dulucas / uvo_challenge Goto Github PK

can you share it one more time ? Thanks a lot!

Hello! Many thanks for this nice GitHub repo. I am wondering on which data split did you evaluate your model. Whether it was UVO sparse test or UVO dense test? In the paper I saw the statement of Challenge final results on UVO-Sparse test dataset. But the testing script in Github code loaded test annotation from the dense split. I guess dense split's test set was what the challenge targeted?

您好，有几个问题想请教您几个问题。我没看太懂track1和track2是什么关系，我理解的track2是利用光流信息进行视频的语义分割吗？请问图里圈出的几个路径分别是什么呢？'PATH/TO/YOUR/UVO_video_test_dense.json'，这个json文件是要自己写的吗？

请问这个CRW模型是用来干嘛的呢

wrap在这里的作用是将光流信息映射到原图上，进行一个对齐操作吗？prev_mask和self.flows[t-1]在这里是不是要求要有一样的大小呢？这个mask是什么呢？不是类似于bounding box之类的吗？

您好~感谢您的开源， 我在跑实验的遇到了这个bug, 经过我的检查，并没有这个属性，不知道是什么原因引起的，您方便的时候能否指点一下？感谢

Firstly, Thank You for your amazing work.

There’s a question I would like to ask regarding the UVO Dataset. I would like to work on the dense dataset. Through the download link in the homepage of the challenge, I could access ‘UVO_video_train_dense.json’, ‘UVO_video_val_dense.json’ and ‘UVO_video_test_dense.json’ that specify the video ids of the train, validation and test datasets. However, I’m unsure how I could obtain the videos. Could you please guide me on how I could obtain the original and annotated videos ?

Many thanks

First of all thank the author for sharing such an excellent work！
When browsing the code, I found that there is a loop call in the get_targets function, is this not a problem?

this checkpoint(seg_swin_l_mixed_pretrained.pth) seem broken.

import torch
torch.load('seg_swin_l_mixed_pretrained.pth')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/chen/miniconda3/envs/sota/lib/python3.9/site-packages/torch/serialization.py", line 600, in load
    with _open_zipfile_reader(opened_file) as opened_zipfile:
  File "/home/chen/miniconda3/envs/sota/lib/python3.9/site-packages/torch/serialization.py", line 242, in __init__
    super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory

Hi, @dulucas ,
In these two config files, many "mask annotations" are used, like in this:

oid_train= dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/oid/',
img_dir = 'images/',
ann_dir = 'masks/',
split = ['train_clean_v2.txt', ],
pipeline=train_pipeline
)
)
and :
uvo_dense_val = dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
img_dir = 'images/dense_val/',
ann_dir = 'masks/dense_val/',
split = ['dense_val_list.txt', ],
pipeline=train_pipeline
)
)

And I tried the code like this(https://github.com/alicranck/coco2voc)(url) to generate the mask pngs, but there maybe something wrong with the generated masks, the training losses are unusual.
Could you please provide the code or scripts that you used to generate the mask? or giving a link you are referred?

Can you explain your training process in detail? Because I see you have a lot of config files, I don't know which ones to use and how to use them

Hello, the pretrained model can not be download

Hi！
Sorry to bother! The links to download weights failed.
Can you offer model weights ? I'm seeking for object detection model.
Thank you!
BR!
George.

Hi! Thanks for the code!

I tried to use your config_file in segmentation dir, but got an error. It seems in config.py there is no "type" key. I'm not familiar with openmmlab. Could you help me figure it out? Thanks a lot!

Script:

config_file = "./segmentation/configs/swin/swin_l_upper_w_jitter_inference.py"
ckpt_file = "../../models/seg_swin_l_uvo_finetuned.pth"
model = init_segmentor(config_file, ckpt_file, device="cuda:0")

Log:

Traceback (most recent call last):
  File "infer.py", line 20, in <module>
    model = init_segmentor(config_file, ckpt_file, device="cuda:0")
  File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmseg/apis/inference.py", line 32, in init_segmentor
    model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
  File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmseg/models/builder.py", line 49, in build_segmentor
    cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
  File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/utils/registry.py", line 212, in build
    return self.build_func(*args, **kwargs, registry=self)
  File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/home/xin/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/utils/registry.py", line 25, in build_from_cfg
    '`cfg` or `default_args` must contain the key "type", '
KeyError: '`cfg` or `default_args` must contain the key "type", but got {\'pretrained\': None, \'backbone\': {\'pretrain_img_size\': 384, \'embed_dims\': 192, \'depths\': [2, 2, 18, 2], \'num_heads\': [6, 12, 24, 48], \'drop_path_rate\': 0.2, \'window_size\': 12}, \'decode_head\': {\'in_channels\': [192, 384, 768, 1536], \'num_classes\': 2, \'loss_decode\': {\'type\': \'CrossEntropyLoss\', \'use_sigmoid\': False, \'loss_weight\': 1.0}}, \'auxiliary_head\': {\'in_channels\': 768, \'num_classes\': 2, \'loss_decode\': {\'type\': \'CrossEntropyLoss\', \'use_sigmoid\': False, \'loss_weight\': 1.0}}, \'train_cfg\': None}\n{\'train_cfg\': None, \'test_cfg\': None}'

I'm a newer mmdetection