Undefined functions appear when running train_CCAM_VOC12.py？

[liaochuanlin]

File "/home/lcl/BECO/BECO-main/CCAM-master/WSSS/train_CCAM_VOC12.py", line 240, in visualize_heatmap(args.tag, images.clone().detach(), ccam, 0, iteration) NameError: name 'visualize_heatmap' is not defined

[Sierkinhane]

Hi, the visualize_heatmap() function was defined in WSSS/utils and please ensure to import it.

[liaochuanlin]

Thank you for your reply, but there are other problems. Traceback (most recent call last): File "/home/lcl/BECO/BECO-main/CCAM-master/WSSS/train_CCAM_VOC12.py", line 273, in torch.save({'state_dict': model.module.state_dict() if (the_number_of_gpu > 1) else model.state_dict(), File "/home/lcl/anaconda3/envs/BECO/lib/python3.10/site-packages/torch/serialization.py", line 440, in save with _open_zipfile_writer(f) as opened_zipfile: File "/home/lcl/anaconda3/envs/BECO/lib/python3.10/site-packages/torch/serialization.py", line 315, in _open_zipfile_writer return container(name_or_buffer) File "/home/lcl/anaconda3/envs/BECO/lib/python3.10/site-packages/torch/serialization.py", line 288, in init super().init(torch._C.PyTorchFileWriter(str(name))) RuntimeError: [enforce fail at inline_container.cc:365] . invalid file name: ./experiments/models/.pth

[Sierkinhane]

Hi, actually the program will automatically create the folder experiments/models/. You can also manually create them.

[liaochuanlin]

Traceback (most recent call last): File "/home/lcl/BECO/BECO-main/CCAM-master/WSSS/inference_crf.py", line 98, in cams = crf_inference_label(np.asarray(ori_image), cams, n_labels=keys.shape[0], t=args.crf_iteration) File "/home/lcl/BECO/BECO-main/CCAM-master/WSSS/tools/ai/demo_utils.py", line 102, in crf_inference_label d = dcrf.DenseCRF2D(w, h, n_labels) AttributeError: module 'pydensecrf.densecrf' has no attribute 'DenseCRF2D'

[Sierkinhane]

Installing pydensecrf from source can address the issue.

[liaochuanlin]

I downloaded the whole folder and added it to wsss, but the above problem still occurred. Is it the wrong way to download the whole file?

[Sierkinhane]

Install using python3 setup.py install.

[liaochuanlin]

When running the evaluation： /home/lcl/anaconda3/envs/BECO/bin/python3.10 /home/lcl/BECO/BECO-main/CCAM-master/WSSS/evaluate_using_background_cues.py --domain train --with_bg_cues --data_dir /home/lcl/BECO/BECO-main/CCAM-master/WSSS/data/VOC2012 True --bg_dir /home/lcl/BECO/BECO-main/CCAM-master/WSSS/experiments/predictions/CCAM_VOC12_MOCO@train@scale=0.5,1.0,1.5,2.0@t=0.3@ccam_inference_crf=10 True usage: evaluate_using_background_cues.py [-h] [--experiment_name EXPERIMENT_NAME] [--domain DOMAIN] [--threshold THRESHOLD] [--predict_dir PREDICT_DIR] [--gt_dir GT_DIR] [--logfile LOGFILE] [--comment COMMENT] [--mode MODE] [--max_th MAX_TH] [--bg_dir BG_DIR] [--with_bg_cues WITH_BG_CUES] evaluate_using_background_cues.py: error: argument --with_bg_cues: expected one argument

[liaochuanlin]

/home/lcl/anaconda3/envs/BECO/bin/python3.10 /home/lcl/BECO/BECO-main/CCAM-master/WSSS/evaluate_using_background_cues.py --domain train --with_bg_cues True --bg_dir /home/lcl/BECO/BECO-main/CCAM-master/WSSS/experiments/predictions/CCAM_VOC12_MOCO@train@scale=0.5,1.0,1.5,2.0@t=0.3@ccam_inference_crf=10 Th=0.05, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.10, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.15, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.20, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.25, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.30, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.35, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.40, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.45, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.50, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.55, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.60, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.65, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.70, mIoU=3.599%, FP=0.0000, FN=1.0000 Th=0.75, mIoU=3.599%, FP=0.0000, FN=1.0000 Best Th=0.05, mIoU=3.599% The end result is very bad.

