Merge remote-tracking branch 'origin/master'

# Conflicts:
#	predict/predict.py

dataset/shapenet.py

@@ -24,13 +24,15 @@ class CustomShapeNet(InMemoryDataset):
     modes = {key: val for val, key in enumerate(['train', 'test', 'predict'])}
 
     def __init__(self, root_dir, collate_per_segment=True, mode='train', transform=None, pre_filter=None,
-                 pre_transform=None, headers=True, has_variations=False, refresh=False, labels_within=False):
+                 pre_transform=None, headers=True, has_variations=False, refresh=False, labels_within=False,
+                 with_normals=False):
         assert mode in self.modes.keys(), f'"mode" must be one of {self.modes.keys()}'
         assert not (collate_per_segment and has_variations), 'Either use each element or pointclouds - with variations'
 
         #Set the Dataset Parameters
         self.has_headers, self.has_variations, self.labels_within = headers, has_variations, labels_within
         self.collate_per_element, self.mode, self.refresh = collate_per_segment, mode, refresh
+        self.with_normals = with_normals
         super(CustomShapeNet, self).__init__(root_dir, transform, pre_transform, pre_filter)
         self.data, self.slices = self._load_dataset()
         print("Initialized")
@@ -38,16 +40,17 @@ class CustomShapeNet(InMemoryDataset):
     @property
     def raw_file_names(self):
         # Maybe add more data like validation sets
-        return list(self.modes.keys())
+        return [self.mode]
 
     @property
     def processed_file_names(self):
-        return [f'{x}.pt' for x in self.raw_file_names]
+        return [f'{self.mode}.pt']
 
     def download(self):
         dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
-        print(f'{dir_count} folders have been found....')
+
         if dir_count:
+            print(f'{dir_count} folders have been found....')
             return dir_count
         raise IOError("No raw pointclouds have been found.")
 
@@ -57,7 +60,7 @@ class CustomShapeNet(InMemoryDataset):
 
     def _load_dataset(self):
         data, slices = None, None
-        filepath = self.processed_paths[self.modes[self.mode]]
+        filepath = self.processed_paths[0]
         if self.refresh:
             try:
                 os.remove(filepath)
@@ -90,7 +93,7 @@ class CustomShapeNet(InMemoryDataset):
 
     def process(self, delimiter=' '):
         datasets = defaultdict(list)
-        idx, data_folder = self.modes[self.mode], self.raw_file_names[self.modes[self.mode]]
+        idx, data_folder = self.modes[self.mode], self.raw_file_names[0]
         path_to_clouds = os.path.join(self.raw_dir, data_folder)
 
         if '.headers' in os.listdir(path_to_clouds):
@@ -110,8 +113,8 @@ class CustomShapeNet(InMemoryDataset):
                 paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}')))
 
             for element in paths:
-                # This was build to filter all variations that aregreater then 25
+                # This was build to filter all full clouds
-                pattern = re.compile('^((6[0-1]|[1-5][0-9])_\w+?\d+?|\d+?_pc)\.(xyz|dat)$')
+                pattern = re.compile('^\d+?_pc\.(xyz|dat)$')
                 if pattern.match(os.path.split(element)[-1]):
                     continue
                 else:
@@ -142,9 +145,10 @@ class CustomShapeNet(InMemoryDataset):
                             y_all = [-1] * points.shape[0]
 
                     y = torch.as_tensor(y_all, dtype=torch.int)
-                    attr_dict = dict(y=y, pos=points[:, :3])
+                    ####################################
-                    if self.mode == 'predict':
+                    # This is where you define the keys
-                        attr_dict.update(normals=points[:, 3:6])
+                    attr_dict = dict(y=y, pos=points[:, :3 if not self.with_normals else 6])
+                    ####################################
                     if self.collate_per_element:
                         data = Data(**attr_dict)
                     else:
@@ -161,14 +165,14 @@ class CustomShapeNet(InMemoryDataset):
                         cloud_variations[int(os.path.split(element)[-1].split('_')[0])].append(data)
             if not self.collate_per_element:
                 if self.has_variations:
-                    for variation in cloud_variations.keys():
+                    for _ in cloud_variations.keys():
                         datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
                 else:
                     datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
 
