easton-cau / sotr Goto Github PK

感谢，测试CPU已经通过，耗时大约0.3s，然后自己想测试GPU版本效果的时候发现时间和CPU效果一致，VisualizationDemo初始化的时候已经修改parallel为true，且开启了多次循环测试，发现时间还是和CPU相当。添加日志证明确实是调用的AsyncPredictor，您是否方便给些指导？已确认环境安装无误

Hello！I read your code recently，i have same questions. First, if xy_pos_emb_shaped=None, get the tensor is same as input.But in your paper say"Position embeddings are added to the blocks to retain positional information, meaning that the position embedding spaces for the column and row are 1∗N ∗C and N ∗1∗C. "How can i add the position information?Second,different training setting with other models in yaml files,(1024, 2048)?Is this an unfair setting?

hello, author! I use the visualize_data.py to visualize the image , but the results of the instance segmentation have the bounding box . However, there is no bounding box in the paper. Can you tell me the reason and how adjust it?

With this command

python tools/train_net.py \
    --config-file configs/SOTR/R101.yaml \
    --num-gpus 1

I get error

File "tools/train_net.py", line 52, in __init__
    super(DefaultTrainer, self).__init__(model, data_loader, optimizer)
TypeError: __init__() takes 1 positional argument but 4 were given

My environment

torch==1.7.1
torchvision==0.9.2
detectron2==0.5

I am not sure what to do with it. Maybe try

detectron2==0.2.1 with torch==1.6

python -m pip install detectron2==0.2.1 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.6/index.html

Can you help me with this?

150ms average, is that normal? I changed everything into GPU.

using r50 version

I am confused about your results.

• AP-s is very poor；
• But AP-l far exceeds the current methods;
  what is the reason for this?

Hi, thanks for your great work!

I tested your pre-trained model (R-101 3x) on test-dev2017 in the coco evaluation server.
when I extract only mask results and save to json files, the segmentation score is matched the results reported on the paper. However, when I save mask results with box results (generated by 511~514 lines in sotr.py) to json files, the score (AP_s, AP_m, AP_l) is different from the paper.

This is the result from json file which is consist of only mask information

overall performance
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.434
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.590
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.731
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.328
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.512
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.590
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733

This is the result from json file which is consist of mask information with box information

overall performance
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.434
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.440
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.552
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.328
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.512
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.536
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.590
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733

As can be seen, (AP_s, AP_m, AP_l) of first result and second result are (0.102, 0.590, 0.731) and (0.194, 0.440, 0.552), respectively.
I don't know the detailed process of the coco evaluation server, so I wonder why there is a difference in segmentation ap due to the presence or absence of box information.

Traceback (most recent call last): File "tools/train_net.py", line 218, in <module> launch( File "/root/miniconda3/lib/python3.8/site-packages/detectron2/engine/launch.py", line 82, in launch main_func(*args) File "tools/train_net.py", line 206, in main trainer = Trainer(cfg) File "tools/train_net.py", line 52, in __init__ super(DefaultTrainer, self).__init__(model, data_loader, optimizer) TypeError: __init__() takes 1 positional argument but 4 were given

Excuse me, what is this problem?

您好，我在运行测试和训练时遇到了几个问题，希望您解答一下。
首先我运行

python tools/train_net.py \
    --config-file configs/SOTR/R101.yaml \
    --eval-only \
    --num-gpus 4 \
    MODEL.WEIGHTS work_dir/SOTR_R101/SOTR_R101.pth

得到的结果是：

|   AP   |  AP50  |  AP75  |  APs   |  APm   |  APl   |
|:------:|:------:|:------:|:------:|:------:|:------:|
| 39.730 | 60.303 | 42.707 | 18.045 | 43.414 | 59.794 |

跟您给出的有一定差距，不知道是哪里出了问题。
另外，在运行训练代码时，tools/train_net.py的第52行super(DefaultTrainer, self).__init__(model, data_loader, optimizer)似乎有问题，我把它改成super(Trainer, self).__init__(cfg)问题消失，但是会出现

FloatingPointError: Loss became infinite or NaN at iteration=2!
loss_dict = {'loss_ins': nan, 'loss_cate': nan}

我的学习率设置是：

SOLVER:
  IMS_PER_BATCH: 4
  BASE_LR: 0.00001
  WARMUP_FACTOR: 0.00001

请问NaN的问题应该如何解决？
期待您的回复，谢谢。

Noticed that SOTR APs is low 10.7 vs MaskRCNN 20, does there any reason for this? Just wonder if APs can boosted the AP overall could even higher.

hi, i can't find the model about SOTR-RT-736 to test pic at high FPS, when i am reshowing your work, can you give me some help?

1. can you provide the model about SOTR-RT-736?
2. can you tell me some details about test the model ?
   thank you very much!

I encountered the following problem when running SOTR with a new data set.
File "/home/xxx/EndoCV2022/detectron2/SOTR/adet/modeling/sotr/sotr.py", line 33, in init
self.scale_ranges = cfg.MODEL.SOTR.FPN_SCALE_RANGES

I checked the configuration file R50.yaml and found that there was no such variable. Do you know how to solve it?

作者您好，我在集群训练的时候出现问题，希望您能解答一下：

我的环境是：
torch == 1.7.1
torchvision == 0.8.2
detectron == 0.2.1

集群显卡使用：
1块显存12G的V100

学习率设置：
IMS_PER_BATCH: 2
BASE_LR: 0.00001
WARMUP_FACTOR: 0.00001
报出结果：NAN

学习率设置：
IMS_PER_BATCH: 4
BASE_LR: 0.00001
WARMUP_FACTOR: 0.00001
报错结果：CUDA out of memory

请问怎么解决这个问题？

请问作者用的是分布式训练方式吗？

尊敬的作者您好，我想使用自己的图片测试您这里的demo程序，但是参数一致填不对，pth文件已经下载，是否方便给一个使用demo.py测试的示例呢

How to run demo, there are a lot of files not found

只有pytorch1.6版本对应有detectron2==0.2.1.
pytorch>1.7版本对应detectron2==0.5

但是要求pytorch>1.7，detectron2==0.2.1.

请问应该如何解决呢
期待作者的回复

super(DefaultTrainer, self).init(model, data_loader, optimizer)
TypeError: init() takes 1 positional argument but 4 were given

does anyone know How to solve this problem?

While training the model, getting following error, can anybody help me here?