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Remove THGeneral (#69041)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69041 `TH_CONCAT_{N}` is still being used by THP so I've moved that into it's own header but all the compiled code is gone. Test Plan: Imported from OSS Reviewed By: anjali411 Differential Revision: D32872477 Pulled By: ngimel fbshipit-source-id: 06c82d8f96dbcee0715be407c61dfc7d7e8be47a
This commit is contained in:
Peter Bell
Facebook GitHub Bot
parent
d049cd2e01
commit
f8fe9a2be1
@ -208,8 +208,7 @@ __device__ static float atomicMin(float* address, float val) {
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#define IABS(a) abs(a)
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// Checks.
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#define CHECKOK C10_CUDA_CHECK
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#define ARGCHECK THArgCheck
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#define ARGCHECK TORCH_CHECK_ARG
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// Math.
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#define NORM3DF(x, y, z) norm3df(x, y, z)
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@ -155,8 +155,7 @@ INLINE void ATOMICADD_F3(T* address, T val) {
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#define IABS(a) abs(a)
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// Checks.
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#define CHECKOK THCheck
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#define ARGCHECK THArgCheck
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#define ARGCHECK TORCH_CHECK_ARG
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// Math.
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#define NORM3DF(x, y, z) sqrtf(x* x + y * y + z * z)
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@ -24,12 +24,10 @@
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// #pragma diag_suppress = 68
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#include <ATen/cuda/CUDAContext.h>
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// #pragma pop
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#include <TH/TH.h>
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#include "../cuda/commands.h"
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#else
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#pragma clang diagnostic push
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#pragma clang diagnostic ignored "-Weverything"
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#include <TH/TH.h>
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#pragma clang diagnostic pop
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#include "../host/commands.h"
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#endif
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@ -32,16 +32,16 @@ Renderer::Renderer(
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const uint& n_channels,
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const uint& n_track) {
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LOG_IF(INFO, PULSAR_LOG_INIT) << "Initializing renderer.";
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THArgCheck(width > 0, 1, "image width must be > 0!");
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THArgCheck(height > 0, 2, "image height must be > 0!");
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THArgCheck(max_n_balls > 0, 3, "max_n_balls must be > 0!");
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THArgCheck(
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TORCH_CHECK_ARG(width > 0, 1, "image width must be > 0!");
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TORCH_CHECK_ARG(height > 0, 2, "image height must be > 0!");
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TORCH_CHECK_ARG(max_n_balls > 0, 3, "max_n_balls must be > 0!");
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TORCH_CHECK_ARG(
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background_normalization_depth > 0.f &&
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background_normalization_depth < 1.f,
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5,
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"background_normalization_depth must be in ]0., 1.[");
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THArgCheck(n_channels > 0, 6, "n_channels must be > 0");
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THArgCheck(
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TORCH_CHECK_ARG(n_channels > 0, 6, "n_channels must be > 0");
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TORCH_CHECK_ARG(
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n_track > 0 && n_track <= MAX_GRAD_SPHERES,
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7,
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("n_track must be > 0 and <" + std::to_string(MAX_GRAD_SPHERES) +
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@ -92,7 +92,7 @@ bool Renderer::operator==(const Renderer& rhs) const {
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};
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void Renderer::ensure_on_device(torch::Device device, bool /*non_blocking*/) {
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THArgCheck(
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TORCH_CHECK_ARG(
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device.type() == c10::DeviceType::CUDA ||
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device.type() == c10::DeviceType::CPU,
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1,
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@ -220,48 +220,48 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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// Check all parameters adhere batch size.
