Functions — github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement

hub / github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement / functions

Functions147 in github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement

⨍ Functions 147◇ Types & classes 27

↓ 11 callersFunctionextract

(a, t, x_shape)

model/ddpm_modules/diffusion.py:64

↓ 8 callersMethod__init__

( self, in_channel=6, out_channel=3, inner_channel=32, norm_groups=32,

model/sr3_modules/unet.py:162

↓ 8 callersMethodset_new_noise_schedule

(self, schedule_opt, schedule_phase='train')

model/model.py:86

↓ 7 callersMethod__init__

( self, in_channel=6, out_channel=3, inner_channel=32, norm_groups=32,

model/ddpm_modules/unet.py:148

↓ 5 callersMethodfeed_data

(self, data)

model/model.py:45

↓ 5 callersMethodget_current_visuals

(self, need_LR=True, sample=False)

model/model.py:98

↓ 5 callersMethodlog_metrics

Log train/validation metrics onto W&B. metrics: dictionary of metrics to be logged

core/wandb_logger.py:44

↓ 4 callersFunctionexists

(x)

model/ddpm_modules/unet.py:7

↓ 3 callersFunctiondefault

(val, d)

model/ddpm_modules/diffusion.py:58

↓ 3 callersFunctionimage_convert_bytes

(img)

data/prepare_data.py:24

↓ 3 callersMethodp_sample

(self, x, t, clip_denoised=True, repeat_noise=False, condition_x=None)

model/ddpm_modules/diffusion.py:190

↓ 3 callersFunctionresize_and_convert

(img, size, resample)

data/prepare_data.py:17

↓ 3 callersFunctionssim

(img1, img2)

core/metrics.py:52

↓ 3 callersMethodtest

(self, continous=False)

model/model.py:60

↓ 2 callersFunction_warmup_beta

(linear_start, linear_end, n_timestep, warmup_frac)

model/sr3_modules/diffusion.py:11

↓ 2 callersFunction_warmup_beta

(linear_start, linear_end, n_timestep, warmup_frac)

model/ddpm_modules/diffusion.py:11

↓ 2 callersFunctiondefault

(val, d)

model/sr3_modules/diffusion.py:58

↓ 2 callersFunctionexists

(x)

model/sr3_modules/unet.py:8

↓ 2 callersMethodget_current_log

(self)

model/model.py:95

↓ 2 callersMethodlog_checkpoint

Log the model checkpoint as W&B artifacts current_epoch: the current epoch current_step: the current batch step

core/wandb_logger.py:70

↓ 2 callersMethodlog_eval_data

Add data row-wise to the initialized table.

core/wandb_logger.py:90

↓ 2 callersMethodlog_eval_table

Log the table

core/wandb_logger.py:109

↓ 2 callersMethodlog_image

Log image array onto W&B. key_name: name of the key image_array: numpy array of image.

core/wandb_logger.py:52

↓ 2 callersMethodoptimize_parameters

(self)

model/model.py:48

↓ 2 callersMethodp_sample

(self, x, t, clip_denoised=True, condition_x=None)

model/sr3_modules/diffusion.py:170

↓ 2 callersMethodp_sample_loop

(self, x_in, continous=False)

model/sr3_modules/diffusion.py:177

↓ 2 callersMethodp_sample_loop

(self, x_in, continous=False)

model/ddpm_modules/diffusion.py:201

↓ 2 callersMethodpredict_start_from_noise

(self, x_t, t, noise)

model/sr3_modules/diffusion.py:141

↓ 2 callersMethodpredict_start_from_noise

(self, x_t, t, noise)

model/ddpm_modules/diffusion.py:158

↓ 2 callersMethodq_sample

(self, x_start, t, noise=None)

model/ddpm_modules/diffusion.py:259

↓ 2 callersMethodsample

(self, batch_size=1, continous=False)

model/model.py:71

↓ 2 callersMethodsave_network

(self, epoch, iter_step)

model/model.py:124

↓ 2 callersMethodset_device

(self, x)

model/base_model.py:29

↓ 2 callersMethodsuper_resolution

(self, x_in, continous=False)

model/sr3_modules/diffusion.py:209

↓ 1 callersFunction_augment

(img)

data/util.py:33

↓ 1 callersFunctionall_threads_inactive

(worker_threads)

data/prepare_data.py:94

↓ 1 callersFunctiondefault

(val, d)

model/sr3_modules/unet.py:12

↓ 1 callersFunctiondefault

(val, d)

model/ddpm_modules/unet.py:11

↓ 1 callersFunctionexists

(x)

model/sr3_modules/diffusion.py:54

↓ 1 callersFunctionexists

(x)

model/ddpm_modules/diffusion.py:54

↓ 1 callersMethodget_network_description

Get the string and total parameters of the network

model/base_model.py:42

↓ 1 callersFunctionget_timestamp

()

core/logger.py:17

↓ 1 callersMethodinc_get

(self)

data/prepare_data.py:61

↓ 1 callersFunctioninit_weights

(net, init_type='kaiming', scale=1, std=0.02)

model/networks.py:60

↓ 1 callersFunctionis_image_file

(filename)

data/util.py:11

↓ 1 callersMethodload_network

(self)

