smalltingting commented 5 months ago

I can't find and download configs and weights of Single Stage models and End to End models such as DETR and FCOS. Could you please tell me how to solve this problem?Thanks you in advance.

zcablii commented 5 months ago

configs and weights are updated

paster489 commented 4 months ago

how to download weights? thanks

zcablii commented 4 months ago

If you can get access to BaiduDisk, here is the link

paster489 commented 4 months ago

It is starting a baiduNetdiskHost program and asking for username and password, i don't know Chinese, I suppose that is what it is asking. I don't have a password...

On Tue, Apr 16, 2024 at 3:22 PM Yuxuan Li @.***> wrote:

If you can get access to BaiduDisk, here is the link https://pan.baidu.com/s/1SuEOl_ImqjoT5Y3pYxZt4w?pwd=c6fo

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zcablii commented 4 months ago

No worries, give me a second, I will upload everything to OneDrive

zcablii commented 4 months ago

https://liveuclac-my.sharepoint.com/:f:/g/personal/zcablii_ucl_ac_uk/EqaZRwGT9VlAsuw54gqwwkoBxSzVd1cyJldFBMUUFDy0jA?e=EY5H2j

paster489 commented 4 months ago

thanks a lot

On Tue, Apr 16, 2024 at 3:49 PM Yuxuan Li @.***> wrote:

https://liveuclac-my.sharepoint.com/:f:/g/personal/zcablii_ucl_ac_uk/EqaZRwGT9VlAsuw54gqwwkoBxSzVd1cyJldFBMUUFDy0jA?e=EY5H2j

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paster489 commented 4 months ago

additional question => in onedrive for images =>

[image: image.png]

in train there are only 663 images: [image: image.png] and not 94K as mentioned:

[image: image.png]

May I have all the training please?

On Tue, Apr 16, 2024 at 3:52 PM Inga Paster @.***> wrote:

thanks a lot

On Tue, Apr 16, 2024 at 3:49 PM Yuxuan Li @.***> wrote:

https://liveuclac-my.sharepoint.com/:f:/g/personal/zcablii_ucl_ac_uk/EqaZRwGT9VlAsuw54gqwwkoBxSzVd1cyJldFBMUUFDy0jA?e=EY5H2j

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zcablii commented 4 months ago

Make sure you are using this link for dataset downloading, as given in the README.

paster489 commented 4 months ago

ok, thanks,

another question, please => in "Annotations" file, I see only the boundary box definition, but there is no label id: car, ship, etc. where is the information please?

On Tue, Apr 16, 2024 at 4:37 PM Yuxuan Li @.***> wrote:

Make sure you are using this link https://liveuclac-my.sharepoint.com/:f:/g/personal/zcablii_ucl_ac_uk/EutczQ-0LB5BmQ1BEguS-PAB8mLXUChRWPVY2Bn5X4-0_w?e=bxPZij for dataset downloading, as given in the README.

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zcablii commented 4 months ago

category labels are denoted as "category_id".

paster489 commented 4 months ago

i see, ok, thanks for quick response

the last question in the checkpoint data you sent me to download, I have weights and config =>

[image: image.png]

but how I know to connect each of them with table?

[image: image.png]

for example for yellow config =>

[image: image.png] the name of config file is fg_frcnn_IN_sup_sar_r50.py which is not in checkpoint link you sent me? maybe the name there is different?

On Tue, Apr 16, 2024 at 5:53 PM Yuxuan Li @.***> wrote:

category labels are denoted as "category_id".

