Vectorize RetinaNet's postprocessing (#2828)

* Vectorize operations, across all feaure levels. * Remove unnecessary other_outputs variable. * Split per feature level. * Perform batched_nms across feature levels. * Add extra parameter for limiting detections before and after nms. * Restoring default threshold. * Apply suggestions from code review Co-authored-by: Francisco Massa <[email protected]> * Renaming variable. Co-authored-by: Francisco Massa <[email protected]>
pytorch · Oct 20, 2020 · 0467c9d · 0467c9d
1 parent d94b45d
commit 0467c9d
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diff --git a/.gitignore b/.gitignore
@@ -22,5 +22,6 @@ htmlcov
 gen.yml
 .mypy_cache
 .vscode/
+.idea/
 *.orig
 *-checkpoint.ipynb
diff --git a/test/expect/ModelTester.test_retinanet_resnet50_fpn_expect.pkl b/test/expect/ModelTester.test_retinanet_resnet50_fpn_expect.pkl
diff --git a/torchvision/models/detection/retinanet.py b/torchvision/models/detection/retinanet.py
@@ -291,6 +291,7 @@ class RetinaNet(nn.Module):
             considered as positive during training.
         bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
             considered as negative during training.
+        topk_candidates (int): Number of best detections to keep before NMS.
 
     Example:
 
@@ -339,7 +340,8 @@ def __init__(self, backbone, num_classes,
                  score_thresh=0.05,
                  nms_thresh=0.5,
                  detections_per_img=300,
-                 fg_iou_thresh=0.5, bg_iou_thresh=0.4):
+                 fg_iou_thresh=0.5, bg_iou_thresh=0.4,
+                 topk_candidates=1000):
         super().__init__()
 
         if not hasattr(backbone, "out_channels"):
@@ -382,6 +384,7 @@ def __init__(self, backbone, num_classes,
         self.score_thresh = score_thresh
         self.nms_thresh = nms_thresh
         self.detections_per_img = detections_per_img
+        self.topk_candidates = topk_candidates
 
         # used only on torchscript mode
         self._has_warned = False
@@ -408,77 +411,63 @@ def compute_loss(self, targets, head_outputs, anchors):
         return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs)
 
     def postprocess_detections(self, head_outputs, anchors, image_shapes):
-        # type: (Dict[str, Tensor], List[Tensor], List[Tuple[int, int]]) -> List[Dict[str, Tensor]]
-        # TODO: Merge this with roi_heads.RoIHeads.postprocess_detections ?
+        # type: (Dict[str, List[Tensor]], List[List[Tensor]], List[Tuple[int, int]]) -> List[Dict[str, Tensor]]
+        class_logits = head_outputs['cls_logits']
+        box_regression = head_outputs['bbox_regression']
 
-        class_logits = head_outputs.pop('cls_logits')
-        box_regression = head_outputs.pop('bbox_regression')
-        other_outputs = head_outputs
-
-        device = class_logits.device
-        num_classes = class_logits.shape[-1]
-
-        scores = torch.sigmoid(class_logits)
-
-        # create labels for each score
-        labels = torch.arange(num_classes, device=device)
-        labels = labels.view(1, -1).expand_as(scores)
+        num_images = len(image_shapes)
 
         detections = torch.jit.annotate(List[Dict[str, Tensor]], [])
 
-        for index, (box_regression_per_image, scores_per_image, labels_per_image, anchors_per_image, image_shape) in \
-                enumerate(zip(box_regression, scores, labels, anchors, image_shapes)):
-
-            boxes_per_image = self.box_coder.decode_single(box_regression_per_image, anchors_per_image)
-            boxes_per_image = box_ops.clip_boxes_to_image(boxes_per_image, image_shape)
-
-            other_outputs_per_image = [(k, v[index]) for k, v in other_outputs.items()]
+        for index in range(num_images):
+            box_regression_per_image = [br[index] for br in box_regression]
+            logits_per_image = [cl[index] for cl in class_logits]
+            anchors_per_image, image_shape = anchors[index], image_shapes[index]
 
             image_boxes = []
             image_scores = []
             image_labels = []
-            image_other_outputs = torch.jit.annotate(Dict[str, List[Tensor]], {})
 
