This trains a small model on CIFAR 10 to test how much distillation benefits.
15import torch.nn as nn
16
17from labml import experiment, logger
18from labml.configs import option
19from labml_nn.experiments.cifar10 import CIFAR10Configs, CIFAR10VGGModel
20from labml_nn.normalization.batch_norm import BatchNorm
We use CIFAR10Configs
which defines all the dataset related configurations, optimizer, and a training loop.
23class Configs(CIFAR10Configs):
30 pass
33class SmallModel(CIFAR10VGGModel):
Create a convolution layer and the activations
40 def conv_block(self, in_channels, out_channels) -> nn.Module:
44 return nn.Sequential(
Convolution layer
46 nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
Batch normalization
48 BatchNorm(out_channels, track_running_stats=False),
ReLU activation
50 nn.ReLU(inplace=True),
51 )
53 def __init__(self):
Create a model with given convolution sizes (channels)
55 super().__init__([[32, 32], [64, 64], [128], [128], [128]])
58@option(Configs.model)
59def _small_model(c: Configs):
63 return SmallModel().to(c.device)
66def main():
Create experiment
68 experiment.create(name='cifar10', comment='small model')
Create configurations
70 conf = Configs()
Load configurations
72 experiment.configs(conf, {
73 'optimizer.optimizer': 'Adam',
74 'optimizer.learning_rate': 2.5e-4,
75 })
Set model for saving/loading
77 experiment.add_pytorch_models({'model': conf.model})
Print number of parameters in the model
79 logger.inspect(params=(sum(p.numel() for p in conf.model.parameters() if p.requires_grad)))
Start the experiment and run the training loop
81 with experiment.start():
82 conf.run()
86if __name__ == '__main__':
87 main()