ii

Author: conansherry

detectron2/engine/defaults.py

@@ -69,7 +69,7 @@ def default_argument_parser():
     # PyTorch still may leave orphan processes in multi-gpu training.
     # Therefore we use a deterministic way to obtain port,
     # so that users are aware of orphan processes by seeing the port occupied.
-    port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14
+    port = 2 ** 15 + 2 ** 14 + hash(os.getpid()) % 2 ** 14
     parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port))
     parser.add_argument(
         "opts",

detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu

@@ -334,7 +334,7 @@ at::Tensor ROIAlign_forward_cuda(
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv((long)output_size, 512L), 4096L));
   dim3 block(512);
 
   if (output.numel() == 0) {
@@ -390,7 +390,7 @@ at::Tensor ROIAlign_backward_cuda(
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv((long)grad.numel(), 512L), 4096L));
   dim3 block(512);
 
   // handle possibly empty gradients

detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu

@@ -348,7 +348,7 @@ at::Tensor ROIAlignRotated_forward_cuda(
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv((long)output_size, 512L), 4096L));
   dim3 block(512);
 
   if (output.numel() == 0) {
@@ -403,7 +403,7 @@ at::Tensor ROIAlignRotated_backward_cuda(
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::cuda::ATenCeilDiv((long)grad.numel(), 512L), 4096L));
   dim3 block(512);
 
   // handle possibly empty gradients

projects/DensePose/densepose/dataset.py

@@ -16,15 +16,15 @@ def get_densepose_metadata():
 
 
 SPLITS = {
-    "densepose_coco_2014_train": ("coco/train2014", "coco/annotations/densepose_train2014.json"),
-    "densepose_coco_2014_minival": ("coco/val2014", "coco/annotations/densepose_minival2014.json"),
+    "densepose_coco_2014_train": ("coco/train2014", "coco/annotations/densepose_coco_2014_train.json"),
+    "densepose_coco_2014_minival": ("coco/val2014", "coco/annotations/densepose_coco_2014_minival.json"),
     "densepose_coco_2014_minival_100": (
         "coco/val2014",
-        "coco/annotations/densepose_minival2014_100.json",
+        "coco/annotations/densepose_coco_2014_minival.json",
     ),
     "densepose_coco_2014_valminusminival": (
         "coco/val2014",
-        "coco/annotations/densepose_valminusminival2014.json",
+        "coco/annotations/densepose_coco_2014_valminusminival.json",
     ),
 }