         if datasets[data_folder]:
             os.makedirs(self.processed_dir, exist_ok=True)
-            torch.save(self.collate(datasets[data_folder]), self.processed_paths[idx])
+            torch.save(self.collate(datasets[data_folder]), self.processed_paths[0])
 
     def __repr__(self):
         return f'{self.__class__.__name__}({len(self)})'
@@ -179,6 +183,7 @@ class ShapeNetPartSegDataset(Dataset):
     Resample raw point cloud to fixed number of points.
     Map raw label from range [1, N] to [0, N-1].
     """
+
     def __init__(self, root_dir, npoints=1024, mode='train', **kwargs):
         super(ShapeNetPartSegDataset, self).__init__()
         self.mode = mode
@@ -191,22 +196,19 @@ class ShapeNetPartSegDataset(Dataset):
 
         # Resample to fixed number of points
         try:
-            choice = np.random.choice(data.pos.shape[0], self.npoints, replace=True)
+            npoints = self.npoints if self.mode != 'predict' else data.pos.shape[0]
+            choice = np.random.choice(data.pos.shape[0], npoints, replace=False if self.mode == 'predict' else True)
         except ValueError:
             choice = []
 
-        points, labels = data.pos[choice, :], data.y[choice]
+        pos, labels = data.pos[choice, :], data.y[choice]
 
         labels -= 1 if self.num_classes() in labels else 0   # Map label from [1, C] to [0, C-1]
 
         sample = {
-            'points': points,  # torch.Tensor (n, 3)
+            'points': pos,  # torch.Tensor (n, 6)
             'labels': labels  # torch.Tensor (n,)
         }
-        if self.mode == 'predict':
-            normals = data.normals[choice]
-            sample.update(normals=normals)
-
         return sample
 
     def __len__(self):

main.py

@@ -33,11 +33,12 @@ parser.add_argument('--npoints', type=int, default=1024, help='resample points n
 parser.add_argument('--model', type=str, default='', help='model path')
 parser.add_argument('--nepoch', type=int, default=250, help='number of epochs to train for')
 parser.add_argument('--outf', type=str, default='checkpoint', help='output folder')
-parser.add_argument('--labels_within', type=strtobool, default=False, help='defines the label location')
+parser.add_argument('--labels_within', type=strtobool, default=True, help='defines the label location')
 parser.add_argument('--batch_size', type=int, default=8, help='input batch size')
 parser.add_argument('--test_per_batches', type=int, default=1000, help='run a test batch per training batches number')
-parser.add_argument('--num_workers', type=int, default=4, help='number of data loading workers')
+parser.add_argument('--num_workers', type=int, default=0, help='number of data loading workers')
 parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
+parser.add_argument('--with_normals', type=strtobool, default=True, help='if training will include normals')
 parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub')
 parser.add_argument('--has_variations', type=strtobool, default=False,
                     help='whether a single pointcloud has variations '
@@ -69,7 +70,7 @@ if __name__ == '__main__':
                               )
 
     TransTransform = GT.RandomTranslate(trans_max_distance)
-    train_transform = GT.Compose([GT.NormalizeScale(), RotTransform, TransTransform])
+    train_transform = GT.Compose([GT.NormalizeScale(), ])
     test_transform = GT.Compose([GT.NormalizeScale(), ])
 
     params = dict(root_dir=opt.dataset,
@@ -78,7 +79,8 @@ if __name__ == '__main__':
                   npoints=opt.npoints,
                   labels_within=opt.labels_within,
                   has_variations=opt.has_variations,
-                  headers=opt.headers
+                  headers=opt.headers,
+                  with_normals=opt.with_normals
                   )
 
     dataset = ShapeNetPartSegDataset(mode='train', **params)
@@ -105,7 +107,7 @@ if __name__ == '__main__':
     dtype = torch.float
     print('cudnn.enabled: ', torch.backends.cudnn.enabled)
 