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zcablii commented 4 months ago

yes, some names are different

paster489 commented 4 months ago

thanks, now additional question, please.

after downloading the weights and config files from onedrive you have sent me => I ran the next simple code test:

from mmengine.utils import get_git_hash from mmengine.utils.dl_utils import collect_env as collect_base_env import mmdet from mmdet.apis import DetInferencer config_path_2 = './SAR_Yuxuan_weights/r101_sar.py' checkpoint_2 = './SAR_Yuxuan_weights/r101_sar_epoch_100.pth' inferencer_2 = DetInferencer(model=config_path_2, weights=checkpoint_2)

and got the next error => KeyError: 'ImageClassifier is not in the mmdet::model registry. Please check whether the value of ImageClassifier is correct or it was registered as expected :

Loads checkpoint by local backend from path: ./SAR_Yuxuan_weights/r101_sar_epoch_100.pth https://file+.vscode-resource.vscode-cdn.net/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/SAR_Yuxuan_weights/r101_sar_epoch_100.pth

Could you advise how to fix it please?

thanks

On Tue, Apr 16, 2024 at 6:29 PM Yuxuan Li @.***> wrote:

yes, some names are different

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zcablii commented 4 months ago

It is because you load a config and model weight for classification, but use a detection environment(mmdet). You can go to mmpretrain for classification model verification.

paster489 commented 4 months ago

I see, my curiosity is to check object detection and not classification. from the config files which you sent me above which one is for detection? thanks

On Thu, Apr 18, 2024 at 7:13 PM Yuxuan Li @.***> wrote:

It is because you load a config and model weight for classification, but use a detection environment(mmdet). You can go to mmpretrain https://github.com/open-mmlab/mmpretrain for classification model verification.

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paster489 commented 4 months ago

by the way, I took another config file (hope it is detection) =>

from mmengine.utils import get_git_hash from mmengine.utils.dl_utils import collect_env as collect_base_env import mmdet from mmdet.apis import DetInferencer

def collect_env(): """Collect the information of the running environments.""" env_info = collect_base_env() env_info['MMDetection'] = f'{mmdet.version}+{get_git_hash()[:7]}' return env_info

def main():

config_file = './mmdetection/SAR_Yuxuan_weights/fg_frcnn_dota_pretrain_sar_wavelet_r50/fg_frcnn_dota_pretrain_sar_wavelet_r50.py' checkpoint_file = './mmdetection/SAR_Yuxuan_weights/fg_frcnn_dota_pretrain_sar_wavelet_r50/best_coco_bbox_mAP_epoch_12.pth'

Initialize the DetInferencer

inferencer_2 = DetInferencer(model=config_file, weights=checkpoint_file)

if name == 'main': main()

for name, val in collect_env().items():

print(f'{name}: {val}')

the error is: KeyError: 'Self_features_model is not in the mmdet::model registry. Please check whether the value of Self_features_model is correct or it was registered as expected

Loads checkpoint by local backend from path: ./mmdetection/SAR_Yuxuan_weights/fg_frcnn_dota_pretrain_sar_wavelet_r50/best_coco_bbox_mAP_epoch_12.pth Traceback (most recent call last): File "/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/test_2.py", line 23, in main() File "/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/test_2.py", line 19, in main inferencer_2 = DetInferencer(model=config_file, weights=checkpoint_file) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/apis/det_inferencer.py", line 98, in init super().init( File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/infer/infer.py", line 180, in init self.model = self._init_model(cfg, weights, device) # type: ignore File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/infer/infer.py", line 483, in _init_model model = MODELS.build(cfg.model) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, args, kwargs, registry=self) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(args) # type: ignore File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/models/detectors/faster_rcnn.py", line 20, in init super().init( File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/models/detectors/two_stage.py", line 34, in init self.backbone = MODELS.build(backbone) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, args, **kwargs, registry=self) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 100, in build_from_cfg raise KeyError( KeyError: 'Self_features_model is not in the mmdet::model registry. Please check whether the value of Self_features_model is correct or it was registered as expected. More details can be found at https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module '

On Thu, Apr 18, 2024 at 8:07 PM Inga Paster @.***> wrote:

I see, my curiosity is to check object detection and not classification. from the config files which you sent me above which one is for detection? thanks

On Thu, Apr 18, 2024 at 7:13 PM Yuxuan Li @.***> wrote:

It is because you load a config and model weight for classification, but use a detection environment(mmdet). You can go to mmpretrain https://github.com/open-mmlab/mmpretrain for classification model verification.

