Trainer Example

This example should illustrate how to use the trainer class.

import torch.nn as nn
from inferno.io.box.cifar import get_cifar10_loaders
from inferno.trainers.basic import Trainer
from inferno.trainers.callbacks.logging.tensorboard import TensorboardLogger
from inferno.extensions.layers.convolutional import ConvELU2D
from inferno.extensions.layers.reshape import Flatten

# lil helper to make sure dirs exits
import os
def ensure_dir(file_path):
    directory = os.path.dirname(file_path)
    if not os.path.exists(directory):
        os.makedirs(directory)
    return directory

# change directories to your needs
LOG_DIRECTORY = ensure_dir('log')
SAVE_DIRECTORY = ensure_dir('save')
DATASET_DIRECTORY = ensure_dir('dataset')


DOWNLOAD_CIFAR = True
USE_CUDA = True

# Build torch model
model = nn.Sequential(
    ConvELU2D(in_channels=3, out_channels=256, kernel_size=3),
    nn.MaxPool2d(kernel_size=2, stride=2),
    ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),
    nn.MaxPool2d(kernel_size=2, stride=2),
    ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),
    nn.MaxPool2d(kernel_size=2, stride=2),
    Flatten(),
    nn.Linear(in_features=(256 * 4 * 4), out_features=10),
    nn.Softmax()
)

# Load loaders
train_loader, validate_loader = get_cifar10_loaders(DATASET_DIRECTORY,
                                        download=DOWNLOAD_CIFAR)

# Build trainer
trainer = Trainer(model) \
.build_criterion('CrossEntropyLoss') \
.build_metric('CategoricalError') \
.build_optimizer('Adam') \
.validate_every((2, 'epochs')) \
.save_every((5, 'epochs')) \
.save_to_directory(SAVE_DIRECTORY) \
.set_max_num_epochs(10) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
                                log_images_every='never'),
              log_directory=LOG_DIRECTORY)

# Bind loaders
trainer \
    .bind_loader('train', train_loader) \
    .bind_loader('validate', validate_loader)

if USE_CUDA:
    trainer.cuda()

# Go!
trainer.fit()

Total running time of the script: ( 0 minutes 0.000 seconds)