Merge pull request #1866 from lRomul/replace_deprecated_np_types

Replace deprecated NumPy aliases of builtin types

timm/data/mixup.py

@@ -120,7 +120,7 @@ def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0
 
     def _params_per_elem(self, batch_size):
         lam = np.ones(batch_size, dtype=np.float32)
-        use_cutmix = np.zeros(batch_size, dtype=np.bool)
+        use_cutmix = np.zeros(batch_size, dtype=bool)
         if self.mixup_enabled:
             if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
                 use_cutmix = np.random.rand(batch_size) < self.switch_prob
@@ -131,7 +131,7 @@ def _params_per_elem(self, batch_size):
             elif self.mixup_alpha > 0.:
                 lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
             elif self.cutmix_alpha > 0.:
-                use_cutmix = np.ones(batch_size, dtype=np.bool)
+                use_cutmix = np.ones(batch_size, dtype=bool)
                 lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
             else:
                 assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."

timm/models/volo.py

@@ -301,8 +301,8 @@ def rand_bbox(size, lam, scale=1):
     W = size[1] // scale
     H = size[2] // scale
     cut_rat = np.sqrt(1. - lam)
-    cut_w = np.int(W * cut_rat)
-    cut_h = np.int(H * cut_rat)
+    cut_w = (W * cut_rat).astype(int)
+    cut_h = (H * cut_rat).astype(int)
 
     # uniform
     cx = np.random.randint(W)