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Functions586 in github.com/MaximeVandegar/Papers-in-100-Lines-of-Code

⨍ Functions 586◇ Types & classes 169
↓ 78 callersMethodstep
(self)
Adam_a_Method_For_Stochastic_Optimization/adam.py:34
↓ 63 callersMethodzero_grad
(self)
Adam_a_Method_For_Stochastic_Optimization/adam.py:29
↓ 25 callersMethod__init__
(self, dim_in, dim_out)
High_Resolution_Image_Synthesis_with_Latent_Diffusion_Models/model.py:340
↓ 17 callersFunctionload_data
(data_path, json_path, train=True, N=25, H=128, W=128)
Learned_Initializations_for_Optimizing_Coordinate_Based_Neural_Representations/nerf_mv.py:60
↓ 13 callersMethodsample
(size)
Sequential_Neural_Likelihood/snl.py:18
↓ 10 callersMethodlen
(self)
Proximal_Policy_Optimization_Algorithms/ppo.py:39
↓ 10 callersMethodsample
(size)
Likelihood_free_MCMC_with_Amortized_Approximate_Ratio_Estimators/AALR-MCMC.py:33
↓ 9 callersMethodstep
(self, env_id, action)
Proximal_Policy_Optimization_Algorithms/ppo.py:51
↓ 8 callersMethodlog_prob
(x_batch)
Sequential_Neural_Likelihood/snl.py:12
↓ 6 callersFunctioncheckerboard_mask
(H, W, invert=False, device=None)
Density_Estimation_Using_Real_NVP/real_nvp.py:27
↓ 5 callersMethod__init__
(self, in_dim, out_dim, scale=1e-10)
Pseudo_Numerical_Methods_for_Diffusion_Models_on_Manifolds/unet.py:63
↓ 5 callersMethod__init__
(self, in_dim, out_dim, scale=1e-10)
Denoising_Diffusion_Probabilistic_Models/unet.py:63
↓ 5 callersMethod__init__
(self, in_dim, out_dim, scale=1e-10)
DPM_Solver_A_Fast_ODE_Solver_for_Diffusion_Probabilistic_Model_Sampling_in_Around_10_Steps/unet.py:63
↓ 5 callersMethod__init__
(self, channels, hidden)
Density_Estimation_Using_Real_NVP/real_nvp.py:93
↓ 5 callersMethod__init__
(self, in_dim, out_dim, scale=1e-10)
Denoising_Diffusion_Implicit_Models/unet.py:63
↓ 5 callersMethod__init__
(self, features, hidden_features, num_layers=10, hidden_layers=2)
Masked_Autoregressive_Flow_for_Density_Estimation/maf.py:127
↓ 5 callersMethod_phi
(self, x, eps, t, t_next)
Pseudo_Numerical_Methods_for_Diffusion_Models_on_Manifolds/pndms_solver.py:92
↓ 5 callersMethodlog_prob
(x_batch)
Likelihood_free_MCMC_with_Amortized_Approximate_Ratio_Estimators/AALR-MCMC.py:27
↓ 5 callersMethodsample
(self, size, device)
Deep_Unsupervised_Learning_using_Nonequilibrium_Thermodynamics/diffusion_models.py:73
↓ 5 callersFunctionsymsqrt
Compute the square root of a positive definite matrix. Retrieved from https://github.com/pytorch/pytorch/issues/25481#issuecomment-544465798.
