mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""
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---
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title: Train a small model on CIFAR 10
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summary: >
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Train a small model on CIFAR 10 to test how much distillation benefits.
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---
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# Train a small model on CIFAR 10
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This trains a small model on CIFAR 10 to test how much [distillation](index.html) benefits.
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[](https://app.labml.ai/run/3b8fda8edaef11eb951eebc4a6e2bfac)
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"""
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import torch.nn as nn
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from labml import experiment, logger
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from labml.configs import option
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from labml_nn.experiments.cifar10 import CIFAR10Configs, CIFAR10VGGModel
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from labml_nn.normalization.batch_norm import BatchNorm
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class Configs(CIFAR10Configs):
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"""
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## Configurations
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We use [`CIFAR10Configs`](../experiments/cifar10.html) which defines all the
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dataset related configurations, optimizer, and a training loop.
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"""
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pass
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class SmallModel(CIFAR10VGGModel):
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"""
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### VGG style model for CIFAR-10 classification
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This derives from the [generic VGG style architecture](../experiments/cifar10.html).
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"""
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def conv_block(self, in_channels, out_channels) -> nn.Module:
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"""
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Create a convolution layer and the activations
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"""
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return nn.Sequential(
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# Convolution layer
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nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
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# Batch normalization
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BatchNorm(out_channels, track_running_stats=False),
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# ReLU activation
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nn.ReLU(inplace=True),
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)
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def __init__(self):
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# Create a model with given convolution sizes (channels)
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super().__init__([[32, 32], [64, 64], [128], [128], [128]])
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@option(Configs.model)
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def _small_model(c: Configs):
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"""
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### Create model
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"""
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return SmallModel().to(c.device)
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def main():
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# Create experiment
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experiment.create(name='cifar10', comment='small model')
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# Create configurations
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conf = Configs()
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# Load configurations
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experiment.configs(conf, {
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'device.cuda_device': 1,
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'optimizer.optimizer': 'Adam',
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'optimizer.learning_rate': 2.5e-4,
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})
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# Set model for saving/loading
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experiment.add_pytorch_models({'model': conf.model})
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# Print number of parameters in the model
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logger.inspect(params=(sum(p.numel() for p in conf.model.parameters() if p.requires_grad)))
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# Start the experiment and run the training loop
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with experiment.start():
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conf.run()
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#
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if __name__ == '__main__':
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main()
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