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Types & classes58 in github.com/csxmli2016/DFDNet

↓ 8 callersClassConvBlock
FaceLandmarkDetection/face_alignment/models.py:13
↓ 4 callersClassStyledUpBlock
models/networks.py:289
↓ 3 callersClassL2Norm
FaceLandmarkDetection/face_alignment/detection/sfd/net_s3fd.py:6
↓ 2 callersClassBlur
models/networks.py:230
↓ 2 callersClassUpResBlock
models/networks.py:617
↓ 1 callersClassCallbackContext
sync_batchnorm/replicate.py:23
↓ 1 callersClassCustomDatasetDataLoader
data/__init__.py:54
↓ 1 callersClassDataParallelWithCallback
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 callersClassEqualLR
models/networks.py:245
↓ 1 callersClassFAN
FaceLandmarkDetection/face_alignment/models.py:145
↓ 1 callersClassFutureResult
A thread-safe future implementation. Used only as one-to-one pipe.
sync_batchnorm/comm.py:18
↓ 1 callersClassHTML
util/html.py:6
↓ 1 callersClassHourGlass
FaceLandmarkDetection/face_alignment/models.py:98
↓ 1 callersClassMSDilateBlock
models/networks.py:160
↓ 1 callersClassResNetDepth
FaceLandmarkDetection/face_alignment/models.py:204
↓ 1 callersClassSlavePipe
Pipe for master-slave communication.
sync_batchnorm/comm.py:46
↓ 1 callersClassSyncMaster
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 callersClassTestOptions
options/test_options.py:4
↓ 1 callersClassUNetDictFace
models/networks.py:423
↓ 1 callersClassVGGFeat
Input: (B, C, H, W), RGB, [-1, 1]
models/networks.py:357
↓ 1 callersClasss3fd
FaceLandmarkDetection/face_alignment/detection/sfd/net_s3fd.py:22
ClassAdaptiveInstanceNorm
models/networks.py:178
ClassAlignedDataset
data/aligned_dataset.py:18
ClassBaseDataLoader
data/base_data_loader.py:1
ClassBaseDataset
data/base_dataset.py:6
ClassBaseModel
models/base_model.py:7
ClassBaseOptions
options/base_options.py:9
ClassBatchNorm2dReimpl
A re-implementation of batch normalization, used for testing the numerical stability. Author: acgtyrant See also: https://github
sync_batchnorm/batchnorm_reimpl.py:18
ClassBlurFunction
models/networks.py:210
ClassBlurFunctionBackward
models/networks.py:190
ClassBottleneck
FaceLandmarkDetection/face_alignment/models.py:58
ClassDlibDetector
FaceLandmarkDetection/face_alignment/detection/dlib/dlib_detector.py:14
ClassEqualConv2d
models/networks.py:272
ClassFaceAlignment
FaceLandmarkDetection/face_alignment/api.py:53
ClassFaceDetector
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
ClassFolderDetector
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
ClassGetData
Download CycleGAN or Pix2Pix Data. Args: technique : str One of: 'cyclegan' or 'pix2pix'. verbose : bool
util/get_data.py:11
ClassImageFolder
data/image_folder.py:42
ClassImagePool
util/image_pool.py:5
ClassLandmarksType
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
ClassNetworkSize
FaceLandmarkDetection/face_alignment/api.py:32
ClassNoiseInjection
models/networks.py:282
ClassSFDDetector
FaceLandmarkDetection/face_alignment/detection/sfd/sfd_detector.py:16
ClassSingleDataset
data/single_dataset.py:7
ClassSynchronizedBatchNorm1d
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
ClassSynchronizedBatchNorm2d
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
ClassSynchronizedBatchNorm3d
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
ClassTVLoss
util/Loss.py:6
ClassTestModel
models/test_model.py:10
ClassTester
FaceLandmarkDetection/test/facealignment_test.py:5
ClassTester
FaceLandmarkDetection/test/test_utils.py:7
ClassTorchTestCase
sync_batchnorm/unittest.py:15
ClassUnalignedDataset
data/unaligned_dataset.py:8
ClassVggClassNet
models/networks.py:632
ClassVisualizer
util/visualizer.py:115
Class_SynchronizedBatchNorm
sync_batchnorm/batchnorm.py:51
Classhinge_loss
util/Loss.py:25
Classhinge_loss_G
util/Loss.py:35