Gromov_Wasserstein_Distances_between_Gaussian_Distributions/GWOT.py:5
↓ 4 callersMethod__init__
(self, data_path)
Improved_Training_of_Wasserstein_GANs/wgan.py:99
↓ 4 callersFunctionchannel_mask
(C, first_half=True, device=None)
Density_Estimation_Using_Real_NVP/real_nvp.py:35
↓ 4 callersMethodget_mean_and_log_var
(self, x)
Auto_Encoding_Variational_Bayes/VAEs.py:38
↓ 4 callersFunctionintrinsics_to_fxfycxcy
(camera_mat, H, W)
Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/splatter_image.py:74
↓ 4 callersFunctionmlp
(x, params)
Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks/maml.py:9
↓ 4 callersMethodpositional_encoding
(x, L)
FastNeRF_High_Fidelity_Neural_Rendering_at_200FPS/fast_nerf.py:32
↓ 3 callersMethod__init__
(self, in_ch, skip_ch, out_ch)
Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/unet.py:42
↓ 3 callersMethod__init__
(self)
Image_to_Image_Translation_with_Conditional_Adversarial_Nets/pix2pix.py:44
↓ 3 callersMethod__init__
(self, size=50)
Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks/cycle_gan.py:78
↓ 3 callersMethod__init__
(self, w0=30, in_dim=2, hidden_dim=256, out_dim=1)
Multiplicative_Filter_Networks/mfn.py:21
↓ 3 callersMethod__init__
(self, in_channels, nd, kd)
Deep_Image_Prior/deep_image_prior.py:12
↓ 3 callersFunctioncompute_loss
(x, label, model, weight_decay)
Optimizing_Millions_of_Hyperparameters_by_Implicit_Differentiation/gradient_based_HO.py:54
↓ 3 callersFunctioncreate_mask
(in_deg, out_deg)
Masked_Autoregressive_Flow_for_Density_Estimation/maf.py:11
↓ 3 callersMethodencode
(self, x)
High_Resolution_Image_Synthesis_with_Latent_Diffusion_Models/model.py:293
↓ 3 callersMethodforward_process
(self, x0, t)
Deep_Unsupervised_Learning_using_Nonequilibrium_Thermodynamics/diffusion_models.py:47
↓ 3 callersMethodinverse
(self, z)
Masked_Autoregressive_Flow_for_Density_Estimation/maf.py:107
↓ 3 callersMethodsample
(self, K)
Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks/maml.py:21
↓ 3 callersFunctionsample_latent
(batch_size, device)
Adversarially_Learned_Inference/ali.py:19
↓ 3 callersFunctionsample_multimodal_distribution
(modes, std, batch_size=1000)
Flow_Straight_and_Fast_Learning_to_Generate_and_Transfer_Data_with_Rectified_Flow/flow_straight_and_fast.py:34
↓ 3 callersFunctionsample_noise
(batch_size, device)
Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks/conv_gan.py:65
↓ 3 callersFunctionsample_noise
(size, dim=256)
Least_Squares_Generative_Adversarial_Networks/lsgan.py:21
↓ 3 callersFunctionsample_noise
(size, dim=100)
Generative_Adversarial_Networks/GANs.py:19
↓ 3 callersFunctionsample_noise
(batch_size, device)
Improved_Training_of_Wasserstein_GANs/wgan.py:118
↓ 3 callersFunctionsample_noise
(size, device, dim=100)
Conditional_Generative_Adversarial_Nets/cgan.py:21
↓ 3 callersMethodstep
(self)
On_the_Variance_of_the_Adaptive_Learning_Rate_and_Beyond/radam.py:36
↓ 3 callersMethodzero_grad
(self)
On_the_Variance_of_the_Adaptive_Learning_Rate_and_Beyond/radam.py:31
↓ 2 callersMethod__init__
(self)
Adversarially_Learned_Inference/ali.py:25
↓ 2 callersMethod__init__
(self, latent_dim=100, output_dim=28 * 28)
Improved_Techniques_for_Training_GANs/semi_supervised_learning.py:52
↓ 2 callersMethod__init__
(self, data_path)
Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks/conv_gan.py:15
↓ 2 callersMethod__init__
