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)