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github.com/csxmli2016/DFDNet
/ types & classes
Types & classes
58 in github.com/csxmli2016/DFDNet
⨍
Functions
280
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Types & classes
58
↓ 8 callers
Class
ConvBlock
FaceLandmarkDetection/face_alignment/models.py:13
↓ 4 callers
Class
StyledUpBlock
models/networks.py:289
↓ 3 callers
Class
L2Norm
FaceLandmarkDetection/face_alignment/detection/sfd/net_s3fd.py:6
↓ 2 callers
Class
Blur
models/networks.py:230
↓ 2 callers
Class
UpResBlock
models/networks.py:617
↓ 1 callers
Class
CallbackContext
sync_batchnorm/replicate.py:23
↓ 1 callers
Class
CustomDatasetDataLoader
data/__init__.py:54
↓ 1 callers
Class
DataParallelWithCallback
Data Parallel with a replication callback. An replication callback `__data_parallel_replicate__` of each module will be invoked after being
sync_batchnorm/replicate.py:50
↓ 1 callers
Class
EqualLR
models/networks.py:245
↓ 1 callers
Class
FAN
FaceLandmarkDetection/face_alignment/models.py:145
↓ 1 callers
Class
FutureResult
A thread-safe future implementation. Used only as one-to-one pipe.
sync_batchnorm/comm.py:18
↓ 1 callers
Class
HTML
util/html.py:6
↓ 1 callers
Class
HourGlass
FaceLandmarkDetection/face_alignment/models.py:98
↓ 1 callers
Class
MSDilateBlock
models/networks.py:160
↓ 1 callers
Class
ResNetDepth
FaceLandmarkDetection/face_alignment/models.py:204
↓ 1 callers
Class
SlavePipe
Pipe for master-slave communication.
sync_batchnorm/comm.py:46
↓ 1 callers
Class
SyncMaster
An abstract `SyncMaster` object. - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
sync_batchnorm/comm.py:56
↓ 1 callers
Class
TestOptions
options/test_options.py:4
↓ 1 callers
Class
UNetDictFace
models/networks.py:423
↓ 1 callers
Class
VGGFeat
Input: (B, C, H, W), RGB, [-1, 1]
models/networks.py:357
↓ 1 callers
Class
s3fd
FaceLandmarkDetection/face_alignment/detection/sfd/net_s3fd.py:22
Class
AdaptiveInstanceNorm
models/networks.py:178
Class
AlignedDataset
data/aligned_dataset.py:18
Class
BaseDataLoader
data/base_data_loader.py:1
Class
BaseDataset
data/base_dataset.py:6
Class
BaseModel
models/base_model.py:7
Class
BaseOptions
options/base_options.py:9
Class
BatchNorm2dReimpl
A re-implementation of batch normalization, used for testing the numerical stability. Author: acgtyrant See also: https://github
sync_batchnorm/batchnorm_reimpl.py:18
Class
BlurFunction
models/networks.py:210
Class
BlurFunctionBackward
models/networks.py:190
Class
Bottleneck
FaceLandmarkDetection/face_alignment/models.py:58
Class
DlibDetector
FaceLandmarkDetection/face_alignment/detection/dlib/dlib_detector.py:14
Class
EqualConv2d
models/networks.py:272
Class
FaceAlignment
FaceLandmarkDetection/face_alignment/api.py:53
Class
FaceDetector
An abstract class representing a face detector. Any other face detection implementation must subclass it. All subclasses must implement ``
FaceLandmarkDetection/face_alignment/detection/core.py:10
Class
FolderDetector
This is a simple helper module that assumes the faces were detected already (either previously or are provided as ground truth). T
FaceLandmarkDetection/face_alignment/detection/folder/folder_detector.py:8
Class
GetData
Download CycleGAN or Pix2Pix Data. Args: technique : str One of: 'cyclegan' or 'pix2pix'. verbose : bool
util/get_data.py:11
Class
ImageFolder
data/image_folder.py:42
Class
ImagePool
util/image_pool.py:5
Class
LandmarksType
Enum class defining the type of landmarks to detect. ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible
FaceLandmarkDetection/face_alignment/api.py:19
Class
NetworkSize
FaceLandmarkDetection/face_alignment/api.py:32
Class
NoiseInjection
models/networks.py:282
Class
SFDDetector
FaceLandmarkDetection/face_alignment/detection/sfd/sfd_detector.py:16
Class
SingleDataset
data/single_dataset.py:7
Class
SynchronizedBatchNorm1d
r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a mini-batch. .. math:: y = \frac{x - mean[x]}{ \
sync_batchnorm/batchnorm.py:148
Class
SynchronizedBatchNorm2d
r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch of 3d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va
sync_batchnorm/batchnorm.py:211
Class
SynchronizedBatchNorm3d
r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch of 4d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va
sync_batchnorm/batchnorm.py:274
Class
TVLoss
util/Loss.py:6
Class
TestModel
models/test_model.py:10
Class
Tester
FaceLandmarkDetection/test/facealignment_test.py:5
Class
Tester
FaceLandmarkDetection/test/test_utils.py:7
Class
TorchTestCase
sync_batchnorm/unittest.py:15
Class
UnalignedDataset
data/unaligned_dataset.py:8
Class
VggClassNet
models/networks.py:632
Class
Visualizer
util/visualizer.py:115
Class
_SynchronizedBatchNorm
sync_batchnorm/batchnorm.py:51
Class
hinge_loss
util/Loss.py:25
Class
hinge_loss_G
util/Loss.py:35