jtchen0528 / pcl-i2g Goto Github PK

Thanks for your work! I'm trying to conduct experiments on the settings of
----------------- Options ---------------
batch_size: 32
beta1: 0.9
checkpoints_dir: ./checkpoints
display_freq: 1000
fake_class_id: 0
fake_im_path: ./dataset\DF
fineSize: 224
gpu_ids: [0] [default: 0]
help: None [default: ==SUPPRESS==]
init_type: xavier
isTrain: True [default: None]
lbda: 10
loadSize: 256
load_model: False
lr: 0.001
lr_policy: constant
max_dataset_size: inf
max_epochs: None
model: patch_inconsistency_discriminator
nThreads: 0
name: patch_inconsistency_discriminator_resnet34_layer4_extra3_size224 [default: ]
overwrite_config: True [default: False]
patience: 10
prefix:
print_freq: 100
real_im_path: ./dataset\original
results_dir: ./results/
save_epoch_freq: 100
save_latest_freq: 1000
seed: 0
suffix:
which_epoch: latest
which_model_netD: resnet34_layer4_extra3
----------------- End -------------------
However, during the training process, i run into this issuse:
File "D:\pythonProject\PCL-I2G\models\patch_inconsistency_discriminator_model.py", line 85, in compute_losses_D
masks = self.mask_down_sampling(masks).reshape(n, h, w)
RuntimeError: shape '[32, 14, 14]' is invalid for input of size 8192

Could you please give me some advice on how to solve this? Thank！

Excuse me，could you please tell me how I can find the original real images？how can I use the FaceForensics++？And I don't know why I need extract frames from videos (with ffmpeg) at the first step of preprocessing. Thank you!

Hello, could you explain what's the function of celebahq_crop when preprocess frames is? I'm not very clear about that.
Thank U!

Hello, thanks for your code implementation.
Please tell me how to prepare the mask when training on the FF++ dataset?

Hello, thanks for your code implementation. I believe the results of in-domain (train and test data from the same dataset) in the original paper are correct, while the out-of-domain (train and test data from different datasets) results are too suprising to me. Previous works, which uses temporal information, can achieve about 0.7-0.8 AUC in CDF (up to ICCV 2021). I really hope that you can answer my question after some experiment.

By the way, few researchers can afford the raw version of FF++ videos : ).

I trained a model using the I2G dataset, but the AUC was very low. The paper states that it is necessary to mix the FF++dataset with the I2G dataset for training.
The paper states that”The first type is the deepfake video frame from the existing deepfake datasets, for which we find the corresponding real video frame and compute the structural dissimilarity (DSSIM) [60] between them. The mask M is then generated by taking a Gaussian blur of DSSIM followed by thresholding.“
However, it seems that this part of the code is not provided in the paper, resulting in the FF+dataset not having a mask. Can you provide guidance.

I have trained a model, named 'PCL-I2G-FF256-32frames-Modified-5e-5'. When I run the test.py with this setting below:

/public/user/anaconda3/envs/PCL-I2G/bin/python3.7 test.py --which_epoch bestval --gpu_ids 0 --partition test --average_mode after_softmax --topn 100 --force_redo --dataset_name FF-DF --real_im_path /data-x/g16/user/outputdir/original/test/ --fake_im_path /data-x/g16/user/outputdir/F2F/test/ --train_config /public/user/PCL-I2G/checkpoints_back/PCL-I2G-FF256-32frames-Modified-5e-5/opt.yml --checkpoints_dir /public/user/PCL-I2G/checkpoints_back/

an error is reported. It looks like I need a mask as an input for testing images.

Traceback (most recent call last):
  File "test.py", line 257, in <module>
    run_eval(opt, output_dir)
  File "test.py", line 62, in run_eval
    model.set_input(inputs)
  File "/public/user/PCL-I2G/models/patch_inconsistency_discriminator_model.py", line 66, in set_input
    self.masks = input['masks'].to(self.device)
KeyError: 'masks'

Would you tell me how to get the mask of testing images? Thanks.

Hi, can you share I2G dataset from FF++, because I meet some errors, but I only want the dataset to train my model, thanks. email is [email protected]

Hi! I re-train a new PCL model by following your codes. The training data is only I2G data. However, the AUC of FF-DF is only 0.5. It seems like that the trained model has no ability of generalization. I have checked the log file, which shows that loss_D is 1.1+ and acc_D is 1.0 in the training phase, but loss_D and acc_D in the validation phase are 17+ and 0.5, respectively. I have no idea about the reason of that. Would you like to give me some suggestions about this problem? Maybe the generated I2G data or some possible errors? And would you like to provide your trained weight of PCL? My email is [email protected]. Thanks you a lot~

100%|███████████████████████████████████████████████████████████████████████████████| 70/70 [00:00<00:00, 69955.03it/s]

after this ，my out file has only empty folder ，no image or mask

Excuse me, when I try to generate I2G data with
python3 generate_I2G.py --real_im_pa dataset --batch_size 512 --out_size 256 --output_dir out
with FF++ dataset, the following error occurs The following error occurs.
The data used is a frame of a real image extracted from the FF++ dataset.
Do you know what the cause is?

-----error-----
ValueError: Sample larger than population or is negative

Could you please give me some advice on how to solve this? Thank！

Dear ,
I want to ask about Pre-processing part
the second step:
Download dlib landmark predictor
How to use this