Replace deprecated `np.int` with `int` (#9307)

Per ``` /content/yolov5/utils/dataloaders.py:458: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations ``` Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Replace deprecated `np.int` with `int` (#9307)
5a134e06 · Glenn Jocher · GitHub · 32794c13 · 5a134e06
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -455,7 +455,7 @@ class LoadImagesAndLabels(Dataset):
        self.im_files = list(cache.keys())  # update
        self.label_files = img2label_paths(cache.keys())  # update
        n = len(shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches
        self.batch = bi  # batch index of image
        self.n = n
@@ -497,7 +497,7 @@ class LoadImagesAndLabels(Dataset):
                elif mini > 1:
                    shapes[i] = [1, 1 / mini]
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
        # Cache images into RAM/disk for faster training (WARNING: large datasets may exceed system resources)
        self.ims = [None] * n
@@ -867,7 +867,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders impo
                    b = x[1:] * [w, h, w, h]  # box
                    # b[2:] = b[2:].max()  # rectangle to square
                    b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)