Recipes¶

Recipes are a way to provide off the shelf algorithms that ce be either used directly from the command line or easily imported in a python script if more flexibility is needed.

A recipe should, as much a possible, be agnostic of the data and the underlying model so that it can be used as a way to quickly try an algorithm on new data or be easily experimented on by changing the model

class torchelie.recipes.recipebase.Recipe(call_fun, loader)¶

Basic recipe that iterates mutiple epochs over a dataset. That loop is instrumented through several configurable callbacks. Callbacks can handle events before and after each batch, before and after each epoch. Each batch is treated with a user supplied function that manipulates it and returns a dict that returns a state usable by the callbacks.

A recipe can be saved by calling its state_dict() member. All its hyper parameters and state will be saved so that it can restart, but requires the exact same setting of callbacks.

You can register multiple objects to a Recipe with register(). If it has a state_dict() member, its state will be saved into the recipe’s when calling the recipe’s state_dict() function. If it has a member to(), moving the recipe to another device will also move those objects.

Parameters:	call_fun (Callable) – A function that takes a batch as an argument and returns a dict of value to feed the state loader (Iterable) – any iterable (most likely a DataLoader)

cpu()¶: Move a recipe and all its movable registered objects to cpu

cuda()¶: Move a recipe and all its movable registered objects to cuda

load_state_dict(state_dict)¶: Restore a recipe

modules()¶: Iterate over all nn.Modules registered in the recipe

register(name, value)¶

Register an object into the recipe as a member. Calling recipe.register('foo', bar) registers bar, and makes it usable through recipe.foo.

Parameters:	name (str) – member’s name value – the object to register

run(epochs)¶

Run the recipe for epochs epochs.

Parameters:	epochs (int) – number of epochs
Returns:	The state

state_dict()¶

Returns:	A state dict

to(device)¶

Move a recipe and all its movable registered objects to a device

Parameters:	device – a torch device

torchelie.recipes.TrainAndCall(model, train_fun, test_fun, train_loader, test_every=100, visdom_env='main', checkpoint='model', log_every=10)¶

Train a model and evaluate it with a custom function. The model is automatically checkpointed, VisdomLogger and StdoutLogger callbacks are also already provided. The gradients are disabled and the model is automatically set to evaluation mode for the evaluation procedure.

Parameters:

model (nn.Model) – a model
train_fun (Callabble) – a function that takes a batch as a single argument, performs a training step and return a dict of values to populate the recipe’s state.
test_fun (Callable) – a function taking no argument that performs something to evaluate your model and returns a dict to populate the state.
train_loader (DataLoader) – Training set dataloader
test_every (int) – testing frequency, in number of iterations (default: 100)
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
checkpoint (str) – checkpointing path or None for no checkpointing
log_every (int) – logging frequency, in number of iterations (default: 10)

Returns:

a configured Recipe

torchelie.recipes.TrainAndTest(model, train_fun, test_fun, train_loader, test_loader, test_every=100, visdom_env='main', log_every=10, checkpoint='model')¶

Perform training and testing on datasets. The model is automatically checkpointed, VisdomLogger and StdoutLogger callbacks are also already provided. The gradients are disabled and the model is automatically set to evaluation mode for the evaluation procedure.

Parameters:

model (nn.Model) – a model
train_fun (Callabble) – a function that takes a batch as a single argument, performs a training step and return a dict of values to populate the recipe’s state.
test_fun (Callable) – a function taking a batch as a single argument then performs something to evaluate your model and returns a dict to populate the state.
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
test_every (int) – testing frequency, in number of iterations (default: 100)
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
log_every (int) – logging frequency, in number of iterations (default: 100)
checkpoint (str) – checkpointing path or None for no checkpointing

torchelie.recipes.Classification(model, train_fun, test_fun, train_loader, test_loader, classes, visdom_env=None, test_every=1000, log_every=100)¶

Classification training and testing loop. Displays Loss, accuracy, gradient based visualization of features, a classification report on worst samples, best samples and confusing samples, a confusion matrix, VisdomLogger, StdLogger, MetricsTable.

Parameters:

model (nn.Model) – a model
train_fun (Callabble) – a function that takes a batch as a single argument, performs a training forward pass and return a dict of values to populate the recipe’s state. It expects the logits predictions under key “preds”, and this batch’s loss under key “loss”.
test_fun (Callable) – a function taking a batch as a single argument then performs something to evaluate your model and returns a dict to populate the state. It expects the logits predictions under key “preds”, and this batch’s loss under key “loss”.
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
classes (list of str) – classes name, in order
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
test_every (int) – testing frequency, in number of iterations (default: 1000)
log_every (int) – logging frequency, in number of iterations (default: 100)

torchelie.recipes.CrossEntropyClassification(model, train_loader, test_loader, classes, lr=0.003, beta1=0.9, wd=0.01, visdom_env='main', test_every=1000, log_every=100)¶

A Classification recipe with a default froward training / testing pass using cross entropy, and extended with RAdamW and ReduceLROnPlateau.

