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Functions785 in github.com/darioShar/DLPM

⨍ Functions 785◇ Types & classes 166
↓ 2 callersFunction_get_c
(alpha, t)
dlpm/methods/LIM/torchlevy/approx_score.py:84
↓ 2 callersFunction_get_c
(alpha, t)
bem/datasets/torchlevy/approx_score.py:84
↓ 2 callersMethod_get_image_from
(self, samples, idx = -1, black_and_w
bem/GenerationManager.py:95
↓ 2 callersMethod_img_to_plt_img
(self, tensor)
bem/GenerationManager.py:92
↓ 2 callersFunction_inception_v3
Wraps `torchvision.models.inception_v3` Skips default weight inititialization if supported by torchvision version. See https://github.com/mse
dlpm/methods/LIM/evaluate/inception.py:167
↓ 2 callersFunction_inception_v3
Wraps `torchvision.models.inception_v3` Skips default weight inititialization if supported by torchvision version. See https://github.com/mse
bem/evaluate/inception.py:167
↓ 2 callersMethod_pdf_simple
simplified version of func `pdf_zolotarev`, assume alpha > 1 and beta = 0
dlpm/methods/LIM/torchlevy/levy.py:82
↓ 2 callersMethod_pdf_simple
simplified version of func `pdf_zolotarev`, assume alpha > 1 and beta = 0
bem/datasets/torchlevy/levy.py:82
↓ 2 callersMethod_plot_data
(self, data, marker='.', animated=False, label=None, ax=None, color=None, alpha=0.5)
bem/GenerationManager.py:140
↓ 2 callersFunction_prd_to_f_beta
Computes F_beta scores for the given precision/recall values. The F_beta scores for all precision/recall pairs will be computed and returned.
bem/evaluate/prd_score.py:194
↓ 2 callersMethod_sample
(self, alpha, beta=0, size=1, type=torch.float32)
dlpm/methods/LIM/torchlevy/levy.py:191
↓ 2 callersMethod_sample
(self, alpha, beta=0, size=1, type=torch.float32)
bem/datasets/torchlevy/levy.py:236
↓ 2 callersMethod_scale_timesteps
(self, t)
dlpm/methods/GenerativeLevyProcess.py:92
↓ 2 callersFunctionadditional_logging
(exp, args)
script_utils.py:135
↓ 2 callersFunctioncft
Numerically evaluate the Fourier Transform of g for the given frequencies
dlpm/methods/LIM/torchlevy/levy_gaussian_origin.py:52
↓ 2 callersFunctioncft
Numerically evaluate the Fourier Transform of g for the given frequencies
dlpm/methods/LIM/torchlevy/levy_gaussian.py:49
↓ 2 callersFunctioncft
Numerically evaluate the Fourier Transform of g for the given frequencies
bem/datasets/torchlevy/levy_gaussian_origin.py:52
↓ 2 callersFunctioncft
Numerically evaluate the Fourier Transform of g for the given frequencies
bem/datasets/torchlevy/levy_gaussian.py:49
↓ 2 callersFunctioncheckpoint
Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas
dlpm/models/nn.py:124
↓ 2 callersMethodcompute_Sigma_tilde_t_1
(self, Gamma_t, Sigma_t_1)
dlpm/methods/dlpm.py:256
↓ 2 callersMethodcompute_Sigmas
(self)
dlpm/methods/dlpm.py:230
↓ 2 callersFunctioncompute_statistics_of_path
(path, model, batch_size, dims, device, num_workers=1)
dlpm/methods/LIM/evaluate/fid_score.py:195
↓ 2 callersFunctioncompute_statistics_of_path
(path, model, batch_size, dims, device, num_workers=1)
bem/evaluate/fid_score.py:196
↓ 2 callersFunctiondrift_fn
The drift function of the reverse-time SDE.
dlpm/methods/LIM/evaluate/likelihood.py:66
↓ 2 callersFunctiondrift_fn
The drift function of the reverse-time SDE.
