Types & classes — github.com/Falling-dow/Unsupervised-Image-Enhancement-with-CNN-and-GAN

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Types & classes40 in github.com/Falling-dow/Unsupervised-Image-Enhancement-with-CNN-and-GAN

⨍ Functions 173◇ Types & classes 40 ↳ Endpoints 3

↓ 9 callersClassConvBlock

↓ 6 callersClassSNConv

↓ 5 callersClassGAM

Global attention module

↓ 5 callersClassTransform

metrics/NIMA/nima/nima/common.py:11

↓ 4 callersClassAVADataset

metrics/NIMA/nima/nima/train/datasets.py:13

↓ 4 callersClassInterpolate

↓ 3 callersClassInputFetcher

data_loader.py:95

↓ 3 callersClassNIMA

metrics/NIMA/nima/nima/model.py:6

↓ 2 callersClassAverageMeter

metrics/NIMA/nima/nima/train/utils.py:27

↓ 2 callersClassDiscriminator

↓ 2 callersClassEDMLoss

metrics/NIMA/nima/nima/train/emd_loss.py:6

↓ 2 callersClassGenerator

Generator network

↓ 2 callersClassInvertedResidual

metrics/NIMA/mobile_net_v2.py:28

↓ 2 callersClassInvertedResidual

metrics/NIMA/nima/nima/mobile_net_v2.py:28

↓ 2 callersClassLogger

Create a tensorboard logger to log_dir.

↓ 2 callersClassReferenceDataset

data_loader.py:39

↓ 1 callersClassBasicBlock

↓ 1 callersClassChannelAttention

↓ 1 callersClassGANLoss

↓ 1 callersClassIdentity

↓ 1 callersClassImagePool

↓ 1 callersClassInferenceModel

metrics/NIMA/nima/nima/inference/inference_model.py:15

↓ 1 callersClassMobileNetV2

metrics/NIMA/mobile_net_v2.py:57

↓ 1 callersClassMobileNetV2

metrics/NIMA/nima/nima/mobile_net_v2.py:57

↓ 1 callersClassMultiscaleRecLoss

↓ 1 callersClassNIMA

metrics/NIMA/CalcNIMA.py:23

↓ 1 callersClassNIMA

metrics/NIMA/test.py:34

↓ 1 callersClassPerceptualLoss

↓ 1 callersClassSpatialAttention

↓ 1 callersClassSwish

↓ 1 callersClassTester

↓ 1 callersClassTrainParams

metrics/NIMA/nima/nima/train/utils.py:13

↓ 1 callersClassTrainer

↓ 1 callersClassVGG19_relu

↓ 1 callersClassValidateParams

metrics/NIMA/nima/nima/train/utils.py:21

ClassAngularLoss

ClassDefaultDataset

data_loader.py:21

ClassGaussianNoise

A gaussian noise module. Args: stddev (float): The standard deviation of the normal distribution. Default: 0.1.

ClassGaussianSmoothing

Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input using a depthwise convolution.