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Assertion macros compatible with pytorch master #540

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Merged
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Jul 3, 2018
Merged

Assertion macros compatible with pytorch master #540

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4 changes: 2 additions & 2 deletions torchvision/csrc/cpu/ROIAlign_cpu.cpp
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Expand Up @@ -223,8 +223,8 @@ at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
const int pooled_height,
const int pooled_width,
const int sampling_ratio) {
AT_ASSERT(!input.type().is_cuda(), "input must be a CPU tensor");
AT_ASSERT(!rois.type().is_cuda(), "rois must be a CPU tensor");
AT_ASSERTM(!input.type().is_cuda(), "input must be a CPU tensor");
AT_ASSERTM(!rois.type().is_cuda(), "rois must be a CPU tensor");

auto num_rois = rois.size(0);
auto channels = input.size(1);
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6 changes: 3 additions & 3 deletions torchvision/csrc/cpu/nms_cpu.cpp
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Expand Up @@ -5,9 +5,9 @@ template <typename scalar_t>
at::Tensor nms_cpu_kernel(const at::Tensor& dets,
const at::Tensor& scores,
const float threshold) {
AT_ASSERT(!dets.type().is_cuda(), "dets must be a CPU tensor");
AT_ASSERT(!scores.type().is_cuda(), "scores must be a CPU tensor");
AT_ASSERT(dets.type() == scores.type(), "dets should have the same type as scores");
AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");
AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");

if (dets.numel() == 0)
return torch::CPU(at::kLong).tensor();
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8 changes: 4 additions & 4 deletions torchvision/csrc/cuda/ROIAlign_cuda.cu
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Expand Up @@ -258,8 +258,8 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
const int pooled_height,
const int pooled_width,
const int sampling_ratio) {
AT_ASSERT(input.type().is_cuda(), "input must be a CUDA tensor");
AT_ASSERT(rois.type().is_cuda(), "rois must be a CUDA tensor");
AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor");
AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");

auto num_rois = rois.size(0);
auto channels = input.size(1);
Expand Down Expand Up @@ -308,8 +308,8 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
const int height,
const int width,
const int sampling_ratio) {
AT_ASSERT(grad.type().is_cuda(), "grad must be a CUDA tensor");
AT_ASSERT(rois.type().is_cuda(), "rois must be a CUDA tensor");
AT_ASSERTM(grad.type().is_cuda(), "grad must be a CUDA tensor");
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_();
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8 changes: 4 additions & 4 deletions torchvision/csrc/cuda/ROIPool_cuda.cu
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Expand Up @@ -110,8 +110,8 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
const float spatial_scale,
const int pooled_height,
const int pooled_width) {
AT_ASSERT(input.type().is_cuda(), "input must be a CUDA tensor");
AT_ASSERT(rois.type().is_cuda(), "rois must be a CUDA tensor");
AT_ASSERTM(input.type().is_cuda(), "input must be a CUDA tensor");
AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");

auto num_rois = rois.size(0);
auto channels = input.size(1);
Expand Down Expand Up @@ -162,8 +162,8 @@ at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
const int channels,
const int height,
const int width) {
AT_ASSERT(grad.type().is_cuda(), "grad must be a CUDA tensor");
AT_ASSERT(rois.type().is_cuda(), "rois must be a CUDA tensor");
AT_ASSERTM(grad.type().is_cuda(), "grad must be a CUDA tensor");
AT_ASSERTM(rois.type().is_cuda(), "rois must be a CUDA tensor");
// TODO add more checks

auto num_rois = rois.size(0);
Expand Down