-    net = PointNet2PartSegmentNet(num_classes)
+    net = PointNet2PartSegmentNet(num_classes, with_normals=opt.with_normals)
 
     if opt.model != '':
         net.load_state_dict(torch.load(opt.model))
@@ -129,12 +131,12 @@ if __name__ == '__main__':
 
         net.train()
         # ToDo: We need different dataloader here to train the network in multiple iterations, maybe move the loop down
-        # for dataloader in ...
         for batch_idx, sample in enumerate(dataLoader):
-            # points: (batch_size, n, 3)
+            # points: (batch_size, n, 6)
+            # pos: (batch_size, n, 3)
             # labels: (batch_size, n)
             points, labels = sample['points'], sample['labels']
-            points = points.transpose(1, 2).contiguous()  # (batch_size, 3, n)
+            points = points.transpose(1, 2).contiguous()  # (batch_size, 3/6, n)
             points, labels = points.to(device, dtype), labels.to(device, torch.long)
 
             optimizer.zero_grad()

model/pointnet2_part_seg.py

@@ -8,15 +8,15 @@ from torch_geometric.utils.num_nodes import maybe_num_nodes
 from torch_geometric.data.data import Data
 from torch_scatter import scatter_add, scatter_max
 
-GLOBAL_POINT_FEATURES = 3
 
 class PointNet2SAModule(torch.nn.Module):
-    def __init__(self, sample_radio, radius, max_num_neighbors, mlp):
+    def __init__(self, sample_radio, radius, max_num_neighbors, mlp, features=3):
         super(PointNet2SAModule, self).__init__()
         self.sample_ratio = sample_radio
         self.radius = radius
         self.max_num_neighbors = max_num_neighbors
         self.point_conv = PointConv(mlp)
+        self.features=features
 
     def forward(self, data):
         x, pos, batch = data
@@ -40,9 +40,10 @@ class PointNet2GlobalSAModule(torch.nn.Module):
     One group with all input points, can be viewed as a simple PointNet module.
     It also return the only one output point(set as origin point).
     '''
-    def __init__(self, mlp):
+    def __init__(self, mlp, features=3):
         super(PointNet2GlobalSAModule, self).__init__()
         self.mlp = mlp
+        self.features = features
 
     def forward(self, data):
         x, pos, batch = data
@@ -52,7 +53,7 @@ class PointNet2GlobalSAModule(torch.nn.Module):
         x1 = scatter_max(x1, batch, dim=0)[0]  # (batch_size, C1)
 
         batch_size = x1.shape[0]
-        pos1 = x1.new_zeros((batch_size, GLOBAL_POINT_FEATURES))  # set the output point as origin
+        pos1 = x1.new_zeros((batch_size, self.features))  # set the output point as origin
         batch1 = torch.arange(batch_size).to(batch.device, batch.dtype)
 
         return x1, pos1, batch1
@@ -158,44 +159,47 @@ class PointNet2PartSegmentNet(torch.nn.Module):
         - https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_part_seg.py
         - https://github.com/rusty1s/pytorch_geometric/blob/master/examples/pointnet++.py
     '''
-    def __init__(self, num_classes):
+    def __init__(self, num_classes, with_normals=False):
         super(PointNet2PartSegmentNet, self).__init__()
         self.num_classes = num_classes
+        self.features = 3 if not with_normals else 6
 
         # SA1
         sa1_sample_ratio = 0.5
         sa1_radius = 0.2
         sa1_max_num_neighbours = 64
-        sa1_mlp = make_mlp(GLOBAL_POINT_FEATURES, [64, 64, 128])
+        sa1_mlp = make_mlp(self.features, [64, 64, 128])
-        self.sa1_module = PointNet2SAModule(sa1_sample_ratio, sa1_radius, sa1_max_num_neighbours, sa1_mlp)
+        self.sa1_module = PointNet2SAModule(sa1_sample_ratio, sa1_radius, sa1_max_num_neighbours, sa1_mlp,
+                                            features=self.features)
 
