Chamfer for Pointclouds object

Summary:
Allow Pointclouds objects and heterogenous data to be provided for Chamfer loss. Remove "none" as an option for point_reduction because it doesn't make sense and in the current implementation is effectively the same as "sum".

Possible improvement: create specialised operations for sum and cosine_similarity of padded tensors, to avoid having to create masks. sum would be useful elsewhere.

Reviewed By: gkioxari

Differential Revision: D20816301

fbshipit-source-id: 0f32073210225d157c029d80de450eecdb64f4d2

pytorch3d/loss/chamfer.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
+from typing import Union
+
 import torch
 import torch.nn.functional as F
-from pytorch3d.ops.nearest_neighbor_points import nn_points_idx
+from pytorch3d.ops.knn import knn_gather, knn_points
+from pytorch3d.structures.pointclouds import Pointclouds
 
 
-def _validate_chamfer_reduction_inputs(batch_reduction: str, point_reduction: str):
+def _validate_chamfer_reduction_inputs(
+    batch_reduction: Union[str, None], point_reduction: str
+):
     """Check the requested reductions are valid.
 
     Args:
         batch_reduction: Reduction operation to apply for the loss across the
-            batch, can be one of ["none", "mean", "sum"].
+            batch, can be one of ["mean", "sum"] or None.
         point_reduction: Reduction operation to apply for the loss across the
-            points, can be one of ["none", "mean", "sum"].
+            points, can be one of ["mean", "sum"].
+    """
+    if batch_reduction is not None and batch_reduction not in ["mean", "sum"]:
+        raise ValueError('batch_reduction must be one of ["mean", "sum"] or None')
+    if point_reduction not in ["mean", "sum"]:
+        raise ValueError('point_reduction must be one of ["mean", "sum"]')
+
+
+def _handle_pointcloud_input(
+    points: Union[torch.Tensor, Pointclouds],
+    lengths: Union[torch.Tensor, None],
+    normals: Union[torch.Tensor, None],
+):
+    """
+    If points is an instance of Pointclouds, retrieve the padded points tensor
+    along with the number of points per batch and the padded normals.
+    Otherwise, return the input points (and normals) with the number of points per cloud
+    set to the size of the second dimension of `points`.
     """
-    if batch_reduction not in ["none", "mean", "sum"]:
-        raise ValueError('batch_reduction must be one of ["none", "mean", "sum"]')
-    if point_reduction not in ["none", "mean", "sum"]:
-        raise ValueError('point_reduction must be one of ["none", "mean", "sum"]')
-    if batch_reduction == "none" and point_reduction == "none":
-        raise ValueError('batch_reduction and point_reduction cannot both be "none".')
+    if isinstance(points, Pointclouds):
+        X = points.points_padded()
+        lengths = points.num_points_per_cloud()
+        normals = points.normals_padded()  # either a tensor or None
+    elif torch.is_tensor(points):
+        if points.ndim != 3:
+            raise ValueError("Expected points to be of shape (N, P, D)")
+        X = points
+        if lengths is not None and (
+            lengths.ndim != 1 or lengths.shape[0] != X.shape[0]
+        ):
+            raise ValueError("Expected lengths to be of shape (N,)")
+        if lengths is None:
+            lengths = torch.full(
+                (X.shape[0],), X.shape[1], dtype=torch.int64, device=points.device
+            )
+        if normals is not None and normals.ndim != 3:
+            raise ValueError("Expected normals to be of shape (N, P, 3")
+    else:
+        raise ValueError(
+            "The input pointclouds should be either "
+            + "Pointclouds objects or torch.Tensor of shape "
+            + "(minibatch, num_points, 3)."
+        )
+    return X, lengths, normals
 
 
 def chamfer_distance(
     x,
     y,
+    x_lengths=None,
+    y_lengths=None,
     x_normals=None,
     y_normals=None,
     weights=None,
-    batch_reduction: str = "mean",
+    batch_reduction: Union[str, None] = "mean",
     point_reduction: str = "mean",
 ):
     """
     Chamfer distance between two pointclouds x and y.
 
     Args:
-        x: FloatTensor of shape (N, P1, D) representing a batch of point clouds
-            with P1 points in each batch element, batch size N and feature
-            dimension D.
-        y: FloatTensor of shape (N, P2, D) representing a batch of point clouds
-            with P2 points in each batch element, batch size N and feature
-            dimension D.
+        x: FloatTensor of shape (N, P1, D) or a Pointclouds object representing
+            a batch of point clouds with at most P1 points in each batch element,
+            batch size N and feature dimension D.
+        y: FloatTensor of shape (N, P2, D) or a Pointclouds object representing
+            a batch of point clouds with at most P2 points in each batch element,
+            batch size N and feature dimension D.
+        x_lengths: Optional LongTensor of shape (N,) giving the number of points in each
+            cloud in x.
+        y_lengths: Optional LongTensor of shape (N,) giving the number of points in each
+            cloud in x.
         x_normals: Optional FloatTensor of shape (N, P1, D).
         y_normals: Optional FloatTensor of shape (N, P2, D).
         weights: Optional FloatTensor of shape (N,) giving weights for
             batch elements for reduction operation.
         batch_reduction: Reduction operation to apply for the loss across the
-            batch, can be one of ["none", "mean", "sum"].
+            batch, can be one of ["mean", "sum"] or None.
         point_reduction: Reduction operation to apply for the loss across the
-            points, can be one of ["none", "mean", "sum"].
+            points, can be one of ["mean", "sum"].
 