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batch_processing = true;
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batch_size = vert_pos.size(0);
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.ndimension() == 3 &&
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vert_col.size(0) == static_cast<int64_t>(batch_size),
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2,
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"vert_col needs to have batch size.");
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_radii.ndimension() == 2 &&
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vert_radii.size(0) == static_cast<int64_t>(batch_size),
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3,
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"vert_radii must be specified per batch.");
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THArgCheck(
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TORCH_CHECK_ARG(
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cam_pos.ndimension() == 2 &&
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cam_pos.size(0) == static_cast<int64_t>(batch_size),
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4,
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"cam_pos must be specified per batch and have the correct batch size.");
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_0_0_center.ndimension() == 2 &&
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pixel_0_0_center.size(0) == static_cast<int64_t>(batch_size),
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5,
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"pixel_0_0_center must be specified per batch.");
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_x.ndimension() == 2 &&
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pixel_vec_x.size(0) == static_cast<int64_t>(batch_size),
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6,
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"pixel_vec_x must be specified per batch.");
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_y.ndimension() == 2 &&
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pixel_vec_y.size(0) == static_cast<int64_t>(batch_size),
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7,
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"pixel_vec_y must be specified per batch.");
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THArgCheck(
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TORCH_CHECK_ARG(
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focal_length.ndimension() == 1 &&
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focal_length.size(0) == static_cast<int64_t>(batch_size),
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8,
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"focal_length must be specified per batch.");
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THArgCheck(
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TORCH_CHECK_ARG(
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principal_point_offsets.ndimension() == 2 &&
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principal_point_offsets.size(0) == static_cast<int64_t>(batch_size),
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9,
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"principal_point_offsets must be specified per batch.");
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if (opacity.has_value()) {
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THArgCheck(
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TORCH_CHECK_ARG(
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opacity.value().ndimension() == 2 &&
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opacity.value().size(0) == static_cast<int64_t>(batch_size),
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13,
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@ -269,14 +269,14 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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}
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// Check all parameters are for a matching number of points.
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n_points = vert_pos.size(1);
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.size(1) == static_cast<int64_t>(n_points),
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2,
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("The number of points for vertex positions (" +
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std::to_string(n_points) + ") and vertex colors (" +
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std::to_string(vert_col.size(1)) + ") doesn't agree.")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_radii.size(1) == static_cast<int64_t>(n_points),
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3,
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("The number of points for vertex positions (" +
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@ -284,50 +284,50 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(vert_col.size(1)) + ") doesn't agree.")
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.c_str());
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if (opacity.has_value()) {
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THArgCheck(
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TORCH_CHECK_ARG(
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opacity.value().size(1) == static_cast<int64_t>(n_points),
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13,
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"Opacity needs to be specified per point.");
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}
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// Check all parameters have the correct last dimension size.
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_pos.size(2) == 3,
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1,
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("Vertex positions must be 3D (have shape " +
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std::to_string(vert_pos.size(2)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.size(2) == this->renderer_vec[0].cam.n_channels,
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2,
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("Vertex colors must have the right number of channels (have shape " +
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std::to_string(vert_col.size(2)) + ", need " +
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std::to_string(this->renderer_vec[0].cam.n_channels) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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cam_pos.size(1) == 3,
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4,
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("Camera position must be 3D (has shape " +
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std::to_string(cam_pos.size(1)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_0_0_center.size(1) == 3,
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5,
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("pixel_0_0_center must be 3D (has shape " +
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std::to_string(pixel_0_0_center.size(1)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_x.size(1) == 3,
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6,
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("pixel_vec_x must be 3D (has shape " +
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std::to_string(pixel_vec_x.size(1)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_y.size(1) == 3,
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7,
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("pixel_vec_y must be 3D (has shape " +
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std::to_string(pixel_vec_y.size(1)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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principal_point_offsets.size(1) == 2,
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9,
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"principal_point_offsets must contain x and y offsets.");
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@ -335,43 +335,43 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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ensure_n_renderers_gte(batch_size);
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} else {
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// Check all parameters are of correct dimension.
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.ndimension() == 2, 2, "vert_col needs to have dimension 2.");
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_radii.ndimension() == 1, 3, "vert_radii must have dimension 1.");
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THArgCheck(cam_pos.ndimension() == 1, 4, "cam_pos must have dimension 1.");
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THArgCheck(
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TORCH_CHECK_ARG(cam_pos.ndimension() == 1, 4, "cam_pos must have dimension 1.");
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TORCH_CHECK_ARG(
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pixel_0_0_center.ndimension() == 1,
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5,
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"pixel_0_0_center must have dimension 1.");
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_x.ndimension() == 1, 6, "pixel_vec_x must have dimension 1.");
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_y.ndimension() == 1, 7, "pixel_vec_y must have dimension 1.");
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THArgCheck(
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TORCH_CHECK_ARG(
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focal_length.ndimension() == 0,
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8,
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"focal_length must have dimension 0.");
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THArgCheck(
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TORCH_CHECK_ARG(
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principal_point_offsets.ndimension() == 1,
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9,
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"principal_point_offsets must have dimension 1.");
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if (opacity.has_value()) {
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THArgCheck(
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TORCH_CHECK_ARG(
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opacity.value().ndimension() == 1,
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13,
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"Opacity needs to be specified per sample.");
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}
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// Check each.
