def test_model8(self): try: import onnx except: unittest.TestCase.skipTest(self, "onnx not found in the libraries") if self.data_dir_local is None: unittest.TestCase.skipTest( self, "DLPY_DATA_DIR_LOCAL is not set in " "the environment variables") m = onnx.load(os.path.join(self.data_dir_local, 'Simple_CNN1.onnx')) model1 = Model.from_onnx_model(self.s, m, offsets=[ 1, 1, 1, ], scale=2, std='std') model1.print_summary()
def test_model30(self): # test specifying output layer in Model.from_onnx_model try: import onnx except: unittest.TestCase.skipTest(self, "onnx not found in the libraries") if self.data_dir_local is None: unittest.TestCase.skipTest(self, "DLPY_DATA_DIR_LOCAL is not set in " "the environment variables") m = onnx.load(os.path.join(self.data_dir_local, 'Simple_CNN1.onnx')) output_layer = OutputLayer(name='test_output', n=50) model1 = Model.from_onnx_model(conn=self.s, onnx_model=m, offsets=[1, 1, 1,], scale=2, std='std', output_layer=output_layer) self.assertTrue(model1.layers[-1].name == 'test_output') self.assertTrue(model1.layers[-1].config['n'] == 50)
def test_imagescaler1(self): # test import model with imagescaler try: import onnx from onnx import helper, TensorProto except: unittest.TestCase.skipTest(self, 'onnx not found') if self.data_dir_local is None: unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR_LOCAL is not set in ' 'the environment variables') import numpy as np n1 = helper.make_node('ImageScaler', ['X'], ['X1'], bias=[0., 0., 0.], scale=1.) n2 = helper.make_node('Conv', inputs=['X1', 'W1'], outputs=['X2'], kernel_shape=[3, 3], pads=[0, 0, 0, 0]) n3 = helper.make_node('MatMul', inputs=['X2', 'W2'], outputs=['X3']) W1 = np.ones((3, 3, 3)).astype(np.float32) W2 = np.ones((9, 2)).astype(np.float32) graph_def = helper.make_graph( [n1, n2, n3], name='test', inputs=[ helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 3, 10, 10]), helper.make_tensor_value_info('W1', TensorProto.FLOAT, [3, 3, 3]), helper.make_tensor_value_info('W2', TensorProto.FLOAT, [9, 2])], outputs=[ helper.make_tensor_value_info('X3', TensorProto.FLOAT, [1, 2])], initializer=[ helper.make_tensor('W1', TensorProto.FLOAT, [3, 3, 3], W1.flatten().astype(np.float32)), helper.make_tensor('W2', TensorProto.FLOAT, [9, 2], W2.flatten().astype(np.float32))]) onnx_model = helper.make_model(graph_def) model1 = Model.from_onnx_model(self.s, onnx_model) l1 = model1.layers[0] self.assertTrue(l1.type == 'input') self.assertTrue(l1.config['offsets'] == [0., 0., 0.]) self.assertTrue(l1.config['scale'] == 1.)