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| python eval.py --val_set /hdd/tam/extend_data/image_split/3dmm/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/3dmm/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/deepfake/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/deepfake/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/image_swap2d/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/image_swap2d/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/image_swap3d/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/image_swap3d/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/monkey/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/monkey/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/reenact/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/reenact/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| python eval.py --val_set /hdd/tam/extend_data/image_split/stargan/ --image_size 256 --checkpoint xception_mydata_checkpoint/ --resume model_pytorch_2.pt --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 1 xception_torch | ||
| python eval.py --val_set /hdd/tam/extend_data/image_split/stargan/ --image_size 256 --checkpoint capsule_mydata_checkpoint/ --resume 4 --adj_brightness=1.0 --adj_contrast=1.0 --gpu_id 0 --batch_size 16 --worker 16 capsule | ||
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| generated/* |
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| """ | ||
| Created on Thu Oct 26 14:19:44 2017 | ||
| @author: Utku Ozbulak - github.com/utkuozbulak | ||
| """ | ||
| import os | ||
| import numpy as np | ||
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| import torch | ||
| from torch.optim import SGD | ||
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| from cnn_visualization.misc_functions import preprocess_image, recreate_image, save_image | ||
| import argparse | ||
| import torch.nn as nn | ||
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| class ClassSpecificImageGeneration(): | ||
| """ | ||
| Produces an image that maximizes a certain class with gradient ascent | ||
| """ | ||
| def __init__(self, model, target_class,image_size): | ||
| self.mean = [-0.485, -0.456, -0.406] | ||
| self.std = [1/0.229, 1/0.224, 1/0.225] | ||
| self.model = model | ||
| self.model.eval() | ||
| self.target_class = target_class | ||
| self.image_size = image_size | ||
| # Generate a random image | ||
| self.created_image = np.uint8(np.random.uniform(0, 255, (image_size, image_size, 3))) | ||
| # Create the folder to export images if not exists | ||
| if not os.path.exists('generated/class_'+str(self.target_class)): | ||
| os.makedirs('generated/class_'+str(self.target_class)) | ||
| print("init xong ... ") | ||
| self.device = torch.device("cuda" if torch.cuda.is_available() | ||
| else "cpu") | ||
| def generate(self, iterations=150): | ||
| """Generates class specific image | ||
| Keyword Arguments: | ||
| iterations {int} -- Total iterations for gradient ascent (default: {150}) | ||
| Returns: | ||
| np.ndarray -- Final maximally activated class image | ||
| """ | ||
| print("bat dau generate xong ... ") | ||
| initial_learning_rate = 200 | ||
| for i in range(1, iterations): | ||
| print(i) | ||
| # Process image and return variable | ||
| self.processed_image = preprocess_image(self.created_image, False) | ||
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| # Define optimizer for the image | ||
| optimizer = SGD([self.processed_image], lr=initial_learning_rate) | ||
| # Forward | ||
| output = self.model(self.processed_image.to(self.device)) | ||
| # Target specific class | ||
| print(output) | ||
| class_loss = -output[0, self.target_class] | ||
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| if i % 1 == 0 or i == iterations-1: | ||
| print('Iteration:', str(i), 'Loss', | ||
| "{0:.2f}".format(class_loss.cpu().data.numpy())) | ||
| # Zero grads | ||
| self.model.zero_grad() | ||
| # Backward | ||
| class_loss.backward() | ||
| # Update image | ||
| optimizer.step() | ||
| # Recreate image | ||
| self.created_image = recreate_image(self.processed_image) | ||
| print(self.created_image.size) | ||
| if i % 1 == 0 or i == iterations-1: | ||
| # Save image | ||
| initial_learning_rate /=2 | ||
| im_path = 'generated/class_'+str(self.target_class)+'/c_'+str(self.target_class)+'_'+'iter_'+str(i)+'.png' | ||
| save_image(self.created_image, im_path) | ||
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| return self.processed_image | ||
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| def parse_args(): | ||
| parser = argparse.ArgumentParser(description="Deepfake detection") | ||
| parser.add_argument('--model_path', default="../../../model/xception/model_pytorch_4.pt", help='path to model ') | ||
| parser.add_argument('--gpu_id',type=int, default=-1, help='path to model ') | ||
| parser.add_argument('--image_size',type=int, default=256, help='path to model ') | ||
| parser.add_argument('--iterations',type=int, default=256, help='iterations random number') | ||
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| subparsers = parser.add_subparsers(dest="model", help='Choose 1 of the model from: capsule,drn,resnext50, resnext ,gan,meso,xception') | ||
