def test_vgg16(self): from dlpy.applications import VGG16 pd.set_option('display.max_columns', 40) model = VGG16(self.s) model.print_summary() res = self.s.fetch(table=model.model_name, sortby='_dllayerid_') print(res) # test random_crop and mutation model1 = VGG16(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', include_top=True) 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_vgg16_2(self): if self.data_dir is None: unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables") model1 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=(103.939, 116.779, 123.68), pre_trained_weights=True, pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True) model2 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=None, random_crop=None, pre_trained_weights=True, pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True) self.assertRaises(ValueError, lambda:VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=None, random_crop='wrong_val', pre_trained_weights=True, pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True))
def initialize_network(s): model_name = request.form.get('model', 'VGG16') resize_x = request.form.get('resizeX', 224) resize_y = request.form.get('resizeY', 224) import sys sys.path.append(app.config.get('DLPY_PATH')) from dlpy.applications import VGG16 try: with Capturing() as dlpy_out: model1 = VGG16(conn=s, model_table='VGG16', n_classes=None, n_channels=3, width=224, height=224) resp = dict(messages=dlpy_out) session['current_network'] = 'VGG16' session['current_network_tbl'] = 'VGG16' except Exception as ex: print(str(ex)) resp = dict(messages=['Error initializing model']) return jsonify(resp)
def test_vgg16_2(self): if self.data_dir is None: unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables") file_dependency = self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5' if not file_exist_on_server(self.s, file_dependency): unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency)) model1 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=(103.939, 116.779, 123.68), pre_trained_weights=True, pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True) model1.print_summary() model2 = VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=None, random_crop=None, pre_trained_weights=True, pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True) model2.print_summary() self.assertRaises(ValueError, lambda: VGG16(self.s, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, offsets=None, random_crop='wrong_val', pre_trained_weights=True, pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True)) # test random_crop and mutation model3 = VGG16(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', pre_trained_weights=True, pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5', include_top=True) model3.print_summary()
def test_vgg16(self): from dlpy.applications import VGG16 model = VGG16(self.s) model.print_summary()