     Returns:
         2-element tuple containing
@@ -61,16 +108,31 @@ def chamfer_distance(
     """
     _validate_chamfer_reduction_inputs(batch_reduction, point_reduction)
 
+    x, x_lengths, x_normals = _handle_pointcloud_input(x, x_lengths, x_normals)
+    y, y_lengths, y_normals = _handle_pointcloud_input(y, y_lengths, y_normals)
+
+    return_normals = x_normals is not None and y_normals is not None
+
     N, P1, D = x.shape
     P2 = y.shape[1]
 
+    # Check if inputs are heterogeneous and create a lengths mask.
+    is_x_heterogeneous = ~(x_lengths == P1).all()
+    is_y_heterogeneous = ~(y_lengths == P2).all()
+    x_mask = (
+        torch.arange(P1, device=x.device)[None] >= x_lengths[:, None]
+    )  # shape [N, P1]
+    y_mask = (
+        torch.arange(P2, device=y.device)[None] >= y_lengths[:, None]
+    )  # shape [N, P2]
+
     if y.shape[0] != N or y.shape[2] != D:
         raise ValueError("y does not have the correct shape.")
     if weights is not None:
         if weights.size(0) != N:
             raise ValueError("weights must be of shape (N,).")
         if not (weights >= 0).all():
-            raise ValueError("weights can not be nonnegative.")
+            raise ValueError("weights cannot be negative.")
         if weights.sum() == 0.0:
             weights = weights.view(N, 1)
             if batch_reduction in ["mean", "sum"]:
@@ -80,46 +142,60 @@ def chamfer_distance(
                 )
             return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)
 
-    return_normals = x_normals is not None and y_normals is not None
     cham_norm_x = x.new_zeros(())
     cham_norm_y = x.new_zeros(())
 
-    x_near, xidx_near, x_normals_near = nn_points_idx(x, y, y_normals)
-    y_near, yidx_near, y_normals_near = nn_points_idx(y, x, x_normals)
+    x_dists, x_idx = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
+    y_dists, y_idx = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
 
-    cham_x = (x - x_near).norm(dim=2, p=2) ** 2.0  # (N, P1)
-    cham_y = (y - y_near).norm(dim=2, p=2) ** 2.0  # (N, P2)
+    cham_x = x_dists[..., 0]  # (N, P1)
+    cham_y = y_dists[..., 0]  # (N, P2)
+
+    if is_x_heterogeneous:
+        cham_x[x_mask] = 0.0
+    if is_y_heterogeneous:
+        cham_y[y_mask] = 0.0
 
     if weights is not None:
         cham_x *= weights.view(N, 1)
         cham_y *= weights.view(N, 1)
 
     if return_normals:
+        # Gather the normals using the indices and keep only value for k=0
+        x_normals_near = knn_gather(y_normals, x_idx, y_lengths)[..., 0, :]
+        y_normals_near = knn_gather(x_normals, y_idx, x_lengths)[..., 0, :]
+
         cham_norm_x = 1 - torch.abs(
             F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)
         )
         cham_norm_y = 1 - torch.abs(
             F.cosine_similarity(y_normals, y_normals_near, dim=2, eps=1e-6)
         )
+
+        if is_x_heterogeneous:
+            cham_norm_x[x_mask] = 0.0
+        if is_y_heterogeneous:
+            cham_norm_y[y_mask] = 0.0
+
         if weights is not None:
             cham_norm_x *= weights.view(N, 1)
             cham_norm_y *= weights.view(N, 1)
 
-    if point_reduction != "none":
-        # If not 'none' then either 'sum' or 'mean'.
-        cham_x = cham_x.sum(1)  # (N,)
-        cham_y = cham_y.sum(1)  # (N,)
+    # Apply point reduction
+    cham_x = cham_x.sum(1)  # (N,)
+    cham_y = cham_y.sum(1)  # (N,)
+    if return_normals:
+        cham_norm_x = cham_norm_x.sum(1)  # (N,)
+        cham_norm_y = cham_norm_y.sum(1)  # (N,)
+    if point_reduction == "mean":
+        cham_x /= x_lengths
+        cham_y /= y_lengths
         if return_normals:
-            cham_norm_x = cham_norm_x.sum(1)  # (N,)
-            cham_norm_y = cham_norm_y.sum(1)  # (N,)
-        if point_reduction == "mean":
-            cham_x /= P1
-            cham_y /= P2
-            if return_normals:
-                cham_norm_x /= P1
-                cham_norm_y /= P2
+            cham_norm_x /= x_lengths
+            cham_norm_y /= y_lengths
 
-    if batch_reduction != "none":
+    if batch_reduction is not None:
+        # batch_reduction == "sum"
         cham_x = cham_x.sum()
         cham_y = cham_y.sum()
         if return_normals:

tests/bm_chamfer.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
+from itertools import product
 
 import torch
 from fvcore.common.benchmark import benchmark
@@ -20,8 +21,23 @@ def bm_chamfer() -> None:
     )
 
     if torch.cuda.is_available():
-        kwargs_list = kwargs_list_naive + [
-            {"batch_size": 1, "P1": 1000, "P2": 3000, "return_normals": False},
-            {"batch_size": 1, "P1": 1000, "P2": 30000, "return_normals": True},
-        ]
+        kwargs_list = []
+        batch_size = [1, 32]
+        P1 = [32, 1000, 10000]
+        P2 = [64, 3000, 30000]
+        return_normals = [True, False]
+        homogeneous = [True, False]
+        test_cases = product(batch_size, P1, P2, return_normals, homogeneous)
+
+        for case in test_cases:
+            b, p1, p2, n, h = case
+            kwargs_list.append(
+                {
+                    "batch_size": b,
+                    "P1": p1,
+                    "P2": p2,
+                    "return_normals": n,
+                    "homogeneous": h,
+                }
+            )
         benchmark(TestChamfer.chamfer_with_init, "CHAMFER", kwargs_list, warmup_iters=1)