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n_points = vert_pos.size(0);
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.size(0) == static_cast<int64_t>(n_points),
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2,
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("The number of points for vertex positions (" +
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std::to_string(n_points) + ") and vertex colors (" +
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std::to_string(vert_col.size(0)) + ") doesn't agree.")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_radii.size(0) == static_cast<int64_t>(n_points),
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3,
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("The number of points for vertex positions (" +
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@ -379,57 +379,57 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(vert_col.size(0)) + ") doesn't agree.")
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.c_str());
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if (opacity.has_value()) {
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THArgCheck(
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TORCH_CHECK_ARG(
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opacity.value().size(0) == static_cast<int64_t>(n_points),
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12,
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"Opacity needs to be specified per point.");
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}
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// Check all parameters have the correct last dimension size.
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_pos.size(1) == 3,
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1,
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("Vertex positions must be 3D (have shape " +
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std::to_string(vert_pos.size(1)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_col.size(1) == this->renderer_vec[0].cam.n_channels,
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2,
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("Vertex colors must have the right number of channels (have shape " +
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std::to_string(vert_col.size(1)) + ", need " +
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std::to_string(this->renderer_vec[0].cam.n_channels) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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cam_pos.size(0) == 3,
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4,
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("Camera position must be 3D (has shape " +
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std::to_string(cam_pos.size(0)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_0_0_center.size(0) == 3,
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5,
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("pixel_0_0_center must be 3D (has shape " +
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std::to_string(pixel_0_0_center.size(0)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_x.size(0) == 3,
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6,
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("pixel_vec_x must be 3D (has shape " +
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std::to_string(pixel_vec_x.size(0)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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pixel_vec_y.size(0) == 3,
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7,
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("pixel_vec_y must be 3D (has shape " +
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std::to_string(pixel_vec_y.size(0)) + ")!")
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.c_str());
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THArgCheck(
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TORCH_CHECK_ARG(
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principal_point_offsets.size(0) == 2,
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9,
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"principal_point_offsets must have x and y component.");
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}
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// Check device placement.
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auto dev = torch::device_of(vert_pos).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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1,
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("Vertex positions must be stored on device " +
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@ -439,7 +439,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(vert_col).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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2,
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("Vertex colors must be stored on device " +
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@ -449,7 +449,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(vert_radii).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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3,
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("Vertex radii must be stored on device " +
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@ -459,7 +459,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(cam_pos).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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4,
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("Camera position must be stored on device " +
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@ -469,7 +469,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(pixel_0_0_center).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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5,
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("pixel_0_0_center must be stored on device " +
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@ -479,7 +479,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(pixel_vec_x).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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6,
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("pixel_vec_x must be stored on device " +
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@ -489,7 +489,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(pixel_vec_y).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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7,
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("pixel_vec_y must be stored on device " +
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@ -499,7 +499,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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std::to_string(dev.index()) + ".")
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.c_str());
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dev = torch::device_of(principal_point_offsets).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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9,
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("principal_point_offsets must be stored on device " +
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@ -510,7 +510,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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.c_str());
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if (opacity.has_value()) {
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dev = torch::device_of(opacity.value()).value();
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THArgCheck(
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TORCH_CHECK_ARG(
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dev.type() == this->device_type && dev.index() == this->device_index,
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13,
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("opacity must be stored on device " +
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@ -521,33 +521,33 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
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.c_str());
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}
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// Type checks.