[liaochuanlin]

type=str)

parser.add_argument('--experiment_name', default='CCAM_VOC12_MOCO@train@scale=0.5,1.0,1.5,2.0@t=0.3@ccam_inference_crf=10', type=str) parser.add_argument("--domain", default='train', type=str) parser.add_argument("--threshold", default=None, type=float)

parser.add_argument("--predict_dir", default='', type=str) parser.add_argument('--gt_dir', default='./data/VOC2012/SegmentationClass', type=str)

parser.add_argument('--logfile', default='', type=str) parser.add_argument('--comment', default='', type=str)

parser.add_argument('--mode', default='npy', type=str) # png parser.add_argument('--max_th', default=0.50, type=float)

parser.add_argument('--bg_dir', default='', type=str) parser.add_argument('--with_bg_cues', default=False, type=bool) Dear author, can you explain this parameter for me?

[liaochuanlin]

Refine the background cues You can use the extracted background cues as pseudo supervision signal to train a saliency detector like PoolNet to further refine the background cues and we provide the code for background cues refinement in the directory ./PoolNet. We also provide our refined background cues at here.Does this have to run? Will it lead to bad results if it doesn't run?

[Sierkinhane]

Could you provide some predicted results of your method and the extracted background cues?

[liaochuanlin]

/home/lcl/anaconda3/envs/BECO/bin/python3.10 /home/lcl/BECO/BECO-main/CCAM-master/WSSS/train_CCAM_VOC12.py --tag CCAM_VOC12_MOCO --pretrained mocov2 --alpha 0.25 [i] CCAM_VOC12_MOCO

[i] mean values is [0.485, 0.456, 0.406] [i] std values is [0.229, 0.224, 0.225] [i] The number of class is 20 [i] train_transform is Compose( Resize(size=(512, 512), interpolation=bilinear, max_size=None, antialias=warn) RandomHorizontalFlip(p=0.5) RandomCrop(size=(448, 448), padding=None) ToTensor() Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) ) [i] test_transform is Compose( <tools.ai.augment_utils.Normalize_For_Segmentation object at 0x7fe2fb7bae90> <tools.ai.augment_utils.Top_Left_Crop_For_Segmentation object at 0x7fe2fb7bb100> <tools.ai.augment_utils.Transpose_For_Segmentation object at 0x7fe2fb7bb340> ) [i] #train data [i] #valid data

[i] log_iteration : 66 [i] val_iteration : 331 [i] max_iteration : 3,310 Loading unsupervised mocov2 pretrained parameters!

