Torchélie
stable
Algorithms and training
Understanding recipes
Using the predefined recipes and callbacks
Recipes
Callbacks for Recipes
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Optimizers
LR Schedulers
Utils
Data Learning
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Models
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Datasets
Transforms
Torchélie
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Index
A
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B
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C
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D
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E
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F
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G
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H
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I
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K
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L
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M
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N
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O
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P
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R
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S
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T
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U
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V
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W
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X
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Z
A
AdaIN2d (class in torchelie.nn)
AdaptPad (class in torchelie.transforms)
AddSign (class in torchelie.optim)
as_multiclass_shape() (in module torchelie.utils)
attention56() (in module torchelie.models)
Attention56Bone (class in torchelie.models)
AutoGAN (class in torchelie.models)
autogan_128() (in module torchelie.models)
autogan_32() (in module torchelie.models)
autogan_64() (in module torchelie.models)
AutoGANGenBlock (class in torchelie.nn.blocks)
B
bgram() (in module torchelie.utils)
C
CachedDataset (class in torchelie.datasets)
Canny (class in torchelie.transforms)
center_crop() (in module torchelie.transforms.differentiable)
ChoiceSampler (class in torchelie.hyper)
ClassCondResNetBone() (in module torchelie.models)
ClassCondResNetDebug() (in module torchelie.models)
Classification() (in module torchelie.recipes)
(in module torchelie.recipes.classification)
Classifier (class in torchelie.models)
Classifier1 (class in torchelie.models)
ColoredColumns (class in torchelie.datasets)
ColoredRows (class in torchelie.datasets)
condition() (torchelie.nn.AdaIN2d method)
(torchelie.nn.CondSeq method)
(torchelie.nn.FiLM2d method)
(torchelie.nn.blocks.Conv2dCondBNReLU method)
(torchelie.nn.blocks.PreactResBlock method)
(torchelie.nn.blocks.ResBlock method)
CondSeq (class in torchelie.nn)
continuous_cross_entropy() (in module torchelie.loss)
ContinuousCEWithLogits (class in torchelie.loss)
Conv1x1() (in module torchelie.nn)
Conv2d() (in module torchelie.nn)
Conv2dBNReLU() (in module torchelie.nn.blocks)
Conv2dCondBNReLU (class in torchelie.nn.blocks)
Conv2dNormReLU() (in module torchelie.nn.blocks)
Conv3x3() (in module torchelie.nn)
CorrelateColors (class in torchelie.data_learning)
cpu() (torchelie.recipes.recipebase.Recipe method)
crop() (in module torchelie.transforms.differentiable)
CrossEntropyClassification() (in module torchelie.recipes)
(in module torchelie.recipes.classification)
cuda() (torchelie.recipes.recipebase.Recipe method)
CurriculumScheduler (class in torchelie.lr_scheduler)
D
Debug (class in torchelie.nn)
DecaySampler (class in torchelie.hyper)
DeepDream (class in torchelie.recipes.deepdream)
DeepDreamLoss (class in torchelie.loss)
DeepDreamOptim (class in torchelie.optim)
DetachedModule (class in torchelie.utils)
dict_by_key() (in module torchelie.utils)
Dummy (class in torchelie.nn)
E
entropy() (in module torchelie.utils)
ExpSampler (class in torchelie.hyper)
F
fake() (in module torchelie.loss.gan.hinge)
(in module torchelie.loss.gan.standard)
FeatureVis (class in torchelie.recipes.feature_vis)
FiLM2d (class in torchelie.nn)
fit() (torchelie.recipes.deepdream.DeepDream method)
(torchelie.recipes.feature_vis.FeatureVis method)
