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Functions965 in github.com/TARTRL/Deepfake_Detection

⨍ Functions 965◇ Types & classes 181

↓ 1 callersFunctiondownsample_conv

( in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None)

dfd/timm/models/resnet.py:249

↓ 1 callersFunctiondrop_block_2d

DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few tr

dfd/timm/models/layers/drop.py:24

↓ 1 callersFunctionfind_images_and_targets

(folder, types=IMG_EXTENSIONS, class_to_idx=None, leaf_name_only=True, sort=True)

dfd/timm/data/dataset.py:23

↓ 1 callersMethodforward

dfd/timm/models/densenet.py:138

↓ 1 callersMethodforward_features

dfd/timm/models/xception.py:186

↓ 1 callersMethodforward_features

dfd/timm/models/pnasnet.py:358

↓ 1 callersMethodforward_features

dfd/timm/models/gluon_xception.py:262

↓ 1 callersMethodforward_features

dfd/timm/models/gluon_xception.py:399

↓ 1 callersMethodforward_features

dfd/timm/models/senet.py:378

↓ 1 callersMethodforward_features

dfd/timm/models/hrnet.py:692

↓ 1 callersMethodforward_features

dfd/timm/models/dpn.py:236

↓ 1 callersMethodforward_features

dfd/timm/models/densenet.py:133

↓ 1 callersMethodforward_features

dfd/timm/models/mobilenetv3.py:126

↓ 1 callersMethodforward_features

dfd/timm/models/inception_v4.py:286

↓ 1 callersMethodforward_features

dfd/timm/models/selecsls.py:143

↓ 1 callersMethodforward_features

dfd/timm/models/nasnet.py:569

↓ 1 callersMethodforward_features

dfd/timm/models/inception_resnet_v2.py:302

↓ 1 callersMethodforward_features

dfd/timm/models/efficientnet.py:320

↓ 1 callersMethodforward_features

dfd/timm/models/efficientnet.py:431

↓ 1 callersMethodforward_features

dfd/timm/models/resnet.py:450

↓ 1 callersFunctionget_all_files

(input_dir, suffix=None)

dfd/timm/data/dataset.py:194

↓ 1 callersFunctionget_base_name

dfd/utils.py:42

↓ 1 callersFunctionget_bn_args_tf

dfd/timm/models/efficientnet_blocks.py:18

↓ 1 callersMethodget_classifier

dfd/timm/models/dla.py:300

↓ 1 callersMethodget_epoch_values

(self, epoch: int)

dfd/timm/scheduler/scheduler.py:61

↓ 1 callersFunctionget_mean_by_model

dfd/timm/data/config.py:84

↓ 1 callersMethodget_num_inchannels

dfd/timm/models/hrnet.py:493

↓ 1 callersFunctionget_padding

(kernel_size, stride, dilation=1)

dfd/timm/models/resnet.py:110

↓ 1 callersFunctionget_std_by_model

dfd/timm/data/config.py:94

↓ 1 callersMethodget_update_values

(self, num_updates: int)

dfd/timm/scheduler/scheduler.py:64

↓ 1 callersFunctionget_version

↓ 1 callersFunctionhard_sigmoid

(x, inplace: bool = False)

dfd/timm/models/layers/activations.py:150

↓ 1 callersFunctionhard_swish

(x, inplace: bool = False)

dfd/timm/models/layers/activations.py:136

↓ 1 callersMethodinit_weights

dfd/timm/models/hrnet.py:674

↓ 1 callersFunctionis_static_pad

(kernel_size: int, stride: int = 1, dilation: int = 1, **_)

dfd/timm/models/layers/padding.py:23

↓ 1 callersFunctionlaunch_main

dfd/runners/train.py:769

↓ 1 callersFunctionload_server_json

dfd/server_json.py:19

↓ 1 callersFunctionload_state_dict

(checkpoint_path, use_ema=False)

dfd/timm/models/helpers.py:8

↓ 1 callersMethodlogits

dfd/timm/models/senet.py:386

↓ 1 callersFunctionmish

(x, _inplace=False)

dfd/timm/models/layers/activations.py:74

↓ 1 callersFunctionmish_jit_bwd

(x, grad_output)

dfd/timm/models/layers/activations.py:57

↓ 1 callersFunctionmish_jit_fwd

dfd/timm/models/layers/activations.py:52

↓ 1 callersFunctionmixup_batch

(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False)

dfd/timm/data/mixup.py:18

↓ 1 callersFunctionmnasnet_100

MNASNet B1, depth multiplier of 1.0.

dfd/timm/models/efficientnet.py:994

↓ 1 callersFunctionpadding_image

dfd/params.py:58

↓ 1 callersFunctionparse_server

dfd/server_json.py:25

↓ 1 callersFunctionplot_csv

(filename, plots_dir)

dfd/timm/utils.py:224

↓ 1 callersFunctionplot_figure

(x_data, y_data, name, plots_dir)

dfd/timm/utils.py:205

↓ 1 callersFunctionpreprocess_for_eval

Preprocesses the given image for evaluation. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. use_bfloat16

dfd/timm/data/tf_preprocessing.py:156

↓ 1 callersFunctionpreprocess_for_train

Preprocesses the given image for evaluation. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. use_bfloat16

dfd/timm/data/tf_preprocessing.py:135

↓ 1 callersFunctionpreprocess_image

Preprocesses the given image. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. is_training: `bool` for whe

