From 938047507479ccb5ba0bcb7f2abff57f570e072a Mon Sep 17 00:00:00 2001 From: Alexander Hirner Date: Mon, 2 Jul 2018 17:20:44 +0000 Subject: [PATCH] Assertion macros compatible with pytorch master --- torchvision/csrc/cpu/ROIAlign_cpu.cpp | 4 ++-- torchvision/csrc/cpu/nms_cpu.cpp | 6 +++--- torchvision/csrc/cuda/ROIAlign_cuda.cu | 8 ++++---- torchvision/csrc/cuda/ROIPool_cuda.cu | 8 ++++---- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/torchvision/csrc/cpu/ROIAlign_cpu.cpp b/torchvision/csrc/cpu/ROIAlign_cpu.cpp index e4ea4cbd8f4..f421f09ebe8 100644 --- a/torchvision/csrc/cpu/ROIAlign_cpu.cpp +++ b/torchvision/csrc/cpu/ROIAlign_cpu.cpp @@ -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); diff --git a/torchvision/csrc/cpu/nms_cpu.cpp b/torchvision/csrc/cpu/nms_cpu.cpp index 6d206b9e8e4..aa4b9b53256 100644 --- a/torchvision/csrc/cpu/nms_cpu.cpp +++ b/torchvision/csrc/cpu/nms_cpu.cpp @@ -5,9 +5,9 @@ template 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(); diff --git a/torchvision/csrc/cuda/ROIAlign_cuda.cu b/torchvision/csrc/cuda/ROIAlign_cuda.cu index 8f633b68965..bc94c8017be 100644 --- a/torchvision/csrc/cuda/ROIAlign_cuda.cu +++ b/torchvision/csrc/cuda/ROIAlign_cuda.cu @@ -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); @@ -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_(); diff --git a/torchvision/csrc/cuda/ROIPool_cuda.cu b/torchvision/csrc/cuda/ROIPool_cuda.cu index 8e886488cea..57da27b8144 100644 --- a/torchvision/csrc/cuda/ROIPool_cuda.cu +++ b/torchvision/csrc/cuda/ROIPool_cuda.cu @@ -110,8 +110,8 @@ std::tuple 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); @@ -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);