luizgh / sigver Goto Github PK

Is transfer learning with using the pretrained signet model a good idea? Adding additional data which is specific to current application and updating the cnn model?

try to run this project , like this:
/projects/projectS/sigver/sigver/featurelearning/train.py
--model signet --logdir ./../../npz --dataset-path /projects/projectS/sigver/npz/mcyt_170_242.npz --users 0 100 --epochs 10

has some problem:

Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1596, in
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 974, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Applications/PyCharm.app/Contents/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/projects/projectS/sigver/sigver/featurelearning/train.py", line 427, in
main(arguments)
File "/projects/projectS/sigver/sigver/featurelearning/train.py", line 368, in main
device, logger, args, logdir)
File "/projects/projectS/sigver/sigver/featurelearning/train.py", line 83, in train
val_metrics = test(val_loader, base_model, classification_layer, device, args.forg, forg_layer)
File "/projects/projectS/sigver/sigver/featurelearning/train.py", line 282, in test
features = base_model(x)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/projects/projectS/sigver/sigver/featurelearning/models/signet.py", line 32, in forward
x = self.fc_layers(x)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 76, in forward
exponential_average_factor, self.eps)
File "/usr/local/lib/python3.6/site-packages/torch/nn/functional.py", line 1619, in batch_norm
raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 2048])

I'm getting the error below with the example.py code and I'd like to know the possible cause.

I am using the following environment:
python == 3.7
pytorch == 1.11
Looking forward to your reply.

Hello,
I am a Phd scholar and I am working on signarure verification. I am trying ti execute your code and want to see how you have computed the EER for all the runs/folds of data. When i run test.py in wd folder i get the following error.
usage: test.py [-h] -m {signet,signet_thin,signet_smaller} --model-path
MODEL_PATH --data-path DATA_PATH [--save-path SAVE_PATH]
[--input-size INPUT_SIZE INPUT_SIZE]
[--exp-users EXP_USERS EXP_USERS]
[--dev-users DEV_USERS DEV_USERS] [--gen-for-test GEN_FOR_TEST]
[--forg-from_exp FORG_FROM_EXP] [--forg-from_dev FORG_FROM_DEV]
[--svm-type {rbf,linear}] [--svm-c SVM_C]
[--svm-gamma SVM_GAMMA] [--gpu-idx GPU_IDX]
[--batch-size BATCH_SIZE] [--folds FOLDS]
test.py: error: the following arguments are required: -m, --model-path, --data-path
An exception has occurred, use %tb to see the full traceback.

SystemExit: 2
Can you please guied me what is the solution of this. Moreover I want to use your eer_user_threshold function in my own implementation. Fot that I need to compute eer for each user and then compute overall EER for each fold/run???

I noticed that you have mentioned " we normalized the input to the neural network by dividing each pixel by the standard deviation of all pixel intensities (from all images in D)" in the paper of SigNet. But I haven't found the relative code either in "preprocessing_signature" function or in "set_up_dataloaders". Do I ignore the code in somewhere else? Or this procedure has been discarded already?

I have preprocessed CEDAR Dataset as per instructions

$ python3 -m sigver.preprocessing.process_dataset --dataset cedar --path /home/atinesh/Downloads/Datasets/CEDAR/signatures --save-path data/cedar/mcyt_170_242.npz

Processing dataset
Number of users: 55
Allocated x of shape: (2640, 170, 242)
 51%|████████████████████████████████▌                               | 28/55 [00:27<00:24,  1.09it/s]/home/atinesh/Desktop/virtual_envs/pytorch_env/lib/python3.6/site-packages/skimage/util/dtype.py:135: UserWarning: Possible precision loss when converting from float64 to uint8
  .format(dtypeobj_in, dtypeobj_out))
100%|████████████████████████████████████████████████████████████████| 55/55 [00:54<00:00,  1.01it/s]

But when I try to run train.py by the following command

$ python3 -m sigver.featurelearning.train --model signet --dataset-path data/cedar/mcyt_170_242.npz --users 1 55 --model signet --epochs 10 --logdir signet

I get error in util.py

train.py, line 330, in main
x, y, yforg, usermapping, filenames = util.load_dataset(args.dataset_path)

util.py, line 31, in load_dataset
user_mapping, filenames = data['user_mapping'], data['filenames']

ValueError: Object arrays cannot be loaded when allow_pickle=False

Dear Luis,

Thank you for making this code publicly available.
I do encounter an error while trying to train on the CEDAR dataset though, perhaps you could help me?

The error looks like this:

The script I use to start training looks like this:

python3.7 -m  sigver.featurelearning.train \
    --dataset cedar \
    --model signet \
    --dataset-path  /data/signature_matching/data/processed/sigver_datasets/cedar.npz \
    --users 10 20 \
    --epochs 2 \
    --logdir /tmp/signet

The cedar.npz dataset has been preprocessed as has been indicated in the README.

Hope you can help me with this.

How exactly is the global and user threshold calculated? given the SVM decision function, do we use user-specific threshold for calculating probabilities?

when I run the train.py in meta-learning, there is an error message(ModuleNotFoundError: No module named 'sigver.datasets.toremove'). It seems that a source file named "toremove.py" was missing in this repositories?

