Merge branch 'facebookresearch:main' into main

sdy623 · Nov 13, 2023 · f51c508 · f51c508
2 parents b695e24 + 017abbf
commit f51c508
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Showing 3 changed files with 39 additions and 24 deletions.
diff --git a/detectron2/data/build.py b/detectron2/data/build.py
@@ -300,9 +300,7 @@ def build_batch_data_loader(
     num_workers=0,
     collate_fn=None,
     drop_last: bool = True,
-    prefetch_factor=None,
-    persistent_workers=False,
-    pin_memory=False,
+    **kwargs,
 ):
     """
     Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are:
@@ -328,6 +326,8 @@ def build_batch_data_loader(
         total_batch_size, world_size
     )
     batch_size = total_batch_size // world_size
+    logger = logging.getLogger(__name__)
+    logger.info("Making batched data loader with batch_size=%d", batch_size)
 
     if isinstance(dataset, torchdata.IterableDataset):
         assert sampler is None, "sampler must be None if dataset is IterableDataset"
@@ -341,9 +341,7 @@ def build_batch_data_loader(
             num_workers=num_workers,
             collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
             worker_init_fn=worker_init_reset_seed,
-            prefetch_factor=prefetch_factor,
-            persistent_workers=persistent_workers,
-            pin_memory=pin_memory,
+            **kwargs
         )  # yield individual mapped dict
         data_loader = AspectRatioGroupedDataset(data_loader, batch_size)
         if collate_fn is None:
@@ -357,9 +355,7 @@ def build_batch_data_loader(
             num_workers=num_workers,
             collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
             worker_init_fn=worker_init_reset_seed,
-            prefetch_factor=prefetch_factor,
-            persistent_workers=persistent_workers,
-            pin_memory=pin_memory,
+            **kwargs
         )
 
 
@@ -499,9 +495,7 @@ def build_detection_train_loader(
     aspect_ratio_grouping=True,
     num_workers=0,
     collate_fn=None,
-    prefetch_factor=None,
-    persistent_workers=False,
-    pin_memory=False,
+    **kwargs
 ):
     """
     Build a dataloader for object detection with some default features.
@@ -553,9 +547,7 @@ def build_detection_train_loader(
         aspect_ratio_grouping=aspect_ratio_grouping,
         num_workers=num_workers,
         collate_fn=collate_fn,
-        prefetch_factor=prefetch_factor,
-        persistent_workers=persistent_workers,
-        pin_memory=pin_memory,
+        **kwargs
     )
 
 

diff --git a/detectron2/engine/defaults.py b/detectron2/engine/defaults.py
@@ -312,8 +312,10 @@ def __call__(self, original_image):
             height, width = original_image.shape[:2]
             image = self.aug.get_transform(original_image).apply_image(original_image)
             image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+            image.to(self.cfg.MODEL.DEVICE)
 
             inputs = {"image": image, "height": height, "width": width}
+
             predictions = self.model([inputs])[0]
             return predictions
 

diff --git a/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h
@@ -225,19 +225,40 @@ HOST_DEVICE_INLINE int convex_hull_graham(
   }
 #else
   // CPU version
-  std::sort(
-      q + 1, q + num_in, [](const Point<T>& A, const Point<T>& B) -> bool {
-        T temp = cross_2d<T>(A, B);
-        if (fabs(temp) < 1e-6) {
-          return dot_2d<T>(A, A) < dot_2d<T>(B, B);
-        } else {
-          return temp > 0;
-        }
-      });
+  // std::sort(
+  //     q + 1, q + num_in, [](const Point<T>& A, const Point<T>& B) -> bool {
+  //       T temp = cross_2d<T>(A, B);
+
+  // if (fabs(temp) < 1e-6) {
+  //   return dot_2d<T>(A, A) < dot_2d<T>(B, B);
+  // } else {
+  //   return temp > 0;
+  // }
+  // });
+  for (int i = 0; i < num_in; i++) {
+    dist[i] = dot_2d<T>(q[i], q[i]);
+  }
+
+  for (int i = 1; i < num_in - 1; i++) {
+    for (int j = i + 1; j < num_in; j++) {
+      T crossProduct = cross_2d<T>(q[i], q[j]);
+      if ((crossProduct < -1e-6) ||
+          (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) {
+        auto q_tmp = q[i];
+        q[i] = q[j];
+        q[j] = q_tmp;
+        auto dist_tmp = dist[i];
+        dist[i] = dist[j];
+        dist[j] = dist_tmp;
+      }
+    }
+  }
+
   // compute distance to origin after sort, since the points are now different.
   for (int i = 0; i < num_in; i++) {
     dist[i] = dot_2d<T>(q[i], q[i]);
   }
+
 #endif
 
   // Step 4: