Distributed training with PyTorch: PointPillars on Waymo

ml3d/configs/pointpillars_waymo.yml

@@ -2,7 +2,7 @@ dataset:
   name: Waymo
   dataset_path: # path/to/your/dataset
   cache_dir: ./logs/cache
-  steps_per_epoch_train: 5000
+  steps_per_epoch_train: 4000
 
 model:
   name: PointPillars
@@ -31,7 +31,7 @@ model:
     max_voxels: [32000, 32000]
 
   voxel_encoder:
-    in_channels: 5
+    in_channels: 4
     feat_channels: [64]
     voxel_size: *vsize
 
@@ -43,7 +43,7 @@ model:
     in_channels: 64
     out_channels: [64, 128, 256]
     layer_nums: [3, 5, 5]
-    layer_strides: [2, 2, 2]
+    layer_strides: [1, 2, 2]
 
   neck:
     in_channels: [64, 128, 256]
@@ -62,17 +62,18 @@ model:
       [-74.88, -74.88, 0, 74.88, 74.88, 0],
     ]
     sizes: [
-      [2.08, 4.73, 1.77],  # car
-      [0.84, 1.81, 1.77],  # cyclist
-      [0.84, 0.91, 1.74]   # pedestrian
+      [2.08, 4.73, 1.77],  # VEHICLE
+      [0.84, 1.81, 1.77],  # CYCLIST
+      [0.84, 0.91, 1.74]   # PEDESTRIAN
     ]
     dir_offset: 0.7854
     rotations: [0, 1.57]
     iou_thr: [[0.4, 0.55], [0.3, 0.5], [0.3, 0.5]]
 
   augment:
     PointShuffle: True
-    ObjectRangeFilter: True
+    ObjectRangeFilter:
+      point_cloud_range: [-74.88, -74.88, -2, 74.88, 74.88, 4]
     ObjectSample:
       min_points_dict:
         VEHICLE: 5
@@ -88,7 +89,7 @@ pipeline:
   name: ObjectDetection
   test_compute_metric: true
   batch_size: 6
-  val_batch_size: 1
+  val_batch_size: 6
   test_batch_size: 1
   save_ckpt_freq: 5
   max_epoch: 200
@@ -102,7 +103,7 @@ pipeline:
     weight_decay: 0.01
 
   # evaluation properties
-  overlaps: [0.5, 0.5, 0.7]
+  overlaps: [0.5, 0.5, 0.5]
   difficulties: [0, 1, 2]
   summary:
     record_for: []

ml3d/datasets/augment/augmentation.py

@@ -493,7 +493,7 @@ def ObjectSample(self, data, db_boxes_dict, sample_dict):
             sampled_points = np.concatenate(
                 [box.points_inside_box for box in sampled], axis=0)
             points = remove_points_in_boxes(points, sampled)
-            points = np.concatenate([sampled_points, points], axis=0)
+            points = np.concatenate([sampled_points[:, :4], points], axis=0)
 
         return {
             'point': points,

ml3d/datasets/utils/operations.py

@@ -4,7 +4,7 @@
 import math
 from scipy.spatial import ConvexHull
 
-from ...metrics import iou_bev
+from open3d.ml.contrib import iou_bev_cpu as iou_bev
 
 
 def create_3D_rotations(axis, angle):

ml3d/datasets/waymo.py

@@ -25,7 +25,6 @@ def __init__(self,
                  name='Waymo',
                  cache_dir='./logs/cache',
                  use_cache=False,
-                 val_split=3,
                  **kwargs):
         """Initialize the function by passing the dataset and other details.
 
@@ -34,7 +33,6 @@ def __init__(self,
             name: The name of the dataset (Waymo in this case).
             cache_dir: The directory where the cache is stored.
             use_cache: Indicates if the dataset should be cached.
-            val_split: The split value to get a set of images for training, validation, for testing.
 
