Aidge learning API

Introduction

Aidge provides its own native learning module. The primary purpose of Aidge Learning is to support Aidge’s IR for automated model optimization, notably advanced QAT, across heterogeneous deployment targets:

• Ability to apply automated quantization schemes that are easily verifiable and customizable by users;

• Formalized and fully reproducible support for industry-standard models and their derivatives;

• Focus on custom hardware development, necessitating specific training strategies.

Basic example of training pipeline in Aidge (for a single epoch):

# Define the model (or load an existing ONNX one)
model = ...
model.set_backend("cuda")

# Initialize parameters (weights and biases)
aidge_core.init_producer(model, "Producer-1>(Conv2D|FC)", aidge_core.he_filler)
aidge_core.init_producer(model, "Producer-2>(Conv2D|FC)", lambda x: aidge_core.constant_filler(x, 0.01))

# Define the data provider (using either aidge_backend_opencv or torch DataLoader)
dataprovider = ...

# Define the optimizer and set the parameters to train
opt = aidge_learning.SGD(momentum=0.9)
opt.set_parameters(aidge_core.producers(model))

# Define the learning rate scheduler
learning_rates = aidge_learning.constant_lr(0.01)
opt.set_learning_rate_scheduler(learning_rates)

scheduler = aidge_core.SequentialScheduler(model)
for i, (input, label) in enumerate(tqdm(dataprovider)):
    # Forward pass
    pred = scheduler.forward(data=[input])[0]
    # Reset the gradient
    opt.reset_grad(model)
    # Compute loss
    loss = aidge_learning.loss.CELoss(pred, label)
    # Compute accuracy (optional)
    acc = aidge_learning.metrics.Accuracy(pred, label, 1)[0]
    # Backward pass
    scheduler.backward()
    # Optimize the parameters
    opt.update()

Components

Fillers

The aidge_core.init_producer method can be used to match in the graph the Producer to be filled with a filler:

Usage example:

# Initialize the weights
aidge_core.init_producer(model, "Producer-1>(Conv2D|FC)", aidge_core.he_filler)
# Initialize the bias
aidge_core.init_producer(model, "Producer-2>(Conv2D|FC)", lambda x: aidge_core.constant_filler(x, 0.01))

Note

Fillers are part of aidge_core and not aidge_learning.

The available fillers are:

aidge_core.constant_filler(tensor: aidge_core.aidge_core.Tensor, value: object) → None

aidge_core.normal_filler(tensor: aidge_core.aidge_core.Tensor, mean: object = 0.0, stdDev: object = 1.0) → None

aidge_core.uniform_filler(tensor: aidge_core.aidge_core.Tensor, min: object, max: object) → None

aidge_core.xavier_uniform_filler(tensor: aidge_core.aidge_core.Tensor, scaling: object = 1.0, varianceNorm: aidge_core.aidge_core.VarianceNorm = <VarianceNorm.FanIn: 0>) → None

aidge_core.xavier_normal_filler(tensor: aidge_core.aidge_core.Tensor, scaling: object = 1.0, varianceNorm: aidge_core.aidge_core.VarianceNorm = <VarianceNorm.FanIn: 0>) → None

aidge_core.he_filler(tensor: aidge_core.aidge_core.Tensor, varianceNorm: aidge_core.aidge_core.VarianceNorm = <VarianceNorm.FanIn: 0>, meanNorm: object = 0.0, scaling: object = 1.0) → None

Losses

aidge_learning.loss.MSE(graph: aidge_core.aidge_core.Tensor, target: aidge_core.aidge_core.Tensor) → aidge_core.aidge_core.Tensor

aidge_learning.loss.BCE(graph: aidge_core.aidge_core.Tensor, target: aidge_core.aidge_core.Tensor) → aidge_core.aidge_core.Tensor

aidge_learning.loss.CELoss(graph: aidge_core.aidge_core.Tensor, target: aidge_core.aidge_core.Tensor) → aidge_core.aidge_core.Tensor

aidge_learning.loss.KD(student_prediction: aidge_core.aidge_core.Tensor, teacher_prediction: aidge_core.aidge_core.Tensor, temperature: SupportsFloat | SupportsIndex = 2.0) → aidge_core.aidge_core.Tensor

aidge_learning.loss.multiStepCELoss(graph: aidge_core.aidge_core.Tensor, target: aidge_core.aidge_core.Tensor, nbTimeSteps: SupportsInt | SupportsIndex) → aidge_core.aidge_core.Tensor

Optimizers

The base optimizer class is :class:` aidge_learning.Optimizer`:

aidge_learning.Optimizer()

The available optimizers are:

aidge_learning.Adam()

aidge_learning.SGD()

Learning rate scheduling

The base learning rate scheduling class is :class:` aidge_learning.LRScheduler`:

aidge_learning.LRScheduler()

The available learning rate schedulers are:

aidge_learning.constant_lr(initial_lr: SupportsFloat | SupportsIndex) → aidge_learning.aidge_learning.LRScheduler

aidge_learning.step_lr(initial_lr: SupportsFloat | SupportsIndex, step_size: SupportsInt | SupportsIndex, gamma: SupportsFloat | SupportsIndex = 0.10000000149011612) → aidge_learning.aidge_learning.LRScheduler

Metrics

aidge_learning.metrics.Accuracy(prediction: aidge_core.aidge_core.Tensor, target: aidge_core.aidge_core.Tensor, axis: SupportsInt | SupportsIndex) → aidge_core.aidge_core.Tensor