = None,
         batch_size: int = 8,
@@ -215,7 +215,7 @@ def from_csv(
             target_col: The column containing the class id.
             categorical_cols: The list of categorical columns.
             numerical_cols: The list of numerical columns.
-            valid_csv: Validation data csv file.
+            val_csv: Validation data csv file.
             test_csv: Test data csv file.
             batch_size: The batchsize to use for parallel loading. Defaults to 64.
             num_workers: The number of workers to use for parallelized loading.
@@ -234,7 +234,7 @@ def from_csv(
             text_data = TabularData.from_files("train.csv", label_field="class", text_field="sentence")
         """
         train_df = pd.read_csv(train_csv, **pandas_kwargs)
-        valid_df = pd.read_csv(valid_csv, **pandas_kwargs) if valid_csv else None
+        val_df = pd.read_csv(val_csv, **pandas_kwargs) if val_csv else None
         test_df = pd.read_csv(test_csv, **pandas_kwargs) if test_csv else None
         predict_df = pd.read_csv(predict_csv, **pandas_kwargs) if predict_csv else None
 
@@ -243,7 +243,7 @@ def from_csv(
             target_col,
             categorical_cols,
             numerical_cols,
-            valid_df,
+            val_df,
             test_df,
             predict_df,
             batch_size,
@@ -268,21 +268,21 @@ def emb_sizes(self) -> list:
     @staticmethod
     def _split_dataframe(
         train_df: DataFrame,
-        valid_df: Optional[DataFrame] = None,
+        val_df: Optional[DataFrame] = None,
         test_df: Optional[DataFrame] = None,
         val_size: float = None,
         test_size: float = None,
     ):
-        if valid_df is None and isinstance(val_size, float) and isinstance(test_size, float):
+        if val_df is None and isinstance(val_size, float) and isinstance(test_size, float):
             assert 0 < val_size < 1
             assert 0 < test_size < 1
-            train_df, valid_df = train_test_split(train_df, test_size=(val_size + test_size))
+            train_df, val_df = train_test_split(train_df, test_size=(val_size + test_size))
 
         if test_df is None and isinstance(test_size, float):
             assert 0 < test_size < 1
-            valid_df, test_df = train_test_split(valid_df, test_size=test_size)
+            val_df, test_df = train_test_split(val_df, test_size=test_size)
 
-        return train_df, valid_df, test_df
+        return train_df, val_df, test_df
 
     @staticmethod
     def _sanetize_cols(cat_cols: Optional[List], num_cols: Optional[List]):
@@ -298,7 +298,7 @@ def from_df(
         target_col: str,
         categorical_cols: Optional[List] = None,
         numerical_cols: Optional[List] = None,
-        valid_df: Optional[DataFrame] = None,
+        val_df: Optional[DataFrame] = None,
         test_df: Optional[DataFrame] = None,
         predict_df: Optional[DataFrame] = None,
         batch_size: int = 8,
@@ -316,7 +316,7 @@ def from_df(
             target_col: The column containing the class id.
             categorical_cols: The list of categorical columns.
             numerical_cols: The list of numerical columns.
-            valid_df: Validation data DataFrame.
+            val_df: Validation data DataFrame.
             test_df: Test data DataFrame.
             batch_size: The batchsize to use for parallel loading. Defaults to 64.
             num_workers: The number of workers to use for parallelized loading.
@@ -334,13 +334,13 @@ def from_df(
         """
         categorical_cols, numerical_cols = cls._sanetize_cols(categorical_cols, numerical_cols)
 
-        train_df, valid_df, test_df = cls._split_dataframe(train_df, valid_df, test_df, val_size, test_size)
+        train_df, val_df, test_df = cls._split_dataframe(train_df, val_df, test_df, val_size, test_size)
 
         preprocess_cls = preprocess_cls or cls.preprocess_cls
 
         preprocess_state = preprocess_cls.generate_state(
             train_df,
-            valid_df,
+            val_df,
             test_df,
             predict_df,
             target_col,
@@ -353,7 +353,7 @@ def from_df(
 
         return cls.from_load_data_inputs(
             train_load_data_input=train_df,
-            valid_load_data_input=valid_df,
+            val_load_data_input=val_df,
             test_load_data_input=test_df,
             predict_load_data_input=predict_df,
             batch_size=batch_size,

flash/text/classification/data.py

@@ -237,7 +237,7 @@ def from_files(
         target: Optional[str] = 'labels',
         filetype: str = "csv",
         backbone: str = "prajjwal1/bert-tiny",
-        valid_file: Optional[str] = None,
+        val_file: Optional[str] = None,
         test_file: Optional[str] = None,
         predict_file: Optional[str] = None,
         max_length: int = 128,
@@ -255,7 +255,7 @@ def from_files(
             target: The field storing the class id of the associated text.
             filetype: .csv or .json
             backbone: Tokenizer backbone to use, can use any HuggingFace tokenizer.
-            valid_file: Path to validation data.
+            val_file: Path to validation data.
             test_file: Path to test data.
             batch_size: the batchsize to use for parallel loading. Defaults to 64.
             num_workers: The number of workers to use for parallelized loading.
@@ -287,7 +287,7 @@ def from_files(
 
         return cls.from_load_data_inputs(
             train_load_data_input=train_file,
-            valid_load_data_input=valid_file,
+            val_load_data_input=val_file,
             test_load_data_input=test_file,
             predict_load_data_input=predict_file,
             batch_size=batch_size,
@@ -327,7 +327,7 @@ def from_file(
             target=None,
             filetype=filetype,
             backbone=backbone,
-            valid_file=None,
+            val_file=None,
             test_file=None,
             predict_file=predict_file,
             max_length=max_length,

flash/text/seq2seq/core/data.py

@@ -168,7 +168,7 @@ def from_files(
         target: Optional[str] = None,
         filetype: str = "csv",
         backbone: str = "sshleifer/tiny-mbart",
-        valid_file: Optional[str] = None,
+        val_file: Optional[str] = None,
         test_file: Optional[str] = None,
         predict_file: Optional[str] = None,
         max_source_length: int = 128,
@@ -185,7 +185,7 @@ def from_files(
             target: The field storing the target translation text.
             filetype: ``csv`` or ``json`` File
             backbone: Tokenizer backbone to use, can use any HuggingFace tokenizer.
-            valid_file: Path to validation data.
+            val_file: Path to validation data.
             test_file: Path to test data.
             max_source_length: Maximum length of the source text. Any text longer will be truncated.
             max_target_length: Maximum length of the target text. Any text longer will be truncated.
@@ -217,7 +217,7 @@ def from_files(
 
         return cls.from_load_data_inputs(
             train_load_data_input=train_file,
-            valid_load_data_input=valid_file,
+            val_load_data_input=val_file,
             test_load_data_input=test_file,
             predict_load_data_input=predict_file,
             batch_size=batch_size,