| ## torch | ||
| parser_capsule = subparsers.add_parser('capsule', help='Capsule') | ||
| parser_drn = subparsers.add_parser('drn', help='DRN ') | ||
| parser_local_nn = subparsers.add_parser('local_nn', help='Local NN ') | ||
| parser_self_attention = subparsers.add_parser('self_attention', help='Self Attention ') | ||
| parser_resnext50 = subparsers.add_parser('resnext50', help='Resnext50 ') | ||
| parser_resnext101 = subparsers.add_parser('resnext101', help='Resnext101 ') | ||
| parser_myresnext = subparsers.add_parser('myresnext', help='My Resnext ') | ||
| parser_mnasnet = subparsers.add_parser('mnasnet', help='mnasnet pytorch ') | ||
| parser_xception_torch = subparsers.add_parser('xception_torch', help='Xception pytorch ') | ||
| parser_xception2_torch = subparsers.add_parser('xception2_torch', help='Xception2 pytorch ') | ||
| parser_dsp_fwa = subparsers.add_parser('dsp_fwa', help='DSP_SWA pytorch ') | ||
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| parser_xception = subparsers.add_parser('xception', help='Xceptionnet') | ||
| parser_efficient = subparsers.add_parser('efficient', help='Efficient Net') | ||
| parser_efficient.add_argument("--type",type=str,required=False,default="0",help="Type efficient net 0-8") | ||
| parser_efficientdual = subparsers.add_parser('efficientdual', help='Efficient Net') | ||
| parser_efft = subparsers.add_parser('efft', help='Efficient Net fft') | ||
| parser_efft.add_argument("--type", type=str, required=False, default="0", help="Type efficient net 0-8") | ||
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| parser_e4dfft = subparsers.add_parser('e4dfft', help='Efficient Net 4d fft') | ||
| parser_e4dfft.add_argument("--type", type=str, required=False, default="0", help="Type efficient net 0-8") | ||
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| return parser.parse_args() | ||
| if __name__ == '__main__': | ||
| target_class = 0 # Flamingo | ||
| # pretrained_model = models.alexnet(pretrained=True) | ||
| args = parse_args() | ||
| print(args) | ||
| model = args.model | ||
| os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) | ||
| gpu_id = 0 if int(args.gpu_id) >=0 else -1 | ||
| image_size = args.image_size | ||
| iterations= args.iterations | ||
| if model== "capsule": | ||
| exit(0) | ||
| pass | ||
| elif model == "drn" : | ||
| from pytorch_model.drn.drn_seg import DRNSub | ||
| model = DRNSub(1) | ||
| pass | ||
| elif model == "local_nn" : | ||
| from pytorch_model.local_nn import local_nn | ||
| model = local_nn() | ||
| elif model == "self_attention": | ||
| from pytorch_model.self_attention import self_attention | ||
| model = self_attention() | ||
| elif model == "resnext50": | ||
| from pytorch_model.model_cnn_pytorch import resnext50 | ||
| model = resnext50(False) | ||
| elif model == "resnext101": | ||
| from pytorch_model.model_cnn_pytorch import resnext101 | ||
| model = resnext101(False) | ||
| elif model == "myresnext": | ||
| from pytorch_model.model_cnn_pytorch import MyResNetX | ||
| model = MyResNetX() | ||
| elif model == "mnasnet": | ||
| from pytorch_model.model_cnn_pytorch import mnasnet | ||
| model = mnasnet(False) | ||
| elif model == "xception_torch": | ||
| from pytorch_model.xception import xception | ||
| model = xception(pretrained=False) | ||
| elif model == "xception2_torch": | ||
| from pytorch_model.xception import xception2 | ||
| model = xception2(pretrained=False) | ||
| elif model == "dsp_fwa": | ||
| from pytorch_model.DSP_FWA.models.classifier import SPPNet | ||
| model = SPPNet(backbone=50, num_class=1) | ||
| elif model == "siamese_torch": | ||
| from pytorch_model.siamese import SiameseNetworkResnet | ||
| model = SiameseNetworkResnet(length_embed = args.length_embed,pretrained=True) | ||
| elif model == "efficient": | ||
| from pytorch_model.efficientnet import EfficientNet | ||
| model = EfficientNet.from_pretrained('efficientnet-b'+args.type,num_classes=1) | ||
| model = nn.Sequential(model,nn.Sigmoid()) | ||
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| elif model == "efft": | ||
| from pytorch_model.efficientnet import EfficientNet | ||
| model = EfficientNet.from_pretrained('efficientnet-b' + args.type, num_classes=1,in_channels=1) | ||
| model = nn.Sequential(model, nn.Sigmoid()) | ||
| elif model == "e4dfft": | ||
| from pytorch_model.efficientnet import EfficientNet | ||
| model = EfficientNet.from_pretrained('efficientnet-b' + args.type, num_classes=1,in_channels=4) | ||
| model = nn.Sequential(model, nn.Sigmoid()) | ||
| elif model == "efficientdual": | ||
| pass | ||
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| from pytorch_model.xception import xception | ||
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| model = xception(pretrained=False) | ||
| device = torch.device("cuda" if torch.cuda.is_available() | ||
| else "cpu") | ||
| model = model.to(device) | ||
| model.load_state_dict(torch.load(args.model_path,map_location=torch.device('cpu'))) | ||
| print("Load xong ... ") | ||
| model.eval() | ||
| csig = ClassSpecificImageGeneration(model, target_class,image_size) | ||
| csig.generate(iterations = iterations) |
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