Meh

utils/.gitignore

@@ -1,3 +1,3 @@
 build
 _ext
-tc_autotune
+*.tc*

utils/pointnet2_utils.py

@@ -12,6 +12,7 @@ import os.path as osp
 from _ext import pointnet2
 
 BASE_DIR = osp.join(osp.abspath(osp.dirname(__file__)), 'tc_autotune')
+tc.GlobalDebugInit(['--dump_cuda=true'])
 
 
 def _tc_wrapper_fn(fn, name):
@@ -31,10 +32,10 @@ def _tc_wrapper_fn(fn, name):
         cache_name += '.tc'
         cache_file = osp.join(BASE_DIR, cache_name)
 
-        if not osp.exists(cache_file + '.cuda'):
+        if not osp.exists(cache_file + '.cuda') and False:
             fn.autotune(*inputs, **tc.autotuner_settings, cache=cache_file)
 
-        return fn(*inputs, cache=cache_file)
+        return fn(*inputs)
 
     return wrapper
 
@@ -96,23 +97,22 @@ furthest_point_sample = FurthestPointSampling.apply
 
 def _make_gather_points():
 
-    forward_lang = """
+    lang = """
         def gather_points(float(B, C, N) points, int32(B, NP) idx) -> (output) {
             output(b, c, np) = points(b, c, idx(b, np))
         }
-    """
 
-    backward_lang = """
         def gather_points_grad(float(B, C, N) points, int32(B, NP) idx, float(B, C, NP) grad_out) -> (grad_points) {
-            grad_points(b, c, idx(b, np)) +=! grad_out(b, c, np)
+            a = idx(b, np)
+            grad_points(b, c, a) +=! grad_out(b, c, np)
         }
     """
 
     fn = tc.define(
-        forward_lang,
+        lang,
-        training=False,
+        training=True,
         name='gather_points',
-        backward=backward_lang
+        backward='gather_points_grad'
     )
 
     return _tc_wrapper_fn(fn, 'gather_points')
@@ -162,6 +162,7 @@ class ThreeNN(Function):
 three_nn = ThreeNN.apply
 
 
+
 class ThreeInterpolate(Function):
 
     @staticmethod
@@ -235,69 +236,28 @@ class ThreeInterpolate(Function):
 three_interpolate = ThreeInterpolate.apply
 
 
-class GroupPoints(Function):
+def _make_group_points():
+    lang = """
+        def group_points(float(B, C, N) points, int32(B, NP, NS) idx) -> (output) {
+            output(b, c, np, ns) = points(b, c, idx(b, np, ns))
+        }
 
-    @staticmethod
+        def group_points_grad(float(B, C, N) points, int32(B, NP, NS) idx, float(B, C, NP, NS) grad_out) -> (grad_points) {
-    def forward(ctx, points: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+            grad_points(b, c, idx(b, np, ns)) +=! grad_out(b, c, np, ns)
-        r"""
+        }
+    """
 
-        Parameters
+    fn = tc.define(
-        ----------
+        lang,
-        points : torch.Tensor
+        training=True,
-            (B, C, N) tensor of points to group
+        name='group_points',
-        idx : torch.Tensor
+        backward='group_points_grad'
-            (B, npoint, nsample) tensor containing the indicies of points to group with
+    )
 
-        Returns
+    return _tc_wrapper_fn(fn, 'group_points')
-        -------
-        torch.Tensor
-            (B, C, npoint, nsample) tensor
-        """
-        assert points.is_contiguous()
-        assert idx.is_contiguous()
-
-        B, npoints, nsample = idx.size()
-        _, C, N = points.size()
-
-        output = torch.cuda.FloatTensor(B, C, npoints, nsample)
-
-        pointnet2.group_points_wrapper(
-            B, C, N, npoints, nsample, points, idx, output
-        )
-
-        ctx.for_backwards = (idx, N)
-        return output
-
-    @staticmethod
-    def backward(ctx,
-                 grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        r"""
-
-        Parameters
-        ----------
-        grad_out : torch.Tensor
-            (B, C, npoint, nsample) tensor of the gradients of the output from forward
-
-        Returns
-        -------
-        torch.Tensor
-            (B, C, N) gradient of the points
-        None
-        """
-        idx, N = ctx.for_backwards
-
-        B, C, npoint, nsample = grad_out.size()
-        grad_points = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
-
-        grad_out_data = grad_out.data.contiguous()
-        pointnet2.group_points_grad_wrapper(
-            B, C, N, npoint, nsample, grad_out_data, idx, grad_points.data
-        )
-
-        return grad_points, None
 
 
-group_points = GroupPoints.apply
+group_points = _make_group_points()
 
 
 class BallQuery(Function):