bem/evaluate/likelihood.py:66
↓ 2 callersMethodema
(self, module)
dlpm/methods/LIM/models/ema.py:24
↓ 2 callersMethodema
(self, module)
dlpm/models/Ema.py:30
↓ 2 callersMethodevaluate_model
(self, models, **kwargs)
bem/evaluate/EvaluationManager.py:111
↓ 2 callersFunctionfid_score
(path1, path2, batch_size=50, device=None, dims=2048, num_workers=4)
dlpm/methods/LIM/evaluate/fid_score.py:229
↓ 2 callersFunctiongaussian_score
(x, mu=0, sigma=torch.sqrt(torch.tensor(2)))
dlpm/methods/LIM/torchlevy/util.py:11
↓ 2 callersFunctiongaussian_score
(x, mu=0, sigma=torch.sqrt(torch.tensor(2)))
bem/datasets/torchlevy/util.py:11
↓ 2 callersMethodgen_noise_schedule
(self, diffusion_steps, scale = 'scale_preserving')
dlpm/methods/dlpm.py:114
↓ 2 callersFunctionget_act
Get activation functions from the config file.
dlpm/models/layers.py:29
↓ 2 callersMethodget_ema_model
(self)
bem/utils_ema.py:55
↓ 2 callersMethodget_eval_hash
(self, p, verbose = False)
bem/utils_exp.py:63
↓ 2 callersFunctionget_extreme_pts
(func)
dlpm/methods/LIM/torchlevy/approx_score.py:100
↓ 2 callersFunctionget_extreme_pts
(func)
bem/datasets/torchlevy/approx_score.py:100
↓ 2 callersFunctionget_features
(path, model, batch_size=50, dims=2048, device='cpu', num_workers=1)
dlpm/methods/LIM/evaluate/fid_score.py:254
↓ 2 callersFunctionget_features
(path, model, batch_size=50, dims=2048, device='cpu', num_workers=1, max_num_files=None)
bem/evaluate/fid_score.py:255
↓ 2 callersMethodget_image
(self, idx = -1, black_and_white=False, # in the case of single channel
bem/GenerationManager.py:78
↓ 2 callersMethodget_one_rv_faster_sampling
(self, shape)
dlpm/methods/dlpm.py:384
↓ 2 callersMethodget_paths_from_param
(self, p, folder_path,
bem/utils_exp.py:97
↓ 2 callersMethodget_plot
(self, plot_original_data = True, limit_nb_datapoints = 10000,
bem/GenerationManager.py:106
↓ 2 callersFunctionimpainted_noise
(data, noise, mask, t, device)
dlpm/methods/LIM/functions/sampler.py:61
↓ 2 callersFunctioninf_train_gen
(img_name, data_size)
bem/datasets/__init__.py:30
↓ 2 callersMethodinit_default_ls
(p, optim)
bem/utils_exp.py:306
↓ 2 callersMethodload_state_dict
(self, state_dict)
dlpm/models/Ema.py:56
↓ 2 callersMethodlog_existing_eval_values
(self, folder='eval')
bem/evaluate/EvaluationManager.py:84
↓ 2 callersFunctionmatch_last_dims
(data, size)
dlpm/methods/LIM/functions/loss.py:5
↓ 2 callersMethodp_mean_variance
(self, model, x, t,
dlpm/methods/GenerativeLevyProcess.py:154
↓ 2 callersFunctionparse_args
()
script_utils.py:155
↓ 2 callersFunctionpdist
(sample_1, sample_2, norm=2)
bem/evaluate/mmd_loss.py:42
↓ 2 callersFunctionprdc
(path1, path2, batch_size=500, device=None, dims=2048, num_workers=0)
dlpm/methods/LIM/evaluate/fid_score.py:298
↓ 2 callersMethodprepare
(self, p = None, checkpoint_dir = None, logger = None)
bem/Experiments.py:140
↓ 2 callersMethodprepare_experiment
(self, logger = None, do_not_load_data=False)
bem/utils_exp.py:442
↓ 2 callersMethodprint_parameters
(self)
bem/Experiments.py:133
↓ 2 callersMethodregister
(self, module)
dlpm/methods/LIM/models/ema.py:9
↓ 2 callersMethodrescale_diffusion
(self, diffusion_steps, time_spacing = None)
dlpm/methods/dlpm.py:176
↓ 2 callersMethodreset
(self, keep_losses = False, keep_evals = False)
bem/evaluate/EvaluationManager.py:62
↓ 2 callersMethodsample_A
(self, shape, diffusion_steps)
dlpm/methods/dlpm.py:226
↓ 2 callersMethodsample_x_t_from_xstart_given_Sigma
(self, xstart, t, Sigma_t, z_t = None)
dlpm/methods/dlpm.py:242
