Customize User Objects

A user may want to define some customized objects with search spaces, such as network architectures, or specialized optimizers. We provide an API to do that.

Example for Constructing a Network

This is an example of doing architecture search as HPO. If you are interested in efficient neural architecture search, please refer another tutorial sec_proxyless_ .

CIFAR ResNet in GluonCV

GluonCV provides CIFARResNet, which allow user to specify how many layers at each stage. For example, we can construct a CIFAR ResNet with only 1 layer per stage:

from gluoncv.model_zoo.cifarresnet import CIFARResNetV1, CIFARBasicBlockV1

layers = [1, 1, 1]
channels = [16, 16, 32, 64]
net = CIFARResNetV1(CIFARBasicBlockV1, layers, channels)

We can visualize the network:

import autogluon as ag
ag.utils.plot_network(net, (1, 3, 32, 32))
../../_images/output_object_d3e86d_3_0.svg

Searchable Network Architecture Using AutoGluon Object

autogluon.obj() enables customized search space to any user defined class. It can also be used within autogluon.Categorical() if you have multiple networks to choose from.

@ag.obj(
    nstage1=ag.space.Int(2, 4),
    nstage2=ag.space.Int(2, 4),
)
class MyCifarResNet(CIFARResNetV1):
    def __init__(self, nstage1, nstage2):
        nstage3 = 9 - nstage1 - nstage2
        layers = [nstage1, nstage2, nstage3]
        channels = [16, 16, 32, 64]
        super().__init__(CIFARBasicBlockV1, layers=layers, channels=channels)
/var/lib/jenkins/miniconda3/envs/autogluon_docs-v0_0_14/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: should_run_async will not call transform_cell automatically in the future. Please pass the result to transformed_cell argument and any exception that happen during thetransform in preprocessing_exc_tuple in IPython 7.17 and above.
  and should_run_async(code)

Create one network instance and print the configuration space:

mynet=MyCifarResNet()
print(mynet.cs)
Configuration space object:
  Hyperparameters:
    nstage1, Type: UniformInteger, Range: [2, 4], Default: 3
    nstage2, Type: UniformInteger, Range: [2, 4], Default: 3

We can also overwrite existing search spaces:

mynet1 = MyCifarResNet(nstage1=1,
                       nstage2=ag.space.Int(5, 10))
print(mynet1.cs)
Configuration space object:
  Hyperparameters:
    nstage2, Type: UniformInteger, Range: [5, 10], Default: 8

Decorate Existing Class

We can also use autogluon.obj() to easily decorate any existing classes. For example, if we want to search learning rate and weight decay for Adam optimizer, we only need to add a decorator:

from mxnet import optimizer as optim
@ag.obj()
class Adam(optim.Adam):
    pass

Then we can create an instance:

myoptim = Adam(learning_rate=ag.Real(1e-2, 1e-1, log=True), wd=ag.Real(1e-5, 1e-3, log=True))
print(myoptim.cs)
Configuration space object:
  Hyperparameters:
    learning_rate, Type: UniformFloat, Range: [0.01, 0.1], Default: 0.0316227766, on log-scale
    wd, Type: UniformFloat, Range: [1e-05, 0.001], Default: 0.0001, on log-scale

Launch Experiments Using AutoGluon Object

AutoGluon Object is compatible with Fit API in AutoGluon tasks, and also works with user-defined training scripts using autogluon.autogluon_register_args(). We can start fitting:

from autogluon import ImageClassification as task
# results = task.fit('cifar10', net=mynet, optimizer=myoptim, num_gpus=1, epochs=1)
# print(results)