model/model.py:146

↓ 1 callersMethodlog_images

Log list of image array onto W&B key_name: name of the key list_images: list of numpy image arrays

core/wandb_logger.py:61

↓ 1 callersFunctionmake_beta_schedule

(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3)

model/sr3_modules/diffusion.py:19

↓ 1 callersFunctionmake_beta_schedule

(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3)

model/ddpm_modules/diffusion.py:19

↓ 1 callersFunctionmkdirs

(paths)

core/logger.py:9

↓ 1 callersFunctionnoise

()

model/ddpm_modules/diffusion.py:74

↓ 1 callersFunctionnoise_like

(shape, device, repeat=False)

model/ddpm_modules/diffusion.py:70

↓ 1 callersMethodp_losses

(self, x_in, noise=None)

model/sr3_modules/diffusion.py:221

↓ 1 callersMethodp_losses

(self, x_in, noise=None)

model/ddpm_modules/diffusion.py:278

↓ 1 callersMethodp_mean_variance

(self, x, t, clip_denoised: bool, condition_x=None)

model/sr3_modules/diffusion.py:151

↓ 1 callersMethodp_mean_variance

(self, x, t, clip_denoised: bool, condition_x=None)

model/ddpm_modules/diffusion.py:174

↓ 1 callersFunctionprepare

(img_path, out_path, n_worker, sizes=(16, 128), resample=Image.BICUBIC, lmdb_save=False)

data/prepare_data.py:100

↓ 1 callersMethodprint_network

(self)

model/model.py:112

↓ 1 callersMethodq_posterior

(self, x_start, x_t, t)

model/sr3_modules/diffusion.py:145

↓ 1 callersMethodq_posterior

(self, x_start, x_t, t)

model/ddpm_modules/diffusion.py:164

↓ 1 callersMethodq_sample

(self, x_start, continuous_sqrt_alpha_cumprod, noise=None)

model/sr3_modules/diffusion.py:212

↓ 1 callersFunctionrepeat_noise

()

model/ddpm_modules/diffusion.py:71

↓ 1 callersFunctionresize_multiple

(img, sizes=(16, 128), resample=Image.BICUBIC, lmdb_save=False)

data/prepare_data.py:30

↓ 1 callersMethodset_loss

(self)

model/model.py:80

↓ 1 callersMethodvalue

(self)

data/prepare_data.py:66

Method__getitem__

(self, index)

data/LRHR_dataset.py:48

Method__init__

(self, opt)

core/wandb_logger.py:7

Method__init__

(self, opt)

model/base_model.py:7

model/sr3_modules/unet.py:19

Method__init__

(self, in_channels, out_channels, use_affine_level=False)

model/sr3_modules/unet.py:35

Method__init__

(self, dim)

model/sr3_modules/unet.py:59

Method__init__

(self, dim)

model/sr3_modules/unet.py:69

Method__init__

(self, dim, dim_out, groups=32, dropout=0)

model/sr3_modules/unet.py:81

Method__init__

(self, dim, dim_out, noise_level_emb_dim=None, dropout=0, use_affine_level=False, norm_groups=32)

model/sr3_modules/unet.py:95

Method__init__

(self, in_channel, n_head=1, norm_groups=32)

model/sr3_modules/unet.py:114

Method__init__

(self, dim, dim_out, *, noise_level_emb_dim=None, norm_groups=32, dropout=0, with_attn=False)

model/sr3_modules/unet.py:146

Method__init__

( self, denoise_fn, image_size, channels=3, loss_type='l1', co

model/sr3_modules/diffusion.py:65

Method__init__

(self, dim)

model/ddpm_modules/unet.py:20

Method__init__

(self, dim)

model/ddpm_modules/unet.py:43

Method__init__

(self, dim)

model/ddpm_modules/unet.py:53

Method__init__

(self, dim, dim_out, groups=32, dropout=0)

model/ddpm_modules/unet.py:65

Method__init__

(self, dim, dim_out, time_emb_dim=None, dropout=0, norm_groups=32)

model/ddpm_modules/unet.py:79

Method__init__

(self, in_channel, n_head=1, norm_groups=32)

model/ddpm_modules/unet.py:100

Method__init__

(self, dim, dim_out, *, time_emb_dim=None, norm_groups=32, dropout=0, with_attn=False)

model/ddpm_modules/unet.py:132

Method__init__

( self, denoise_fn, image_size, channels=3, loss_type='l1', co

model/ddpm_modules/diffusion.py:79

Method__init__

(self, dataroot, datatype, l_resolution=16, r_resolution=128, split='train', data_len=-1, need_LR=False)

data/LRHR_dataset.py:10

Method__init__

(self, resize_fn, lmdb_save, out_path, env, sizes)

data/prepare_data.py:51

Method__len__

(self)

data/LRHR_dataset.py:45

(img_list, hflip=True, rot=True, split='val')

data/util.py:27

Functioncalculate_psnr

(img1, img2)

core/metrics.py:42

Functioncalculate_ssim

calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255]

core/metrics.py:75

Functioncreate_dataloader

create dataloader

data/__init__.py:7

Functioncreate_dataset

dict to string for logger

core/logger.py:115

Functiondict_to_nonedict

model/base_model.py:14

next →1–100 of 147, ranked by callers