Fix type error in 2_new_data_mode (#6469)

* Always map location to cpu when load checkpoint (#6405)

* configs: update groie README (#6401)

Signed-off-by: Leonardo Rossi <leonardo.rossi@unipr.it>

* [Fix] fix config path in docs (#6396)

* [Enchance] Set a random seed when the user does not set a seed. (#6457)

* fix random seed bug

* add comment

* enchance random seed

* rename

Co-authored-by: Haobo Yuan <yuanhaobo@whu.edu.cn>

* [BugFixed] fix wrong trunc_normal_init use (#6432)

* fix wrong trunc_normal_init use

* fix wrong trunc_normal_init use

* fix #6446

Co-authored-by: Uno Wu <st9007a@gmail.com>
Co-authored-by: Leonardo Rossi <leonardo.rossi@unipr.it>
Co-authored-by: BigDong <yudongwang@tju.edu.cn>
Co-authored-by: Haian Huang(深度眸) <1286304229@qq.com>
Co-authored-by: Haobo Yuan <yuanhaobo@whu.edu.cn>
Co-authored-by: Shusheng Yang <shusheng.yang@qq.com>

configs/groie/README.md

@@ -25,9 +25,6 @@ performance.
 ## Results and models
 
 The results on COCO 2017 minival (5k images) are shown in the below table.
-You can find
-[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT)
-the trained models.
 
 ### Application of GRoIE to different architectures
 
@@ -42,24 +39,24 @@ the trained models.
 | R-50-FPN  | GC-Net           |   1x    |  40.7  |  36.5   | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) |
 | R-50-FPN  | + GRoIE          |   1x    |  41.0  |  37.8   | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) |
 | R-101-FPN | GC-Net           |   1x    |  42.2  |  37.8   | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) |
-| R-101-FPN | + GRoIE          |   1x    |   |    | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |
+| R-101-FPN | + GRoIE          |   1x    |  42.6  |  38.7   | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |
 
 ## Citation
 
 If you use this work or benchmark in your research, please cite this project.
 
 ```latex
-@misc{rossi2020novel,
-    title={A novel Region of Interest Extraction Layer for Instance Segmentation},
-    author={Leonardo Rossi and Akbar Karimi and Andrea Prati},
-    year={2020},
-    eprint={2004.13665},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
+@inproceedings{rossi2021novel,
+  title={A novel region of interest extraction layer for instance segmentation},
+  author={Rossi, Leonardo and Karimi, Akbar and Prati, Andrea},
+  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+  pages={2203--2209},
+  year={2021},
+  organization={IEEE}
 }
 ```
 
 ## Contact
 
-The implementation of GROI is currently maintained by
+The implementation of GRoIE is currently maintained by
 [Leonardo Rossi](https://github.com/hachreak/).

docs/1_exist_data_model.md

@@ -300,7 +300,7 @@ Assuming that you have already downloaded the checkpoints to the directory `chec
 
    ```shell
    python tools/test.py \
-       configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
        checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
        --show-dir faster_rcnn_r50_fpn_1x_results
    ```

docs/2_new_data_model.md

@@ -257,7 +257,7 @@ For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).
 To test the trained model, you can simply run
 
 ```shell
-python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py/latest.pth --eval bbox segm
+python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon/latest.pth --eval bbox segm
 ```
 
 For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).

docs_zh-CN/1_exist_data_model.md

@@ -285,7 +285,7 @@ bash tools/dist_test.sh \
 
    ```shell
    python tools/test.py \
-       configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
        checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
        --show-dir faster_rcnn_r50_fpn_1x_results
    ```

mmdet/apis/__init__.py

@@ -2,10 +2,11 @@
 from .inference import (async_inference_detector, inference_detector,
                         init_detector, show_result_pyplot)
 from .test import multi_gpu_test, single_gpu_test
-from .train import get_root_logger, set_random_seed, train_detector
+from .train import (get_root_logger, init_random_seed, set_random_seed,
+                    train_detector)
 
 __all__ = [
     'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector',
     'async_inference_detector', 'inference_detector', 'show_result_pyplot',
-    'multi_gpu_test', 'single_gpu_test'
+    'multi_gpu_test', 'single_gpu_test', 'init_random_seed'
 ]

mmdet/apis/inference.py

@@ -39,8 +39,7 @@ def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
     config.model.train_cfg = None
     model = build_detector(config.model, test_cfg=config.get('test_cfg'))
     if checkpoint is not None:
-        map_loc = 'cpu' if device == 'cpu' else None
-        checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
+        checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
         if 'CLASSES' in checkpoint.get('meta', {}):
             model.CLASSES = checkpoint['meta']['CLASSES']
         else:

mmdet/apis/train.py

@@ -4,10 +4,11 @@
 
 import numpy as np
 import torch
+import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
                          Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner)
+                         build_runner, get_dist_info)
 from mmcv.utils import build_from_cfg
 
 from mmdet.core import DistEvalHook, EvalHook
@@ -16,6 +17,39 @@
 from mmdet.utils import get_root_logger
 
