add group norm config and minor fix

configs/mask_rcnn_r50_fpn_gn_2x.py

+# model settings
+model = dict(
+    type='MaskRCNN',
+    pretrained='tools/resnet50-GN.path',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        normalize=dict(
+            type='GN',
+            num_groups=32)),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        normalize=dict(
+            type='GN',
+            num_groups=32)),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_scales=[8],
+        anchor_ratios=[0.5, 1.0, 2.0],
+        anchor_strides=[4, 8, 16, 32, 64],
+        target_means=[.0, .0, .0, .0],
+        target_stds=[1.0, 1.0, 1.0, 1.0],
+        use_sigmoid_cls=True),
+    bbox_roi_extractor=dict(
+        type='SingleRoIExtractor',
+        roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+        out_channels=256,
+        featmap_strides=[4, 8, 16, 32]),
+    bbox_head=dict(
+        type='ConvFCBBoxHead',
+        num_shared_convs=4,
+        num_shared_fcs=1,
+        in_channels=256,
+        conv_out_channels=256,
+        fc_out_channels=1024,
+        roi_feat_size=7,
+        num_classes=81,
+        target_means=[0., 0., 0., 0.],
+        target_stds=[0.1, 0.1, 0.2, 0.2],
+        reg_class_agnostic=False,
+        normalize=dict(
+            type='GN',
+            num_groups=32)),
+    mask_roi_extractor=dict(
+        type='SingleRoIExtractor',
+        roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+        out_channels=256,
+        featmap_strides=[4, 8, 16, 32]),
+    mask_head=dict(
+        type='FCNMaskHead',
+        num_convs=4,
+        in_channels=256,
+        conv_out_channels=256,
+        num_classes=81,
+        normalize=dict(
+            type='GN',
+            num_groups=32)))
+
+# model training and testing settings
+train_cfg = dict(
+    rpn=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.7,
+            neg_iou_thr=0.3,
+            min_pos_iou=0.3,
+            ignore_iof_thr=-1),
+        sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False),
+        allowed_border=0,
+        pos_weight=-1,
+        smoothl1_beta=1 / 9.0,
+        debug=False),
+    rcnn=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.5,
+            ignore_iof_thr=-1),
+        sampler=dict(
+            type='RandomSampler',
+            num=512,
+            pos_fraction=0.25,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=True),
+        mask_size=28,
+        pos_weight=-1,
+        debug=False))
+test_cfg = dict(
+    rpn=dict(
+        nms_across_levels=False,
+        nms_pre=2000,
+        nms_post=2000,
+        max_num=2000,
+        nms_thr=0.7,
+        min_bbox_size=0),
+    rcnn=dict(
+        score_thr=0.05,
+        nms=dict(type='nms', iou_thr=0.5),
+        max_per_img=100,
+        mask_thr_binary=0.5))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+data = dict(
+    imgs_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0.5,
+        with_mask=True,
+        with_crowd=True,
+        with_label=True),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0,
+        with_mask=True,
+        with_crowd=True,
+        with_label=True),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0,
+        with_mask=False,
+        with_label=False,
+        test_mode=True))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[16, 22])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 24
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/mask_rcnn_r50_fpn_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

mmdet/models/backbones/resnet.py

@@ -33,7 +33,7 @@ class BasicBlock(nn.Module):
                  downsample=None,
                  style='pytorch',
                  with_cp=False,
-                 normalize=dict(type='GN')):
+                 normalize=dict(type='BN')):
         super(BasicBlock, self).__init__()
         self.conv1 = conv3x3(inplanes, planes, stride, dilation)
 
@@ -252,16 +252,14 @@ class ResNet(nn.Module):
         self.depth = depth
         self.num_stages = num_stages
         assert num_stages >= 1 and num_stages <= 4
-        assert len(strides) == len(dilations) == num_stages
-        assert max(out_indices) < num_stages
         self.strides = strides
         self.dilations = dilations
         assert len(strides) == len(dilations) == num_stages
         self.out_indices = out_indices
         assert max(out_indices) < num_stages
         self.style = style
-        self.with_cp = with_cp
         self.frozen_stages = frozen_stages
+        self.with_cp = with_cp
 
         assert isinstance(normalize, dict) and 'type' in normalize
         assert normalize['type'] in ['BN', 'GN']
@@ -357,7 +355,7 @@ class ResNet(nn.Module):
         if mode and self.frozen_stages >= 0:
             for param in self.conv1.parameters():
                 param.requires_grad = False
- 
+
             stem_norm = getattr(self, self.stem_norm_name)
             stem_norm.eval()
             for param in stem_norm.parameters():
@@ -447,7 +445,8 @@ class ResNetClassifier(ResNet):
             if 'blobs' in cf_state:
                 cf_state = cf_state['blobs']
             for i, (py_k, cf_k) in enumerate(mapping.items(), 1):
-                print('[{}/{}] Loading {} to {}'.format(i, len(mapping), cf_k, py_k))
+                print('[{}/{}] Loading {} to {}'.format(
+                    i, len(mapping), cf_k, py_k))
                 assert py_k in py_state and cf_k in cf_state
                 py_state[py_k] = torch.Tensor(cf_state[cf_k])
         self.load_state_dict(py_state)