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()