[Misc] Using the aliases of builtin types like np.int is deprecated (#7300)

examples/pytorch/ggnn/train_gc.py

@@ -60,7 +60,7 @@ def main(args):
                 labels = labels.data.numpy().tolist()
                 dev_preds += preds
                 dev_labels += labels
-        acc = np.equal(dev_labels, dev_preds).astype(np.float).tolist()
+        acc = np.equal(dev_labels, dev_preds).astype(float).tolist()
         acc = sum(acc) / len(acc)
         print(f"Epoch {epoch}, Dev acc {acc}")
 
@@ -80,7 +80,7 @@ def main(args):
                 labels = labels.data.numpy().tolist()
                 test_preds += preds
                 test_labels += labels
-        acc = np.equal(test_labels, test_preds).astype(np.float).tolist()
+        acc = np.equal(test_labels, test_preds).astype(float).tolist()
         acc = sum(acc) / len(acc)
         test_acc_list.append(acc)
 

examples/pytorch/hilander/utils/evaluate.py

@@ -40,7 +40,7 @@ def evaluate(gt_labels, pred_labels, metric="pairwise"):
         "evaluate with {}{}{}".format(TextColors.FATAL, metric, TextColors.ENDC)
     ):
         result = metric_func(gt_labels, pred_labels)
-    if isinstance(result, np.float):
+    if isinstance(result, float):
         print(
             "{}{}: {:.4f}{}".format(
                 TextColors.OKGREEN, metric, result, TextColors.ENDC

examples/pytorch/mvgrl/graph/dataset.py

@@ -68,7 +68,7 @@ def process(dataset):
                             for attr in re.split("[,\s]+", line.strip("\s\n"))
                             if attr
                         ],
-                        dtype=np.float,
+                        dtype=float,
                     )
                 )
     else:
@@ -130,7 +130,7 @@ def process(dataset):
                 f[graph.degree[u[0]]] = 1.0
                 if "label" in u[1]:
                     f = np.concatenate(
-                        (np.array(u[1]["label"], dtype=np.float), f)
+                        (np.array(u[1]["label"], dtype=float), f)
                     )
                 graph.nodes[u[0]]["feat"] = f
     return graphs, pprs

examples/pytorch/ogb/deepwalk/reading_data.py

@@ -213,13 +213,13 @@ class DeepwalkDataset:
             node_degree = self.G.out_degrees(self.valid_seeds).numpy()
             node_degree = np.power(node_degree, 0.75)
             node_degree /= np.sum(node_degree)
-            node_degree = np.array(node_degree * 1e8, dtype=np.int)
+            node_degree = np.array(node_degree * 1e8, dtype=int)
             self.neg_table = []
 
             for idx, node in enumerate(self.valid_seeds):
                 self.neg_table += [node] * node_degree[idx]
             self.neg_table_size = len(self.neg_table)
-            self.neg_table = np.array(self.neg_table, dtype=np.long)
+            self.neg_table = np.array(self.neg_table, dtype=int)
             del node_degree
 
     def create_sampler(self, i):

examples/pytorch/ogb/line/reading_data.py

@@ -209,13 +209,13 @@ class LineDataset:
             node_degree = self.G.out_degrees(self.valid_nodes).numpy()
             node_degree = np.power(node_degree, 0.75)
             node_degree /= np.sum(node_degree)
-            node_degree = np.array(node_degree * 1e8, dtype=np.int)
+            node_degree = np.array(node_degree * 1e8, dtype=int)
             self.neg_table = []
 
             for idx, node in enumerate(self.valid_nodes):
                 self.neg_table += [node] * node_degree[idx]
             self.neg_table_size = len(self.neg_table)
-            self.neg_table = np.array(self.neg_table, dtype=np.long)
+            self.neg_table = np.array(self.neg_table, dtype=int)
             del node_degree
 
     def create_sampler(self, i):

examples/pytorch/pointcloud/pct/ShapeNet.py

@@ -132,7 +132,7 @@ class ShapeNetDataset(Dataset):
                     [t.split("\n")[0].split(" ") for t in f.readlines()]
                 ).astype(np.float)
             data_list.append(data[:, 0 : self.dim])
-            label_list.append(data[:, 6].astype(np.int))
+            label_list.append(data[:, 6].astype(int))
             category_list.append(shapenet.synset_dict[fn.split("/")[-2]])
         self.data = data_list
         self.label = label_list
@@ -157,5 +157,5 @@ class ShapeNetDataset(Dataset):
         if self.mode == "train":
             x = self.translate(x, size=self.dim)
         x = x.astype(np.float)
-        y = y.astype(np.int)
+        y = y.astype(int)
         return x, y, cat

examples/pytorch/pointcloud/point_transformer/ShapeNet.py

@@ -130,9 +130,9 @@ class ShapeNetDataset(Dataset):
             with open(fn) as f:
                 data = np.array(
                     [t.split("\n")[0].split(" ") for t in f.readlines()]
-                ).astype(np.float)
+                ).astype(float)
             data_list.append(data[:, 0 : self.dim])
-            label_list.append(data[:, 6].astype(np.int))
+            label_list.append(data[:, 6].astype(int))
             category_list.append(shapenet.synset_dict[fn.split("/")[-2]])
         self.data = data_list
         self.label = label_list
@@ -156,6 +156,6 @@ class ShapeNetDataset(Dataset):
         cat = self.category[i]
         if self.mode == "train":
             x = self.translate(x, size=self.dim)
-        x = x.astype(np.float)
-        y = y.astype(np.int)
+        x = x.astype(float)
+        y = y.astype(int)
         return x, y, cat

examples/pytorch/pointcloud/pointnet/ShapeNet.py

@@ -130,9 +130,9 @@ class ShapeNetDataset(Dataset):
             with open(fn) as f:
                 data = np.array(
                     [t.split("\n")[0].split(" ") for t in f.readlines()]
-                ).astype(np.float)
+                ).astype(float)
             data_list.append(data[:, 0 : self.dim])
-            label_list.append(data[:, 6].astype(np.int))
+            label_list.append(data[:, 6].astype(int))
             category_list.append(shapenet.synset_dict[fn.split("/")[-2]])
         self.data = data_list
         self.label = label_list
@@ -156,6 +156,6 @@ class ShapeNetDataset(Dataset):
         cat = self.category[i]
         if self.mode == "train":
             x = self.translate(x, size=self.dim)
-        x = x.astype(np.float)
-        y = y.astype(np.int)
+        x = x.astype(float)
+        y = y.astype(int)
         return x, y, cat

examples/pytorch/rrn/sudoku_solver.py

@@ -13,7 +13,7 @@ def solve_sudoku(puzzle):
     :param puzzle: an array-like data with shape [9, 9], blank positions are filled with 0
     :return: a [9, 9] shaped numpy array
     """
-    puzzle = np.array(puzzle, dtype=np.long).reshape([-1])
+    puzzle = np.array(puzzle, dtype=int).reshape([-1])
     model_path = "ckpt"
     if not os.path.exists(model_path):
         os.mkdir(model_path)