From 196cd7aed196f74dbc80062c168ec531a4375c10 Mon Sep 17 00:00:00 2001 From: Valery Chernov Date: Fri, 3 Jun 2022 03:14:20 +0300 Subject: [PATCH] Pylint fix after #11647 (#11704) --- .../python/tools/quantization/calibrate.py | 18 +++- .../python/quantization/test_calibration.py | 89 +++++++++++++------ 2 files changed, 77 insertions(+), 30 deletions(-) diff --git a/onnxruntime/python/tools/quantization/calibrate.py b/onnxruntime/python/tools/quantization/calibrate.py index 38d7c22fbf..79d31d6f31 100644 --- a/onnxruntime/python/tools/quantization/calibrate.py +++ b/onnxruntime/python/tools/quantization/calibrate.py @@ -90,7 +90,9 @@ class CalibraterBase: sess_options = onnxruntime.SessionOptions() sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL self.infer_session = onnxruntime.InferenceSession( - self.augmented_model_path, sess_options=sess_options, providers=self.execution_providers, + self.augmented_model_path, + sess_options=sess_options, + providers=self.execution_providers, ) def select_tensors_to_calibrate(self, model): @@ -170,7 +172,11 @@ class MinMaxCalibrater(CalibraterBase): :param averaging_constant: constant smoothing factor to use when computing the moving average. """ super(MinMaxCalibrater, self).__init__( - model, op_types_to_calibrate, augmented_model_path, symmetric, use_external_data_format, + model, + op_types_to_calibrate, + augmented_model_path, + symmetric, + use_external_data_format, ) self.intermediate_outputs = [] self.calibrate_tensors_range = None @@ -221,7 +227,9 @@ class MinMaxCalibrater(CalibraterBase): add_reduce_min_max(tensor, "ReduceMax") onnx.save( - model, self.augmented_model_path, save_as_external_data=self.use_external_data_format, + model, + self.augmented_model_path, + save_as_external_data=self.use_external_data_format, ) self.augment_model = model @@ -368,7 +376,9 @@ class HistogramCalibrater(CalibraterBase): model.graph.node.extend(added_nodes) model.graph.output.extend(added_outputs) onnx.save( - model, self.augmented_model_path, save_as_external_data=self.use_external_data_format, + model, + self.augmented_model_path, + save_as_external_data=self.use_external_data_format, ) self.augment_model = model diff --git a/onnxruntime/test/python/quantization/test_calibration.py b/onnxruntime/test/python/quantization/test_calibration.py index b62935555e..84773c4ada 100644 --- a/onnxruntime/test/python/quantization/test_calibration.py +++ b/onnxruntime/test/python/quantization/test_calibration.py @@ -61,11 +61,16 @@ class TestCalibrate(unittest.TestCase): vi_b = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1, 3, 3]) vi_e = helper.make_tensor_value_info("E", TensorProto.FLOAT, [1, 1, 5, 1]) vi_f = helper.make_tensor_value_info("F", TensorProto.FLOAT, [1, 1, 5, 1]) - conv_node = onnx.helper.make_node( - "Conv", ["A", "B"], ["C"], name="Conv", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + conv_node = helper.make_node( + "Conv", + ["A", "B"], + ["C"], + name="Conv", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], ) - clip_node = onnx.helper.make_node("Clip", ["C"], ["D"], name="Clip") - matmul_node = onnx.helper.make_node("MatMul", ["D", "E"], ["F"], name="MatMul") + clip_node = helper.make_node("Clip", ["C"], ["D"], name="Clip") + matmul_node = helper.make_node("MatMul", ["D", "E"], ["F"], name="MatMul") graph = helper.make_graph([conv_node, clip_node, matmul_node], "test_graph_1", [vi_a, vi_b, vi_e], [vi_f]) model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)]) @@ -139,11 +144,21 @@ class TestCalibrate(unittest.TestCase): vi_h = helper.make_tensor_value_info("H", TensorProto.FLOAT, [1, 1, 3, 3]) vi_j = helper.make_tensor_value_info("J", TensorProto.FLOAT, [1, 1, 3, 3]) vi_k = helper.make_tensor_value_info("K", TensorProto.FLOAT, [1, 1, 5, 5]) - conv_node_1 = onnx.helper.make_node( - "Conv", ["G", "H"], ["I"], name="Conv1", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + conv_node_1 = helper.make_node( + "Conv", + ["G", "H"], + ["I"], + name="Conv1", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], ) - conv_node_2 = onnx.helper.make_node( - "Conv", ["I", "J"], ["K"], name="Conv2", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + conv_node_2 = helper.make_node( + "Conv", + ["I", "J"], + ["K"], + name="Conv2", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], ) graph = helper.make_graph([conv_node_1, conv_node_2], "test_graph_2", [vi_g, vi_h, vi_j], [vi_k]) model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)]) @@ -185,12 +200,17 @@ class TestCalibrate(unittest.TestCase): vi_l = helper.make_tensor_value_info("L", TensorProto.FLOAT, [1, 1, 5, 5]) vi_n = helper.make_tensor_value_info("N", TensorProto.FLOAT, [1, 1, 3, 3]) vi_q = helper.make_tensor_value_info("Q", TensorProto.FLOAT, [1, 1, 5, 5]) - relu_node = onnx.helper.make_node("Relu", ["L"], ["M"], name="Relu") - conv_node = onnx.helper.make_node( - "Conv", ["M", "N"], ["O"], name="Conv", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + relu_node = helper.make_node("Relu", ["L"], ["M"], name="Relu") + conv_node = helper.make_node( + "Conv", + ["M", "N"], + ["O"], + name="Conv", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], ) - clip_node = onnx.helper.make_node("Clip", ["O"], ["P"], name="Clip") - matmul_node = onnx.helper.make_node("MatMul", ["P", "M"], ["Q"], name="MatMul") + clip_node = helper.make_node("Clip", ["O"], ["P"], name="Clip") + matmul_node = helper.make_node("MatMul", ["P", "M"], ["Q"], name="MatMul") graph = helper.make_graph([relu_node, conv_node, clip_node, matmul_node], "test_graph_3", [vi_l, vi_n], [vi_q]) model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)]) test_model_path = "./test_model_3.onnx" @@ -273,12 +293,12 @@ class TestCalibrate(unittest.TestCase): b3 = generate_input_initializer([3], np.float32, "B3") w5 = generate_input_initializer([3, 3, 1, 1], np.float32, "W5") b5 = generate_input_initializer([3], np.float32, "B5") - relu_node_1 = onnx.helper.make_node("Relu", ["input"], ["X1"], name="Relu1") - conv_node_1 = onnx.helper.make_node("Conv", ["X1", "W1", "B1"], ["X2"], name="Conv1") - relu_node_2 = onnx.helper.make_node("Relu", ["X2"], ["X3"], name="Relu2") - conv_node_2 = onnx.helper.make_node("Conv", ["X3", "W3", "B3"], ["X4"], name="Conv2") - conv_node_3 = onnx.helper.make_node("Conv", ["X1", "W5", "B5"], ["X5"], name="Conv3") - add_node = onnx.helper.make_node("Add", ["X4", "X5"], ["X6"], name="Add") + relu_node_1 = helper.make_node("Relu", ["input"], ["X1"], name="Relu1") + conv_node_1 = helper.make_node("Conv", ["X1", "W1", "B1"], ["X2"], name="Conv1") + relu_node_2 = helper.make_node("Relu", ["X2"], ["X3"], name="Relu2") + conv_node_2 = helper.make_node("Conv", ["X3", "W3", "B3"], ["X4"], name="Conv2") + conv_node_3 = helper.make_node("Conv", ["X1", "W5", "B5"], ["X5"], name="Conv3") + add_node = helper.make_node("Add", ["X4", "X5"], ["X6"], name="Add") graph = helper.make_graph( [relu_node_1, conv_node_1, relu_node_2, conv_node_2, conv_node_3, add_node], "test_graph_4", @@ -307,7 +327,9 @@ class TestCalibrate(unittest.TestCase): sess_options = onnxruntime.SessionOptions() sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL infer_session = onnxruntime.InferenceSession( - test_model_path, sess_options=sess_options, providers=["CPUExecutionProvider"], + test_model_path, + sess_options=sess_options, + providers=["CPUExecutionProvider"], ) data_reader.rewind() rmin = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf], dtype=np.float32) @@ -339,14 +361,29 @@ class TestCalibrate(unittest.TestCase): vi_n = helper.make_tensor_value_info("N", TensorProto.FLOAT, [0]) vi_o = helper.make_tensor_value_info("O", TensorProto.FLOAT, [1, 1, 5, 5]) # O = helper.make_tensor_value_info('O', TensorProto.FLOAT, None) - conv_node_1 = onnx.helper.make_node( - "Conv", ["G", "conv1_w"], ["I"], name="Conv1", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + conv_node_1 = helper.make_node( + "Conv", + ["G", "conv1_w"], + ["I"], + name="Conv1", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], ) - conv_node_2 = onnx.helper.make_node( - "Conv", ["I", "conv2_w"], ["K"], name="Conv2", kernel_shape=[3, 3], pads=[1, 1, 1, 1], + conv_node_2 = helper.make_node( + "Conv", + ["I", "conv2_w"], + ["K"], + name="Conv2", + kernel_shape=[3, 3], + pads=[1, 1, 1, 1], + ) + resize_node_1 = helper.make_node("Resize", ["K", "M", "N"], ["O"], name="Reize1") + graph = helper.make_graph( + [conv_node_1, conv_node_2, resize_node_1], + "test_graph_5", + [vi_g, vi_m, vi_n], + [vi_o], ) - resize_node_1 = onnx.helper.make_node("Resize", ["K", "M", "N"], ["O"], name="Reize1") - graph = helper.make_graph([conv_node_1, conv_node_2, resize_node_1], "test_graph_5", [vi_g, vi_m, vi_n], [vi_o],) conv1_w = generate_input_initializer([1, 1, 3, 3], np.float32, "conv1_w") conv2_w = generate_input_initializer([1, 1, 3, 3], np.float32, "conv2_w") graph.initializer.extend([conv1_w, conv2_w])