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THArgCheck(
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TORCH_CHECK_ARG(
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vert_pos.scalar_type() == c10::kFloat, 1, "pulsar requires float types.");
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THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
vert_col.scalar_type() == c10::kFloat, 2, "pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
vert_radii.scalar_type() == c10::kFloat,
|
||||
3,
|
||||
"pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
cam_pos.scalar_type() == c10::kFloat, 4, "pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
pixel_0_0_center.scalar_type() == c10::kFloat,
|
||||
5,
|
||||
"pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
pixel_vec_x.scalar_type() == c10::kFloat,
|
||||
6,
|
||||
"pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
pixel_vec_y.scalar_type() == c10::kFloat,
|
||||
7,
|
||||
"pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
focal_length.scalar_type() == c10::kFloat,
|
||||
8,
|
||||
"pulsar requires float types.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
// Unfortunately, the PyTorch interface is inconsistent for
|
||||
// Int32: in Python, there exists an explicit int32 type, in
|
||||
// C++ this is currently `c10::kInt`.
|
||||
@ -555,68 +555,68 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
|
||||
9,
|
||||
"principal_point_offsets must be provided as int32.");
|
||||
if (opacity.has_value()) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
opacity.value().scalar_type() == c10::kFloat,
|
||||
13,
|
||||
"opacity must be a float type.");
|
||||
}
|
||||
// Content checks.
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(vert_radii > FEPS).all().item<bool>(),
|
||||
3,
|
||||
("Vertex radii must be > FEPS (min is " +
|
||||
std::to_string(vert_radii.min().item<float>()) + ").")
|
||||
.c_str());
|
||||
if (this->orthogonal()) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(focal_length == 0.f).all().item<bool>(),
|
||||
8,
|
||||
("for an orthogonal projection focal length must be zero (abs max: " +
|
||||
std::to_string(focal_length.abs().max().item<float>()) + ").")
|
||||
.c_str());
|
||||
} else {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(focal_length > FEPS).all().item<bool>(),
|
||||
8,
|
||||
("for a perspective projection focal length must be > FEPS (min " +
|
||||
std::to_string(focal_length.min().item<float>()) + ").")
|
||||
.c_str());
|
||||
}
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
gamma <= 1.f && gamma >= 1E-5f,
|
||||
10,
|
||||
("gamma must be in [1E-5, 1] (" + std::to_string(gamma) + ").").c_str());
|
||||
if (min_depth == 0.f) {
|
||||
min_depth = focal_length.max().item<float>() + 2.f * FEPS;
|
||||
}
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
min_depth > focal_length.max().item<float>(),
|
||||
12,
|
||||
("min_depth must be > focal_length (" + std::to_string(min_depth) +
|
||||
" vs. " + std::to_string(focal_length.max().item<float>()) + ").")
|
||||
.c_str());
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
max_depth > min_depth + FEPS,
|
||||
11,
|
||||
("max_depth must be > min_depth + FEPS (" + std::to_string(max_depth) +
|
||||
" vs. " + std::to_string(min_depth + FEPS) + ").")
|
||||
.c_str());
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
percent_allowed_difference >= 0.f && percent_allowed_difference < 1.f,
|
||||
14,
|
||||
("percent_allowed_difference must be in [0., 1.[ (" +
|
||||
std::to_string(percent_allowed_difference) + ").")
|
||||
.c_str());
|
||||
THArgCheck(max_n_hits > 0, 14, "max_n_hits must be > 0!");
|
||||
THArgCheck(mode < 2, 15, "mode must be in {0, 1}.");
|
||||
TORCH_CHECK_ARG(max_n_hits > 0, 14, "max_n_hits must be > 0!");
|
||||
TORCH_CHECK_ARG(mode < 2, 15, "mode must be in {0, 1}.");
|
||||
torch::Tensor real_bg_col;
|
||||
if (bg_col.has_value()) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
bg_col.value().device().type() == this->device_type &&
|
||||
bg_col.value().device().index() == this->device_index,
|
||||
13,
|
||||
"bg_col must be stored on the renderer device!");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
bg_col.value().ndimension() == 1 &&
|
||||
bg_col.value().size(0) == renderer_vec[0].cam.n_channels,
|
||||
13,
|
||||
@ -629,11 +629,11 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
|
||||
.to(c10::kFloat);
|
||||
}
|
||||
if (opacity.has_value()) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(opacity.value() >= 0.f).all().item<bool>(),
|
||||
13,
|
||||
"opacity must be >= 0.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(opacity.value() <= 1.f).all().item<bool>(),
|
||||
13,
|
||||
"opacity must be <= 1.");
|
||||
@ -941,7 +941,7 @@ Renderer::backward(
|
||||
max_n_hits,
|
||||
mode);
|
||||
// Additional checks for the gradient computation.