[i] Architecture is resnet50 [i] Total Params: 23.54M

[i] Epoch[0/10], iteration=100, learning_rate=0.0010, loss=1.1635, positive_loss=0.0400, negative_loss=0.9580, time=40sec [i] Epoch[0/10], iteration=200, learning_rate=0.0009, loss=1.0110, positive_loss=0.0428, negative_loss=0.9016, time=38sec [i] Epoch[0/10], iteration=300, learning_rate=0.0009, loss=0.9442, positive_loss=0.0486, negative_loss=0.7862, time=38sec Is Negative: True [i] save model [i] Epoch[1/10], iteration=100, learning_rate=0.0009, loss=0.8853, positive_loss=0.0506, negative_loss=0.7656, time=51sec [i] Epoch[1/10], iteration=200, learning_rate=0.0009, loss=0.8497, positive_loss=0.0600, negative_loss=0.6674, time=39sec [i] Epoch[1/10], iteration=300, learning_rate=0.0008, loss=0.8274, positive_loss=0.0543, negative_loss=0.6699, time=39sec [i] save model [i] Epoch[2/10], iteration=100, learning_rate=0.0008, loss=0.7983, positive_loss=0.0608, negative_loss=0.6137, time=51sec [i] Epoch[2/10], iteration=200, learning_rate=0.0008, loss=0.7801, positive_loss=0.0557, negative_loss=0.6619, time=39sec [i] Epoch[2/10], iteration=300, learning_rate=0.0007, loss=0.7643, positive_loss=0.0592, negative_loss=0.6105, time=39sec [i] save model [i] Epoch[3/10], iteration=100, learning_rate=0.0007, loss=0.7495, positive_loss=0.0554, negative_loss=0.6402, time=51sec [i] Epoch[3/10], iteration=200, learning_rate=0.0007, loss=0.7369, positive_loss=0.0654, negative_loss=0.5693, time=39sec [i] Epoch[3/10], iteration=300, learning_rate=0.0006, loss=0.7246, positive_loss=0.0611, negative_loss=0.5761, time=39sec [i] save model [i] Epoch[4/10], iteration=100, learning_rate=0.0006, loss=0.7191, positive_loss=0.0576, negative_loss=0.6141, time=50sec [i] Epoch[4/10], iteration=200, learning_rate=0.0006, loss=0.7047, positive_loss=0.0568, negative_loss=0.6006, time=38sec [i] Epoch[4/10], iteration=300, learning_rate=0.0005, loss=0.7014, positive_loss=0.0627, negative_loss=0.5613, time=38sec [i] save model [i] Epoch[5/10], iteration=100, learning_rate=0.0005, loss=0.6958, positive_loss=0.0568, negative_loss=0.5652, time=51sec [i] Epoch[5/10], iteration=200, learning_rate=0.0005, loss=0.6861, positive_loss=0.0660, negative_loss=0.5346, time=39sec [i] Epoch[5/10], iteration=300, learning_rate=0.0004, loss=0.6819, positive_loss=0.0610, negative_loss=0.5482, time=39sec [i] save model [i] Epoch[6/10], iteration=100, learning_rate=0.0004, loss=0.6764, positive_loss=0.0598, negative_loss=0.5811, time=51sec [i] Epoch[6/10], iteration=200, learning_rate=0.0004, loss=0.6716, positive_loss=0.0674, negative_loss=0.5235, time=38sec [i] Epoch[6/10], iteration=300, learning_rate=0.0003, loss=0.6669, positive_loss=0.0519, negative_loss=0.5649, time=38sec [i] save model [i] Epoch[7/10], iteration=100, learning_rate=0.0003, loss=0.6641, positive_loss=0.0515, negative_loss=0.5690, time=50sec [i] Epoch[7/10], iteration=200, learning_rate=0.0003, loss=0.6602, positive_loss=0.0650, negative_loss=0.4885, time=38sec [i] Epoch[7/10], iteration=300, learning_rate=0.0002, loss=0.6572, positive_loss=0.0578, negative_loss=0.5534, time=38sec [i] save model [i] Epoch[8/10], iteration=100, learning_rate=0.0002, loss=0.6574, positive_loss=0.0664, negative_loss=0.5179, time=50sec [i] Epoch[8/10], iteration=200, learning_rate=0.0002, loss=0.6533, positive_loss=0.0598, negative_loss=0.5154, time=39sec [i] Epoch[8/10], iteration=300, learning_rate=0.0001, loss=0.6497, positive_loss=0.0643, negative_loss=0.4860, time=39sec [i] save model [i] Epoch[9/10], iteration=100, learning_rate=0.0001, loss=0.6512, positive_loss=0.0623, negative_loss=0.5379, time=50sec [i] Epoch[9/10], iteration=200, learning_rate=0.0001, loss=0.6496, positive_loss=0.0590, negative_loss=0.5055, time=38sec [i] Epoch[9/10], iteration=300, learning_rate=0.0000, loss=0.6486, positive_loss=0.0569, negative_loss=0.5523, time=38sec [i] save model CCAM_VOC12_MOCO

Process finished with exit code 0

[Sierkinhane]

I mean you can go to the directory./experiments/images/ and show whether the predicted cues are correct.

[liaochuanlin]

Yes, the colormap.jpg picture is generated in / home/lcl/BECO/BECO-main/CCAM-master/WSSS/experiments/images/CCAM_VOC12_MOCO/train/colormaps

[Sierkinhane]

That's good. How about the generated background cues? And your cams to be refined. Could you please provide some examples?

[liaochuanlin]

Yes, the png picture is generated in / home/lcl/BECO/BECO-main/CCAM-master/WSSS/experiments/predictions/CCAM_VOC12_MOCO@train@scale=0.5,1.0,1.5,2.0@t=0.3@ccam_inference_crf=10

[liaochuanlin]

May I add your contact information? I'm also a student.

[Sierkinhane]

python3 evaluate_using_background_cues.py --experiment_name you_experiment_name --domain train --data_dir path/to/your/data --with_bg_cues True --bg_dir path/to/your/background cues in which you_experiment_name means that the directory storing your extracted cams and bg_dir indicates the background cues.

[Sierkinhane]

If your extracted cams and background cues are correct, the evaluation results may be good.

[liaochuanlin]

Okay, I'll try again.  

爩 @.***

 

------------------ 原始邮件 ------------------ 发件人: "Jinheng @.>; 发送时间: 2023年9月5日(星期二) 下午3:39 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [CVI-SZU/CCAM] Undefined functions appear when running train_CCAM_VOC12.py？ (Issue #19)

If your extracted cams and background cues are correct, the evaluation results may be good.

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