dfd/timm/data/tf_preprocessing.py:176

↓ 1 callersFunctionrand_augment_ops

(magnitude=10, hparams=None, transforms=None)

dfd/timm/data/auto_augment.py:609

↓ 1 callersFunctionrand_augment_transform

Create a RandAugment transform :param config_str: String defining configuration of random augmentation. Consists of multiple sections separa

dfd/timm/data/auto_augment.py:631

↓ 1 callersMethodreset

dfd/timm/utils.py:157

↓ 1 callersMethodreset_classifier

(self, num_classes, global_pool='avg')

dfd/timm/models/dla.py:303

↓ 1 callersMethodreset_parameters

dfd/timm/models/layers/cond_conv2d.py:72

↓ 1 callersFunctionresize

dfd/params.py:45

↓ 1 callersFunctionresolve_data_config

(args, default_cfg={}, model=None, verbose=True)

dfd/timm/data/config.py:5

↓ 1 callersFunctionresume_checkpoint

(model, checkpoint_path)

dfd/timm/models/helpers.py:47

↓ 1 callersMethodsave_checkpoint

(self, model, optimizer, args, epoch, model_ema=None, metric=None, use_amp=False)

dfd/timm/utils.py:66

↓ 1 callersMethodsave_recovery

(self, model, optimizer, args, epoch, model_ema=None, use_amp=False, batch_idx=0)

dfd/timm/utils.py:128

↓ 1 callersFunctionsemnasnet_100

MNASNet A1 (w/ SE), depth multiplier of 1.0.

dfd/timm/models/efficientnet.py:1028

↓ 1 callersFunctionsetup_default_logging

(default_level=logging.INFO)

dfd/timm/utils.py:353

↓ 1 callersMethodstep_update

(self, num_updates: int, metric: float = None)

dfd/timm/scheduler/scheduler.py:74

↓ 1 callersFunctionswish

(x, _inplace=False)

dfd/timm/models/layers/activations.py:47

↓ 1 callersFunctionswish_jit_bwd

(x, grad_output)

dfd/timm/models/layers/activations.py:25

↓ 1 callersFunctionswish_jit_fwd

dfd/timm/models/layers/activations.py:20

↓ 1 callersMethodsync_lookahead

dfd/timm/optim/lookahead.py:39

↓ 1 callersFunctiontest_img

(model_path, img_files)

dfd/runners/test.py:29

↓ 1 callersFunctiontrain_epoch

( epoch, model, loader, optimizer, loss_fn, args, lr_scheduler=None, saver=None, output_dir=''

dfd/runners/train.py:594

↓ 1 callersFunctiontransforms_deepfake_train

If separate==True, the transforms are returned as a tuple of 3 separate transforms for use in a mixing dataset that passes * all data th

dfd/timm/data/transforms_factory.py:16

↓ 1 callersFunctiontransforms_deepfake_train_v1

If separate==True, the transforms are returned as a tuple of 3 separate transforms for use in a mixing dataset that passes * all data th

dfd/timm/data/transforms_factory.py:76

↓ 1 callersFunctiontransforms_imagenet_eval

( img_size=224, crop_pct=None, interpolation='bilinear', use_prefetcher=False,

dfd/timm/data/transforms_factory.py:321

↓ 1 callersFunctiontransforms_imagenet_train

If separate==True, the transforms are returned as a tuple of 3 separate transforms for use in a mixing dataset that passes * all data th

dfd/timm/data/transforms_factory.py:239

↓ 1 callersFunctionupdate_summary

(epoch, train_metrics, eval_metrics, filename, plots_dir, write_header=False)

dfd/timm/utils.py:238

↓ 1 callersMethodzero_init_last_bn

dfd/timm/models/resnet.py:147

dfd/timm/data/auto_augment.py:337

dfd/timm/data/auto_augment.py:500

dfd/timm/data/auto_augment.py:622

dfd/timm/data/auto_augment.py:761

(self, pil_img)

dfd/timm/data/transforms.py:12

(self, pil_imgs)

dfd/timm/data/transforms.py:21

(self, np_imgs)

dfd/timm/data/transforms.py:30

(self, pil_img)

dfd/timm/data/transforms.py:40

Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized ima

dfd/timm/data/transforms.py:147

Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized ima

dfd/timm/data/transforms.py:188

Args: img (PIL Image): Image to be flipped. Returns: PIL Image: Randomly flipped image.

dfd/timm/data/transforms.py:227

dfd/timm/data/transforms.py:248

Args: img (PIL Image): Image to be flipped. Returns: PIL Image: Randomly flipped image.

dfd/timm/data/transforms.py:265

Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized ima

dfd/timm/data/transforms.py:288

Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.

dfd/timm/data/transforms.py:312

Args: img (PIL Image): Input image. Returns: PIL Image: Color jittered image.

dfd/timm/data/transforms.py:333

dfd/timm/data/transforms.py:350

(self, image_bytes)

dfd/timm/data/tf_preprocessing.py:219

dfd/timm/data/mixup.py:35

dfd/timm/data/random_erasing.py:81

Build the blocks Args: in_chs: Number of input-channels passed to first block model_block_args: A list of lists, oute

dfd/timm/models/efficientnet_builder.py:276

Build the blocks Args: in_chs: Number of input-channels passed to first block model_block_args: A list of lists, oute

dfd/timm/models/efficientnet_builder.py:448

(self, output, target)

dfd/timm/loss/jsd.py:26

Method__getitem__

dfd/timm/data/dataset.py:101

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