Functions — github.com/Falling-dow/Unsupervised-Image-Enhancement-with-CNN-and-GAN

hub / github.com/Falling-dow/Unsupervised-Image-Enhancement-with-CNN-and-GAN / functions

Functions173 in github.com/Falling-dow/Unsupervised-Image-Enhancement-with-CNN-and-GAN

⨍ Functions 173◇ Types & classes 40 ↳ Endpoints 3

↓ 17 callersFunctiondenorm

(x)

utils.py:128

↓ 9 callersMethod__init__

(self, in_nc, out_nc, reduction=8, bias=False, use_sn=False, norm=False)

models.py:266

↓ 5 callersMethod__init__

(self, tv_loss_weight=1)

losses.py:168

↓ 5 callersFunctioncreate_folder

(root_dir, path, version)

utils.py:115

↓ 5 callersFunctiondis_conv_block

(in_channels, out_channels, kernel_size, stride, padding, dilation, use_bias, norm_fun, act_fun, use_sn)

models.py:158

↓ 5 callersFunctiondis_pred_conv_block

(in_channels, out_channels, kernel_size, stride, padding, dilation, use_bias, type)

models.py:170

↓ 5 callersMethodupdate

(self, val, n=1)

metrics/NIMA/nima/nima/train/utils.py:37

↓ 4 callersFunctionSpectralNorm

(module, mode=True)

models.py:185

↓ 4 callersMethodget_target_tensor

(self, input, target_is_real)

losses.py:282

↓ 4 callersMethodget_zero_tensor

(self, input)

losses.py:294

↓ 3 callersFunctionssim

(img1, img2)

metrics/CalcPSNR.py:95

↓ 3 callersFunctionssim

(img1, img2)

metrics/CalcSSIM.py:93

↓ 3 callersFunctionvalidate

(model, loader, criterion, writer=None, global_step=None, name=None)

metrics/NIMA/nima/nima/train/main.py:38

↓ 2 callersMethod__init__

Initialize summary writer.

utils.py:55

↓ 2 callersFunctionbgr2ycbcr

same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

metrics/CalcPSNR.py:139

↓ 2 callersFunctionbgr2ycbcr

same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

metrics/CalcSSIM.py:137

↓ 2 callersFunctioncalc_nima

(img_path, result_save_path, epoch)

metrics/NIMA/CalcNIMA.py:58

↓ 2 callersFunctioncalc_psnr

(folder_Gen, folder_GT, result_save_path, epoch)

metrics/CalcPSNR.py:11

↓ 2 callersFunctioncalc_ssim

(folder_Gen, folder_GT, result_save_path, epoch)

metrics/CalcSSIM.py:11

↓ 2 callersFunctiondownload_file

(url, local_filename, chunk_size=1024)

metrics/NIMA/nima/nima/common.py:51

↓ 2 callersFunctionget_act_fun

(act_fun_type='LeakyReLU')

models.py:299

↓ 2 callersFunctionget_mean_score

(score)

metrics/NIMA/nima/nima/common.py:38

↓ 2 callersFunctionget_norm_fun

(norm_fun_type='none')

models.py:322

↓ 2 callersFunctionget_test_loader

(root, img_size=512, batch_size=8, shuffle=False, num_workers=0)

data_loader.py:85

↓ 2 callersMethodinit_weights

(self, net, init_type='kaiming', gain=0.02)

trainer.py:361

↓ 2 callersFunctionlistdir

(dname)

data_loader.py:15

↓ 2 callersMethodloss

(self, real_preds, fake_preds, target_is_real, for_real=None, for_fake=None, for_discriminator=True)

losses.py:300

↓ 2 callersFunctionmobile_net_v2

(pretrained=True)

metrics/NIMA/mobile_net_v2.py:122

↓ 2 callersMethodpredict

(self, image)

metrics/NIMA/nima/nima/inference/inference_model.py:37

↓ 2 callersMethodprint_network

Print out the network information.

tester.py:124

↓ 2 callersMethodprint_network

Print out the network information.

trainer.py:397

↓ 2 callersMethodtensor_size

(t)

losses.py:183

↓ 2 callersMethodtrain

Train UEGAN .

trainer.py:41

↓ 1 callersMethod__init__

(self, n_class=1000, input_size=224, width_mult=1.)

metrics/NIMA/mobile_net_v2.py:58

↓ 1 callersMethod__init__

(self, n_class=1000, input_size=224, width_mult=1.)

metrics/NIMA/nima/nima/mobile_net_v2.py:58

↓ 1 callersFunction_create_train_data_part

(params: TrainParams)

metrics/NIMA/nima/nima/train/main.py:53

↓ 1 callersFunction_create_val_data_part

(params: TrainParams)

metrics/NIMA/nima/nima/train/main.py:67

↓ 1 callersMethod_fetch_refs

(self)

data_loader.py:100

↓ 1 callersMethod_initialize_weights

(self)

metrics/NIMA/mobile_net_v2.py:106

↓ 1 callersMethod_initialize_weights

(self)

metrics/NIMA/nima/nima/mobile_net_v2.py:106

↓ 1 callersMethod_make_dataset

(self, root)

data_loader.py:44

↓ 1 callersFunction_read_ava_txt

(path_to_ava: str)

metrics/NIMA/nima/nima/train/clean_dataset.py:41

↓ 1 callersMethodbuild_model

Create a generator and a discriminator.