↓ 2 callersFunctionsde_score_update
input: x_s, s, t output: x_t
dlpm/methods/LIM/functions/sampler.py:115
↓ 2 callersMethodset_parameter
(self, p, config_path = None)
bem/utils_exp.py:424
↓ 2 callersMethodstate_dict
(self)
dlpm/models/Ema.py:53
↓ 2 callersMethodstop
(self)
bem/Logger.py:26
↓ 2 callersFunctiontimestep_embedding
Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may b
dlpm/models/nn.py:103
↓ 2 callersMethodtrain
(self, total_epoch, **kwargs)
bem/TrainingManager.py:62
↓ 2 callersFunctionupdate_experiment_after_loading
(exp, args)
script_utils.py:115
↓ 2 callersFunctionupdate_parameters_before_loading
(p, args)
script_utils.py:4
↓ 1 callersMethod__init__
(self, root, classes="train", transform=None, target_transform=None)
dlpm/methods/LIM/datasets/lsun.py:75
↓ 1 callersMethod__init__
(self, in_features, out_features, device)
dlpm/models/Embeddings.py:28
↓ 1 callersMethod__init__
(self, root, classes="train", transform=None, target_transform=None)
bem/datasets/lsun.py:75
↓ 1 callersMethod__len__
(self)
dlpm/methods/LIM/datasets/vision.py:31
↓ 1 callersMethod__len__
(self)
bem/datasets/vision.py:31
↓ 1 callersFunction_cluster_into_bins
Clusters the union of the data points and returns the cluster distribution. Clusters the union of eval_data and ref_data into num_clusters using mi
bem/evaluate/prd_score.py:108
↓ 1 callersFunction_compute_fans
(shape, in_axis=1, out_axis=0)
dlpm/methods/LIM/models/layers.py:60
↓ 1 callersFunction_compute_fans
(shape, in_axis=1, out_axis=0)
dlpm/methods/LIM/models/layerspp.py:122
↓ 1 callersFunction_compute_fans
(shape, in_axis=1, out_axis=0)
dlpm/models/layers.py:60
↓ 1 callersFunction_compute_fans
(shape, in_axis=1, out_axis=0)
dlpm/models/layerspp.py:122
↓ 1 callersFunction_einsum
(a, b, c, x, y)
dlpm/methods/LIM/models/layers.py:534
↓ 1 callersFunction_einsum
(a, b, c, x, y)
dlpm/methods/LIM/models/layerspp.py:56
↓ 1 callersFunction_einsum
(a, b, c, x, y)
dlpm/models/layers.py:534
↓ 1 callersFunction_einsum
(a, b, c, x, y)
dlpm/models/layerspp.py:56
↓ 1 callersMethod_ema
(self, module)
bem/utils_ema.py:31
↓ 1 callersMethod_evaluate_model
(self, models, data_to_generate, batch
bem/evaluate/EvaluationManager.py:117
↓ 1 callersFunction_get_c_t
(alpha)
dlpm/methods/LIM/torchlevy/approx_score.py:25
↓ 1 callersFunction_get_c_t
(alpha)
bem/datasets/torchlevy/approx_score.py:25
↓ 1 callersMethod_get_device
()
bem/Experiments.py:31
↓ 1 callersFunction_get_pdf_dict
(alpha)
dlpm/methods/LIM/torchlevy/levy.py:227
↓ 1 callersFunction_get_pdf_dict
(alpha)
bem/datasets/torchlevy/levy.py:272
↓ 1 callersMethod_initialize
(self)
bem/Experiments.py:112
↓ 1 callersFunction_lim_ddpm_unet
(p)
dlpm/dlpm_experiment.py:60
↓ 1 callersFunction_lim_ncsnpp
(p)
dlpm/dlpm_experiment.py:64
↓ 1 callersMethod_load_experiment
(self, model_path, eval_path, logge
bem/utils_exp.py:452
↓ 1 callersMethod_make_paths
(self, train_path, val_path, test_path, wnids_path, words_path)
bem/datasets/tinyimagenet.py:83
↓ 1 callersMethod_optimize_gpu
(device)
bem/Experiments.py:49
↓ 1 callersMethod_pdf
(self, x: torch.Tensor, alpha, beta=0)
dlpm/methods/LIM/torchlevy/levy.py:33
↓ 1 callersMethod_pdf
(self, x: torch.Tensor, alpha, beta=0)
bem/datasets/torchlevy/levy.py:33
↓ 1 callersMethod_prepare_data_directories
(self, dataset_name, dataset_files, remove_existing_eval_files, num_real_data, hash_params)
bem/utils_exp.py:179
↓ 1 callersMethod_set_seed
(seed, device)
bem/Experiments.py:58
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