Support YOLOv8 Ins Segmentation Inference

configs/yolov8/ins_seg/yolov8_ins_s_syncbn_fast_8xb16-500e_coco.py

@@ -0,0 +1,59 @@
+_base_ = '../yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py'
+
+# Batch size of a single GPU during validation
+val_batch_size_per_gpu = 16
+# Worker to pre-fetch data for each single GPU during validation
+val_num_workers = 8
+
+batch_shapes_cfg = dict(
+    _delete_=True,
+    type='BatchShapePolicy',
+    batch_size=val_batch_size_per_gpu,
+    img_size=_base_.img_scale[0],
+    # The image scale of padding should be divided by pad_size_divisor
+    size_divisor=32,
+    # Additional paddings for pixel scale
+    extra_pad_ratio=0.5)
+
+# Testing take a long time due to model_test_cfg.
+# If you want to speed it up, you can increase score_thr
+# or decraese nms_pre and max_per_img
+model_test_cfg = dict(
+    multi_label=True,
+    nms_pre=30000,
+    min_bbox_size=0,
+    score_thr=0.001,
+    nms=dict(type='nms', iou_threshold=0.7),
+    max_per_img=300,
+    mask_thr_binary=0.5,
+    # fast_test: Whether to use fast test methods. When set
+    # to False, the implementation here is the same as the
+    # official, with higher mAP. If set to True, mask will first
+    # be upsampled to origin image shape through Pytorch, and
+    # then use mask_thr_binary to determine which pixels belong
+    # to the object. If set to False, will first use
+    # mask_thr_binary to determine which pixels belong to the
+    # object , and then use opencv to upsample mask to origin
+    # image shape. Default to False.
+    fast_test=False)
+
+# ===============================Unmodified in most cases====================
+model = dict(
+    bbox_head=dict(
+        type='YOLOv8InsHead',
+        head_module=dict(
+            type='YOLOv8InsHeadModule', masks_channels=32,
+            protos_channels=256)),
+    test_cfg=model_test_cfg)
+
+val_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=val_num_workers,
+    dataset=dict(batch_shapes_cfg=batch_shapes_cfg))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(metric=['bbox', 'segm'])
+test_evaluator = val_evaluator
+
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')

mmyolo/datasets/transforms/transforms.py

@@ -264,7 +264,7 @@ def _resize_img(self, results: dict):
             results['pad_param_origin'] = results['pad_param'] * \
                                           np.repeat(ratio, 2)
 
-        if self.half_pad_param:
+        if self.half_pad_param or 'gt_masks' in results:
             results['pad_param'] = np.array(
                 [padding_h / 2, padding_h / 2, padding_w / 2, padding_w / 2],
                 dtype=np.float32)

mmyolo/models/dense_heads/__init__.py

@@ -9,6 +9,7 @@
 from .yolov6_head import YOLOv6Head, YOLOv6HeadModule
 from .yolov7_head import YOLOv7Head, YOLOv7HeadModule, YOLOv7p6HeadModule
 from .yolov8_head import YOLOv8Head, YOLOv8HeadModule
+from .yolov8_ins_head import YOLOv8InsHead, YOLOv8InsHeadModule
 from .yolox_head import YOLOXHead, YOLOXHeadModule
 from .yolox_pose_head import YOLOXPoseHead, YOLOXPoseHeadModule
 
@@ -19,5 +20,6 @@
     'YOLOv7HeadModule', 'YOLOv7p6HeadModule', 'YOLOv8Head', 'YOLOv8HeadModule',
     'RTMDetRotatedHead', 'RTMDetRotatedSepBNHeadModule', 'RTMDetInsSepBNHead',
     'RTMDetInsSepBNHeadModule', 'YOLOv5InsHead', 'YOLOv5InsHeadModule',
-    'YOLOXPoseHead', 'YOLOXPoseHeadModule'
+    'YOLOv8InsHead', 'YOLOv8InsHeadModule', 'YOLOXPoseHead',
+    'YOLOXPoseHeadModule'
 ]