— Reply to this email directly, view it on GitHub https://github.com/zcablii/SARDet_100K/issues/6#issuecomment-2064402256, or unsubscribe https://github.com/notifications/unsubscribe-auth/AO3HQPRNHG5LZHEGVMO2EQDY57WLLAVCNFSM6AAAAABFRHLADOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANRUGQYDEMRVGY . You are receiving this because you commented.Message ID: @.***>

zcablii commented 4 months ago

please use the configs I provided in the repo. The configs with the weight files are a bit out of date because I made some code refactory and renaming.

paster489 commented 4 months ago

I see thanks.. but i took the config file from your repository and weights from Onefrove you sent me and still it is not working:

rom mmengine.utils import get_git_hash from mmengine.utils.dl_utils import collect_env as collect_base_env import mmdet from mmdet.apis import DetInferencer

def collect_env(): """Collect the information of the running environments.""" env_info = collect_base_env() env_info['MMDetection'] = f'{mmdet.version}+{get_git_hash()[:7]}' return env_info

def main():

config_file = './local_configs/SARDet/other_backbones/fg_frcnn_dota_pretrain_sar_r101_wavelet.py' checkpoint_file = './mmdetection/SAR_Yuxuan_weights/fg_frcnn_dota_pretrain_sar_r101_wavelet/best_coco_bbox_mAP_epoch_12.pth'

Initialize the DetInferencer

inferencer_2 = DetInferencer(model=config_file, weights=checkpoint_file)

if name == 'main': main()

for name, val in collect_env().items():

print(f'{name}: {val}')

error: KeyError: 'MSFA is not in the mmdet::model registry. Please check whether the value of MSFA is correct or it was registered as expected.

Loads checkpoint by local backend from path: ./mmdetection/SAR_Yuxuan_weights/fg_frcnn_dota_pretrain_sar_r101_wavelet/best_coco_bbox_mAP_epoch_12.pth Traceback (most recent call last): File "/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/test_2.py", line 23, in main() File "/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/test_2.py", line 19, in main inferencer_2 = DetInferencer(model=config_file, weights=checkpoint_file) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/apis/det_inferencer.py", line 98, in init super().init( File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/infer/infer.py", line 180, in init self.model = self._init_model(cfg, weights, device) # type: ignore File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/infer/infer.py", line 483, in _init_model model = MODELS.build(cfg.model) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, args, kwargs, registry=self) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(args) # type: ignore File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/models/detectors/faster_rcnn.py", line 20, in init super().init( File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmdet/models/detectors/two_stage.py", line 34, in init self.backbone = MODELS.build(backbone) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build return self.build_func(cfg, args, kwargs, registry=self) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/paster/anaconda3/envs/MSFA/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 100, in build_from_cfg raise KeyError( KeyError: 'MSFA is not in the mmdet::model registry. Please check whether the value of MSFA is correct or it was registered as expected. More details can be found at https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module ' (base) **@.***:~/Documents/SAR_inference/SARDet_100K/MSFA$

thank you very much for help

On Thu, Apr 18, 2024 at 11:59 PM Yuxuan Li @.***> wrote:

please use the configs I provided in the repo. The configs with the weight files are a bit out of date because I made some code refractory and renaming.

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zcablii commented 4 months ago

Your environment is not properly installed.

paster489 commented 4 months ago

I see... I did exactly according to repo instructions:

change directory into the project main code

cd MSFA

create env

conda create -y -n MSFA python=3.8 conda activate MSFA

install pytorch

conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia

or

pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118

install dependencies of openmmlab

pip install -U openmim mim install "mmengine==0.8.4" mim install "mmcv==2.0.1" mim install "mmdet==3.1.0"

install other dependencies

pip install -r requirements.txt

install MSFA

pip install -v -e .

