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)