(self, input_dim=28 * 28, hidden_dim=28 * 28, output_dim=10, depth=8)
Self_Normalizing_Neural_Networks/selu.py:37
↓ 2 callersMethod__init__
(self, in_dim=2, hidden_dim=256, out_dim=1)
Implicit_Neural_Representations_with_Periodic_Activation_Functions/siren.py:46
↓ 2 callersMethod__init__
(self, data_path='data')
Wasserstein_GAN/wgan.py:15
↓ 2 callersMethod__init__
(self)
Adversarial_Feature_Learning/adversarial_feature_learning.py:24
↓ 2 callersMethod_compute_batch_stats
(self, x)
Masked_Autoregressive_Flow_for_Density_Estimation/maf.py:48
↓ 2 callersMethod_tok
(self, prompt: str)
DreamBooth_Fine_Tuning_Text_to_Image_Diffusion_Models_for_Subject_Driven_Generation/dreambooth.py:46
↓ 2 callersFunctioncompute_pred
(x, label, model)
Optimizing_Millions_of_Hyperparameters_by_Implicit_Differentiation/gradient_based_HO.py:60
↓ 2 callersMethoddecode
(self, z)
High_Resolution_Image_Synthesis_with_Latent_Diffusion_Models/model.py:303
↓ 2 callersFunctiondecode_gaussians
(raw, source_c2w, fx, fy, cx, cy, znear, zfar, opacity_threshold=0.0)
Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/splatter_image.py:91
↓ 2 callersMethodencode_text
(self, input_ids)
High_Resolution_Image_Synthesis_with_Latent_Diffusion_Models/sample.py:31
↓ 2 callersMethodforward_and_logdet
(self, y)
Masked_Autoregressive_Flow_for_Density_Estimation/maf.py:136
↓ 2 callersFunctiongaussian_log_pdf
Arguments: ---------- - z: a batch of m data points (size: m x data_dim)
Variational_Inference_with_Normalizing_Flows/Flows.py:9
↓ 2 callersFunctionget_minibatch
(batch_size, device)
Adversarial_Feature_Learning/adversarial_feature_learning.py:17
↓ 2 callersFunctionget_minibatch
(batch_size, device)
Conditional_Generative_Adversarial_Nets/cgan.py:14
↓ 2 callersMethodinvert
(self, z)
NICE_Non_linear_Independent_Components_Estimation/NICE.py:68
↓ 2 callersFunctionperform_k_training_steps
(model, task, k, batch_size=10)
On_First_Order_Meta_Learning_Algorithms/reptile.py:50
↓ 2 callersFunctionperform_k_training_steps
(nerf_model, task, k, optimizer, batch_size=128, device='cpu', hn=2., hf=6., nb_b
Learned_Initializations_for_Optimizing_Coordinate_Based_Neural_Representations/nerf_mv.py:104
↓ 2 callersFunctionperform_k_training_steps
(params, task, batch_size, inner_training_steps, alpha, device='cpu')
Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks/maml.py:35
↓ 2 callersMethodpositional_encoding
(self, x, L, step, is_pos=False)
FreeNeRF_Improving_Few_shot_Neural_Rendering_with_Free_Frequency_Regularization/freenerf.py:48
↓ 2 callersMethodpositional_encoding
(x, L)
InfoNeRF_Ray_Entropy_Minimization_for_Few_Shot_Neural_Volume_Rendering/infonerf.py:51
↓ 2 callersMethodpositional_encoding
(x, L)
NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis/nerf.py:65
↓ 2 callersMethodpositional_encoding
(x, L)
KiloNeRF_Speeding_up_Neural_Radiance_Fields_with_Thousands_of_Tiny_MLPs/kilo_nerf.py:53
↓ 2 callersMethodpositional_encoding
(x, L)
Neural_Radiance_Fields_Without_Known_Camera_Parameters/nerfmm.py:50
↓ 2 callersMethodquery
(self, image)
Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks/cycle_gan.py:82
↓ 2 callersFunctionrender
(pos, color, opacity_raw, sigma, c2w, H, W, fx, fy, cx, cy, near=2e-3, far=100, pix_guard=64, T=16,
Splatter_Image_Ultra_Fast_Single_View_3D_Reconstruction/gaussian_splatting.py:114
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