Parameters:

model (nn.Module) – a model learnable with cross entropy
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
classes (list of str) – classes name, in order
lr (float) – the learning rate
beta1 (float) – RAdamW’s beta1
wd (float) – weight decay
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
test_every (int) – testing frequency, in number of iterations (default: 1000)
log_every (int) – logging frequency, in number of iterations (default: 1000)

torchelie.recipes.gan.GANRecipe(G, D, G_fun, D_fun, loader, visdom_env='main', log_every=10)¶

(unstable)

Model Training¶

torchelie.recipes.trainandtest.TrainAndTest(model, train_fun, test_fun, train_loader, test_loader, test_every=100, visdom_env='main', log_every=10, checkpoint='model')¶

Perform training and testing on datasets. The model is automatically checkpointed, VisdomLogger and StdoutLogger callbacks are also already provided. The gradients are disabled and the model is automatically set to evaluation mode for the evaluation procedure.

Parameters:

model (nn.Model) – a model
train_fun (Callabble) – a function that takes a batch as a single argument, performs a training step and return a dict of values to populate the recipe’s state.
test_fun (Callable) – a function taking a batch as a single argument then performs something to evaluate your model and returns a dict to populate the state.
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
test_every (int) – testing frequency, in number of iterations (default: 100)
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
log_every (int) – logging frequency, in number of iterations (default: 100)
checkpoint (str) – checkpointing path or None for no checkpointing

torchelie.recipes.classification.Classification(model, train_fun, test_fun, train_loader, test_loader, classes, visdom_env=None, test_every=1000, log_every=100)¶

Classification training and testing loop. Displays Loss, accuracy, gradient based visualization of features, a classification report on worst samples, best samples and confusing samples, a confusion matrix, VisdomLogger, StdLogger, MetricsTable.

Parameters:

model (nn.Model) – a model
train_fun (Callabble) – a function that takes a batch as a single argument, performs a training forward pass and return a dict of values to populate the recipe’s state. It expects the logits predictions under key “preds”, and this batch’s loss under key “loss”.
test_fun (Callable) – a function taking a batch as a single argument then performs something to evaluate your model and returns a dict to populate the state. It expects the logits predictions under key “preds”, and this batch’s loss under key “loss”.
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
classes (list of str) – classes name, in order
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
test_every (int) – testing frequency, in number of iterations (default: 1000)
log_every (int) – logging frequency, in number of iterations (default: 100)

torchelie.recipes.classification.CrossEntropyClassification(model, train_loader, test_loader, classes, lr=0.003, beta1=0.9, wd=0.01, visdom_env='main', test_every=1000, log_every=100)¶

A Classification recipe with a default froward training / testing pass using cross entropy, and extended with RAdamW and ReduceLROnPlateau.

Parameters:

model (nn.Module) – a model learnable with cross entropy
train_loader (DataLoader) – Training set dataloader
test_loader (DataLoader) – Testing set dataloader
classes (list of str) – classes name, in order
lr (float) – the learning rate
beta1 (float) – RAdamW’s beta1
wd (float) – weight decay
visdom_env (str) – name of the visdom environment to use, or None for not using Visdom (default: None)
test_every (int) – testing frequency, in number of iterations (default: 1000)
log_every (int) – logging frequency, in number of iterations (default: 1000)

Deep Dream¶

Deep Dream recipe.

Performs the algorithm described in https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

This implementation differs from the original one: the image is optimized in Fourier space, for greater details and colors, the model and layers are customizable.

A commandline interface is provided through python3 -m torchelie.recipes.deepdream, and a DeepDreamRecipe is provided.

class torchelie.recipes.deepdream.DeepDream(model, dream_layer)¶

Deep Dream recipe

First instantiate the recipe then call recipe(n_iter, img)

Parameters:	model (nn.Module) – the trained model to use dream_layer (str) – the layer to use on which activations will be maximized

fit(ref, iters, lr=0.0003, device='cpu', visdom_env='deepdream')¶

Parameters:	lr (float, optional) – the learning rate visdom_env (str or None) – the name of the visdom env to use, or None to disable Visdom

Feature visualization¶

Feature visualization

This optimizes an image to maximize some neuron in order to visualize the features it captures

A commandline is provided with python3 -m torchelie.recipes.feature_vis

class torchelie.recipes.feature_vis.FeatureVis(model, layer, input_size, lr=0.001, device='cpu', visdom_env='feature_vis')¶

Feature viz

First instantiate the recipe then call recipe(n_iter, img)

Parameters:

model (nn.Module) – the trained model to use
layer (str) – the layer to use on which activations will be maximized
input_size (int) – the size of the square image the model accepts as input
lr (float, optional) – the learning rate
device (device) – where to run the computation
visdom_env (str or None) – the name of the visdom env to use, or None to disable Visdom

fit(n_iters, neuron)¶

Run the recipe

Parameters:	n_iters (int) – number of iterations to run neuron (int) – the feature map to maximize
Returns:	the optimized image

Neural Style¶

Neural Style from Leon Gatys

A commandline interface is provided through python3 -m torchelie.recipes.neural_style

class torchelie.recipes.neural_style.NeuralStyle(lr=0.01, device='cpu', visdom_env='style')¶

Neural Style Recipe

First instantiate the recipe then call recipe(n_iter, img)

Parameters:	lr (float, optional) – the learning rate device (device) – where to run the computation visdom_env (str or None) – the name of the visdom env to use, or None to disable Visdom

fit(iters, content_img, style_img, style_ratio, content_layers=None)¶

Run the recipe

Parameters:	n_iters (int) – number of iterations to run content (PIL.Image) – content image style (PIL.Image) – style image ratio (float) – weight of style loss content_layers (list of str) – layers on which to reconstruct content

Deep Image Prior¶

Deep Image Prior as a command line tool is in this recipe, it provides super-resolution and denoising.

python3 -m torchelie.recipes.image_prior for the usage message