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Functions576 in github.com/JCBrouwer/maua-stylegan2

⨍ Functions 576◇ Types & classes 120
↓ 31 callersMethodload
(self, num=-1)
lucidrains.py:1141
↓ 31 callersMethodload_state_dict
(self, G_state_dict, D_state_dict)
lookahead_minimax.py:79
↓ 21 callersMethodstate_dict
(self)
lookahead_minimax.py:76
↓ 17 callersMethod__init__
(self, kernel, pad, upsample_factor=1)
models/stylegan2.py:77
↓ 17 callersMethod__init__
(self, in_channels=3)
models/autoencoder.py:239
↓ 16 callersMethodstep
Performs a single Lookahead optimization step on BOTH optimizers after the generator's optimizer step. This allows the discriminator
lookahead_minimax.py:135
↓ 16 callersMethodzero_grad
(self)
lookahead_minimax.py:70
↓ 15 callersMethodbackward
(self, trainer, loss, optimizer, optimizer_idx)
lightning.py:202
↓ 15 callersFunctionupdate
(state_dict, new)
convert_weight.py:93
↓ 13 callersMethod__init__
(self, output_size=1920, checkpoint=None)
models/stylegan1.py:510
↓ 13 callersFunctionrandom_mat_apply
(p, transform, prev, eye)
augment.py:143
↓ 13 callersMethodsave
(self, num)
lucidrains.py:1137
↓ 12 callersMethod__init__
(self, beta)
lucidrains.py:64
↓ 10 callersMethodencode
(self, inputs)
models/autoencoder.py:261
↓ 7 callersFunctiongaussian_filter
(x, sigma)
generate_video.py:14
↓ 6 callersFunctionaugment
(img, p, transform_matrix=(None, None))
augment.py:364
↓ 6 callersFunctioncreate_decoder_single_conv
(in_chs, out_chs, kernel)
models/autoencoder.py:563
↓ 6 callersFunctioncreate_encoder_single_conv
(in_chs, out_chs, kernel)
models/autoencoder.py:481
↓ 6 callersMethoddecode
(self, inp)
models/autoencoder.py:269
↓ 6 callersFunctionget_rank
()
distributed.py:7
↓ 6 callersFunctionimage_noise
(n, im_size)
lucidrains.py:178
↓ 6 callersMethodmake_noise
(self, batch, batch_size=None)
lightning.py:119
↓ 5 callersMethod__init__
( self, pnet_type="vgg", pnet_rand=False, pnet_tune=False, use_dropout
validation/lpips/networks_basic.py:22
↓ 5 callersMethodapply
(module, name, n_power_iterations, dim, eps, normalize=True)
validation/spectral_norm.py:54
↓ 5 callersFunctionconvert_conv
(vars, source_name, target_name, bias=True, start=0)
convert_weight.py:40
↓ 5 callersFunctionnoise_list
(n, layers, latent_dim)
lucidrains.py:165
↓ 5 callersMethodrandom_indices
(self, shape)
models/autoencoder.py:254
↓ 5 callersFunctionstyles_def_to_tensor
(styles_def)
lucidrains.py:194
↓ 5 callersMethodtrain
(self)
lucidrains.py:756
↓ 5 callersMethodupdate_viewport
(self)
select_latents.py:213
↓ 5 callersFunctionupfirdn2d
(input, kernel, up=1, down=1, pad=(0, 0))
op/upfirdn2d.py:145
↓ 4 callersMethod__init__
Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return featur
validation/inception.py:33
↓ 4 callersMethod__init__
(self, modulation, h, w)
audioreactive/bend.py:82
↓ 4 callersMethod_create_upsampling_module
(self, input_channels, pooling_kenel)
models/autoencoder.py:622
↓ 4 callersMethod_mousewheel_bind
(self, widget)
select_latents.py:79
↓ 4 callersMethod_mousewheel_unbind
(self)
select_latents.py:82
↓ 4 callersMethodcalculate_loss
(self)
contrastive_learner.py:223
↓ 4 callersMethodforward
(self, in0, in1, retPerLayer=None)
validation/lpips/networks_basic.py:176
↓ 4 callersMethodgenerate_truncated
(self, S, G, style, noi, trunc_psi=0.75, num_image_tiles=8)
lucidrains.py:1099
↓ 4 callersFunctionleaky_relu
(p=0.2)
lucidrains.py:182
↓ 4 callersFunctionlognormal_sample
(size, mean=0, std=1)
augment.py:120
↓ 4 callersMethodmean_latent
(self, n_latent)
models/stylegan1.py:576
↓ 4 callersFunctionnormalize
(x)
validation/calc_ppl.py:12
↓ 4 callersFunctionrequires_grad
(model, flag=True)
train_profile.py:41
↓ 4 callersFunctionrequires_grad
(model, flag=True)
train.py:40
↓ 4 callersFunctionrequires_grad
(model, flag=True)
lightning.py:20
↓ 4 callersFunctionuniform_sample
(size, low, high)
augment.py:131
↓ 3 callersMethod__init__
(self, beta)
contrastive_learner.py:96
↓ 3 callersMethod__init__
(self, requires_grad=False, pretrained=True)
validation/lpips/pretrained_networks.py:99
↓ 3 callersMethod_create_downsampling_module
(self, input_channels, pooling_kenel)
models/autoencoder.py:539
↓ 3 callersFunctioncategory_sample
(size, categories)
augment.py:124
↓ 3 callersFunctionconvert_dense
