def test_time_out_image_classification():
time_limit = 30
from gluoncv.auto.tasks import ImageClassification
task = ImageClassification({'num_trials': 1, 'epochs': 50})
tic = time.time()
classifier = task.fit(IMAGE_CLASS_DATASET, time_limit=time_limit)
# check time_limit with a little bit overhead
assert (time.time() - tic) < time_limit + 180
def test_torch_image_classification_custom_net():
from gluoncv.auto.tasks import ImageClassification
from timm import create_model
import torch.nn as nn
net = create_model('resnet18')
net.fc = nn.Linear(512, 4)
task = ImageClassification({'num_trials': 1, 'epochs': 1, 'custom_net': net, 'batch_size': 8})
classifier = task.fit(IMAGE_CLASS_DATASET)
assert task.fit_summary().get('valid_acc', 0) > 0
test_result = classifier.predict(IMAGE_CLASS_TEST)
def test_image_classification():
from gluoncv.auto.tasks import ImageClassification
task = ImageClassification({
'model': 'resnet18_v1',
'num_trials': 1,
'epochs': 1,
'batch_size': 8
})
classifier = task.fit(IMAGE_CLASS_DATASET)
assert task.fit_summary().get('valid_acc', 0) > 0
test_result = classifier.predict(IMAGE_CLASS_TEST)
def test_image_classification_custom_net():
from gluoncv.auto.tasks import ImageClassification
from gluoncv.model_zoo import get_model
net = get_model('resnet18_v1')
task = ImageClassification({
'num_trials': 1,
'epochs': 1,
'custom_net': net
})
classifier = task.fit(IMAGE_CLASS_DATASET)
assert task.fit_summary().get('valid_acc', 0) > 0
test_result = classifier.predict(IMAGE_CLASS_TEST)
def test_image_classification():
from gluoncv.auto.tasks import ImageClassification
task = ImageClassification({'num_trials': 1})
classifier = task.fit(IMAGE_CLASS_DATASET)
assert task.fit_summary.get('valid_acc', 0) > 0
test_result = classifier.predict(IMAGE_CLASS_TEST)
ag.Int(4, 7), # [16, 32, 64, 128]
'momentum':
ag.Real(0.85, 0.95),
'wd':
ag.Real(1e-6, 1e-2, log=True),
'epochs':
15,
'num_trials':
args.num_trials,
'search_strategy':
'bayesopt'
}
# specify learning task
task = ImageClassification(config)
# specify dataset
dataset = Dataset.get(args.dataset)
train_data, valid_data = dataset.split(0.8)
# fit auto estimator
classifier = task.fit(train_data, valid_data)
# evaluate auto estimator
top1, top5 = classifier.evaluate(valid_data)
logging.info('evaluation: top1={}, top5={}'.format(top1, top5))
# save and load auto estimator
classifier.save('classifier.pkl')
classifier = ImageClassification.load('classifier.pkl')