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Functions295 in github.com/294coder/Dif-PAN

⨍ Functions 295◇ Types & classes 68

utils/_metric_legacy.py:382

Methodadd_noise

Compute the noised input xt = alpha_t * x + sigma_t * noise. Args: x: A `torch.Tensor` with shape `(batch_size, *shape)

solver/dpm_solver.py:1020

Functionclamp_fn

diffusion_engine.py:43

Methodclear_history

utils/metric.py:41

Functioncompare_index

utils/_metric_legacy.py:423

Functioncompute_index

(img_base, img_out, ratio)

utils/_metric_legacy.py:262

Functioncompute_iters

(size, bs, drop_last=False)

utils/misc.py:204

Functionconcat_dif_timesteps_tensor

( tensor_list, n: int, out_size )

utils/misc.py:164

Functiondefault

utils/misc.py:18

Methoddynamic_thresholding_fn

The dynamic thresholding method.

solver/dpm_solver.py:424

Methodema_model_state_dict

utils/optim_utils.py:79

(self, x, mode="train", *args, **kwargs)

diffusion/diffusion_ddpm_pan.py:768

(self, pred, gt)

utils/loss_utils.py:69

(self, pred, gt)

utils/loss_utils.py:81

(self, pred, gt)

utils/loss_utils.py:93

(self, pred, gt)

utils/loss_utils.py:108

(self, img1, img2)

utils/loss_utils.py:129

(self, img1, img2)

utils/loss_utils.py:186

(self, x: torch.Tensor)

models/unet_model_google.py:27

models/unet_model_google.py:49

models/unet_model_google.py:58

models/unet_model_google.py:68

models/unet_model_google.py:79

models/unet_model_google.py:89

models/unet_model_google.py:108

(self, x, time_emb, guidance=None)

models/unet_model_google.py:134

models/unet_model_google.py:157

(self, x, time_emb, guidance=None)

models/unet_model_google.py:204

suit with dpm-solver api

models/unet_model_google.py:379

(self, x, time, cond=None, self_cond=None)

models/sr3_dwt.py:169

(self, noise_level)

models/sr3_dwt.py:228

(self, x, noise_embed)

models/sr3_dwt.py:249

models/sr3_dwt.py:262

models/sr3_dwt.py:272

models/sr3_dwt.py:281

models/sr3_dwt.py:299

(self, x, time_emb)

models/sr3_dwt.py:322

models/sr3_dwt.py:341

models/sr3_dwt.py:369

(self, x, cond)

models/sr3_dwt.py:389

(self, x, cond)

models/sr3_dwt.py:442

(self, x, cond)

models/sr3_dwt.py:536

(self, x, cond)

models/sr3_dwt.py:604

(self, x, time_emb, cond=None)

models/sr3_dwt.py:658

models/pansharpen_model.py:37

models/pansharpen_model.py:47

models/pansharpen_model.py:74

( self, lms, pan, schedule, n, pre_cal_fm: List[List[torch.Ten

models/pansharpen_model.py:151

(self, x, time, cond=None, self_cond=None)

models/sr3.py:162

(self, noise_level)

models/sr3.py:216

(self, x, noise_embed)

models/sr3.py:237

models/sr3.py:250

models/sr3.py:260

models/sr3.py:269

models/sr3.py:287

(self, x, time_emb)

models/sr3.py:310

models/sr3.py:329

models/sr3.py:357

(self, x, cond)

models/sr3.py:377

(self, x, cond)

models/sr3.py:430

(self, x, cond)

models/sr3.py:526

(self, x, cond)

models/sr3.py:594

(self, x, time_emb, cond=None)

models/sr3.py:655

Functionget_inner_feature_hook

(m: nn.Module, input, output, saved_list)

utils/model_hook.py:10

Functionget_loss

utils/loss_utils.py:190

Functionimg_batch2one_img

(batched_img: torch.Tensor)

utils/misc.py:178

diffusion_engine.py:44

Inverse the sample `x` from time `t_start` to `t_end` by DPM-Solver. For discrete-time DPMs, we use `t_start=1/N`, where `N` is the t

solver/dpm_solver.py:1040

utils/metric.py:38

Methodload_ema_params

utils/optim_utils.py:60

Methodload_model_params

utils/optim_utils.py:64

Methodlog_image

(self, tag: str, image: Tensor, step: int)

utils/logger.py:83

Methodlog_images

( self, tag: str, images: Tensor, step: int, *, dataformats: str = "NCHW" )

utils/logger.py:88

Functionnon_wavelet_ref_loop

(dl: data.DataLoader)

dataset/for_loops.py:4

Functionnon_wavelet_unref_loop

(dl: data.DataLoader)

dataset/for_loops.py:12

diffusion_engine.py:33

Methodnorm_func

dataset/pan_dataset.py:127

Functionnormalize_to_01

utils/metric.py:101

Methodon_fly_model_state_dict

utils/optim_utils.py:69

Methodpredict_start_from_xprev

(self, x_t, t, xprev)

diffusion/diffusion_ddpm_pan.py:289

(self, *args, **kwargs)

utils/logger.py:131

Methodprint_str

(*args, **kwargs)

utils/metric.py:18

Functionpsnr_batch_tensor_metric

calculate PSNR for batch tensor images :param b_gt: tensor, shape [B, C, H, W] :param b_test: tensor, shape [B, C, H, W] :return:

utils/metric.py:125

utils/_metric_legacy.py:15

Methodq_mean_variance

(self, x_start, t)

diffusion/diffusion_ddpm_pan.py:278

Functionshow_distribution

diffusion_engine.py:26

(img1, img2, win_size=11, data_range=1, size_average=True)

utils/loss_utils.py:150

Functionssim_batch_tensor_metric

utils/metric.py:160

(self, epoch=None)

utils/lr_scheduler.py:37

(self, epoch=None)

utils/lr_scheduler.py:77

(self, epoch=None)

utils/lr_scheduler.py:95

diffusion_engine.py:38

Functionwavelet_ref_loop

(dl: data.DataLoader)

dataset/for_loops.py:8

Functionwavelet_unref_loop

(dl: data.DataLoader)

dataset/for_loops.py:16

Methodweighted_diffusion_guidance_loss

(self, l1, l2, iteration)

utils/optim_utils.py:13

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