KPlanes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance/kplanes.py:81
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, step, hn=0, hf=0.5, nb_bins=192)
FreeNeRF_Improving_Few_shot_Neural_Rendering_with_Free_Frequency_Regularization/freenerf.py:77
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192, T=0.1)
InfoNeRF_Ray_Entropy_Minimization_for_Few_Shot_Neural_Volume_Rendering/infonerf.py:76
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
PlenOctrees_for_Real_time_Rendering_of_Neural_Radiance_Fields/nerf-sg.py:87
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
Fourier_Features_Let_Networks_Learn_High_Frequency_Functions_in_Low_Dimensional_Domains/inverse_rendering.py:60
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
Learned_Initializations_for_Optimizing_Coordinate_Based_Neural_Representations/nerf_mv.py:36
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
Plenoxels_Radiance_Fields_without_Neural_Networks/plenoxels.py:68
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis/nerf.py:89
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
KiloNeRF_Speeding_up_Neural_Radiance_Fields_with_Thousands_of_Tiny_MLPs/kilo_nerf.py:93
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
Instant_Neural_Graphics_Primitives_with_a_Multiresolution_Hash_Encoding/ngp.py:98
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
Neural_Radiance_Fields_Without_Known_Camera_Parameters/nerfmm.py:72
↓ 2 callersFunctionrender_rays
(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192)
FastNeRF_High_Fidelity_Neural_Rendering_at_200FPS/fast_nerf.py:98
↓ 2 callersMethodreset_env
(self, env_id)
Proximal_Policy_Optimization_Algorithms/ppo.py:42
↓ 2 callersMethodreverse
(self, xt, t)
Deep_Unsupervised_Learning_using_Nonequilibrium_Thermodynamics/diffusion_models.py:64
↓ 2 callersFunctionsample
(tokenizer, text_encoder, vae, unet, prompts, size=512, device="cuda:0")
DreamBooth_Fine_Tuning_Text_to_Image_Diffusion_Models_for_Subject_Driven_Generation/dreambooth.py:14
↓ 2 callersMethodsample
(self, size)
Gromov_Wasserstein_Distances_between_Gaussian_Distributions/GWOT.py:37
↓ 2 callersMethodsample
(self, x)
Adversarially_Learned_Inference/ali.py:47
↓ 2 callersFunctionsample_bach
(batch_size, device)
Adversarially_Learned_Inference/ali.py:14
↓ 2 callersFunctionsample_batch
(size)
Deep_Unsupervised_Learning_using_Nonequilibrium_Thermodynamics/diffusion_models.py:10
↓ 2 callersFunctionsample_batch
(camera_extrinsics, camera_intrinsics, images, batch_size, H, W, img_index=0, sample_all=False)
Neural_Radiance_Fields_Without_Known_Camera_Parameters/nerfmm.py:146
↓ 2 callersFunctionsample_from_mcmc
(prior, likelihood_function, observation_x, T=5000, thinning=10, num_chains=10, transitio
Sequential_Neural_Likelihood/snl.py:67
↓ 2 callersFunctionsample_latent
(batch_size, device)
Adversarial_Feature_Learning/adversarial_feature_learning.py:13
↓ 2 callersFunctionsample_noise
(batch_size, device)
Wasserstein_GAN/wgan.py:80
↓ 2 callersFunctionsample_task
()
On_First_Order_Meta_Learning_Algorithms/reptile.py:38
↓ 2 callersFunctionsample_task
(rays_o, rays_d, gt_pixels)
Learned_Initializations_for_Optimizing_Coordinate_Based_Neural_Representations/nerf_mv.py:99
↓ 2 callersFunctionsample_task
()
Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks/maml.py:29
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