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avoid converting a TensorOptions from float to integer

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Summary: pytorch is adding checks that mean integer tensors with requires_grad=True need to be avoided. Fix accidentally creating them.

Reviewed By: jcjohnson, gkioxari

Differential Revision: D21576712

fbshipit-source-id: 008218997986800a36d93caa1a032ee91f2bffcd
This commit is contained in:
Jeremy Reizenstein
2020-05-14 13:13:36 -07:00
committed by Facebook GitHub Bot
parent 6a365d203f
commit 728179e848
9 changed files with 15 additions and 12 deletions
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6
pytorch3d/csrc/rasterize_points/rasterize_points.cu
View File

@@ -177,7 +177,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> RasterizePointsNaiveCuda(
AT_ERROR(ss.str());
}
auto int_opts = points.options().dtype(at::kInt);
auto int_opts = num_points_per_cloud.options().dtype(at::kInt);
auto float_opts = points.options().dtype(at::kFloat);
at::Tensor point_idxs = at::full({N, S, S, K}, -1, int_opts);
at::Tensor zbuf = at::full({N, S, S, K}, -1, float_opts);
@@ -372,7 +372,7 @@ at::Tensor RasterizePointsCoarseCuda(
ss << "Got " << num_bins << "; that's too many!";
AT_ERROR(ss.str());
}
auto opts = points.options().dtype(at::kInt);
auto opts = num_points_per_cloud.options().dtype(at::kInt);
at::Tensor points_per_bin = at::zeros({N, num_bins, num_bins}, opts);
at::Tensor bin_points = at::full({N, num_bins, num_bins, M}, -1, opts);
@@ -509,7 +509,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> RasterizePointsFineCuda(
if (K > kMaxPointsPerPixel) {
AT_ERROR("Must have num_closest <= 150");
}
auto int_opts = points.options().dtype(at::kInt);
auto int_opts = bin_points.options().dtype(at::kInt);
auto float_opts = points.options().dtype(at::kFloat);
at::Tensor point_idxs = at::full({N, S, S, K}, -1, int_opts);
at::Tensor zbuf = at::full({N, S, S, K}, -1, float_opts);

4
pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp
View File

@@ -25,7 +25,7 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsNaiveCpu(
const int K = points_per_pixel;
// Initialize output tensors.
auto int_opts = points.options().dtype(torch::kInt32);
auto int_opts = num_points_per_cloud.options().dtype(torch::kInt32);
auto float_opts = points.options().dtype(torch::kFloat32);
torch::Tensor point_idxs = torch::full({N, S, S, K}, -1, int_opts);
torch::Tensor zbuf = torch::full({N, S, S, K}, -1, float_opts);
@@ -105,7 +105,7 @@ torch::Tensor RasterizePointsCoarseCpu(
const int B = 1 + (image_size - 1) / bin_size; // Integer division round up
const int M = max_points_per_bin;
auto opts = points.options().dtype(torch::kInt32);
auto opts = num_points_per_cloud.options().dtype(torch::kInt32);
torch::Tensor points_per_bin = torch::zeros({N, B, B}, opts);
torch::Tensor bin_points = torch::full({N, B, B, M}, -1, opts);