Clean up pyre lines not suppressing any errors (#1620)

Summary:
Pull Request resolved: #1620

As title. Looks like `Any` is OK now...thank goodness

Reviewed By: sarahtranfb

Differential Revision: D77395444

fbshipit-source-id: 87b5d3373b8a6cd7170d9506d43ee16865e6b4c0

Author: craymichael

captum/_utils/av.py

@@ -400,8 +400,6 @@ def _compute_and_save_activations(
             AV.save(path, model_id, identifier, unsaved_layers, new_activations, num_id)
 
     @staticmethod
-    # pyre-fixme[3]: Return annotation cannot be `Any`.
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     def _unpack_data(data: Union[Any, Tuple[Any, Any]]) -> Any:
         r"""
         Helper to extract input from labels when getting items from a Dataset. Assumes

captum/_utils/common.py

@@ -251,8 +251,6 @@ def _format_tensor_into_tuples(
     return inputs
 
 
-# pyre-fixme[3]: Return annotation cannot be `Any`.
-# pyre-fixme[2]: Parameter annotation cannot be `Any`.
 def _format_inputs(inputs: Any, unpack_inputs: bool = True) -> Any:
     return (
         inputs
@@ -569,7 +567,6 @@ def future_forward(*args: Any, **kwargs: Any):
 def _run_forward(
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
     forward_func: Callable,
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     inputs: Any,
     target: TargetType = None,
     additional_forward_args: Optional[object] = None,
@@ -754,7 +751,6 @@ def _extract_device(
 
 def _reduce_list(
     val_list: Sequence[TupleOrTensorOrBoolGeneric],
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
     # pyre-fixme[24]: Generic type `list` expects 1 type parameter, use
     #  `typing.List[<element type>]` to avoid runtime subscripting errors.
     red_func: Callable[[List], Any] = torch.cat,
@@ -832,7 +828,6 @@ def _flatten_tensor_or_tuple(inp: TensorOrTupleOfTensorsGeneric) -> Tensor:
     return torch.cat([single_inp.flatten() for single_inp in inp])
 
 
-# pyre-fixme[3]: Return annotation cannot be `Any`.
 def _get_module_from_name(model: Module, layer_name: str) -> Any:
     r"""
     Returns the module (layer) object, given its (string) name

captum/_utils/gradient.py

@@ -227,7 +227,6 @@ def _forward_layer_eval(
 def _forward_layer_distributed_eval(
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
     forward_fn: Callable,
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     inputs: Any,
     layer: ModuleOrModuleList,
     target_ind: TargetType = None,
@@ -399,10 +398,8 @@ def _extract_device_ids(
     ):
         if (
             hasattr(forward_fn, "device_ids")
-            # pyre-fixme[33]: Given annotation cannot be `Any`.
             and cast(Any, forward_fn).device_ids is not None
         ):
-            # pyre-fixme[33]: Given annotation cannot be `Any`.
             device_ids = cast(Any, forward_fn).device_ids
         else:
             raise AssertionError(
@@ -820,7 +817,6 @@ def _extract_parameters_from_layers(layer_modules):
 
 def _compute_jacobian_wrt_params(
     model: Module,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
     inputs: Tuple[Any, ...],
     labels: Optional[Tensor] = None,
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
@@ -893,10 +889,8 @@ def _compute_jacobian_wrt_params(
         return tuple(grads)
 
 
-# pyre-fixme[3]: Return annotation cannot contain `Any`.
 def _compute_jacobian_wrt_params_with_sample_wise_trick(
     model: Module,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
     inputs: Tuple[Any, ...],
     labels: Optional[Tensor] = None,
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.

captum/attr/_utils/baselines.py

@@ -22,7 +22,6 @@ class ProductBaselines:
 
     def __init__(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         baseline_values: Union[
             List[List[Any]],
             Dict[Union[str, Tuple[str, ...]], List[Any]],
@@ -38,7 +37,6 @@ def __init__(
         self.dict_keys = dict_keys
         self.baseline_values = baseline_values
 
-    # pyre-fixme[3]: Return annotation cannot contain `Any`.
     def sample(self) -> Union[List[Any], Dict[str, Any]]:
         baselines = [
             random.choice(baseline_list) for baseline_list in self.baseline_values
@@ -57,7 +55,6 @@ def sample(self) -> Union[List[Any], Dict[str, Any]]:
 
         return dict_baselines
 
-    # pyre-fixme[3]: Return annotation cannot contain `Any`.
     def __call__(self) -> Union[List[Any], Dict[str, Any]]:
         """
         Returns:

captum/attr/_utils/batching.py

@@ -136,7 +136,6 @@ def _tuple_splice_range(
     )
 
 
-# pyre-fixme[3]: Return annotation cannot contain `Any`.
 def _batched_generator(
     inputs: TensorOrTupleOfTensorsGeneric,
     additional_forward_args: Optional[object] = None,
@@ -221,8 +220,6 @@ def _batched_operator(
     return _reduce_list(all_outputs)
 
