def test_ssd_mobilenet_v2_model(): model = DLRModel(MODEL_PATH.as_posix()) data = np.load(DATA_PATH) assert model.get_input_names() == ['image_tensor'] assert model.get_output_names() == [ 'detection_scores:0', 'detection_classes:0', 'num_detections:0' ] assert model.get_input_dtypes() == ['uint8'] assert model.get_output_dtypes() == ['float32', 'float32', 'float32'] outputs = model.run({"image_tensor": data}) assert outputs[0].shape == (1, 100, 4) assert outputs[1].shape == (1, 100) assert outputs[2].shape == (1, 100) detections = np.multiply(np.ceil(outputs[1]), outputs[2]) expected = np.zeros(detections.shape) expected[:, :6] = np.array([[1., 1., 1., 2., 3., 1]]) comparison = detections == expected assert comparison.all()
def test_resnet(): # Load the model model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resnet18_v1') device = 'cpu' model = DLRModel(model_path, device) # Run the model image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32) #flatten within a input array input_data = {'data': image} print('Testing inference on resnet18...') probabilities = model.run(input_data) #need to be a list of input arrays matching input names assert probabilities[0].argmax() == 151 assert model.get_input_names() == ["data"] assert model.get_input_dtypes() == ["float32"] assert model.get_output_dtypes() == ["float32"] assert model.get_input_dtype(0) == "float32" assert model.get_output_dtype(0) == "float32"
def test_ssd_mobilenet_v2_model(): model = DLRModel(MODEL_PATH.as_posix()) data = np.load(DATA_PATH) assert model.get_input_names() == ['image_tensor'] assert model.get_output_names() == [ 'detection_classes:0', 'num_detections:0', 'detection_boxes:0', 'detection_scores:0' ] assert model.get_input_dtypes() == ['uint8'] assert model.get_output_dtypes() == [ 'float32', 'float32', 'float32', 'float32' ] outputs = model.run({"image_tensor": data}) assert outputs[0].shape == (1, 100) assert outputs[1].shape == (1, ) assert outputs[2].shape == (1, 100, 4) assert outputs[3].shape == (1, 100) detections = np.multiply(np.ceil(outputs[3]), outputs[0]) expected = np.zeros(detections.shape) assert np.count_nonzero(detections) == outputs[1][0]
def test_mobilenet_v1_0_75_224_quant(): # Load the model model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'mobilenet_v1_0.75_224_quant') device = 'cpu' model = DLRModel(model_path, device) # load image (dtype: uint8) image = np.load( os.path.join(os.path.dirname(os.path.abspath(__file__)), 'cat_224_uint8.npy')) print('Testing inference on mobilenet_v1_0.75_224_quant...') probabilities = model.run({'input': image}) assert probabilities[0].argmax() == 282 assert model.get_input_names() == ["input"] assert model.get_input_dtypes() == ["uint8"] assert model.get_output_dtypes() == ["uint8"] assert model.get_input_dtype(0) == "uint8" assert model.get_output_dtype(0) == "uint8" input2 = model.get_input("input") assert input2.dtype == 'uint8' assert input2.shape == (1, 224, 224, 3) assert (input2 == image).all()
def test_tf_model(dev_type=None, dev_id=None): _generate_frozen_graph() model = DLRModel(FROZEN_GRAPH_PATH, dev_type, dev_id) inp_names = model.get_input_names() assert inp_names == ['import/input1:0', 'import/input2:0'] out_names = model.get_output_names() assert out_names == [ 'import/preproc/output1:0', 'import/preproc/output2:0' ] inp1 = [[4., 1.], [3., 2.]] inp2 = [[0., 1.], [1., 0.]] res = model.run({'import/input1:0': inp1, 'import/input2:0': inp2}) assert res is not None assert len(res) == 2 assert np.alltrue(res[0] == [[36., 361.], [49., 324.]]) assert res[1] == 1 m_inp1 = model.get_input('import/input1:0') m_inp2 = model.get_input('import/input2:0') assert np.alltrue(m_inp1 == inp1) assert np.alltrue(m_inp2 == inp2)
def test_tflite_model(): _generate_tflite_file() m = DLRModel(TFLITE_FILE_PATH) inp_names = m.get_input_names() assert sorted(inp_names) == ['input1', 'input2'] out_names = m.get_output_names() assert sorted(out_names) == ['preproc/output1', 'preproc/output2'] inp1 = np.array([[4., 1.], [3., 2.]]).astype("float32") inp2 = np.array([[0., 1.], [1., 0.]]).astype("float32") res = m.run({'input1': inp1, 'input2': inp2}) assert res is not None assert len(res) == 2 exp_out0 = np.array([[36., 361.], [49., 324.]]).astype("float32") assert np.alltrue(res[0] == exp_out0) assert res[1] == 1 m_inp1 = m.get_input('input1') m_inp2 = m.get_input('input2') assert np.alltrue(m_inp1 == inp1) assert np.alltrue(m_inp2 == inp2)