(torchelie.recipes.neural_style.NeuralStyle method)
focal_loss() (in module torchelie.loss)
FocalLoss (class in torchelie.loss)
forever() (in module torchelie.utils)
forward() (torchelie.data_learning.CorrelateColors method)
(torchelie.data_learning.ParameterizedImg method)
(torchelie.data_learning.PixelImage method)
(torchelie.data_learning.SpectralImage method)
(torchelie.loss.ContinuousCEWithLogits method)
(torchelie.loss.DeepDreamLoss method)
(torchelie.loss.FocalLoss method)
(torchelie.loss.NeuralStyleLoss method)
(torchelie.loss.OrthoLoss method)
(torchelie.loss.PerceptualLoss method)
(torchelie.loss.TemperedCrossEntropyLoss method)
(torchelie.loss.TotalVariationLoss method)
(torchelie.models.AutoGAN method)
(torchelie.models.Classifier method)
(torchelie.models.Classifier1 method)
(torchelie.models.PixelCNN method)
(torchelie.models.ProjectionDiscr method)
(torchelie.models.VggClassCondGeneratorDebug method)
(torchelie.models.VggImg2ImgGeneratorDebug method)
(torchelie.nn.AdaIN2d method)
(torchelie.nn.CondSeq method)
(torchelie.nn.Debug method)
(torchelie.nn.Dummy method)
(torchelie.nn.FiLM2d method)
(torchelie.nn.ImageNetInputNorm method)
(torchelie.nn.Lambda method)
(torchelie.nn.MaskedConv2d method)
(torchelie.nn.Noise method)
(torchelie.nn.PixelNorm method)
(torchelie.nn.Reshape method)
(torchelie.nn.TopLeftConv2d method)
(torchelie.nn.VQ method)
(torchelie.nn.WithSavedActivations method)
(torchelie.nn.blocks.AutoGANGenBlock method)
(torchelie.nn.blocks.Conv2dCondBNReLU method)
(torchelie.nn.blocks.HardSigmoid method)
(torchelie.nn.blocks.HardSwish method)
(torchelie.nn.blocks.PreactResBlock method)
(torchelie.nn.blocks.ResBlock method)
(torchelie.nn.blocks.SEBlock method)
(torchelie.nn.blocks.SNResidualDiscrBlock method)
freeze() (in module torchelie.utils)
FrozenModule (class in torchelie.utils)
G
GANRecipe() (in module torchelie.recipes.gan)
GaussianMixture (class in torchelie.distributions)
gblur() (in module torchelie.transforms.differentiable)
generated() (in module torchelie.loss.gan.hinge)
(in module torchelie.loss.gan.standard)
get_acts_() (torchelie.loss.DeepDreamLoss method)
get_params() (torchelie.transforms.ResizedCrop static method)
get_style_content_() (torchelie.loss.NeuralStyleLoss method)
gram() (in module torchelie.utils)
H
HardSigmoid (class in torchelie.nn.blocks)
HardSwish (class in torchelie.nn.blocks)
HorizontalConcatDataset (class in torchelie.datasets)
Hourglass (class in torchelie.models)
HyperparamSampler (class in torchelie.hyper)
HyperparamSearch (class in torchelie.hyper)
I
ilerp() (in module torchelie.utils)
ImageNetInputNorm (class in torchelie.nn)
invert() (torchelie.data_learning.CorrelateColors method)
K
kaiming() (in module torchelie.utils)
L
Lambda (class in torchelie.nn)
layer_by_name() (in module torchelie.utils)
lerp() (in module torchelie.utils)
load_recursive_state_dict() (in module torchelie.utils)
load_state_dict() (torchelie.optim.Lookahead method)
(torchelie.recipes.recipebase.Recipe method)
log_prob() (torchelie.distributions.GaussianMixture method)
(torchelie.distributions.LogisticMixture method)
log_result() (torchelie.hyper.HyperparamSearch method)
Logistic (class in torchelie.distributions)
LogisticMixture (class in torchelie.distributions)
Lookahead (class in torchelie.optim)
M
make_resnet_shortcut() (in module torchelie.nn.blocks)
MaskedConv2d (class in torchelie.nn)
mblur() (in module torchelie.transforms.differentiable)
MConvBNrelu() (in module torchelie.nn.blocks)
MConvNormReLU() (in module torchelie.nn.blocks)
mean (torchelie.distributions.GaussianMixture attribute)
(torchelie.distributions.LogisticMixture attribute)
mixup() (in module torchelie.datasets)
MixUpDataset (class in torchelie.datasets)
modules() (torchelie.recipes.recipebase.Recipe method)
MultiBranch (class in torchelie.transforms)
N
n002() (in module torchelie.utils)
nb_parameters() (in module torchelie.utils)
NeuralStyle (class in torchelie.recipes.neural_style)
NeuralStyleLoss (class in torchelie.loss)
NoexceptDataset (class in torchelie.datasets)
Noise (class in torchelie.nn)
O
OneCycle (class in torchelie.lr_scheduler)
ortho() (in module torchelie.loss)
OrthoLoss (class in torchelie.loss)
P
PairedDataset (class in torchelie.datasets)
ParameterizedImg (class in torchelie.data_learning)
Patch16() (in module torchelie.models)
Patch286() (in module torchelie.models)
