论文复现使用PaddleDetection复现OrientedRepPoints的复现笔记

Posted 2022-12-16 songyuc

1 复现流程

复现流程表：

1. 翻译原始论文；
2. 学习PaddleDetection配置参数
3. 对齐Dataloader；

2 MMRotate原始模型代码

2.1 数据集配置：DOTA1.0

2.1.1 在Win11上使用mklink创建软链接

示例代码：

mklink /j "D:\\Professional\\Paddle\\mmrotate_oriented_reppoints\\data\\DOTA" "D:\\Professional\\Paddle\\DOTA"

2.1.2 划分数据集：mmrotate: split dota dataset

1024×1024 patches with an overlap of 200

Ss_trainval:

python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_trainval.json

Ss_test:

python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_test.json

Multiple scale dataset

Ms_trainval:

python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ms_trainval.json

ms_test:

python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ms_test.json

Clone原始模型代码：

git clone https://github.com/open-mmlab/mmrotate.git mmrotate_oriented_reppoints

2.2 配置mmrotate环境

验证mmrotate安装时出现错误：“RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)”

在使用mmrotate官方示例验证其安装时，出现错误：

[mmrotate_oriented_reppoints]：
…
File “d:\\professional\\xxx\\mmrotate\\models\\roi_heads\\bbox_heads\\rotated_bbox_head.py”, line 418, in get_bboxes
det_bboxes, det_labels = multiclass_nms_rotated(
File “d:\\professional\\xxx\\mmrotate\\core\\post_processing\\bbox_nms_rotated.py”, line 58, in multiclass_nms_rotated
bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds]
RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)

（1）将测试命令修改为使用cpu进行推理：

python demo/image_demo.py demo/demo.jpg oriented_rcnn_r50_fpn_1x_dota_le90.py oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth --out-file result.jpg --device cpu

（2）将出错位置的张量索引操作改成一致的设备

大概率出错的是这两句代码：

      def assign(self,
               points,
               gt_rbboxes,
               gt_rbboxes_ignore=None,
               gt_labels=None,
               overlaps=None):
        ......
        gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6)
        scale = self.scale
        gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) +
                          torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int()
        gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max)

        # stores the assigned gt index of each point
        assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long)
        # stores the assigned gt dist (to this point) of each point
        assigned_gt_dist = points.new_full((num_points, ), float('inf'))
        points_range = torch.arange(points.shape[0])				# NOT specify a device

        for idx in range(num_gts):
            gt_lvl = gt_bboxes_lvl[idx]
            # get the index of points in this level
            lvl_idx = gt_lvl == points_lvl
            points_index = points_range[lvl_idx]					# <-- error1
            ...
            # the index of nearest k points to gt center in this level
            min_dist_points_index = points_index[min_dist_index]	# <-- error2

我看了一下，points_index也是来自于points_range，很大的可能性是points_range的设备不一致，然后track上去，发现points_range = torch.arange(points.shape[0])这一句代码没有显示指定设备，可能会分配到CPU上从而引发错误，于是把代码修改为：

points_range = torch.arange(points.shape[0]).to(gt_bboxes_lvl.device) # 跟gt_bboxes_lvl的设备保持一致

4 PaddleDetection学习笔记

4.1 配置环境&Clone代码

创建conda环境：

conda create --name conda-paddle python=3.10

Clone代码：

git clone https://github.com/PaddlePaddle/PaddleDetection.git Paddle_oriented_reppoints

4.2 PaddleDetection配置文件说明

_BASE_：基础配置

‘…/…/datasets/dota.yml’：DOTA数据集配置文件

‘…/…/runtime.yml’：运行时配置

5 重写配置文件

5.1 超参数换算表

mmrorate	paddleDetection
lr_config.step	schedulers.milestones = step - 1
Backbone:
frozen_stages (1)	freeze_at = frozen_stages - 1 (0)
out_indices	return_idx = out_indices + 1
FPN:
start_level	extra_stage = start_level + 1
Head:
anchor_generator.scales	anchor_scales = scales

关于schedulers.milestones的设置

这个我已经跟鑫鑫确认了，schedulers.milestones = step - 1，聊天记录如下：