Updates

data/Indoor3DSemSegLoader.py

@@ -16,12 +16,10 @@ def _load_data_file(name):
 
 
 class Indoor3DSemSeg(data.Dataset):
-    def __init__(self,
-                 num_points,
-                 root,
-                 train=True,
-                 download=True,
-                 data_precent=1.0):
+
+    def __init__(
+            self, num_points, root, train=True, download=True, data_precent=1.0
+    ):
         super().__init__()
         self.data_precent = data_precent
         root = os.path.abspath(root)
@@ -32,18 +30,23 @@ class Indoor3DSemSeg(data.Dataset):
         if download and not os.path.exists(self.data_dir):
             zipfile = os.path.join(root, os.path.basename(self.url))
             subprocess.check_call(
-                shlex.split("curl {} -o {}".format(self.url, zipfile)))
+                shlex.split("curl {} -o {}".format(self.url, zipfile))
+            )
 
-            subprocess.check_call(shlex.split("unzip {} -d {}".format(zipfile, root)))
+            subprocess.check_call(
+                shlex.split("unzip {} -d {}".format(zipfile, root))
+            )
 
             subprocess.check_call(shlex.split("rm {}".format(zipfile)))
 
         self.train, self.num_points = train, num_points
 
         all_files = _get_data_files(
-            os.path.join(self.data_dir, "all_files.txt"))
+            os.path.join(self.data_dir, "all_files.txt")
+        )
         room_filelist = _get_data_files(
-            os.path.join(self.data_dir, "room_filelist.txt"))
+            os.path.join(self.data_dir, "room_filelist.txt")
+        )
 
         data_batchlist, label_batchlist = [], []
         for f in all_files:
@@ -74,9 +77,11 @@ class Indoor3DSemSeg(data.Dataset):
         np.random.shuffle(pt_idxs)
 
         current_points = torch.from_numpy(self.points[idx, pt_idxs, :]).type(
-            torch.FloatTensor)
+            torch.FloatTensor
+        )
         current_labels = torch.from_numpy(self.labels[idx, pt_idxs]).type(
-            torch.LongTensor)
+            torch.LongTensor
+        )
 
         return current_points, current_labels
 

data/ModelNet40Loader.py

@@ -19,12 +19,10 @@ def _load_data_file(name):
 
 
 class ModelNet40Cls(data.Dataset):
-    def __init__(self,
-                 num_points,
-                 root,
-                 transforms=None,
-                 train=True,
-                 download=True):
+
+    def __init__(
+            self, num_points, root, transforms=None, train=True, download=True
+    ):
         super().__init__()
 
         self.transforms = transforms
@@ -37,9 +35,12 @@ class ModelNet40Cls(data.Dataset):
         if download and not os.path.exists(self.data_dir):
             zipfile = os.path.join(root, os.path.basename(self.url))
             subprocess.check_call(
-                shlex.split("curl {} -o {}".format(self.url, zipfile)))
+                shlex.split("curl {} -o {}".format(self.url, zipfile))
+            )
 
-            subprocess.check_call(shlex.split("unzip {} -d {}".format(zipfile, root)))
+            subprocess.check_call(
+                shlex.split("unzip {} -d {}".format(zipfile, root))
+            )
 
             subprocess.check_call(shlex.split("rm {}".format(zipfile)))
 
@@ -83,9 +84,10 @@ class ModelNet40Cls(data.Dataset):
     def randomize(self):
         self.actual_number_of_points = min(
             max(
-                np.random.randint(self.num_points * 0.8,
-                                  self.num_points * 1.2), 1),
-            self.points.shape[1])
+                np.random.randint(self.num_points * 0.8, self.num_points * 1.2),
+                1
+            ), self.points.shape[1]
+        )
 
 
 if __name__ == "__main__":