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Functions526 in github.com/bbaaii/DreamDiffusion

⨍ Functions 526◇ Types & classes 106

Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

code/dc_ldm/modules/diffusionmodules/openaimodel.py:390

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:725

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:957

(ctx, run_function, length, *args)

code/dc_ldm/modules/diffusionmodules/util.py:121

code/dc_ldm/modules/diffusionmodules/util.py:215

(self, c_concat, c_crossattn)

code/dc_ldm/modules/diffusionmodules/util.py:258

code/dc_ldm/modules/diffusionmodules/model.py:53

code/dc_ldm/modules/diffusionmodules/model.py:72

(self, x, temb)

code/dc_ldm/modules/diffusionmodules/model.py:121

code/dc_ldm/modules/diffusionmodules/model.py:178

(self, x, t=None, context=None)

code/dc_ldm/modules/diffusionmodules/model.py:316

code/dc_ldm/modules/diffusionmodules/model.py:434

code/dc_ldm/modules/diffusionmodules/model.py:535

code/dc_ldm/modules/diffusionmodules/model.py:594

code/dc_ldm/modules/diffusionmodules/model.py:641

code/dc_ldm/modules/diffusionmodules/model.py:680

code/dc_ldm/modules/diffusionmodules/model.py:705

code/dc_ldm/modules/diffusionmodules/model.py:722

code/dc_ldm/modules/diffusionmodules/model.py:741

(self, x, scale_factor=1.0)

code/dc_ldm/modules/diffusionmodules/model.py:763

code/dc_ldm/modules/diffusionmodules/model.py:822

(self, batch, key=None)

code/dc_ldm/modules/encoders/modules.py:28

code/dc_ldm/modules/encoders/modules.py:45

code/dc_ldm/modules/encoders/modules.py:64

code/dc_ldm/modules/encoders/modules.py:94

code/dc_ldm/modules/encoders/modules.py:126

code/dc_ldm/modules/encoders/modules.py:155

code/dc_ldm/modules/encoders/modules.py:184

code/dc_ldm/modules/encoders/modules.py:215

code/dc_ldm/modules/encoders/modules.py:259

(self, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, co

code/dc_ldm/modules/losses/vqperceptual.py:99

(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=

code/dc_ldm/modules/losses/contperceptual.py:46

(self, z, temp=None, rescale_logits=False, return_logits=False)

code/dc_ldm/models/autoencoder.py:75

(self, input, return_pred_indices=False)

code/dc_ldm/models/autoencoder.py:238

(self, input, sample_posterior=True)

code/dc_ldm/models/autoencoder.py:457

(self, x, *args, **kwargs)

code/dc_ldm/models/autoencoder.py:564

(self, x_noisy, t, *args, **kwargs)

code/dc_ldm/models/diffusion/classifier.py:120

(self, x, *args, **kwargs)

code/dc_ldm/models/diffusion/ddpm.py:340

(self, x, c, label, image_raw, *args, **kwargs)

code/dc_ldm/models/diffusion/ddpm.py:1043

(self, x, t, c_concat: list = None, c_crossattn: list = None)

code/dc_ldm/models/diffusion/ddpm.py:1567

(self, x, c, label, image_raw, *args, **kwargs)

code/dc_ldm/models/diffusion/ddpm.py:1613

code/dc_ldm/modules/encoders/modules.py:210

Methodfreeze_cond_stage

code/dc_ldm/models/diffusion/ddpm.py:668

Methodfreeze_diffusion_model

code/dc_ldm/models/diffusion/ddpm.py:660

Methodfreeze_whole_model

code/dc_ldm/models/diffusion/ddpm.py:687

(self, fmri_embedding, num_samples, ddim_steps, HW=None, limit=None, state=None, output_path = None)

code/dc_ldm/ldm_for_eeg.py:310

Functionget_1d_sincos_pos_embed

grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/

code/sc_mbm/utils.py:6

Functionget_img_label

(class_index:dict, img_filename:list, naive_label_set=None)

code/dataset.py:158

Methodget_last_layer

code/dc_ldm/modules/diffusionmodules/model.py:364

Methodget_model_output

code/dc_ldm/models/diffusion/plms.py:179

Methodget_x_prev_and_pred_x0

code/dc_ldm/models/diffusion/plms.py:200

Functiongroup_by_key_prefix

code/dc_ldm/modules/x_transformer.py:106

Functionhinge_d_loss_with_exemplar_weights

(logits_real, logits_fake, weights)

