Searchable Objects¶
When defining custom Python objects such as network architectures, or specialized optimizers, it may be hard to decide what values to set for all of their attributes. AutoGluon provides an API that allows you to instead specify a search space of possible values to consider for such attributes, within which the optimal value will be automatically searched for at runtime. This tutorial demonstrates how easy this is to do, without having to modify your existing code at all!
Example for Constructing a Network¶
This tutorial covers an example of selecting a neural network’s
architecture as a hyperparameter optimization (HPO) task. If you are
interested in efficient neural architecture search (NAS), please refer
to this other tutorial instead: 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.core as ag
from autogluon.vision.utils import plot_network
plot_network(net, (1, 3, 32, 32))
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)
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.vision import ImagePredictor
classifier = ImagePredictor().fit('cifar10', hyperparameters={'net': mynet, 'optimizer': myoptim, 'epochs': 1}, ngpus_per_trial=1)
INFO:root:time_limit=auto set to time_limit=7200. INFO:gluoncv.auto.tasks.image_classification:Starting fit without HPO INFO:ImageClassificationEstimator:modified configs(<old> != <new>): { INFO:ImageClassificationEstimator:root.img_cls.model resnet50_v1 != resnet50_v1b INFO:ImageClassificationEstimator:root.train.batch_size 128 != 16 INFO:ImageClassificationEstimator:root.train.num_workers 4 != 8 INFO:ImageClassificationEstimator:root.train.rec_train_idx ~/.mxnet/datasets/imagenet/rec/train.idx != auto INFO:ImageClassificationEstimator:root.train.data_dir ~/.mxnet/datasets/imagenet != auto INFO:ImageClassificationEstimator:root.train.lr 0.1 != 0.01 INFO:ImageClassificationEstimator:root.train.rec_train ~/.mxnet/datasets/imagenet/rec/train.rec != auto INFO:ImageClassificationEstimator:root.train.epochs 10 != 1 INFO:ImageClassificationEstimator:root.train.rec_val ~/.mxnet/datasets/imagenet/rec/val.rec != auto INFO:ImageClassificationEstimator:root.train.num_training_samples 1281167 != -1 INFO:ImageClassificationEstimator:root.train.rec_val_idx ~/.mxnet/datasets/imagenet/rec/val.idx != auto INFO:ImageClassificationEstimator:root.valid.num_workers 4 != 8 INFO:ImageClassificationEstimator:root.valid.batch_size 128 != 16 INFO:ImageClassificationEstimator:} INFO:ImageClassificationEstimator:Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/docs/_build/eval/tutorials/course/2a4cd35f/.trial_0/config.yaml INFO:ImageClassificationEstimator:Start training from [Epoch 0] INFO:ImageClassificationEstimator:Epoch[0] Batch [49] Speed: 102.315576 samples/sec accuracy=0.168750 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [99] Speed: 104.529078 samples/sec accuracy=0.238750 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [149] Speed: 103.558884 samples/sec accuracy=0.290833 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [199] Speed: 103.046291 samples/sec accuracy=0.323125 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [249] Speed: 102.486269 samples/sec accuracy=0.344250 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [299] Speed: 101.998146 samples/sec accuracy=0.362708 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [349] Speed: 101.418116 samples/sec accuracy=0.381786 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [399] Speed: 100.685982 samples/sec accuracy=0.401406 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [449] Speed: 100.247008 samples/sec accuracy=0.415833 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [499] Speed: 99.598979 samples/sec accuracy=0.429000 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [549] Speed: 99.049519 samples/sec accuracy=0.440000 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [599] Speed: 98.481920 samples/sec accuracy=0.450104 lr=0.010000 INFO:ImageClassificationEstimator:Epoch[0] Batch [649] Speed: 98.070448 samples/sec 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/var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/docs/_build/eval/tutorials/course/2a4cd35f/.trial_0/best_checkpoint.pkl INFO:gluoncv.auto.tasks.image_classification:Finished, total runtime is 603.28 s INFO:gluoncv.auto.tasks.image_classification:{ 'best_config': { 'batch_size': 16, 'custom_net': MyCifarResNet( (features): HybridSequential( (0): Conv2D(None -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): HybridSequential( (0): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (1): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (2): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) ) (3): HybridSequential( (0): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(16 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) (downsample): HybridSequential( (0): Conv2D(16 -> 32, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (1): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (2): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (3): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (4): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (5): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (6): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) (7): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) ) (4): HybridSequential( (0): CIFARBasicBlockV1( (body): HybridSequential( (0): Conv2D(32 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) (2): Activation(relu) (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) (downsample): HybridSequential( (0): Conv2D(32 -> 64, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None) ) ) ) (5): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW) ) (output): Dense(64 -> 10, linear) ), 'custom_optimizer': <__main__.Adam object at 0x7f202fab0b50>, 'dist_ip_addrs': None, 'epochs': 1, 'estimator': <class 'gluoncv.auto.estimators.image_classification.image_classification.ImageClassificationEstimator'>, 'final_fit': False, 'gpus': [0], 'log_dir': '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/docs/_build/eval/tutorials/course/2a4cd35f', 'lr': 0.01, 'model': 'resnet50_v1b', 'ngpus_per_trial': 1, 'nthreads_per_trial': 128, 'num_trials': 1, 'num_workers': 8, 'search_strategy': 'random', 'seed': 520, 'time_limits': 7200, 'wall_clock_tick': 1614920754.4082444}, 'total_time': 584.1629416942596, 'train_acc': 0.6343148148148148, 'valid_acc': 0.901}
print(classifier.fit_summary())
{'train_acc': 0.6343148148148148, 'valid_acc': 0.901, 'total_time': 584.1629416942596, 'best_config': {'model': 'resnet50_v1b', 'lr': 0.01, 'num_trials': 1, 'epochs': 1, 'batch_size': 16, 'nthreads_per_trial': 128, 'ngpus_per_trial': 1, 'time_limits': 7200, 'search_strategy': 'random', 'dist_ip_addrs': None, 'log_dir': '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/docs/_build/eval/tutorials/course/2a4cd35f', 'custom_net': MyCifarResNet(
(features): HybridSequential(
(0): Conv2D(None -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(1): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(2): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(3): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
(downsample): HybridSequential(
(0): Conv2D(16 -> 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(1): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(2): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(3): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(4): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(5): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(6): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(7): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(4): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
(downsample): HybridSequential(
(0): Conv2D(32 -> 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(5): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)
)
(output): Dense(64 -> 10, linear)
), 'custom_optimizer': <__main__.Adam object at 0x7f202fab0b50>, 'num_workers': 8, 'gpus': [0], 'seed': 520, 'final_fit': False, 'estimator': <class 'gluoncv.auto.estimators.image_classification.image_classification.ImageClassificationEstimator'>, 'wall_clock_tick': 1614920754.4082444}, 'fit_history': {'train_acc': 0.6343148148148148, 'valid_acc': 0.901, 'total_time': 584.1629416942596, 'best_config': {'model': 'resnet50_v1b', 'lr': 0.01, 'num_trials': 1, 'epochs': 1, 'batch_size': 16, 'nthreads_per_trial': 128, 'ngpus_per_trial': 1, 'time_limits': 7200, 'search_strategy': 'random', 'dist_ip_addrs': None, 'log_dir': '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/docs/_build/eval/tutorials/course/2a4cd35f', 'custom_net': MyCifarResNet(
(features): HybridSequential(
(0): Conv2D(None -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(1): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(2): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(3): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(16 -> 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
(downsample): HybridSequential(
(0): Conv2D(16 -> 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(1): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(2): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(3): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(4): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(5): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(6): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
(7): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(4): HybridSequential(
(0): CIFARBasicBlockV1(
(body): HybridSequential(
(0): Conv2D(32 -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): Activation(relu)
(3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
(downsample): HybridSequential(
(0): Conv2D(32 -> 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
)
)
)
(5): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)
)
(output): Dense(64 -> 10, linear)
), 'custom_optimizer': <__main__.Adam object at 0x7f202fab0b50>, 'num_workers': 8, 'gpus': [0], 'seed': 520, 'final_fit': False, 'estimator': <class 'gluoncv.auto.estimators.image_classification.image_classification.ImageClassificationEstimator'>, 'wall_clock_tick': 1614920754.4082444}}}