 
+def init_random_seed(seed=None, device='cuda'):
+    """Initialize random seed.
+
+    If the seed is not set, the seed will be automatically randomized,
+    and then broadcast to all processes to prevent some potential bugs.
+
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+
+    Returns:
+        int: Seed to be used.
+    """
+    if seed is not None:
+        return seed
+
+    # Make sure all ranks share the same random seed to prevent
+    # some potential bugs. Please refer to
+    # https://github.com/open-mmlab/mmdetection/issues/6339
+    rank, world_size = get_dist_info()
+    seed = np.random.randint(2**31)
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+    dist.broadcast(random_num, src=0)
+    return random_num.item()
+
+
 def set_random_seed(seed, deterministic=False):
     """Set random seed.
 

mmdet/models/backbones/pvt.py

@@ -9,6 +9,7 @@
                       constant_init, normal_init, trunc_normal_init)
 from mmcv.cnn.bricks.drop import build_dropout
 from mmcv.cnn.bricks.transformer import MultiheadAttention
+from mmcv.cnn.utils.weight_init import trunc_normal_
 from mmcv.runner import (BaseModule, ModuleList, Sequential, _load_checkpoint,
                          load_state_dict)
 from torch.nn.modules.utils import _pair as to_2tuple
@@ -315,7 +316,7 @@ def __init__(self, pos_shape, pos_dim, drop_rate=0., init_cfg=None):
         self.drop = nn.Dropout(p=drop_rate)
 
     def init_weights(self):
-        trunc_normal_init(self.pos_embed, std=0.02)
+        trunc_normal_(self.pos_embed, std=0.02)
 
     def resize_pos_embed(self, pos_embed, input_shape, mode='bilinear'):
         """Resize pos_embed weights.
@@ -526,9 +527,7 @@ def init_weights(self):
                         f'training start from scratch')
             for m in self.modules():
                 if isinstance(m, nn.Linear):
-                    trunc_normal_init(m.weight, std=.02)
-                    if m.bias is not None:
-                        constant_init(m.bias, 0)
+                    trunc_normal_init(m, std=.02, bias=0.)
                 elif isinstance(m, nn.LayerNorm):
                     constant_init(m.bias, 0)
                     constant_init(m.weight, 1.0)

mmdet/models/backbones/swin.py

@@ -8,6 +8,7 @@
 import torch.utils.checkpoint as cp
 from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
 from mmcv.cnn.bricks.transformer import FFN, build_dropout
+from mmcv.cnn.utils.weight_init import trunc_normal_
 from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
 from mmcv.utils import to_2tuple
 
@@ -74,7 +75,7 @@ def __init__(self,
         self.softmax = nn.Softmax(dim=-1)
 
     def init_weights(self):
-        trunc_normal_init(self.relative_position_bias_table, std=0.02)
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
 
     def forward(self, x, mask=None):
         """
@@ -672,12 +673,10 @@ def init_weights(self):
                         f'{self.__class__.__name__}, '
                         f'training start from scratch')
             if self.use_abs_pos_embed:
-                trunc_normal_init(self.absolute_pos_embed, std=0.02)
+                trunc_normal_(self.absolute_pos_embed, std=0.02)
             for m in self.modules():
                 if isinstance(m, nn.Linear):
-                    trunc_normal_init(m.weight, std=.02)
-                    if m.bias is not None:
-                        constant_init(m.bias, 0)
+                    trunc_normal_init(m, std=.02, bias=0.)
                 elif isinstance(m, nn.LayerNorm):
                     constant_init(m.bias, 0)
                     constant_init(m.weight, 1.0)

tools/train.py

@@ -13,7 +13,7 @@
 from mmcv.utils import get_git_hash
 
 from mmdet import __version__
-from mmdet.apis import set_random_seed, train_detector
+from mmdet.apis import init_random_seed, set_random_seed, train_detector
 from mmdet.datasets import build_dataset
 from mmdet.models import build_detector
 from mmdet.utils import collect_env, get_root_logger
@@ -148,12 +148,12 @@ def main():
     logger.info(f'Config:\n{cfg.pretty_text}')
 
     # set random seeds
-    if args.seed is not None:
-        logger.info(f'Set random seed to {args.seed}, '
-                    f'deterministic: {args.deterministic}')
-        set_random_seed(args.seed, deterministic=args.deterministic)
-    cfg.seed = args.seed
-    meta['seed'] = args.seed
+    seed = init_random_seed(args.seed)
+    logger.info(f'Set random seed to {seed}, '
+                f'deterministic: {args.deterministic}')
+    set_random_seed(seed, deterministic=args.deterministic)
+    cfg.seed = seed
+    meta['seed'] = seed
     meta['exp_name'] = osp.basename(args.config)
 
     model = build_detector(