Accumulate points (#4)

Summary:
Code for accumulating points in the z-buffer in three ways:
1. weighted sum
2. normalised weighted sum
3. alpha compositing

Pull Request resolved: fairinternal/pytorch3d#4

Reviewed By: nikhilaravi

Differential Revision: D20522422

Pulled By: gkioxari

fbshipit-source-id: 5023baa05f15e338f3821ef08f5552c2dcbfc06c

Author: oawiles

docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

@@ -673,9 +673,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "pytorch3d (local)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pytorch3d_local"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -687,7 +687,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5+"
+   "version": "3.7.6"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

docs/tutorials/render_coloured_points.ipynb

@@ -0,0 +1,187 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+#include <torch/extension.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <stdio.h>
+#include <vector>
+
+// TODO(gkioxari) support all data types once AtomicAdd supports doubles.
+// Currently, support is for floats only.
+__global__ void alphaCompositeCudaForwardKernel(
+    // clang-format off
+    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> result,
+    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
+    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
+    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+  // clang-format on
+  const int64_t batch_size = result.size(0);
+  const int64_t C = features.size(0);
+  const int64_t H = points_idx.size(2);
+  const int64_t W = points_idx.size(3);
+
+  // Get the batch and index
+  const int batch = blockIdx.x;
+
+  const int num_pixels = C * W * H;
+  const int num_threads = gridDim.y * blockDim.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
+
+  // Iterate over each feature in each pixel
+  for (int pid = tid; pid < num_pixels; pid += num_threads) {
+    int ch = pid / (W * H);
+    int j = (pid % (W * H)) / H;
+    int i = (pid % (W * H)) % H;
+
+    // alphacomposite the different values
+    float cum_alpha = 1.;
+    // Iterate through the closest K points for this pixel
+    for (int k = 0; k < points_idx.size(1); ++k) {
+      int n_idx = points_idx[batch][k][j][i];
+
+      // Sentinel value is -1 indicating no point overlaps the pixel
+      if (n_idx < 0) {
+        continue;
+      }
+
+      float alpha = alphas[batch][k][j][i];
+      // TODO(gkioxari) It might be more efficient to have threads write in a
+      // local variable, and move atomicAdd outside of the loop such that
+      // atomicAdd is executed once per thread.
+      atomicAdd(
+          &result[batch][ch][j][i], features[ch][n_idx] * cum_alpha * alpha);
+      cum_alpha = cum_alpha * (1 - alpha);
+    }
+  }
+}
+
+// TODO(gkioxari) support all data types once AtomicAdd supports doubles.
+// Currently, support is for floats only.
+__global__ void alphaCompositeCudaBackwardKernel(
+    // clang-format off
+    torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> grad_features,
+    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_alphas,
+    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_outputs,
+    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
+    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
+    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+  // clang-format on
+  const int64_t batch_size = points_idx.size(0);
+  const int64_t C = features.size(0);
+  const int64_t H = points_idx.size(2);
+  const int64_t W = points_idx.size(3);
+
+  // Get the batch and index
+  const int batch = blockIdx.x;
+
+  const int num_pixels = C * W * H;
+  const int num_threads = gridDim.y * blockDim.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
+
+  // Parallelize over each feature in each pixel in images of size H * W,
+  // for each image in the batch of size batch_size
+  for (int pid = tid; pid < num_pixels; pid += num_threads) {
+    int ch = pid / (W * H);
+    int j = (pid % (W * H)) / H;
+    int i = (pid % (W * H)) % H;
+
+    // alphacomposite the different values
+    float cum_alpha = 1.;
+    // Iterate through the closest K points for this pixel
+    for (int k = 0; k < points_idx.size(1); ++k) {
+      int n_idx = points_idx[batch][k][j][i];
+
+      // Sentinel value is -1 indicating no point overlaps the pixel
+      if (n_idx < 0) {
+        continue;
+      }
+      float alpha = alphas[batch][k][j][i];
+
+      // TODO(gkioxari) It might be more efficient to have threads write in a
+      // local variable, and move atomicAdd outside of the loop such that
+      // atomicAdd is executed once per thread.
+      atomicAdd(
+          &grad_alphas[batch][k][j][i],
+          cum_alpha * features[ch][n_idx] * grad_outputs[batch][ch][j][i]);
+      atomicAdd(
+          &grad_features[ch][n_idx],
+          cum_alpha * alpha * grad_outputs[batch][ch][j][i]);
+
+      // Iterate over all (K-1) nearest points to update gradient
+      for (int t = 0; t < k; ++t) {
+        int t_idx = points_idx[batch][t][j][i];
+        // Sentinel value is -1, indicating no point overlaps this pixel
+        if (t_idx < 0) {
+          continue;
+        }
+        float alpha_tvalue = alphas[batch][t][j][i];
+        // TODO(gkioxari) It might be more efficient to have threads write in a
+        // local variable, and move atomicAdd outside of the loop such that
+        // atomicAdd is executed once per thread.
+        atomicAdd(
+            &grad_alphas[batch][t][j][i],
+            -grad_outputs[batch][ch][j][i] * features[ch][n_idx] * cum_alpha *
+                alpha / (1 - alpha_tvalue));
+      }
+
+      cum_alpha = cum_alpha * (1 - alphas[batch][k][j][i]);
+    }
+  }
+}
+
+torch::Tensor alphaCompositeCudaForward(
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx) {
+  const int64_t batch_size = points_idx.size(0);
+  const int64_t C = features.size(0);
+  const int64_t H = points_idx.size(2);
+  const int64_t W = points_idx.size(3);
+
+  auto result = torch::zeros({batch_size, C, H, W}, features.options());
+
+  const dim3 threadsPerBlock(64);
+  const dim3 numBlocks(batch_size, 1024 / batch_size + 1);
+
+  // TODO(gkioxari) add AT_DISPATCH_FLOATING_TYPES once atomicAdd supports
+  // doubles. Currently, support is for floats only.
+  alphaCompositeCudaForwardKernel<<<numBlocks, threadsPerBlock>>>(
+      // clang-format off
+      result.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
+      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
+      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
+      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+  // clang-format on
+
+  return result;
+}
+
+std::tuple<torch::Tensor, torch::Tensor> alphaCompositeCudaBackward(
+    const torch::Tensor& grad_outputs,
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx) {
+  auto grad_features = torch::zeros_like(features);
+  auto grad_alphas = torch::zeros_like(alphas);
+
+  const int64_t bs = alphas.size(0);
+
+  const dim3 threadsPerBlock(64);
+  const dim3 numBlocks(bs, 1024 / bs + 1);
+
+  // TODO(gkioxari) add AT_DISPATCH_FLOATING_TYPES once atomicAdd supports
+  // doubles. Currently, support is for floats only.
+  alphaCompositeCudaBackwardKernel<<<numBlocks, threadsPerBlock>>>(
+      // clang-format off
+      grad_features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
+      grad_alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
+      grad_outputs.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
+      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
+      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
+      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+  // clang-format on
+
+  return std::make_tuple(grad_features, grad_alphas);
+}