code/dc_ldm/modules/losses/vqperceptual.py:12

Functionidentity

code/dataset.py:18

Functionidentity

code/eval_metrics.py:27

Functionimg_norm

code/dataset.py:85

code/dc_ldm/modules/attention.py:29

(*args, **kwargs)

code/dc_ldm/modules/x_transformer.py:65

Functioninterpolate_pos_embed

(model, checkpoint_model)

code/sc_mbm/utils.py:46

code/dc_ldm/modules/losses/vqperceptual.py:36

code/dc_ldm/modules/losses/vqperceptual.py:40

Functionlarger_the_better

code/eval_metrics.py:12

Functionlist_get_all_index

code/dataset.py:210

Functionload_model

(config, model, checkpoint_path )

code/sc_mbm/utils.py:98

Methodlog_images

(self, batch, only_inputs=False, plot_ema=False, **kwargs)

code/dc_ldm/models/autoencoder.py:354

Methodlog_images

(self, batch, only_inputs=False, **kwargs)

code/dc_ldm/models/autoencoder.py:523

Methodlog_images

(self, batch, N=8, *args, **kwargs)

code/dc_ldm/models/diffusion/classifier.py:238

Methodlog_images

(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs)

code/dc_ldm/models/diffusion/ddpm.py:526

Methodlog_images

(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, qua

code/dc_ldm/models/diffusion/ddpm.py:1399

Functionmake_dataset

code/dataset.py:222

Functionmax_neg_value

code/dc_ldm/modules/attention.py:25

Functionmean_flat

https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all no

code/dc_ldm/util.py:63

Functionmetrics_only

(pred_imgs, gt_imgs, metric, *args, **kwargs)

code/eval_metrics.py:107

code/dc_ldm/modules/distributions/distributions.py:9

code/dc_ldm/modules/distributions/distributions.py:61

Functionmse_metric

code/eval_metrics.py:18

Functionn_way_scores

(pred_imgs, gt_imgs, metric, is_sucess, n=2, n_trials=100)

code/eval_metrics.py:79

(self, sample, dims=[1,2,3])

code/dc_ldm/modules/distributions/distributions.py:53

Functionnormalize

code/eeg_ldm.py:96

Functionnormalize

code/gen_eval_eeg.py:28

Functionnormalize2

code/dataset.py:355

Methodon_train_batch_end

(self, *args, **kwargs)

code/dc_ldm/models/autoencoder.py:213

Methodon_train_batch_end

(self, *args, **kwargs)

code/dc_ldm/models/diffusion/ddpm.py:514

Methodon_train_batch_start

(self, batch, batch_idx, dataloader_idx)

code/dc_ldm/models/diffusion/ddpm.py:632

Methodon_train_epoch_start

code/dc_ldm/models/diffusion/classifier.py:157

Methodon_validation_start

code/dc_ldm/models/diffusion/classifier.py:206

Functionpad_to_length

code/dataset.py:24

Functionpair_wise_score

(pred_imgs, gt_imgs, metric, is_sucess)

code/eval_metrics.py:58

Functionparallel_data_prefetch

( func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False )

code/dc_ldm/util.py:107

Functionpatchify

imgs: (N, 1, num_voxels) x: (N, L, patch_size)

code/sc_mbm/utils.py:103

Functionpcc_metric

code/eval_metrics.py:21

Functionpick_and_pop

code/dc_ldm/modules/x_transformer.py:88

Functionplot_recon_figures2

(model, device, dataset, output_path, num_figures = 5, config=None, logger=None, model_without_ddp=None)

code/stageA1_eeg_pretrain.py:239

Functionprocess_voxel_ts

v: voxel timeseries of a subject. (1200, num_voxels) p: patch size t: time step of the averaging window for v. Kamitani used 8 ~ 12s

code/dataset.py:37

Methodrecon_loss

imgs: [N, 1, num_voxels] pred: [N, L, p] mask: [N, L], 0 is keep, 1 is remove,

code/dc_ldm/models/diffusion/ddpm.py:1090

Methodregister_schedule

(self, given_betas=None, beta_schedule="linear", timesteps=1000,

code/dc_ldm/models/diffusion/ddpm.py:647

Functionremove_repeats

code/dataset.py:194

Methodrescale_bbox

code/dc_ldm/models/diffusion/ddpm.py:1108

code/dc_ldm/modules/distributions/distributions.py:6

code/dc_ldm/modules/distributions/distributions.py:17

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