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View Code? Open in Web Editor NEW[PR] How to Reduce Change Detection to Semantic Segmentation
[PR] How to Reduce Change Detection to Semantic Segmentation
Hello, I appreciate your efforts.
I want to train the model on the ChangeSim.
However, I cannot find the below files.
Could you provide the files?
├── dark_test_list.txt
├── dust_test_list.txt
├── idx2color.txt
├── test_list.txt
├── test_split.txt
├── train_list.txt
└── train_split.txt
When trying to run the train script I get the ModuleNotFoundError: No module named 'correlation_cuda'
error. I have tried this on my local machine and on a server from lambdalabs but both don't seem to work and keep getting stuck on the correlation_cuda issue
in the main train.py script you can pass the device param. but there is a lot of stuff cuda Is used for and can't use the CPU to do anything? is this meant to do stuff like cuda:4
or so?
Hello, thank you for bringing new ideas. May I ask if it is possible to distinguish the appearance, disappearance, and exchange of change targets from the output results of the model?
Hi and thanks for the amazing work!
I was wondering if you could give me any advice on how to perform the inference on my own set of images.
Thank you,
Laura
How can I test model on new images data.
inference code on custom dataset........
When loading the PCD dataset, select the Set by data-cv option to load the image from the pre-separated train/test folder.
Please explain the code or method for separating images into folders for the same performance comparison.
Hello, I use the pre-trained weight "resnet18_id_4_deeplabv3_VL_CMU_CD.pth" to run the shell command "resnet18_mtf_id_msf4_deeplabv3_cmu", but the result is very bad, the following is my running log and results, can you help me find the reason?
PS C:\Users\TEST\Desktop\C-3PO-main> c:; cd 'c:\Users\TEST\Desktop\C-3PO-main'; & 'C:\Users\TEST\anaconda3\envs\pc\python.exe' 'c:\Users\TEST.vscode\extensions\ms-python.python-2023.4.1\pythonFiles\lib\python\debugpy\adapter/../..\debugpy\launcher' '52990' '--' 'C:\Users\TEST\Desktop\C-3PO-main/src/train.py' '--test-only' '--save-imgs' '--model' 'vgg16bn_mtf_msf_deeplabv3' '--mtf' 'id' '--msf' '4' '--train-dataset' 'VL_CMU_CD' '--test-dataset' 'VL_CMU_CD' '--input-size' '512' '--resume' 'C:\Users\TEST\Downloads\vgg16bn_id_4_deeplabv3_VL_CMU_CD.pth'
Not using distributed mode
Namespace(train_dataset='VL_CMU_CD', test_dataset='VL_CMU_CD', test_dataset2='', input_size=512, randomflip=0.5, randomrotate=False, randomcrop=False, data_cv=0, model='vgg16bn_mtf_msf_deeplabv3', mtf='id', msf=4, device='cuda', batch_size=4, epochs=100, workers=16, loss='bi', loss_weight=False, opt='adam', lr_scheduler='cosine', lr=0.0001, warmup=False, momentum=0.9, weight_decay=0, print_freq=50, resume='C:\Users\TEST\Downloads\vgg16bn_id_4_deeplabv3_VL_CMU_CD.pth', pretrained='', start_epoch=0, eval_every=1, test_only=True, save_imgs=True, save_local=False, world_size=1, dist_url='env://', output_dir='C:/Users/TEST/Desktop/output\vgg16bn_mtf_msf_deeplabv3_VL_CMU_CD_0/2023-03-13_19-29-23', distributed=False)
Train Aug:
DATA AUG: resize (512, 512)
DATA AUG: random flip 0.5
VL_CMU_CD train: 1008
Test Aug:
DATA AUG: resize (512, 512)
VL_CMU_CD test: 354
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torch\utils\data\dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8 (cpuset
is not taken into account), which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(
MSF: [128, 256, 512, 512]
MTF: mode: id kernel_size: 3
MTF: mode: id kernel_size: 3
MTF: mode: id kernel_size: 3
MTF: mode: id kernel_size: 3
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\models_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed
in the future, please use 'weights' instead.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\models_utils.py:223: UserWarning: Arguments other than a weight enum orNone
for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passingweights=VGG16_BN_Weights.IMAGENET1K_V1
. You can also useweights=VGG16_BN_Weights.DEFAULT
to get the most up-to-date weights.
warnings.warn(msg)
Loss: bi
LR Scheduler: cosine
load from: C:\Users\TEST\Downloads\vgg16bn_id_4_deeplabv3_VL_CMU_CD.pth
load ret:
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torch\utils\data\dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 8 (cpuset
is not taken into account), which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
C:\Users\TEST\anaconda3\envs\pc\lib\site-packages\torchvision\transforms\functional.py:442: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(
Test: [ 0/354] eta: 5:26:46 Prec: 0.999 (0.999) Rec: 0.034 (0.034) Acc: 0.092 (0.092) F1score: 0.0656 (0.0656) time: 55.3849 data: 53.1827 max mem: 717
Test: [100/354] eta: 0:03:54 Prec: 1.000 (0.994) Rec: 0.188 (0.113) Acc: 0.269 (0.250) F1score: 0.3163 (0.1955) time: 0.3864 data: 0.0014 max mem: 721
Test: [200/354] eta: 0:01:39 Prec: 0.995 (0.996) Rec: 0.115 (0.111) Acc: 0.157 (0.236) F1score: 0.2069 (0.1886) time: 0.3468 data: 0.0016 max mem: 721
Test: [300/354] eta: 0:00:30 Prec: 0.994 (0.981) Rec: 0.127 (0.104) Acc: 0.169 (0.214) F1score: 0.2245 (0.1784) time: 0.4156 data: 0.0014 max mem: 721
Test: Total time: 0:03:13
Test: Total: 354 Metric Prec: 0.9834 Recall: 0.0997 F1: 0.1728
tensor(0.1728)
Hi, congratulations on the excellent work! Where can be accessed the weights of the models presented in the results table?
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