pytorch3d/csrc/compositing/alpha_composite.cu

@@ -0,0 +1,110 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+#include <torch/extension.h>
+#include "pytorch3d_cutils.h"
+
+#include <vector>
+
+// Perform alpha compositing of points in a z-buffer.
+//
+// Inputs:
+//    features: FloatTensor of shape (C, P) which gives the features
+//            of each point where C is the size of the feature and
+//            P the number of points.
+//    alphas: FloatTensor of shape (N, points_per_pixel, W, W) where
+//            points_per_pixel is the number of points in the z-buffer
+//            sorted in z-order, and W is the image size.
+//    points_idx: IntTensor of shape (N, points_per_pixel, W, W) giving the
+//            indices of the nearest points at each pixel, sorted in z-order.
+// Returns:
+//    weighted_fs: FloatTensor of shape (N, C, W, W) giving the accumulated
+//            feature for each point. Concretely, it gives:
+//                 weighted_fs[b,c,i,j] = sum_k cum_alpha_k *
+//                   features[c,points_idx[b,k,i,j]]
+//                 where cum_alpha_k =
+//                    alphas[b,k,i,j] * prod_l=0..k-1 (1 - alphas[b,l,i,j])
+
+// CUDA declarations
+#ifdef WITH_CUDA
+torch::Tensor alphaCompositeCudaForward(
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx);
+
+std::tuple<torch::Tensor, torch::Tensor> alphaCompositeCudaBackward(
+    const torch::Tensor& grad_outputs,
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx);
+#endif
+
+// C++ declarations
+torch::Tensor alphaCompositeCpuForward(
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx);
+
+std::tuple<torch::Tensor, torch::Tensor> alphaCompositeCpuBackward(
+    const torch::Tensor& grad_outputs,
+    const torch::Tensor& features,
+    const torch::Tensor& alphas,
+    const torch::Tensor& points_idx);
+
+torch::Tensor alphaCompositeForward(
+    torch::Tensor& features,
+    torch::Tensor& alphas,
+    torch::Tensor& points_idx) {
+  features = features.contiguous();
+  alphas = alphas.contiguous();
+  points_idx = points_idx.contiguous();
+
+  if (features.type().is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CONTIGUOUS_CUDA(features);
+    CHECK_CONTIGUOUS_CUDA(alphas);
+    CHECK_CONTIGUOUS_CUDA(points_idx);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+    return alphaCompositeCudaForward(features, alphas, points_idx);
+  } else {
+    CHECK_CONTIGUOUS(features);
+    CHECK_CONTIGUOUS(alphas);
+    CHECK_CONTIGUOUS(points_idx);
+
+    return alphaCompositeCpuForward(features, alphas, points_idx);
+  }
+}
+
+std::tuple<torch::Tensor, torch::Tensor> alphaCompositeBackward(
+    torch::Tensor& grad_outputs,
+    torch::Tensor& features,
+    torch::Tensor& alphas,
+    torch::Tensor& points_idx) {
+  grad_outputs = grad_outputs.contiguous();
+  features = features.contiguous();
+  alphas = alphas.contiguous();
+  points_idx = points_idx.contiguous();
+
+  if (grad_outputs.type().is_cuda()) {
+#ifdef WITH_CUDA
+    CHECK_CONTIGUOUS_CUDA(grad_outputs);
+    CHECK_CONTIGUOUS_CUDA(features);
+    CHECK_CONTIGUOUS_CUDA(alphas);
+    CHECK_CONTIGUOUS_CUDA(points_idx);
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+
+    return alphaCompositeCudaBackward(
+        grad_outputs, features, alphas, points_idx);
+  } else {
+    CHECK_CONTIGUOUS(grad_outputs);
+    CHECK_CONTIGUOUS(features);
+    CHECK_CONTIGUOUS(alphas);
+    CHECK_CONTIGUOUS(points_idx);
+
+    return alphaCompositeCpuBackward(
+        grad_outputs, features, alphas, points_idx);
+  }
+}