Fix cuda FloatTensor error

.gitignore

@@ -1,5 +1,7 @@
 .vscode
 notebooks
+experiments
+tests
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

oft/__init__.py

@@ -1,4 +1,4 @@
 from .model import OftNet, huber_loss, hard_neg_mining_loss
 from .data import KittiObjectDataset, ObjectEncoder, AugmentedObjectDataset
 from .utils import MetricDict, Timer, convert_figure, make_grid
-from .visualization import vis_score, vis_uncertainty
+from .visualization import vis_score, vis_uncertainty, visualize_objects

oft/data/encoder.py

@@ -112,14 +112,14 @@ def _encode_positions(self, positions, indices, grid):
         positions = positions.index_select(0, indices.view(-1)).view(C, D, W, 3)
 
         # Compute relative offsets and normalize
-        pos_offsets = (positions - centers) / self.pos_std
+        pos_offsets = (positions - centers) / self.pos_std.to(positions)
         return pos_offsets.permute(0, 3, 1, 2)
 
     def _encode_dimensions(self, classids, dimensions, indices):
 
         # Convert mean and std to tensors
-        log_dim_mean = self.log_dim_mean[classids]
-        log_dim_std = self.log_dim_std[classids]
+        log_dim_mean = self.log_dim_mean.to(dimensions)[classids]
+        log_dim_std = self.log_dim_std.to(dimensions)[classids]
 
         # Compute normalized log scale offset
         dim_offsets = (torch.log(dimensions) - log_dim_mean) / log_dim_std
@@ -146,7 +146,7 @@ def _encode_empty(self, grid):
         pos_offsets = grid.new_zeros((self.nclass, 3, depth-1, width-1))
         dim_offsets = grid.new_zeros((self.nclass, 3, depth-1, width-1))
         ang_offsets = grid.new_zeros((self.nclass, 2, depth-1, width-1))
-        mask = grid.new_zeros((self.nclass, depth-1, width-1)).byte()
+        mask = grid.new_zeros((self.nclass, depth-1, width-1)).bool()
 
         return heatmaps, pos_offsets, dim_offsets, ang_offsets, mask
 

oft/model/oftnet.py

@@ -14,7 +14,7 @@ def __init__(self, num_classes=1, frontend='resnet18', topdown_layers=8,
 
         # Construct frontend network
         assert frontend in ['resnet18', 'resnet34'], 'unrecognised frontend'
-        self.frontend = getattr(resnet, frontend)(pretrained=True)
+        self.frontend = getattr(resnet, frontend)(pretrained=False)
 
         # Lateral layers convert resnet outputs to a common feature size
         self.lat8 = nn.Conv2d(128, 256, 1)

oft/utils.py

@@ -99,6 +99,34 @@ def gaussian_kernel(sigma=1., trunc=2.):
     return kernel2d / kernel2d.sum()
 
 
+def bbox_corners(obj):
+    """
+    Return the 2D
+    """
+
+    # Get corners of bounding box in object space
+    offsets = torch.tensor([
+        [-.5,  0., -.5],    # Back-left lower
+        [ .5,  0., -.5],    # Front-left lower
+        [-.5,  0.,  .5],    # Back-right lower
+        [ .5,  0.,  .5],    # Front-right lower
+        [-.5, -1., -.5],    # Back-left upper
+        [ .5, -1., -.5],    # Front-left upper
+        [-.5, -1.,  .5],    # Back-right upper
+        [ .5, -1.,  .5],    # Front-right upper
+    ])
+    corners = offsets * torch.tensor(obj.dimensions)
+    # corners = corners[:, [2, 0, 1]]
+
+    # Apply y-axis rotation
+    corners = rotate(corners, torch.tensor(obj.angle))
+
+    # Apply translation
+    corners = corners + torch.tensor(obj.position)
+    return corners
+
+
+
 def collate(batch):
 
     idxs, images, calibs, objects, grids = zip(*batch)

train.py

@@ -181,8 +181,7 @@ def parse_args():
     parser = ArgumentParser()
 
     # Data options
-    parser.add_argument('--root', type=str, 
-                        default='data/kitti',
+    parser.add_argument('--root', type=str, default='data/kitti',
                         help='root directory of the KITTI dataset')
     parser.add_argument('--grid-size', type=float, nargs=2, default=(80., 80.),
                         help='width and depth of validation grid, in meters')