replace uses of np.ndarray with npt.NDArray

Summary:
X-link: meta-pytorch/opacus#681

X-link: meta-pytorch/captum#1389

X-link: meta-pytorch/botorch#2586

X-link: pytorch/audio#3846

This replaces uses of `numpy.ndarray` in type annotations with `numpy.typing.NDArray`. In Numpy-1.24.0+ `numpy.ndarray` is annotated as generic type. Without template parameters it triggers static analysis errors:
```counterexample
Generic type `ndarray` expects 2 type parameters.
```
`numpy.typing.NDArray` is an alias that provides default template parameters.

Reviewed By: ryanthomasjohnson

Differential Revision: D64619891

fbshipit-source-id: dffc096b1ce90d11e73d475f0bbcb8867ed9ef01

Author: igorsugak

torchbenchmark/models/pytorch_unet/pytorch_unet/predict.py

@@ -3,6 +3,7 @@
 import os
 
 import numpy as np
+import numpy.typing as npt
 import torch
 import torch.nn.functional as F
 from PIL import Image
@@ -102,7 +103,7 @@ def _generate_name(fn):
     return args.output or list(map(_generate_name, args.input))
 
 
-def mask_to_image(mask: np.ndarray):
+def mask_to_image(mask: npt.NDArray):
     if mask.ndim == 2:
         return Image.fromarray((mask * 255).astype(np.uint8))
     elif mask.ndim == 3:

torchbenchmark/models/sam/predictor.py

@@ -7,6 +7,7 @@
 from typing import Optional, Tuple
 
 import numpy as np
+import numpy.typing as npt
 import torch
 
 from .sam import Sam
@@ -32,7 +33,7 @@ def __init__(
 
     def set_image(
         self,
-        image: np.ndarray,
+        image: npt.NDArray,
         image_format: str = "RGB",
     ) -> None:
         """
@@ -92,13 +93,13 @@ def set_torch_image(
 
     def predict(
         self,
-        point_coords: Optional[np.ndarray] = None,
-        point_labels: Optional[np.ndarray] = None,
-        box: Optional[np.ndarray] = None,
-        mask_input: Optional[np.ndarray] = None,
+        point_coords: Optional[npt.NDArray] = None,
+        point_labels: Optional[npt.NDArray] = None,
+        box: Optional[npt.NDArray] = None,
+        mask_input: Optional[npt.NDArray] = None,
         multimask_output: bool = True,
         return_logits: bool = False,
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> Tuple[npt.NDArray, npt.NDArray, npt.NDArray]:
         """
         Predict masks for the given input prompts, using the currently set image.
 

torchbenchmark/models/sam/transforms.py

@@ -8,6 +8,7 @@
 from typing import Tuple
 
 import numpy as np
+import numpy.typing as npt
 import torch
 from torch.nn import functional as F
 from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
@@ -23,7 +24,7 @@ class ResizeLongestSide:
     def __init__(self, target_length: int) -> None:
         self.target_length = target_length
 
-    def apply_image(self, image: np.ndarray) -> np.ndarray:
+    def apply_image(self, image: npt.NDArray) -> npt.NDArray:
         """
         Expects a numpy array with shape HxWxC in uint8 format.
         """
@@ -33,8 +34,8 @@ def apply_image(self, image: np.ndarray) -> np.ndarray:
         return np.array(resize(to_pil_image(image), target_size))
 
     def apply_coords(
-        self, coords: np.ndarray, original_size: Tuple[int, ...]
-    ) -> np.ndarray:
+        self, coords: npt.NDArray, original_size: Tuple[int, ...]
+    ) -> npt.NDArray:
         """
         Expects a numpy array of length 2 in the final dimension. Requires the
         original image size in (H, W) format.
@@ -49,8 +50,8 @@ def apply_coords(
         return coords
 
     def apply_boxes(
-        self, boxes: np.ndarray, original_size: Tuple[int, ...]
-    ) -> np.ndarray:
+        self, boxes: npt.NDArray, original_size: Tuple[int, ...]
+    ) -> npt.NDArray:
         """
         Expects a numpy array shape Bx4. Requires the original image size
         in (H, W) format.