tester.py:107

↓ 1 callersMethodbuild_model

Create a generator and a discriminator.

trainer.py:317

↓ 1 callersMethodbuild_tensorboard

Build a tensorboard logger.

tester.py:149

↓ 1 callersFunctioncalc_mean_std

(feat, eps=1e-5)

models.py:204

↓ 1 callersFunctioncalculate_psnr

(img1, img2, data_range=255)

metrics/CalcPSNR.py:85

↓ 1 callersFunctionclean_and_split

(path_to_ava_txt: str, path_to_save_csv: str, path_to_images: str)

metrics/NIMA/nima/nima/train/clean_dataset.py:50

↓ 1 callersFunctioncli

()

metrics/NIMA/nima/nima/cli.py:10

↓ 1 callersFunctionconv_1x1_bn

(inp, oup)

metrics/NIMA/mobile_net_v2.py:20

↓ 1 callersFunctionconv_1x1_bn

(inp, oup)

metrics/NIMA/nima/nima/mobile_net_v2.py:20

↓ 1 callersFunctionconv_bn

(inp, oup, stride)

metrics/NIMA/mobile_net_v2.py:12

↓ 1 callersFunctionconv_bn

(inp, oup, stride)

metrics/NIMA/nima/nima/mobile_net_v2.py:12

↓ 1 callersMethodcreate_model

(cls)

metrics/NIMA/nima/nima/inference/inference_model.py:17

↓ 1 callersFunctionformat_output

(mean_score, std_score, prob)

metrics/NIMA/nima/nima/inference/utils.py:1

↓ 1 callersFunctionget_config

()

config.py:7

↓ 1 callersFunctionget_std_score

(scores)

metrics/NIMA/nima/nima/common.py:44

↓ 1 callersFunctionget_train_loader

(root, img_size=512, resize_size=256, batch_size=8, shuffle=True, num_workers=8, drop_last=True)

data_loader.py:72

↓ 1 callersMethodimages_summary

Log a list of images.

utils.py:64

↓ 1 callersMethodload_pretrained_model

(self, resume_epochs)

tester.py:133

↓ 1 callersMethodload_pretrained_model

(self, resume_epochs)

trainer.py:406

↓ 1 callersFunctionmain

(args)

main.py:14

↓ 1 callersFunctionmain

(model)

metrics/NIMA/test.py:69

↓ 1 callersFunctionmobile_net_v2

(pretrained=True)

metrics/NIMA/nima/nima/mobile_net_v2.py:122

↓ 1 callersMethodmodel_validation

(self, step)

trainer.py:217

↓ 1 callersMethodpredict_from_file

(self, image_path)

metrics/NIMA/nima/nima/inference/inference_model.py:29

↓ 1 callersMethodpredict_from_pil_image

(self, image)

metrics/NIMA/nima/nima/inference/inference_model.py:33

↓ 1 callersFunctionprepare_image

(image)

metrics/NIMA/CalcNIMA.py:45

↓ 1 callersFunctionprepare_image

(image)

metrics/NIMA/test.py:56

↓ 1 callersMethodprint_info

(self, step, total_steps, pbar)

trainer.py:175

↓ 1 callersMethodquery

(self, images)

utils.py:30

↓ 1 callersFunctionremove_all_not_found_image

(df: pd.DataFrame, path_to_images: str, num_workers: int = 64)

metrics/NIMA/nima/nima/train/clean_dataset.py:28

↓ 1 callersMethodreset

(self)

metrics/NIMA/nima/nima/train/utils.py:31

↓ 1 callersMethodsave_params

(self, file_path: str)

metrics/NIMA/nima/nima/train/utils.py:16

↓ 1 callersFunctionsetup_seed

(seed)

utils.py:149

↓ 1 callersFunctionstart_check_model

(params: ValidateParams)

metrics/NIMA/nima/nima/train/main.py:110

↓ 1 callersFunctionstart_train

(params: TrainParams)

metrics/NIMA/nima/nima/train/main.py:81

↓ 1 callersMethodtest

Test UEGAN .

tester.py:41

↓ 1 callersFunctiontrain

(model, loader, optimizer, criterion, writer=None, global_step=None, name=None)

metrics/NIMA/nima/nima/train/main.py:20

(self, real_preds, fake_preds, target_is_real, for_real=None, for_fake=None, for_discriminator=True)

metrics/NIMA/nima/nima/train/datasets.py:22

Method__init__

(self, loaders, args)

(self, channels=3, kernel_size=21, sigma=3, dim=2)

utils.py:167

Method__init__

(self, mean=0.0, stddev=0.1)

utils.py:237

Method__init__

(self, conv_dim, norm_fun, act_fun, use_sn)

models.py:12

Method__init__

(self, in_channels, out_channels, kernel_size, stride, padding, dilation, use_bias, use_sn)

models.py:78

Method__init__

(self, in_channels, out_channels, kernel_size, stride, padding, dilation, use_bias, norm_fun, act_fun, use_sn)

models.py:89

Method__init__

(self, conv_dim, norm_fun, act_fun, use_sn, adv_loss_type)

models.py:105

Method__init__

(self, scale_factor, mode, align_corners)

models.py:192

Method__init__

(self, channel, ratio=4)

next →1–100 of 173, ranked by callers