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Functions401 in github.com/beibuwandeluori/DFGC_Detection

Methodforward
(self, x)
data_preparation/train_baseline_model/resnet18/resnet18.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet18/resnet18.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19_bn.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19_bn.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet34.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet34.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet50.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet50.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16.py:81
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16_bn.py:58
Methodforward
(self, x)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16_bn.py:81
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example2.py:30
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example2.py:44
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example2.py:54
Methodforward
(self, x)
data_preparation/generate_adversarial/attack_ensemble_example2.py:117
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example3.py:28
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example3.py:42
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example3.py:52
Methodforward
(self, x)
data_preparation/generate_adversarial/attack_ensemble_example3.py:116
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example5.py:28
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example5.py:42
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example5.py:52
Methodforward
(self, x)
data_preparation/generate_adversarial/attack_ensemble_example5.py:115
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example1.py:29
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example1.py:43
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example1.py:53
Methodforward
(self, x)
data_preparation/generate_adversarial/attack_ensemble_example1.py:117
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example4.py:28
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example4.py:42
Methodforward
(self, input)
data_preparation/generate_adversarial/attack_ensemble_example4.py:52
Methodforward
(self, x)
data_preparation/generate_adversarial/attack_ensemble_example4.py:115
Methodforward
(self, x)
data_preparation/generate_adversarial/models/xception.py:39
Methodforward
(self, inp)
data_preparation/generate_adversarial/models/xception.py:84
Methodforward
(self, input)
data_preparation/generate_adversarial/models/xception.py:182
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg19_bn.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg19_bn.py:81
Methodforward
(self, x)
data_preparation/generate_adversarial/models/resnet34.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/resnet34.py:81
Methodforward
(self, x)
data_preparation/generate_adversarial/models/resnet50.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/resnet50.py:81
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg16.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg16.py:81
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg16_bn.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg16_bn.py:81
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg19.py:58
Methodforward
(self, x)
data_preparation/generate_adversarial/models/vgg19.py:81
Methodforward
(self, x, target)
loss/losses.py:12
Methodforward
(self, output1, output2, label)
loss/losses.py:41
Functionget_default_mask
Set the default mask for cli
dataset/DeepFakeMask.py:32
Methodget_image_size
Get the input image size for a given efficientnet model. Args: model_name (str): Name for efficientnet. Returns:
submission_Det/efficientnet.py:396
Functionget_same_padding_maxPool2d
Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of mo
submission_Det/utils.py:275
Functioninit_imagenet_weight
(_conv_stem_weight, input_channel=3)
network/models.py:22
Functionload_landmarks
:param landmarks_file: input landmarks json file name :return: all_landmarks: having the shape of 5x2 list. represent left eye,
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/celeb_df_v2_dataset.py:22
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/resnet18/train_binary_resnet18.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/vgg19_vgg19_bn/train_binary_vgg19.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/vgg19_vgg19_bn/train_binary_vgg19_bn.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/train_binary_resnet34.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/train_binary_resnet50.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/train_binary_vgg16_bn.py:32
Functionload_network
(network, save_filename)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/train_binary_vgg16.py:32
Methodload_real_video_json
(self)
data_preparation/train_baseline_model/vgg19_vgg19_bn/celeb_df_v2_dataset.py:102
Functionsave_landmarks
(input_dir, save_dir)
data_preparation/extract_face/generate_landmarks_dlib.py:24
Methodsearch_similar_face
(self, video_name, this_landmark)
data_preparation/train_baseline_model/vgg19_vgg19_bn/celeb_df_v2_dataset.py:113
Methodset_swish
Sets swish function as memory efficient (for training) or standard (for export). Args: memory_efficient (bool): Whether to use me
submission_Det/efficientnet.py:235
Functionshow_metrics
(labels, outputs, preds=None, describe='Image')
dataset/utils.py:161
Methodtensor_flip
img size: [H, W] :return a flipped list
dataset/utils.py:73
Methodtensor_inverse_flip
img size: [H, W] :return a flipped list
utils/utils.py:68
Methodtensor_inverse_flip
img size: [H, W] :return a flipped list
dataset/utils.py:81
Methodtensor_inverse_rotation
img size: [H, W] rotation degree: [90 180 270] :return a rotated list
utils/utils.py:51
Methodtensor_inverse_rotation
img size: [H, W] rotation degree: [90 180 270] :return a rotated list
dataset/utils.py:64
Methodtensor_rotation
img size: [H, W] rotation degree: [90 180 270] :return a rotated list
dataset/utils.py:55
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/resnet18/resnet18.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19_bn.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet34.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet50.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16.py:22
Functionweights_init_classifier
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16_bn.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/vgg19_bn.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/resnet34.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/resnet50.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/vgg16.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/vgg16_bn.py:22
Functionweights_init_classifier
(m)
data_preparation/generate_adversarial/models/vgg19.py:22
Functionweights_init_kaiming
(m)
network/models.py:8
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/resnet18/resnet18.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19_bn.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/vgg19_vgg19_bn/vgg19.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet34.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/resnet50.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16.py:9
Functionweights_init_kaiming
(m)
data_preparation/train_baseline_model/resnet34_resnet50_vgg16_vgg16_bn/vgg16_bn.py:9
Functionweights_init_kaiming
(m)
data_preparation/generate_adversarial/models/vgg19_bn.py:9
Functionweights_init_kaiming
(m)
data_preparation/generate_adversarial/models/resnet34.py:9
Functionweights_init_kaiming
(m)
data_preparation/generate_adversarial/models/resnet50.py:9
Functionweights_init_kaiming
(m)
data_preparation/generate_adversarial/models/vgg16.py:9
Functionweights_init_kaiming
(m)
data_preparation/generate_adversarial/models/vgg16_bn.py:9
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