-            for class_index in range(num_classes):
+            for box_regression_per_level, logits_per_level, anchors_per_level in \
+                    zip(box_regression_per_image, logits_per_image, anchors_per_image):
+                num_classes = logits_per_level.shape[-1]
+
                 # remove low scoring boxes
-                inds = torch.gt(scores_per_image[:, class_index], self.score_thresh)
-                boxes_per_class, scores_per_class, labels_per_class = \
-                    boxes_per_image[inds], scores_per_image[inds, class_index], labels_per_image[inds, class_index]
-                other_outputs_per_class = [(k, v[inds]) for k, v in other_outputs_per_image]
+                scores_per_level = torch.sigmoid(logits_per_level).flatten()
+                keep_idxs = scores_per_level > self.score_thresh
+                scores_per_level = scores_per_level[keep_idxs]
+                topk_idxs = torch.where(keep_idxs)[0]
 
-                # remove empty boxes
-                keep = box_ops.remove_small_boxes(boxes_per_class, min_size=1e-2)
-                boxes_per_class, scores_per_class, labels_per_class = \
-                    boxes_per_class[keep], scores_per_class[keep], labels_per_class[keep]
-                other_outputs_per_class = [(k, v[keep]) for k, v in other_outputs_per_class]
+                # keep only topk scoring predictions
+                num_topk = min(self.topk_candidates, topk_idxs.size(0))
+                scores_per_level, idxs = scores_per_level.topk(num_topk)
+                topk_idxs = topk_idxs[idxs]
 
-                # non-maximum suppression, independently done per class
-                keep = box_ops.nms(boxes_per_class, scores_per_class, self.nms_thresh)
+                anchor_idxs = topk_idxs // num_classes
+                labels_per_level = topk_idxs % num_classes
 
-                # keep only topk scoring predictions
-                keep = keep[:self.detections_per_img]
-                boxes_per_class, scores_per_class, labels_per_class = \
-                    boxes_per_class[keep], scores_per_class[keep], labels_per_class[keep]
-                other_outputs_per_class = [(k, v[keep]) for k, v in other_outputs_per_class]
+                boxes_per_level = self.box_coder.decode_single(box_regression_per_level[anchor_idxs],
+                                                               anchors_per_level[anchor_idxs])
+                boxes_per_level = box_ops.clip_boxes_to_image(boxes_per_level, image_shape)
+
+                image_boxes.append(boxes_per_level)
+                image_scores.append(scores_per_level)
+                image_labels.append(labels_per_level)
 
-                image_boxes.append(boxes_per_class)
-                image_scores.append(scores_per_class)
-                image_labels.append(labels_per_class)
+            image_boxes = torch.cat(image_boxes, dim=0)
+            image_scores = torch.cat(image_scores, dim=0)
+            image_labels = torch.cat(image_labels, dim=0)
 
-                for k, v in other_outputs_per_class:
-                    if k not in image_other_outputs:
-                        image_other_outputs[k] = []
-                    image_other_outputs[k].append(v)
+            # non-maximum suppression
+            keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh)
+            keep = keep[:self.detections_per_img]
 
             detections.append({
-                'boxes': torch.cat(image_boxes, dim=0),
-                'scores': torch.cat(image_scores, dim=0),
-                'labels': torch.cat(image_labels, dim=0),
+                'boxes': image_boxes[keep],
+                'scores': image_scores[keep],
+                'labels': image_labels[keep],
             })
 
-            for k, v in image_other_outputs.items():
-                detections[-1].update({k: torch.cat(v, dim=0)})
-
         return detections
 
     def forward(self, images, targets=None):
@@ -557,8 +546,23 @@ def forward(self, images, targets=None):
             # compute the losses
             losses = self.compute_loss(targets, head_outputs, anchors)
         else:
+            # recover level sizes
+            num_anchors_per_level = [x.size(2) * x.size(3) for x in features]
+            HW = 0
+            for v in num_anchors_per_level:
+                HW += v
+            HWA = head_outputs['cls_logits'].size(1)
+            A = HWA // HW
+            num_anchors_per_level = [hw * A for hw in num_anchors_per_level]
+
+            # split outputs per level
+            split_head_outputs: Dict[str, List[Tensor]] = {}
+            for k in head_outputs:
+                split_head_outputs[k] = list(head_outputs[k].split(num_anchors_per_level, dim=1))
+            split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors]
+
             # compute the detections
-            detections = self.postprocess_detections(head_outputs, anchors, images.image_sizes)
+            detections = self.postprocess_detections(split_head_outputs, split_anchors, images.image_sizes)
             detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
 
         if torch.jit.is_scripting():