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Remove .type().tensor() #626

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Merged
merged 1 commit into from
Oct 13, 2018
Merged

Remove .type().tensor() #626

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4 changes: 2 additions & 2 deletions torchvision/csrc/cpu/ROIAlign_cpu.cpp
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Expand Up @@ -231,8 +231,8 @@ at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
auto height = input.size(2);
auto width = input.size(3);

at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});

at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
auto output_size = num_rois * pooled_height * pooled_width * channels;

if (output.numel() == 0)
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4 changes: 2 additions & 2 deletions torchvision/csrc/cpu/ROIPool_cpu.cpp
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Expand Up @@ -16,8 +16,8 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cpu(const at::Tensor &input,
int input_height = input.size(2);
int input_width = input.size(3);

at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
at::Tensor argmax = input.type().toScalarType(at::kInt).tensor({num_rois, channels, pooled_height, pooled_width}).zero_();
at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
at::Tensor argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type().toScalarType(at::kInt));

// define accessors for indexing
auto input_a = input.accessor<float, 4>();
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7 changes: 3 additions & 4 deletions torchvision/csrc/cpu/nms_cpu.cpp
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@@ -1,6 +1,5 @@
#include "cpu/vision.h"


template <typename scalar_t>
at::Tensor nms_cpu_kernel(const at::Tensor& dets,
const at::Tensor& scores,
Expand All @@ -10,7 +9,7 @@ at::Tensor nms_cpu_kernel(const at::Tensor& dets,
AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");

if (dets.numel() == 0)
return torch::CPU(at::kLong).tensor();
return at::empty({0}, at::device(at::kCPU).dtype(at::kLong));

auto x1_t = dets.select(1, 0).contiguous();
auto y1_t = dets.select(1, 1).contiguous();
Expand All @@ -22,7 +21,7 @@ at::Tensor nms_cpu_kernel(const at::Tensor& dets,
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));

auto ndets = dets.size(0);
at::Tensor suppressed_t = at::zeros(torch::CPU(at::kByte), {ndets});
at::Tensor suppressed_t = at::zeros({ndets}, at::device(at::kCPU).dtype(at::kByte));

auto suppressed = suppressed_t.data<uint8_t>();
auto order = order_t.data<int64_t>();
Expand Down Expand Up @@ -66,7 +65,7 @@ at::Tensor nms_cpu(const at::Tensor& dets,
const at::Tensor& scores,
const float threshold) {

auto result = dets.type().tensor();
auto result = at::empty({0}, dets.type());

AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
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4 changes: 2 additions & 2 deletions torchvision/csrc/cuda/ROIAlign_cuda.cu
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Expand Up @@ -267,7 +267,7 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
auto height = input.size(2);
auto width = input.size(3);

at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options());

auto output_size = num_rois * pooled_height * pooled_width * channels;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
Expand Down Expand Up @@ -313,7 +313,7 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");

auto num_rois = rois.size(0);
at::Tensor grad_input = grad.type().tensor({batch_size, channels, height, width}).zero_();
at::Tensor grad_input = at::zeros({batch_size, channels, height, width}, grad.options());

cudaStream_t stream = at::cuda::getCurrentCUDAStream();

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4 changes: 2 additions & 2 deletions torchvision/csrc/cuda/ROIPool_cuda.cu
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Expand Up @@ -116,8 +116,8 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
auto height = input.size(2);
auto width = input.size(3);

at::Tensor output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
at::Tensor argmax = input.type().toScalarType(at::kInt).tensor({num_rois, channels, pooled_height, pooled_width}).zero_();
at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type());
at::Tensor argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.type().toScalarType(at::kInt));

auto output_size = num_rois * pooled_height * pooled_width * channels;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
Expand Down