Patch32() (in module torchelie.models)
Patch70() (in module torchelie.models)
patch_discr() (in module torchelie.models)
PerceptualLoss (class in torchelie.loss)
PerceptualNet() (in module torchelie.models)
PixelCNN (class in torchelie.models)
PixelImage (class in torchelie.data_learning)
PixelNorm (class in torchelie.nn)
PreactResBlock (class in torchelie.nn.blocks)
PreactResNetDebug() (in module torchelie.models)
proj_patch_discr() (in module torchelie.models)
ProjectionDiscr (class in torchelie.models)
ProjPatch32() (in module torchelie.models)
R
RAdamW (class in torchelie.optim)
real() (in module torchelie.loss.gan.hinge)
(in module torchelie.loss.gan.standard)
Recipe (class in torchelie.recipes.recipebase)
recursive_state_dict() (in module torchelie.utils)
register() (torchelie.recipes.recipebase.Recipe method)
render() (torchelie.data_learning.ParameterizedImg method)
ResBlock (class in torchelie.nn.blocks)
Reshape (class in torchelie.nn)
ResizedCrop (class in torchelie.transforms)
ResizeNoCrop (class in torchelie.transforms)
ResNetBone() (in module torchelie.models)
ResNetDebug() (in module torchelie.models)
roll() (in module torchelie.transforms.differentiable)
run() (torchelie.recipes.recipebase.Recipe method)
S
sample() (torchelie.hyper.ChoiceSampler method)
(torchelie.hyper.DecaySampler method)
(torchelie.hyper.ExpSampler method)
(torchelie.hyper.HyperparamSampler method)
(torchelie.hyper.HyperparamSearch method)
(torchelie.hyper.Sampler method)
(torchelie.models.PixelCNN method)
Sampler (class in torchelie.hyper)
SEBlock (class in torchelie.nn.blocks)
send_to_device() (in module torchelie.utils)
set_content() (torchelie.loss.NeuralStyleLoss method)
set_keep_layers() (torchelie.nn.WithSavedActivations method)
set_style() (torchelie.loss.NeuralStyleLoss method)
snres_discr() (in module torchelie.models)
snres_discr_4l() (in module torchelie.models)
snres_discr_5l() (in module torchelie.models)
snres_projdiscr() (in module torchelie.models)
snres_projdiscr_4l() (in module torchelie.models)
snres_projdiscr_5l() (in module torchelie.models)
SNResidualDiscrBlock (class in torchelie.nn.blocks)
SpadeResBlock (class in torchelie.nn.blocks)
SpectralImage (class in torchelie.data_learning)
state_dict() (torchelie.optim.Lookahead method)
(torchelie.recipes.recipebase.Recipe method)
step() (torchelie.lr_scheduler.CurriculumScheduler method)
(torchelie.lr_scheduler.OneCycle method)
(torchelie.optim.AddSign method)
(torchelie.optim.DeepDreamOptim method)
(torchelie.optim.Lookahead method)
(torchelie.optim.RAdamW method)
Subset (class in torchelie.datasets)
T
tempered_cross_entropy() (in module torchelie.loss)
tempered_log_softmax() (in module torchelie.loss)
tempered_nll_loss() (in module torchelie.loss)
tempered_softmax() (in module torchelie.loss)
TemperedCrossEntropyLoss (class in torchelie.loss)
to() (torchelie.recipes.recipebase.Recipe method)
TopLeftConv2d (class in torchelie.nn)
torchelie.callbacks (module)
torchelie.data_learning (module)
torchelie.distributions (module)
torchelie.hyper (module)
torchelie.loss.gan.hinge (module)
torchelie.loss.gan.standard (module)
torchelie.lr_scheduler (module)
torchelie.nn.blocks (module)
torchelie.recipes (module)
torchelie.recipes.deepdream (module)
torchelie.recipes.feature_vis (module)
torchelie.recipes.image_prior (module)
torchelie.recipes.neural_style (module)
torchelie.transforms.differentiable (module)
torchelie.utils (module)
total_variation() (in module torchelie.loss)
TotalVariationLoss (class in torchelie.loss)
TrainAndCall() (in module torchelie.recipes)
TrainAndTest() (in module torchelie.recipes)
(in module torchelie.recipes.trainandtest)
U
UNet() (in module torchelie.models)
UNetBone (class in torchelie.models)
unfreeze() (in module torchelie.utils)
V
VectorCondResNetBone() (in module torchelie.models)
VectorCondResNetDebug() (in module torchelie.models)
VggBNBone() (in module torchelie.models)
VggClassCondGeneratorDebug (class in torchelie.models)
VggDebug() (in module torchelie.models)
VggImg2ImgGeneratorDebug (class in torchelie.models)
VQ (class in torchelie.nn)
W
WithIndexDataset (class in torchelie.datasets)
WithSavedActivations (class in torchelie.nn)
X
xavier() (in module torchelie.utils)
Z
zero_grad() (torchelie.optim.Lookahead method)
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