(vars, source_name, target_name)
convert_weight.py:79
↓ 3 callersFunctionconvert_modconv
(vars, source_name, target_name, flip=False)
convert_weight.py:14
↓ 3 callersFunctionevaluate_in_chunks
(max_batch_size, model, *args)
lucidrains.py:186
↓ 3 callersMethodforward
Function computes the distance between image patches in0 and in1 INPUTS in0, in1 - torch.Tensor object of shape Nx3xXxY - image p
validation/lpips/dist_model.py:124
↓ 3 callersFunctiongaussian_filter
Smooth tensors along time (first) axis with gaussian kernel. Args: x (th.tensor): Tensor to be smoothed sigma (float): Standard d
audioreactive/signal.py:319
↓ 3 callersFunctionget_world_size
()
distributed.py:32
↓ 3 callersMethodinitialize
INPUTS model - ['net-lin'] for linearly calibrated network ['net'] for off-the-shelf network
validation/lpips/dist_model.py:19
↓ 3 callersFunctionlatent_to_w
(style_vectorizer, latent_descr)
lucidrains.py:174
↓ 3 callersFunctionmake_kernel
(k)
models/stylegan2.py:23
↓ 3 callersFunctionmake_noise
(batch_size, latent_dim, prob)
train_profile.py:59
↓ 3 callersFunctionmake_noise
(batch_size, latent_dim, prob)
train.py:58
↓ 3 callersFunctionnoise
(n, latent_dim)
lucidrains.py:161
↓ 3 callersFunctionreduce_sum
(tensor)
distributed.py:42
↓ 3 callersFunctionrotate_mat
(theta)
augment.py:35
↓ 3 callersMethodsave_network
(self, network, path, network_label, epoch_label)
validation/lpips/base_model.py:35
↓ 3 callersFunctionscale_mat
(s_x, s_y)
augment.py:47
↓ 3 callersFunctionupdate_moving_average
(ema_updater, ma_model, current_model)
contrastive_learner.py:106
↓ 2 callersMethod_make_mouse_wheel_handler
(widget, orient, factor=1 / 120, what="units")
select_latents.py:114
↓ 2 callersMethod_register_hook
(self)
contrastive_learner.py:137
↓ 2 callersFunctionaccumulate
(model1, model2, decay=0.5 ** (32.0 / 10_000))
train_profile.py:46
↓ 2 callersFunctionaccumulate
(model1, model2, decay=0.5 ** (32.0 / 10_000))
train.py:45
↓ 2 callersMethodaccumulate_g
(self, decay=0.5 ** (32.0 / (10_000)))
lightning.py:53
↓ 2 callersFunctioncast_list
(el)
lucidrains.py:123
↓ 2 callersFunctioncompute_nearest_neighbour_distances
(input_features, nearest_k, metric)
validation/metrics.py:195
↓ 2 callersFunctioncompute_pairwise_distance
(data_x, data_y=None, metric="l2")
validation/metrics.py:179
↓ 2 callersMethodconfig
(self)
lucidrains.py:732
↓ 2 callersFunctionconvert_torgb
(vars, source_name, target_name)
convert_weight.py:58
↓ 2 callersMethodevaluate
(self, num=0, num_image_tiles=8)
lucidrains.py:947
↓ 2 callersFunctionfill_statedict
(state_dict, vars, size)
convert_weight.py:131
↓ 2 callersFunctionfused_leaky_relu
(input, bias, negative_slope=0.2, scale=2 ** 0.5)
op/fused_act.py:86
↓ 2 callersMethodget_latent
(self, inputs)
models/stylegan2.py:489
↓ 2 callersFunctionget_tensors
(gpu_only=True)
gpu_profile.py:95
↓ 2 callersFunctiongpu_profile
(frame, event, arg)
gpu_profile.py:28
↓ 2 callersFunctionim2tensor
(image, imtype=np.uint8, cent=1.0, factor=255.0 / 2.0)
validation/lpips/util.py:93
↓ 2 callersMethodinit_GAN
(self)
lucidrains.py:702
↓ 2 callersMethodinit_folders
(self)
lucidrains.py:1127
↓ 2 callersFunctioninterpolant
(t)
generate_video.py:54
↓ 2 callersFunctionlerp
(val, low, high)
generate_video.py:50
↓ 2 callersFunctionlerp
(a, b, t)
validation/metrics.py:217
↓ 2 callersFunctionlerp
(a, b, t)
validation/calc_ppl.py:27
↓ 2 callersMethodlerp
(a, b, t)
lucidrains.py:1052
↓ 2 callersMethodloss
(self, real, fake, mu, log_var)
models/autoencoder.py:130
↓ 2 callersMethodmodel_name
(self, num)
lucidrains.py:1124
↓ 2 callersFunctionnormal_sample
(size, mean=0, std=1)
augment.py:135
↓ 2 callersFunctionnp2tensor
(np_obj)
validation/lpips/util.py:41
↓ 2 callersFunctionpercentile_clip
Normalize signal between 0 and 1, clipping peak values above given percentile Args: signal (th.tensor): Signal to normalize p (in
audioreactive/signal.py:271
↓ 2 callersFunctionperlin_pls
(resolution)
audioreactive/examples/kelp.py:97
↓ 2 callersFunctionprdc
(real_features, fake_features, nearest_k=10, metric="l2")
validation/metrics.py:201
↓ 2 callersFunctionraw_chroma
Creates chromagram Args: audio (np.array): Audio signal sr (int): Sampling rate of the audio type (str, optional): ["cens
audioreactive/signal.py:102
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