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(grad_im.ndimension() == 3 + batch_processing &&
|
||||
static_cast<uint>(grad_im.size(0 + batch_processing)) ==
|
||||
this->height() &&
|
||||
@ -950,7 +950,7 @@ Renderer::backward(
|
||||
this->renderer_vec[0].cam.n_channels),
|
||||
1,
|
||||
"The gradient image size is not correct.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(image.ndimension() == 3 + batch_processing &&
|
||||
static_cast<uint>(image.size(0 + batch_processing)) == this->height() &&
|
||||
static_cast<uint>(image.size(1 + batch_processing)) == this->width() &&
|
||||
@ -958,32 +958,32 @@ Renderer::backward(
|
||||
this->renderer_vec[0].cam.n_channels),
|
||||
2,
|
||||
"The result image size is not correct.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
grad_im.scalar_type() == c10::kFloat,
|
||||
1,
|
||||
"The gradient image must be of float type.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
image.scalar_type() == c10::kFloat,
|
||||
2,
|
||||
"The image must be of float type.");
|
||||
if (dif_opy) {
|
||||
THArgCheck(opacity.has_value(), 13, "dif_opy set requires opacity values.");
|
||||
TORCH_CHECK_ARG(opacity.has_value(), 13, "dif_opy set requires opacity values.");
|
||||
}
|
||||
if (batch_processing) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
grad_im.size(0) == static_cast<int64_t>(batch_size),
|
||||
1,
|
||||
"Gradient image batch size must agree.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
image.size(0) == static_cast<int64_t>(batch_size),
|
||||
2,
|
||||
"Image batch size must agree.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
forw_info.size(0) == static_cast<int64_t>(batch_size),
|
||||
3,
|
||||
"forward info must have batch size.");
|
||||
}
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
(forw_info.ndimension() == 3 + batch_processing &&
|
||||
static_cast<uint>(forw_info.size(0 + batch_processing)) ==
|
||||
this->height() &&
|
||||
@ -993,13 +993,13 @@ Renderer::backward(
|
||||
3 + 2 * this->n_track()),
|
||||
3,
|
||||
"The forward info image size is not correct.");
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
forw_info.scalar_type() == c10::kFloat,
|
||||
3,
|
||||
"The forward info must be of float type.");
|
||||
// Check device.
|
||||
auto dev = torch::device_of(grad_im).value();
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
dev.type() == this->device_type && dev.index() == this->device_index,
|
||||
1,
|
||||
("grad_im must be stored on device " +
|
||||
@ -1009,7 +1009,7 @@ Renderer::backward(
|
||||
std::to_string(dev.index()) + ".")
|
||||
.c_str());
|
||||
dev = torch::device_of(image).value();
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
dev.type() == this->device_type && dev.index() == this->device_index,
|
||||
2,
|
||||
("image must be stored on device " +
|
||||
@ -1019,7 +1019,7 @@ Renderer::backward(
|
||||
std::to_string(dev.index()) + ".")
|
||||
.c_str());
|
||||
dev = torch::device_of(forw_info).value();
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
dev.type() == this->device_type && dev.index() == this->device_index,
|
||||
3,
|
||||
("forw_info must be stored on device " +
|
||||
@ -1029,7 +1029,7 @@ Renderer::backward(
|
||||
std::to_string(dev.index()) + ".")
|
||||
.c_str());
|
||||
if (dbg_pos.has_value()) {
|
||||
THArgCheck(
|
||||
TORCH_CHECK_ARG(
|
||||
dbg_pos.value().first < this->width() &&
|
||||
dbg_pos.value().second < this->height(),
|
||||
23,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user