 
-# pyre-fixme[3]: Return annotation cannot be `Any`.
-# pyre-fixme[2]: Parameter annotation cannot be `Any`.
 def _select_example(curr_arg: Any, index: int, bsz: int) -> Any:
     if curr_arg is None:
         return None

captum/attr/_utils/class_summarizer.py

@@ -60,7 +60,6 @@ def update(  # type: ignore
 
         num_labels = 1
 
-        # pyre-fixme[33]: Given annotation cannot contain `Any`.
         labels_typed: Union[List[Any], Tensor]
         if isinstance(labels, list) or isinstance(labels, Tensor):
             labels_typed = labels

captum/concept/_core/tcav.py

@@ -315,8 +315,6 @@ def __init__(
         self.model_id = model_id
         self.concepts: Set[Concept] = set()
         self.classifier = classifier
-        # pyre-fixme[4]: Attribute `classifier_kwargs` of class `TCAV`
-        # must have a type other than `Any`.
         self.classifier_kwargs: Any = classifier_kwargs
         # pyre-fixme[8]: Attribute has type `Dict[str, Dict[str, CAV]]`; used as
         #  `DefaultDict[Variable[_KT], DefaultDict[Variable[_KT], Variable[_VT]]]`.
@@ -740,7 +738,6 @@ def interpret(
             classes = []
             for concepts in experimental_sets:
                 concepts_key = concepts_to_str(concepts)
-                # pyre-fixme[33]: Given annotation cannot contain `Any`.
                 cavs_stats = cast(Dict[str, Any], self.cavs[concepts_key][layer].stats)
                 cavs.append(cavs_stats["weights"].float().detach().tolist())
                 classes.append(cavs_stats["classes"])

captum/influence/_core/influence.py

@@ -31,8 +31,6 @@ def __init_(self, model: Module, train_dataset: Dataset, **kwargs: Any) -> None:
         self.train_dataset = train_dataset
 
     @abstractmethod
-    # pyre-fixme[3]: Return annotation cannot be `Any`.
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     def influence(self, inputs: Any = None, **kwargs: Any) -> Any:
         r"""
         Args:

captum/influence/_core/influence_function.py

@@ -263,7 +263,6 @@ def __init__(
     @abstractmethod
     def self_influence(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs_dataset: Optional[Union[Tuple[Any, ...], DataLoader]] = None,
         show_progress: bool = False,
     ) -> Tensor:
@@ -315,7 +314,6 @@ def self_influence(
     @abstractmethod
     def _get_k_most_influential(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs: Union[Tuple[Any, ...], DataLoader],
         k: int = 5,
         proponents: bool = True,
@@ -367,7 +365,6 @@ def _get_k_most_influential(
     @abstractmethod
     def _influence(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs: Union[Tuple[Any, ...], DataLoader],
         show_progress: bool = False,
     ) -> Tensor:
@@ -486,7 +483,6 @@ class IntermediateQuantitiesInfluenceFunction(InfluenceFunctionBase):
     # pyre-fixme[3]: Return type must be annotated.
     def compute_intermediate_quantities(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs_dataset: Union[Tuple[Any, ...], DataLoader],
         aggregate: bool = False,
         show_progress: bool = False,
@@ -961,7 +957,6 @@ def _retrieve_projections_naive_influence_function(
 
     def compute_intermediate_quantities(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs_dataset: Union[Tuple[Any, ...], DataLoader],
         aggregate: bool = False,
         show_progress: bool = False,
@@ -1172,7 +1167,6 @@ def influence(  # type: ignore[override]
 
     def _get_k_most_influential(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs: Union[Tuple[Any, ...], DataLoader],
         k: int = 5,
         proponents: bool = True,
@@ -1246,7 +1240,6 @@ def _get_k_most_influential(
 
     def _influence(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs: Union[Tuple[Any, ...], DataLoader],
         show_progress: bool = False,
     ) -> Tensor:
@@ -1287,7 +1280,6 @@ def _influence(
     @log_usage(part_of_slo=True, skip_self_logging=True)
     def self_influence(
         self,
-        # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
         inputs_dataset: Optional[Union[Tuple[Any, ...], DataLoader]] = None,
         show_progress: bool = False,
     ) -> Tensor:
@@ -1333,7 +1325,6 @@ def self_influence(
 
 def _basic_computation_naive_influence_function(
     influence_inst: InfluenceFunctionBase,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
     inputs: Tuple[Any, ...],
     targets: Optional[Tensor] = None,
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.

captum/influence/_core/similarity_influence.py

@@ -163,7 +163,6 @@ def influence(  # type: ignore[override]
         self,
         inputs: Union[Tensor, Tuple[Tensor, ...]],
         top_k: int = 1,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Optional[Any] = None,
         load_src_from_disk: bool = True,
         **kwargs: Any,