Merge branch 'master' into cubic-feature-sampling

Author: nicolas-chaulet

test/test_chamfer_dist.py

@@ -12,7 +12,7 @@
 ROOT = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..")
 sys.path.insert(0, ROOT)
 
-from torch_points_kernels import ChamferFunction, chamfer_dist
+from torch_points_kernels.chamfer_dist import ChamferFunction, chamfer_dist
 
 
 class TestChamferDistance(unittest.TestCase):

torch_points_kernels/chamfer_dist.py

@@ -0,0 +1,59 @@
+import torch
+
+if torch.cuda.is_available():
+    import torch_points_kernels.points_cuda as tpcuda
+
+
+class ChamferFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, xyz1, xyz2):
+        if not torch.cuda.is_available():
+            raise NotImplementedError(
+                "CPU version is not available for Chamfer Distance"
+            )
+
+        dist1, dist2, idx1, idx2 = tpcuda.chamfer_dist(xyz1, xyz2)
+        ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
+
+        return dist1, dist2
+
+    @staticmethod
+    def backward(ctx, grad_dist1, grad_dist2):
+        xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
+        grad_xyz1, grad_xyz2 = tpcuda.chamfer_dist_grad(
+            xyz1, xyz2, idx1, idx2, grad_dist1, grad_dist2
+        )
+        return grad_xyz1, grad_xyz2
+
+
+def chamfer_dist(xyz1, xyz2, ignore_zeros=False):
+    r"""
+    Calcuates the distance between B pairs of point clouds
+
+    Parameters
+    ----------
+    xyz1 : torch.Tensor (dtype=torch.float32)
+        (B, n1, 3) B point clouds containing n1 points
+    xyz2 : torch.Tensor (dtype=torch.float32)
+        (B, n2, 3) B point clouds containing n2 points
+    ignore_zeros : bool
+        ignore the point whose coordinate is (0, 0, 0) or not
+
+    Returns
+    -------
+    dist: torch.Tensor
+        (B, ): the distances between B pairs of point clouds
+    """
+    if len(xyz1.shape) != 3 or xyz1.size(2) != 3 or len(xyz2.shape) != 3 or xyz2.size(2) != 3:
+        raise ValueError('The input point cloud should be of size (B, n_pts, 3)')
+
+    batch_size = xyz1.size(0)
+    if batch_size == 1 and ignore_zeros:
+        non_zeros1 = torch.sum(xyz1, dim=2).ne(0)
+        non_zeros2 = torch.sum(xyz2, dim=2).ne(0)
+        xyz1 = xyz1[non_zeros1].unsqueeze(dim=0)
+        xyz2 = xyz2[non_zeros2].unsqueeze(dim=0)
+
+    dist1, dist2 = ChamferFunction.apply(xyz1, xyz2)
+    return torch.mean(dist1) + torch.mean(dist2)
+

torch_points_kernels/torchpoints.py

@@ -235,50 +235,3 @@ def ball_query(
         return ball_query_dense(radius, nsample, x, y, sort=sort)
     else:
         raise Exception("unrecognized mode {}".format(mode))
-
-
-class ChamferFunction(Function):
-    @staticmethod
-    def forward(ctx, xyz1, xyz2):
-        dist1, dist2, idx1, idx2 = tpcuda.chamfer_dist(xyz1, xyz2)
-        ctx.save_for_backward(xyz1, xyz2, idx1, idx2)
-
-        return dist1, dist2
-
-    @staticmethod
-    def backward(ctx, grad_dist1, grad_dist2):
-        xyz1, xyz2, idx1, idx2 = ctx.saved_tensors
-        grad_xyz1, grad_xyz2 = tpcuda.chamfer_dist_grad(
-            xyz1, xyz2, idx1, idx2, grad_dist1, grad_dist2
-        )
-        return grad_xyz1, grad_xyz2
-
-
-def chamfer_dist(xyz1, xyz2, ignore_zeros=False):
-    r"""
-    Calcuates the distance between B pairs of point clouds
-
-    Parameters
-    ----------
-    xyz1 : torch.Tensor (dtype=torch.float32)
-        (B, n1, 3) B point clouds containing n1 points
-    xyz2 : torch.Tensor (dtype=torch.float32)
-        (B, n2, 3) B point clouds containing n2 points
-    ignore_zeros : bool
-        ignore the point whose coordinate is (0, 0, 0) or not
-
-    Returns
-    -------
-    dist: torch.Tensor
-        (B, ): the distances between B pairs of point clouds
-    """
-    batch_size = xyz1.size(0)
-    if batch_size == 1 and ignore_zeros:
-        non_zeros1 = torch.sum(xyz1, dim=2).ne(0)
-        non_zeros2 = torch.sum(xyz2, dim=2).ne(0)
-        xyz1 = xyz1[non_zeros1].unsqueeze(dim=0)
-        xyz2 = xyz2[non_zeros2].unsqueeze(dim=0)
-
-    dist1, dist2 = ChamferFunction.apply(xyz1, xyz2)
-    return torch.mean(dist1) + torch.mean(dist2)
-