In the file "sigver/datasets/util.py" the function "process_dataset_images" loops through all the users and set the y-value of each signature to the index of the user instead of the value.

In other words, it does this:

for i, user in enumerate(tqdm(users)):
    ...
    y[indexes] = i

but I believe it should be like this:

for i, user in enumerate(tqdm(users)):
    ...
    y[indexes] = user

Is this an error or did I understood something incorrectly?

CUDA_VISIBLE_DEVICES=0 python -m sigver.featurelearning.train --model signet --dataset-path /home/dell/Documents/Prasad_AI/sign_similarity/sigver/dataset_npz/dataset_npz.npz --users 20 55 --model signet --epochs 60 --forg --lamb 0.95 --logdir signet_f_lamb0.95
Namespace(batch_size=32, checkpoint=None, dataset_path='/home/dell/Documents/Prasad_AI/sign_similarity/sigver/dataset_npz/dataset_npz.npz', epochs=60, forg=True, gpu_idx=0, input_size=(150, 220), lamb=0.95, logdir='signet_f_lamb0.95', loss_type='L2', lr=0.001, lr_decay=0.1, lr_decay_times=3, model='signet', momentum=0.9, seed=42, test=False, users=[20, 55], visdomport=None, weight_decay=0.0001)
Using device: cuda:0
Loading Data
Initializing Model
Training
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:180: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
x = torch.tensor(x, dtype=torch.float).to(device)
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:181: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
y = torch.tensor(y, dtype=torch.long).to(device)
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:182: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
yforg = torch.tensor(batch[2], dtype=torch.float).to(device)
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:278: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
x = torch.tensor(x, dtype=torch.float).to(device)
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:279: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
y = torch.tensor(y, dtype=torch.long).to(device)
/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py:280: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
yforg = torch.tensor(yforg, dtype=torch.float).to(device)
Epoch 0. Val loss: 3.4440, Val acc: 10.22%,Val forg loss: 0.5179, Val forg acc: 79.69%
Epoch 1. Val loss: 3.2476, Val acc: 20.76%,Val forg loss: 0.4181, Val forg acc: 84.38%
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py", line 428, in
main(arguments)
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py", line 369, in main
device, logger, args, logdir)
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py", line 81, in train
epoch, optimizer, lr_scheduler, callback, device, args)
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/sigver/featurelearning/train.py", line 201, in train_epoch
class_loss = F.cross_entropy(logits, y[yforg == 0])
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/myenv/lib/python3.6/site-packages/torch/nn/functional.py", line 1970, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/home/dell/Documents/Prasad_AI/sign_similarity/sigver/myenv/lib/python3.6/site-packages/torch/nn/functional.py", line 1790, in nll_loss
ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: invalid argument 2: non-empty vector or matrix expected at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:32

Sir, after training WD classifier and meta learning can you tell us how to predict forgery or genuine for a single image

Hi,
So, as the title says, I got an assertion error running example.py. I printed out the value of

(features.cpu() - expected_features).abs().max()

and got 0.0010.

Versions of required libraries:
matplotlib: 3.0.2
torch: 1.0.1
torchvision: 0.2.1

As a beginner of Python, I want to ask
How can the pickle file be transformed to a readable format to obtain the FAR, AUC, owner prediction and etc?

Hi,
I have trained the writer-dependent classifiers using your pre-trained models. But EER score is not the same as reported in your papers.

If I understood the paper correctly, the exploitation set is used only to train the feature extractor, if that's the case why is it necessary to specify the users for the exploitation set to train the WD model?

I tried to pass an empty set as the exploitation set but that caused an error.

I was tried with Pre-trained models,

SigNet
SigNet-F lambda 0.95

with Cosine similarity between two image features (like: one genuine, one forgery signature).

feature1 = base_model(input1)
feature2 = base_model(input2)
cosine_similarity(features1, features2)

real1.png, real2.png -> similarity: 0.31142098
real1.png, fake1.png -> similarity: 0.2714578
real1.png, fake2.png -> similarity: 0.6426417
real2.png, fake1.png -> similarity: 0.18943718
real2.png, fake2.png -> similarity: 0.6238067

here how i concluded is 60% above similarity means, both as verified, else rejected.
but 2/5 cases successfully verified. 40% accuracy only i got it from pretrained model.

I have few doubts @luizgh, @gonultasbu and @atinesh-s sir. please help to resolve,

• when i am testing with real time noisy images (like signature written in paper images) not getting good results. why? how to resolve these issue?

• How i will improve the accuary of the model SigNet?

Thanks & Regards
Murugan Rajenthiran

I'm working on an offline signature verification project right now. I really need those benchmark databases to compare my work on. I searched the whole Internet for CEDAR and Brazilian PUC-PR database but ended up in vain. The paper cited for CEDAR leaves no link and the email the author left was abandoned. The paper of Brazilian PUC-PR was in Portuguese and I simply don't understand a word.
Could you please share the link of these two databases. I'd be very grateful if you help me.