         # SA2
         sa2_sample_ratio = 0.25
         sa2_radius = 0.4
         sa2_max_num_neighbours = 64
-        sa2_mlp = make_mlp(128+GLOBAL_POINT_FEATURES, [128, 128, 256])
+        sa2_mlp = make_mlp(128+self.features, [128, 128, 256])
-        self.sa2_module = PointNet2SAModule(sa2_sample_ratio, sa2_radius, sa2_max_num_neighbours, sa2_mlp)
+        self.sa2_module = PointNet2SAModule(sa2_sample_ratio, sa2_radius, sa2_max_num_neighbours, sa2_mlp,
+                                            features=self.features)
 
         # SA3
-        sa3_mlp = make_mlp(256+GLOBAL_POINT_FEATURES, [256, 512, 1024])
+        sa3_mlp = make_mlp(256+self.features, [256, 512, 1024])
-        self.sa3_module = PointNet2GlobalSAModule(sa3_mlp)
+        self.sa3_module = PointNet2GlobalSAModule(sa3_mlp, self.features)
 
         ##
-        knn_num = GLOBAL_POINT_FEATURES
+        knn_num = self.features
 
         # FP3, reverse of sa3
         fp3_knn_num = 1  # After global sa module, there is only one point in point cloud
-        fp3_mlp = make_mlp(1024+256+GLOBAL_POINT_FEATURES, [256, 256])
+        fp3_mlp = make_mlp(1024+256+self.features, [256, 256])
         self.fp3_module = PointNet2FPModule(fp3_knn_num, fp3_mlp)
 
         # FP2, reverse of sa2
         fp2_knn_num = knn_num
-        fp2_mlp = make_mlp(256+128+GLOBAL_POINT_FEATURES, [256, 128])
+        fp2_mlp = make_mlp(256+128+self.features, [256, 128])
         self.fp2_module = PointNet2FPModule(fp2_knn_num, fp2_mlp)
 
         # FP1, reverse of sa1
         fp1_knn_num = knn_num
-        fp1_mlp = make_mlp(128+GLOBAL_POINT_FEATURES, [128, 128, 128])
+        fp1_mlp = make_mlp(128+self.features, [128, 128, 128])
         self.fp1_module = PointNet2FPModule(fp1_knn_num, fp1_mlp)
 
         self.fc1 = Lin(128, 128)
@@ -252,11 +256,12 @@ class PointNet2PartSegmentNet(torch.nn.Module):
 
 if __name__ == '__main__':
     num_classes = 10
-    net = PointNet2PartSegmentNet(num_classes)
+    num_features = 6
+    net = PointNet2PartSegmentNet(num_classes, features=num_features)
 
     #
     print('Test dense input ..')
-    data1 = torch.rand((2, GLOBAL_POINT_FEATURES, 1024))  # (batch_size, 3, num_points)
+    data1 = torch.rand((2, num_features, 1024))  # (batch_size, 3, num_points)
     print('data1: ', data1.shape)
 
     out1 = net(data1)
@@ -272,7 +277,7 @@ if __name__ == '__main__':
         data_batch = Data()
 
         # data_batch.x = None
-        data_batch.pos = torch.cat([torch.rand(pos_num1, GLOBAL_POINT_FEATURES), torch.rand(pos_num2, GLOBAL_POINT_FEATURES)], dim=0)
+        data_batch.pos = torch.cat([torch.rand(pos_num1, num_features), torch.rand(pos_num2, num_features)], dim=0)
         data_batch.batch = torch.cat([torch.zeros(pos_num1, dtype=torch.long), torch.ones(pos_num2, dtype=torch.long)])
 
         return data_batch

predict/clusters.txt

@@ -0,0 +1,200 @@
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