[Feature] Support RTMDet Ins Segmentation Inference

configs/rtmdet/rtmdet-ins_s_syncbn_fast_8xb32-300e_coco.py

@@ -0,0 +1,31 @@
+_base_ = './rtmdet_s_syncbn_fast_8xb32-300e_coco.py'
+
+widen_factor = 0.5
+
+model = dict(
+    bbox_head=dict(
+        type='RTMDetInsSepBNHead',
+        head_module=dict(
+            type='RTMDetInsSepBNHeadModule',
+            use_sigmoid_cls=True,
+            widen_factor=widen_factor),
+        loss_mask=dict(
+            type='mmdet.DiceLoss', loss_weight=2.0, eps=5e-6,
+            reduction='mean')),
+    test_cfg=dict(
+        multi_label=True,
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100,
+        mask_thr_binary=0.5))
+
+_base_.test_pipeline[-2] = dict(
+    type='LoadAnnotations', with_bbox=True, with_mask=True, _scope_='mmdet')
+
+val_dataloader = dict(dataset=dict(pipeline=_base_.test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(metric=['bbox', 'segm'])
+test_evaluator = val_evaluator

mmyolo/datasets/utils.py

@@ -19,13 +19,18 @@ def yolov5_collate(data_batch: Sequence,
     """
     batch_imgs = []
     batch_bboxes_labels = []
+    batch_masks = []
     for i in range(len(data_batch)):
         datasamples = data_batch[i]['data_samples']
         inputs = data_batch[i]['inputs']
         batch_imgs.append(inputs)
 
         gt_bboxes = datasamples.gt_instances.bboxes.tensor
         gt_labels = datasamples.gt_instances.labels
+        if 'masks' in datasamples.gt_instances:
+            masks = datasamples.gt_instances.masks.to_tensor(
+                dtype=torch.bool, device=gt_bboxes.device)
+            batch_masks.append(masks)
         batch_idx = gt_labels.new_full((len(gt_labels), 1), i)
         bboxes_labels = torch.cat((batch_idx, gt_labels[:, None], gt_bboxes),
                                   dim=1)
@@ -36,6 +41,8 @@ def yolov5_collate(data_batch: Sequence,
             'bboxes_labels': torch.cat(batch_bboxes_labels, 0)
         }
     }
+    if len(batch_masks) > 0:
+        collated_results['data_samples']['masks'] = torch.cat(batch_masks, 0)
 
     if use_ms_training:
         collated_results['inputs'] = batch_imgs

mmyolo/models/data_preprocessors/data_preprocessor.py

@@ -96,12 +96,14 @@ def forward(self, data: dict, training: bool = False) -> dict:
                 inputs, data_samples = batch_aug(inputs, data_samples)
 
         img_metas = [{'batch_input_shape': inputs.shape[2:]}] * len(inputs)
-        data_samples = {
+        data_samples_output = {
             'bboxes_labels': data_samples['bboxes_labels'],
             'img_metas': img_metas
         }
+        if 'masks' in data_samples:
+            data_samples_output['masks'] = data_samples['masks']
 
-        return {'inputs': inputs, 'data_samples': data_samples}
+        return {'inputs': inputs, 'data_samples': data_samples_output}
 
 
 @MODELS.register_module()

mmyolo/models/dense_heads/__init__.py

@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .ppyoloe_head import PPYOLOEHead, PPYOLOEHeadModule
 from .rtmdet_head import RTMDetHead, RTMDetSepBNHeadModule
+from .rtmdet_ins_head import RTMDetInsSepBNHead, RTMDetInsSepBNHeadModule
 from .rtmdet_rotated_head import (RTMDetRotatedHead,
                                   RTMDetRotatedSepBNHeadModule)
 from .yolov5_head import YOLOv5Head, YOLOv5HeadModule
@@ -14,5 +15,6 @@
     'YOLOv6HeadModule', 'YOLOXHeadModule', 'RTMDetHead',
     'RTMDetSepBNHeadModule', 'YOLOv7Head', 'PPYOLOEHead', 'PPYOLOEHeadModule',
     'YOLOv7HeadModule', 'YOLOv7p6HeadModule', 'YOLOv8Head', 'YOLOv8HeadModule',
-    'RTMDetRotatedHead', 'RTMDetRotatedSepBNHeadModule'
+    'RTMDetRotatedHead', 'RTMDetRotatedSepBNHeadModule', 'RTMDetInsSepBNHead',
+    'RTMDetInsSepBNHeadModule'
 ]