From 5a134e06530a8c24fdb9774c2c4ab0b513b08260 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 7 Sep 2022 10:11:30 +0300 Subject: [PATCH] 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 Signed-off-by: Glenn Jocher --- utils/dataloaders.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/utils/dataloaders.py b/utils/dataloaders.py index c1ad1f1..d8ef11f 100755 --- 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)