[NumPy] Remove references to deprecated NumPy type aliases.

This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str).

NumPy 1.24 drops the deprecated aliases, so we must remove uses before updating NumPy.

PiperOrigin-RevId: 497174980

Author: hawkinsp

tensorflow_graphics/projects/local_implicit_grid/core/evaluator.py

@@ -311,7 +311,7 @@ def eval_grid(self, grid):
     """
     # initialize output feature grid
     ogrid = np.zeros([self.ijk.shape[0], self.codelen])
-    niters = np.ceil(self.ijk.shape[0] / self.grid_batch).astype(np.int)
+    niters = np.ceil(self.ijk.shape[0] / self.grid_batch).astype(int)
     for idx in range(niters):
       sid = idx * self.grid_batch
       eid = min(sid + self.grid_batch, self.ijk.shape[0])
@@ -580,7 +580,7 @@ def evaluate_feature_grid(self, feature_grid, mask, res_per_part=4,
     output_grid = np.ones([res_per_part*s[0],
                            res_per_part*s[1],
                            res_per_part*s[2]], dtype=np.float32).reshape(-1)
-    mask = mask.astype(np.bool)
+    mask = mask.astype(bool)
     if self.overlap:
       mask = np.stack([mask[:-1, :-1, :-1],
                        mask[:-1, :-1, 1:],

tensorflow_graphics/projects/local_implicit_grid/core/point_utils.py

@@ -91,7 +91,7 @@ def np_shifted_crop(v, idx_grid, shift, crop_size, ntarget):
   vall = v.copy()
   point_idxs = np.arange(v.shape[0])
   point_grid_idx = np.floor(vxyz / crop_size).astype(np.int32)
-  valid_mask = np.ones(point_grid_idx.shape[0]).astype(np.bool)
+  valid_mask = np.ones(point_grid_idx.shape[0]).astype(bool)
   for i in range(3):
     valid_mask = np.logical_and(valid_mask, point_grid_idx[:, i] >= 0)
     valid_mask = np.logical_and(valid_mask,

tensorflow_graphics/projects/local_implicit_grid/core/reconstruction.py

@@ -241,7 +241,7 @@ def feature_grid(self):
 
 
 def occupancy_sparse_to_dense(occ_idx, grid_shape):
-  dense = np.zeros(grid_shape, dtype=np.bool).ravel()
+  dense = np.zeros(grid_shape, dtype=bool).ravel()
   occ_idx_f = (occ_idx[:, 0] * grid_shape[1] * grid_shape[2] +
                occ_idx[:, 1] * grid_shape[2] + occ_idx[:, 2])
   dense[occ_idx_f] = True