use int64_t instead of long in cuda kernels (#1131)

mmdet/ops/masked_conv/src/masked_conv2d_kernel.cu

@@ -18,9 +18,9 @@ __global__ void MaskedIm2colForward(const int n, const scalar_t *data_im,
                                     const int height, const int width,
                                     const int kernel_h, const int kernel_w,
                                     const int pad_h, const int pad_w,
-                                    const long *mask_h_idx,
-                                    const long *mask_w_idx, const int mask_cnt,
-                                    scalar_t *data_col) {
+                                    const int64_t *mask_h_idx,
+                                    const int64_t *mask_w_idx,
+                                    const int mask_cnt, scalar_t *data_col) {
   // mask_cnt * channels
   CUDA_1D_KERNEL_LOOP(index, n) {
     const int m_index = index % mask_cnt;
@@ -59,8 +59,8 @@ int MaskedIm2colForwardLaucher(const at::Tensor bottom_data, const int height,
   AT_DISPATCH_FLOATING_TYPES_AND_HALF(
       bottom_data.scalar_type(), "MaskedIm2colLaucherForward", ([&] {
         const scalar_t *bottom_data_ = bottom_data.data<scalar_t>();
-        const long *mask_h_idx_ = mask_h_idx.data<long>();
-        const long *mask_w_idx_ = mask_w_idx.data<long>();
+        const int64_t *mask_h_idx_ = mask_h_idx.data<int64_t>();
+        const int64_t *mask_w_idx_ = mask_w_idx.data<int64_t>();
         scalar_t *top_data_ = top_data.data<scalar_t>();
         MaskedIm2colForward<scalar_t>
             <<<GET_BLOCKS(output_size), THREADS_PER_BLOCK>>>(
@@ -74,16 +74,15 @@ int MaskedIm2colForwardLaucher(const at::Tensor bottom_data, const int height,
 template <typename scalar_t>
 __global__ void MaskedCol2imForward(const int n, const scalar_t *data_col,
                                     const int height, const int width,
-                                    const int channels, const long *mask_h_idx,
-                                    const long *mask_w_idx, const int mask_cnt,
-                                    scalar_t *data_im) {
+                                    const int channels,
+                                    const int64_t *mask_h_idx,
+                                    const int64_t *mask_w_idx,
+                                    const int mask_cnt, scalar_t *data_im) {
   CUDA_1D_KERNEL_LOOP(index, n) {
     const int m_index = index % mask_cnt;
     const int h_im = mask_h_idx[m_index];
     const int w_im = mask_w_idx[m_index];
     const int c_im = index / mask_cnt;
-    // int kernel_extent_w = (kernel_w - 1) + 1;
-    // int kernel_extent_h = (kernel_h - 1) + 1;
     // compute the start and end of the output
     data_im[(c_im * height + h_im) * width + w_im] = data_col[index];
   }
@@ -99,8 +98,8 @@ int MaskedCol2imForwardLaucher(const at::Tensor bottom_data, const int height,
   AT_DISPATCH_FLOATING_TYPES_AND_HALF(
       bottom_data.scalar_type(), "MaskedCol2imLaucherForward", ([&] {
         const scalar_t *bottom_data_ = bottom_data.data<scalar_t>();
-        const long *mask_h_idx_ = mask_h_idx.data<long>();
-        const long *mask_w_idx_ = mask_w_idx.data<long>();
+        const int64_t *mask_h_idx_ = mask_h_idx.data<int64_t>();
+        const int64_t *mask_w_idx_ = mask_w_idx.data<int64_t>();
         scalar_t *top_data_ = top_data.data<scalar_t>();
 
         MaskedCol2imForward<scalar_t>

mmdet/ops/sigmoid_focal_loss/src/sigmoid_focal_loss_cuda.cu

@@ -23,7 +23,7 @@
 template <typename scalar_t>
 __global__ void SigmoidFocalLossForward(const int nthreads,
                                         const scalar_t *logits,
-                                        const long *targets,
+                                        const int64_t *targets,
                                         const int num_classes,
                                         const float gamma, const float alpha,
                                         const int num, scalar_t *losses) {
@@ -60,7 +60,7 @@ __global__ void SigmoidFocalLossForward(const int nthreads,
 
 template <typename scalar_t>
 __global__ void SigmoidFocalLossBackward(
-    const int nthreads, const scalar_t *logits, const long *targets,
+    const int nthreads, const scalar_t *logits, const int64_t *targets,
     const scalar_t *d_losses, const int num_classes, const float gamma,
     const float alpha, const int num, scalar_t *d_logits) {
   CUDA_1D_KERNEL_LOOP(i, nthreads) {
@@ -109,7 +109,8 @@ at::Tensor SigmoidFocalLoss_forward_cuda(const at::Tensor &logits,
   auto losses = at::empty({num_samples, logits.size(1)}, logits.options());
   auto losses_size = num_samples * logits.size(1);
 
-  dim3 grid(std::min(THCCeilDiv((long)losses_size, 512L), 4096L));
+  dim3 grid(
+      std::min(THCCeilDiv((int64_t)losses_size, (int64_t)512), (int64_t)4096));
   dim3 block(512);
 
   if (losses.numel() == 0) {
@@ -121,7 +122,7 @@ at::Tensor SigmoidFocalLoss_forward_cuda(const at::Tensor &logits,
       logits.scalar_type(), "SigmoidFocalLoss_forward", [&] {
         SigmoidFocalLossForward<scalar_t><<<grid, block>>>(
             losses_size, logits.contiguous().data<scalar_t>(),
-            targets.contiguous().data<long>(), num_classes, gamma, alpha,
+            targets.contiguous().data<int64_t>(), num_classes, gamma, alpha,
             num_samples, losses.data<scalar_t>());
       });
   THCudaCheck(cudaGetLastError());
@@ -147,7 +148,8 @@ at::Tensor SigmoidFocalLoss_backward_cuda(const at::Tensor &logits,
   auto d_logits = at::zeros({num_samples, num_classes}, logits.options());
   auto d_logits_size = num_samples * logits.size(1);
 
-  dim3 grid(std::min(THCCeilDiv((long)d_logits_size, 512L), 4096L));
+  dim3 grid(std::min(THCCeilDiv((int64_t)d_logits_size, (int64_t)512),
+                     (int64_t)4096));
   dim3 block(512);
 
   if (d_logits.numel() == 0) {
@@ -159,7 +161,7 @@ at::Tensor SigmoidFocalLoss_backward_cuda(const at::Tensor &logits,
       logits.scalar_type(), "SigmoidFocalLoss_backward", [&] {
         SigmoidFocalLossBackward<scalar_t><<<grid, block>>>(
             d_logits_size, logits.contiguous().data<scalar_t>(),
-            targets.contiguous().data<long>(),
+            targets.contiguous().data<int64_t>(),
             d_losses.contiguous().data<scalar_t>(), num_classes, gamma, alpha,
             num_samples, d_logits.data<scalar_t>());
       });