         Returns:
             class: The corresponding class.
@@ -43,7 +41,6 @@ def __init__(self,
                          name=name,
                          cache_dir=cache_dir,
                          use_cache=use_cache,
-                         val_split=val_split,
                          **kwargs)
 
         cfg = self.cfg
@@ -52,22 +49,27 @@ def __init__(self,
         self.dataset_path = cfg.dataset_path
         self.num_classes = 4
         self.label_to_names = self.get_label_to_names()
+        self.shuffle = kwargs.get('shuffle', False)
 
         self.all_files = sorted(
             glob(join(cfg.dataset_path, 'velodyne', '*.bin')))
         self.train_files = []
         self.val_files = []
+        self.test_files = []
 
         for f in self.all_files:
-            idx = Path(f).name.replace('.bin', '')[:3]
-            idx = int(idx)
-            if idx < cfg.val_split:
+            if 'train' in f:
                 self.train_files.append(f)
-            else:
+            elif 'val' in f:
                 self.val_files.append(f)
-
-        self.test_files = glob(
-            join(cfg.dataset_path, 'testing', 'velodyne', '*.bin'))
+            elif 'test' in f:
+                self.test_files.append(f)
+            else:
+                log.warning(
+                    f"Skipping {f}, prefix must be one of train, test or val.")
+        if self.shuffle:
+            log.info("Shuffling training files...")
+            self.rng.shuffle(self.train_files)
 
     @staticmethod
     def get_label_to_names():
@@ -90,18 +92,21 @@ def read_lidar(path):
         """Reads lidar data from the path provided.
 
         Returns:
-            A data object with lidar information.
+            pc: pointcloud data with shape [N, 6], where
+                the format is xyzRGB.
         """
-        assert Path(path).exists()
-
         return np.fromfile(path, dtype=np.float32).reshape(-1, 6)
 
     @staticmethod
     def read_label(path, calib):
-        """Reads labels of bound boxes.
+        """Reads labels of bounding boxes.
+
+        Args:
+            path: The path to the label file.
+            calib: Calibration as returned by read_calib().
 
         Returns:
-            The data objects with bound boxes information.
+            The data objects with bounding boxes information.
         """
         if not Path(path).exists():
             return None
@@ -131,24 +136,22 @@ def read_calib(path):
         Returns:
             The camera and the camera image used in calibration.
         """
-        assert Path(path).exists()
-
         with open(path, 'r') as f:
             lines = f.readlines()
         obj = lines[0].strip().split(' ')[1:]
-        P0 = np.array(obj, dtype=np.float32)
+        unused_P0 = np.array(obj, dtype=np.float32)
 
         obj = lines[1].strip().split(' ')[1:]
-        P1 = np.array(obj, dtype=np.float32)
+        unused_P1 = np.array(obj, dtype=np.float32)
 
         obj = lines[2].strip().split(' ')[1:]
         P2 = np.array(obj, dtype=np.float32)
 
         obj = lines[3].strip().split(' ')[1:]
-        P3 = np.array(obj, dtype=np.float32)
+        unused_P3 = np.array(obj, dtype=np.float32)
 
         obj = lines[4].strip().split(' ')[1:]
-        P4 = np.array(obj, dtype=np.float32)
+        unused_P4 = np.array(obj, dtype=np.float32)
 
         obj = lines[5].strip().split(' ')[1:]
         R0 = np.array(obj, dtype=np.float32).reshape(3, 3)
@@ -162,7 +165,7 @@ def read_calib(path):
         Tr_velo_to_cam = Waymo._extend_matrix(Tr_velo_to_cam)
 
         world_cam = np.transpose(rect_4x4 @ Tr_velo_to_cam)
-        cam_img = np.transpose(P2)
+        cam_img = np.transpose(np.vstack((P2.reshape(3, 4), [0, 0, 0, 1])))
 
         return {'world_cam': world_cam, 'cam_img': cam_img}
 
@@ -209,7 +212,7 @@ def get_split_list(self, split):
         else:
             raise ValueError("Invalid split {}".format(split))
 
-    def is_tested():
+    def is_tested(attr):
         """Checks if a datum in the dataset has been tested.
 
         Args:
@@ -219,16 +222,16 @@ def is_tested():
             If the datum attribute is tested, then return the path where the
                 attribute is stored; else, returns false.
         """
-        pass
+        raise NotImplementedError()
 
-    def save_test_result():
+    def save_test_result(results, attr):
         """Saves the output of a model.
 