The conda list is:

packages in environment at /home/paster/anaconda3/envs/MSFA:

#

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge addict 2.4.0 pypi_0 pypi aliyun-python-sdk-core 2.15.1 pypi_0 pypi aliyun-python-sdk-kms 2.16.2 pypi_0 pypi appdirs 1.4.4 pypi_0 pypi asttokens 2.4.1 pyhd8ed1ab_0 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge blas 1.0 mkl brotli-python 1.0.9 py38heb0550a_2 bzip2 1.0.8 h7b6447c_0 ca-certificates 2024.2.2 hbcca054_0 conda-forge certifi 2024.2.2 py38h06a4308_0 cffi 1.16.0 pypi_0 pypi charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 pypi_0 pypi colorama 0.4.6 pypi_0 pypi comm 0.2.2 pypi_0 pypi configparser 7.0.0 pypi_0 pypi contourpy 1.1.1 pypi_0 pypi crcmod 1.7 pypi_0 pypi cryptography 42.0.5 pypi_0 pypi cuda-cudart 11.8.89 0 nvidia cuda-cupti 11.8.87 0 nvidia cuda-libraries 11.8.0 0 nvidia cuda-nvrtc 11.8.89 0 nvidia cuda-nvtx 11.8.86 0 nvidia cuda-runtime 11.8.0 0 nvidia cycler 0.12.1 pypi_0 pypi debugpy 1.8.1 py38h17151c0_0 conda-forge decorator 5.1.1 pyhd8ed1ab_0 conda-forge executing 2.0.1 pyhd8ed1ab_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch filelock 3.13.1 py38h06a4308_0 fonttools 4.51.0 pypi_0 pypi freetype 2.11.0 h70c0345_0 giflib 5.2.1 h7b6447c_0 gmp 6.2.1 h295c915_3 gmpy2 2.1.2 py38heeb90bb_0 gnutls 3.6.15 he1e5248_0 idna 3.4 py38h06a4308_0 importlib-metadata 7.1.0 pyha770c72_0 conda-forge importlib-resources 6.4.0 pypi_0 pypi importlib_metadata 7.1.0 hd8ed1ab_0 conda-forge intel-openmp 2021.4.0 h06a4308_3561 ipykernel 6.14.0 py38h7f3c49e_0 conda-forge ipython 8.4.0 py38h578d9bd_0 conda-forge ipywidgets 8.1.2 pypi_0 pypi jedi 0.19.1 pyhd8ed1ab_0 conda-forge jinja2 3.1.3 py38h06a4308_0 jmespath 0.10.0 pypi_0 pypi jpeg 9e h7f8727e_0 jupyter_client 8.6.1 pyhd8ed1ab_0 conda-forge jupyter_core 5.7.2 py38h578d9bd_0 conda-forge jupyterlab-widgets 3.0.10 pypi_0 pypi kiwisolver 1.4.5 pypi_0 pypi kymatio 0.3.0 pypi_0 pypi lame 3.100 h7b6447c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 libcublas 11.11.3.6 0 nvidia libcufft 10.9.0.58 0 nvidia libcufile 1.9.1.3 0 nvidia libcurand 10.3.5.147 0 nvidia libcusolver 11.4.1.48 0 nvidia libcusparse 11.7.5.86 0 nvidia libffi 3.3 he6710b0_2 libgcc-ng 13.2.0 h807b86a_5 conda-forge libgomp 13.2.0 h807b86a_5 conda-forge libiconv 1.16 h7f8727e_2 libidn2 2.3.2 h7f8727e_0 libnpp 11.8.0.86 0 nvidia libnvjpeg 11.9.0.86 0 nvidia libpng 1.6.37 hbc83047_0 libsodium 1.0.18 h36c2ea0_1 conda-forge libstdcxx-ng 13.2.0 h7e041cc_5 conda-forge libtasn1 4.16.0 h27cfd23_0 libtiff 4.2.0 h2818925_1 libunistring 0.9.10 h27cfd23_0 libwebp 1.2.2 h55f646e_0 libwebp-base 1.2.2 h7f8727e_0 lz4-c 1.9.3 h295c915_1 markdown 3.6 pypi_0 pypi markdown-it-py 3.0.0 pypi_0 pypi markupsafe 2.1.1 py38h7f8727e_0 matplotlib 3.7.5 pypi_0 pypi matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge mdurl 0.1.2 pypi_0 pypi mkl 2021.4.0 h06a4308_640 mkl-service 2.4.0 py38h7f8727e_0 mkl_fft 1.3.1 py38hd3c417c_0 mkl_random 1.2.2 py38h51133e4_0 mmcv 2.0.1 pypi_0 pypi mmdet 3.1.0 pypi_0 pypi mmengine 0.8.4 pypi_0 pypi model-index 0.1.11 pypi_0 pypi mpc 1.1.0 h10f8cd9_1 mpfr 4.0.2 hb69a4c5_1 mpmath 1.3.0 py38h06a4308_0 msfa 3.1.0 dev_0 ncurses 6.3 h7f8727e_2 nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge nettle 3.7.3 hbbd107a_1 networkx 3.1 py38h06a4308_0 numpy 1.22.3 py38he7a7128_0 numpy-base 1.22.3 py38hf524024_0 opencv-python 4.9.0.80 pypi_0 pypi opendatalab 0.0.10 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openmim 0.3.9 pypi_0 pypi openssl 1.1.1w hd590300_0 conda-forge openxlab 0.0.38 pypi_0 pypi ordered-set 4.1.0 pypi_0 pypi oss2 2.17.0 pypi_0 pypi packaging 24.0 pyhd8ed1ab_0 conda-forge pandas 2.0.3 pypi_0 pypi parso 0.8.4 pyhd8ed1ab_0 conda-forge pexpect 4.9.0 pyhd8ed1ab_0 conda-forge pickleshare 0.7.5 py_1003 conda-forge pillow 9.0.1 py38h22f2fdc_0 pip 23.3.1 py38h06a4308_0 platformdirs 4.2.0 pyhd8ed1ab_0 conda-forge prompt-toolkit 3.0.42 pyha770c72_0 conda-forge psutil 5.9.8 py38h01eb140_0 conda-forge ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge pycocotools 2.0.7 pypi_0 pypi pycparser 2.22 pypi_0 pypi pycryptodome 3.20.0 pypi_0 pypi pygments 2.17.2 pyhd8ed1ab_0 conda-forge pyparsing 3.1.2 pypi_0 pypi pysocks 1.7.1 py38h06a4308_0 python 3.8.13 h12debd9_0 python-dateutil 2.9.0.post0 pypi_0 pypi python_abi 3.8 2_cp38 conda-forge pytorch 2.0.1 py3.8_cuda11.8_cudnn8.7.0_0 pytorch pytorch-cuda 11.8 h7e8668a_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.4 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi pyzmq 26.0.0 py38h34c975a_0 conda-forge readline 8.1.2 h7f8727e_1 requests 2.28.2 pypi_0 pypi rich 13.4.2 pypi_0 pypi scipy 1.10.1 pypi_0 pypi setuptools 60.2.0 pypi_0 pypi shapely 2.0.4 pypi_0 pypi six 1.16.0 pyhd3eb1b0_1 sqlite 3.38.5 hc218d9a_0 stack_data 0.6.2 pyhd8ed1ab_0 conda-forge sympy 1.12 py38h06a4308_0 tabulate 0.9.0 pypi_0 pypi termcolor 2.4.0 pypi_0 pypi terminaltables 3.1.10 pypi_0 pypi timm 0.4.12 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tomli 2.0.1 pypi_0 pypi torchaudio 2.0.2 py38_cu118 pytorch torchhaarfeatures 0.0.2 pypi_0 pypi torchtriton 2.0.0 py38 pytorch torchvision 0.15.2 py38_cu118 pytorch tornado 6.4 py38h01eb140_0 conda-forge tqdm 4.65.2 pypi_0 pypi traitlets 5.14.2 pyhd8ed1ab_0 conda-forge typing_extensions 4.9.0 py38h06a4308_1 tzdata 2024.1 pypi_0 pypi urllib3 1.26.18 pypi_0 pypi wcwidth 0.2.13 pyhd8ed1ab_0 conda-forge wheel 0.41.2 py38h06a4308_0 widgetsnbextension 4.0.10 pypi_0 pypi xz 5.2.5 h7f8727e_1 yapf 0.40.1 pypi_0 pypi zeromq 4.3.5 h59595ed_1 conda-forge zipp 3.18.1 pypi_0 pypi zlib 1.2.12 h7f8727e_2 zstd 1.5.2 ha4553b6_0 (MSFA) @.***:

Could you send me one config file and one weight file (doesn't matter at this stage the model) which you are sure that are updated and working and exact python code to see detection please ?

On Fri, Apr 19, 2024 at 2:11 PM Yuxuan Li @.***> wrote:

Your environment is not properly installed.

— Reply to this email directly, view it on GitHub https://github.com/zcablii/SARDet_100K/issues/6#issuecomment-2066351911, or unsubscribe https://github.com/notifications/unsubscribe-auth/AO3HQPVFV7ATGFVYVT4GWALY6D3WNAVCNFSM6AAAAABFRHLADOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANRWGM2TCOJRGE . You are receiving this because you commented.Message ID: @.***>

zcablii commented 4 months ago

Please use the provided code and weights for model training and testing, as we verified they are working well. I am not sure why you try to run MSFA code under mmdet repo. But I think you should at lease include import msfa for using msfa models.

zcablii commented 4 months ago

Your environment looks fine. I can give you some debugging advice: prepare the dataset and change the dataset paths accordingly in the configuration, then run (for example) python test.py ./local_configs/SARDet/other_backbones/fg_frcnn_dota_pretrain_sar_r101_wavelet.py SOME_PATH/fg_frcnn_dota_pretrain_sar_r101_wavelet/best_coco_ bbox_mAP_epoch_12.pth to check if you get a valid result. If you encounter some general errors, you can look them up on Google. If the error is related to the mmdet framework, you can go to the "Issues" on the official mmdetection github and search for a solution. It's also possible to drill down to where the error is occurring and trace it back to the code to figure out the underlying problem (most likely some file path issue).

paster489 commented 4 months ago

it is working. thanks a lot

On Fri, Apr 19, 2024 at 3:47 PM Yuxuan Li @.***> wrote:

Please use the provided code and weights for model training and testing, as we verified they are working well. I am not sure why you try to run MSFA code under mmdet repo. But I think you should at lease include import msfa for using msfa models.

— Reply to this email directly, view it on GitHub https://github.com/zcablii/SARDet_100K/issues/6#issuecomment-2066503100, or unsubscribe https://github.com/notifications/unsubscribe-auth/AO3HQPWWYW7HCFIIL4OBFODY6EG6RAVCNFSM6AAAAABFRHLADOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANRWGUYDGMJQGA . You are receiving this because you commented.Message ID: @.***>

zcablii / SARDet_100K

can't download configs and weights #6

Loads checkpoint by local backend from path: ./SAR_Yuxuan_weights/r101_sar_epoch_100.pth https://file+.vscode-resource.vscode-cdn.net/home/paster/Documents/SAR_inference/SARDet_100K/MSFA/SAR_Yuxuan_weights/r101_sar_epoch_100.pth

Initialize the DetInferencer

for name, val in collect_env().items():

print(f'{name}: {val}')

Initialize the DetInferencer

for name, val in collect_env().items():

print(f'{name}: {val}')

change directory into the project main code

create env

install pytorch

or

install dependencies of openmmlab

install other dependencies

install MSFA

packages in environment at /home/paster/anaconda3/envs/MSFA:

Name Version Build Channel