Point rend

configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py

@@ -1,12 +1,12 @@
 _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-preprocess_cfg = dict(
+data_preprocessor = dict(
     mean=[103.530, 116.280, 123.675],
     std=[1.0, 1.0, 1.0],
-    to_rgb=False,
+    bgr_to_rgb=False,
     pad_size_divisor=32)
 model = dict(
     # use caffe img_norm
-    preprocess_cfg=preprocess_cfg,
+    data_preprocessor=data_preprocessor,
     backbone=dict(
         norm_cfg=dict(requires_grad=False),
         style='caffe',
@@ -18,8 +18,9 @@
     dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
     dict(
         type='RandomChoiceResize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)]),
+        scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                (1333, 768), (1333, 800)],
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs'),
 ]

configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py

@@ -1,4 +1,18 @@
 _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
+
+max_epochs = 36
+
 # learning policy
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=max_epochs,
+        by_epoch=True,
+        milestones=[28, 34],
+        gamma=0.1)
+]
+
+train_cfg = dict(max_epochs=max_epochs)

mmdet/models/detectors/point_rend.py

@@ -1,4 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import ConfigDict
+
+from mmdet.core.utils import OptConfigType, OptMultiConfig
 from mmdet.registry import MODELS
 from .two_stage import TwoStageDetector
 
@@ -13,20 +16,20 @@ class PointRend(TwoStageDetector):
     """
 
     def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None,
-                 init_cfg=None):
-        super(PointRend, self).__init__(
+                 backbone: ConfigDict,
+                 rpn_head: ConfigDict,
+                 roi_head: ConfigDict,
+                 train_cfg: ConfigDict,
+                 test_cfg: ConfigDict,
+                 neck: OptConfigType = None,
+                 data_preprocessor: OptConfigType = None,
+                 init_cfg: OptMultiConfig = None) -> None:
+        super().__init__(
             backbone=backbone,
             neck=neck,
             rpn_head=rpn_head,
             roi_head=roi_head,
             train_cfg=train_cfg,
             test_cfg=test_cfg,
-            pretrained=pretrained,
-            init_cfg=init_cfg)
+            init_cfg=init_cfg,
+            data_preprocessor=data_preprocessor)

mmdet/models/roi_heads/mask_heads/coarse_mask_head.py

@@ -1,7 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.cnn import ConvModule, Linear
-from mmcv.runner import ModuleList, auto_fp16
+from mmengine.model import ModuleList
+from torch import Tensor
 
+from mmdet.core.utils import MultiConfig
 from mmdet.registry import MODELS
 from .fcn_mask_head import FCNMaskHead
 
@@ -14,29 +16,29 @@ class CoarseMaskHead(FCNMaskHead):
     the input feature map instead of upsample it.
 
     Args:
-        num_convs (int): Number of conv layers in the head. Default: 0.
-        num_fcs (int): Number of fc layers in the head. Default: 2.
+        num_convs (int): Number of conv layers in the head. Defaults to 0.
+        num_fcs (int): Number of fc layers in the head. Defaults to 2.
         fc_out_channels (int): Number of output channels of fc layer.
-            Default: 1024.
+            Defaults to 1024.
         downsample_factor (int): The factor that feature map is downsampled by.
-            Default: 2.
+            Defaults to 2.
         init_cfg (dict or list[dict], optional): Initialization config dict.
     """
 
     def __init__(self,
-                 num_convs=0,
-                 num_fcs=2,
-                 fc_out_channels=1024,
-                 downsample_factor=2,
-                 init_cfg=dict(
+                 num_convs: int = 0,
+                 num_fcs: int = 2,
+                 fc_out_channels: int = 1024,
+                 downsample_factor: int = 2,
+                 init_cfg: MultiConfig = dict(
                      type='Xavier',
                      override=[
                          dict(name='fcs'),
                          dict(type='Constant', val=0.001, name='fc_logits')
                      ]),
                  *arg,
-                 **kwarg):
-        super(CoarseMaskHead, self).__init__(
+                 **kwarg) -> None:
+        super().__init__(
             *arg,
             num_convs=num_convs,
             upsample_cfg=dict(type=None),
@@ -81,11 +83,19 @@ def __init__(self,
         output_channels = self.num_classes * self.output_area
         self.fc_logits = Linear(last_layer_dim, output_channels)
 
-    def init_weights(self):
+    def init_weights(self) -> None:
+        """Initialize weights."""
         super(FCNMaskHead, self).init_weights()
 
-    @auto_fp16()
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward features from the upstream network.
+
+        Args:
+            x (Tensor): Extract mask RoI features.
+
+        Returns:
+            Tensor: Predicted foreground masks.
+        """
         for conv in self.convs:
             x = conv(x)