#

Train a ResNet on CIFAR 10

12from typing import List, Optional
13
14from torch import nn
15
16from labml import experiment
17from labml.configs import option
18from labml_nn.experiments.cifar10 import CIFAR10Configs
19from labml_nn.resnet import ResNetBase

#

Configurations

We use CIFAR10Configs which defines all the dataset related configurations, optimizer, and a training loop.

22class Configs(CIFAR10Configs):

#

Number fo blocks for each feature map size

31    n_blocks: List[int] = [3, 3, 3]

#

Number of channels for each feature map size

33    n_channels: List[int] = [16, 32, 64]

#

Bottleneck sizes

35    bottlenecks: Optional[List[int]] = None

#

Kernel size of the initial convolution layer

37    first_kernel_size: int = 3

#

Create model

40@option(Configs.model)
41def _resnet(c: Configs):

#

ResNet

46    base = ResNetBase(c.n_blocks, c.n_channels, c.bottlenecks, img_channels=3, first_kernel_size=c.first_kernel_size)

#

Linear layer for classification

48    classification = nn.Linear(c.n_channels[-1], 10)

#

Stack them

51    model = nn.Sequential(base, classification)

#

Move the model to the device

53    return model.to(c.device)

#

56def main():

#

Create experiment

58    experiment.create(name='resnet', comment='cifar10')

#

Create configurations

60    conf = Configs()

#

Load configurations

62    experiment.configs(conf, {
63        'bottlenecks': [8, 16, 16],
64        'n_blocks': [6, 6, 6],
65
66        'optimizer.optimizer': 'Adam',
67        'optimizer.learning_rate': 2.5e-4,
68
69        'epochs': 500,
70        'train_batch_size': 256,
71
72        'train_dataset': 'cifar10_train_augmented',
73        'valid_dataset': 'cifar10_valid_no_augment',
74    })

#

Set model for saving/loading

76    experiment.add_pytorch_models({'model': conf.model})

#

Start the experiment and run the training loop

78    with experiment.start():
79        conf.run()

#

83if __name__ == '__main__':
84    main()