Python API

Architectures

class bob.learn.pytorch.architectures.CASIANet(num_cls, drop_rate=0.5)

Bases: torch.nn.modules.module.Module

The class defining the CASIA-Net CNN model.

This class implements the CNN described in: “Learning Face Representation From Scratch”, D. Yi, Z. Lei, S. Liao and S.z. Li, 2014

num_classes

int – The number of classes.

drop_rate

float – The probability for dropout.

conv

torch.nn.Module – The output of the convolutional / maxpool layers

avgpool

torch.nn.Module – The output of the average pooling layer (used as embedding)

classifier

torch.nn.Module – The output of the last linear (logits)

forward(x)

Propagate data through the network

Parameters:x (torch.Tensor) – The data to forward through the network Returns:x – The last layer of the network Return type:torch.Tensor

class bob.learn.pytorch.architectures.CNN8(num_cls, drop_rate=0.5)

Bases: torch.nn.modules.module.Module

The class defining the CNN8 model.

num_classes

int – The number of classes.

drop_rate

float – The probability for dropout.

conv

torch.nn.Module – The output of the convolutional / maxpool layers

avgpool

torch.nn.Module – The output of the average pooling layer (used as embedding)

classifier

torch.nn.Module – The output of the last linear (logits)

forward(x)

Propagate data through the network

Parameters:x (torch.Tensor) – The data to forward through the network Returns:x – The last layer of the network Return type:torch.Tensor

Datasets

class bob.learn.pytorch.datasets.CasiaDataset(root_dir, frontal_only=False, transform=None, start_index=0)

Bases: torch.utils.data.dataset.Dataset

Casia WebFace dataset.

Class representing the CASIA WebFace dataset

Parameters

root-dir: path

The path to the data

frontal_only: boolean

If you want to only use frontal faces

transform: torchvision.transforms

The transform(s) to apply to the face images

class bob.learn.pytorch.datasets.CasiaWebFaceDataset(root_dir, transform=None, start_index=0)

Bases: torch.utils.data.dataset.Dataset

Casia WebFace dataset (for CNN training).

Class representing the CASIA WebFace dataset

Parameters

root-dir: path

The path to the data

frontal_only: boolean

If you want to only use frontal faces

transform: torchvision.transforms

The transform(s) to apply to the face images

class bob.learn.pytorch.datasets.ConcatDataset(datasets)

Bases: torch.utils.data.dataset.Dataset

Class to concatenate two or more datasets for DR-GAN training

Parameters

datasets: list

The list of datasets (as torch.utils.data.Dataset)

class bob.learn.pytorch.datasets.FaceCropper(cropped_height, cropped_width)

Bases: object

Class to crop a face, based on eyes position

class bob.learn.pytorch.datasets.Normalize(mean, std)

Bases: object

class bob.learn.pytorch.datasets.Resize(size)

Bases: object

class bob.learn.pytorch.datasets.RollChannels

Bases: object

Class to transform a bob image into skimage. i.e. CxHxW to HxWxC

class bob.learn.pytorch.datasets.ToTensor

Bases: object

bob.learn.pytorch.datasets.map_labels(raw_labels, start_index=0)

Map the ID label to [0 - # of IDs]

Scripts

Trainers

class bob.learn.pytorch.trainers.CNNTrainer(network, batch_size=64, use_gpu=False, verbosity_level=2)

Bases: object

Class to train a CNN

network

torch.nn.Module – The network to train

batch_size

int – The size of your minibatch

use_gpu

boolean – If you would like to use the gpu

verbosity_level

int – The level of verbosity output to stdout

load_model(model_filename)

Loads an existing model

Parameters:model_file (str) – The filename of the model to load Returns:

• start_epoch (int) – The epoch to start with
• start_iteration (int) – The iteration to start with
• losses (list(float)) – The list of losses from previous training

save_model(output_dir, epoch=0, iteration=0, losses=None)

Save the trained network

Parameters:

• output_dir (str) – The directory to write the models to
• epoch (int) – the current epoch
• iteration (int) – the current (last) iteration
• losses (list(float)) – The list of losses since the beginning of training

train(dataloader, n_epochs=20, learning_rate=0.01, output_dir='out', model=None)

Performs the training.

Parameters:

• dataloader (torch.utils.data.DataLoader) – The dataloader for your data
• n_epochs (int) – The number of epochs you would like to train for
• learning_rate (float) – The learning rate for SGD optimizer.
• output_dir (str) – The directory where you would like to save models