def test_from_model(self):
from dlpy.applications import ResNet18_Caffe, VGG11
vgg11 = VGG11(self.s)
backbone1 = vgg11.to_functional_model(vgg11.layers[-2])
self.assertEqual(backbone1.layers[-1].__class__.__name__, 'Dense')
model_resnet18 = ResNet18_Caffe(self.s, n_classes = 6, random_crop = 'none', width = 400, height = 400)
backbone2 = model_resnet18.to_functional_model(model_resnet18.layers[-3])
self.assertEqual(backbone2.layers[-1].__class__.__name__, 'BN')
def test_vgg11(self):
from dlpy.applications import VGG11
model = VGG11(self.s)
model.print_summary()
# test random_crop and mutation
model1 = VGG11(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random')
model1.print_summary()
res1 = self.s.fetch(table=model1.model_name, sortby='_dllayerid_')
print(res1)
self.assertEqual(res1['Fetch'].iloc[10, 3], 4)
self.assertEqual(res1['Fetch'].iloc[4, 3], 2)
def test_vgg11(self):
from dlpy.applications import VGG11
model = VGG11(self.s)
model.print_summary()