         Args:
             results: The output of a model for the datum associated with the attribute passed.
             attr: The attributes that correspond to the outputs passed in results.
         """
-        pass
+        raise NotImplementedError()
 
 
 class WaymoSplit():
@@ -273,11 +276,9 @@ def get_attr(self, idx):
 
 
 class Object3d(BEVBox3D):
-    """The class stores details that are object-specific, such as bounding box
-    coordinates, occlusion and so on.
-    """
 
     def __init__(self, center, size, label, calib):
+        # ground truth files doesn't have confidence value.
         confidence = float(label[15]) if label.__len__() == 16 else -1.0
 
         world_cam = calib['world_cam']

ml3d/torch/dataloaders/concat_batcher.py

@@ -4,6 +4,7 @@
 import pickle
 import torch
 import yaml
+import math
 from os import listdir
 from os.path import exists, join, isdir
 
@@ -434,6 +435,22 @@ def to(self, device):
         self.feat = [feat.to(device) for feat in self.feat]
         self.label = [label.to(device) for label in self.label]
 
+    @staticmethod
+    def scatter(batch, num_gpu):
+        batch_size = len(batch.batch_lengths)
+
+        new_batch_size = math.ceil(batch_size / num_gpu)
+        batches = [SparseConvUnetBatch([]) for _ in range(num_gpu)]
+        for i in range(num_gpu):
+            start = new_batch_size * i
+            end = min(new_batch_size * (i + 1), batch_size)
+            batches[i].point = batch.point[start:end]
+            batches[i].feat = batch.feat[start:end]
+            batches[i].label = batch.label[start:end]
+            batches[i].batch_lengths = batch.batch_lengths[start:end]
+
+        return [b for b in batches if len(b.point)]  # filter empty batch
+
 
 class PointTransformerBatch:
 
@@ -486,7 +503,6 @@ def __init__(self, batches):
         self.attr = []
 
         for batch in batches:
-            self.attr.append(batch['attr'])
             data = batch['data']
             self.point.append(torch.tensor(data['point'], dtype=torch.float32))
             self.labels.append(
@@ -519,6 +535,23 @@ def to(self, device):
             if self.bboxes[i] is not None:
                 self.bboxes[i] = self.bboxes[i].to(device)
 
+    @staticmethod
+    def scatter(batch, num_gpu):
+        batch_size = len(batch.point)
+
+        new_batch_size = math.ceil(batch_size / num_gpu)
+        batches = [ObjectDetectBatch([]) for _ in range(num_gpu)]
+        for i in range(num_gpu):
+            start = new_batch_size * i
+            end = min(new_batch_size * (i + 1), batch_size)
+            batches[i].point = batch.point[start:end]
+            batches[i].labels = batch.labels[start:end]
+            batches[i].bboxes = batch.bboxes[start:end]
+            batches[i].bbox_objs = batch.bbox_objs[start:end]
+            batches[i].attr = batch.attr[start:end]
+
+        return [b for b in batches if len(b.point)]  # filter empty batch
+
 
 class ConcatBatcher(object):
     """ConcatBatcher for KPConv."""

ml3d/torch/models/base_model_objdet.py

@@ -25,7 +25,7 @@ def __init__(self, **kwargs):
         self.rng = np.random.default_rng(kwargs.get('seed', None))
 
     @abstractmethod
-    def loss(self, results, inputs):
+    def get_loss(self, results, inputs):
         """Computes the loss given the network input and outputs.
 
         Args:

ml3d/torch/models/point_pillars.py

@@ -137,7 +137,7 @@ def get_optimizer(self, cfg):
         optimizer = torch.optim.AdamW(self.parameters(), **cfg)
         return optimizer, None
 
-    def loss(self, results, inputs):
+    def get_loss(self, results, inputs):
         scores, bboxes, dirs = results
         gt_labels = inputs.labels
         gt_bboxes = inputs.bboxes

ml3d/torch/models/point_rcnn.py

@@ -183,7 +183,7 @@ def step(self):
 
         return optimizer, scheduler
 
-    def loss(self, results, inputs):
+    def get_loss(self, results, inputs):
         if self.mode == "RPN":
             return self.rpn.loss(results, inputs)
         else: