# Preprocessing: create a Numpy array numpy_array = numpy.array([[1.0,2.0,3.0], [4.0,5.0,6.0]], dtype=float) print('Original Numpy array:\n{}\n'.format(numpy_array)) # Convert the Numpy array to a TensorProto tensor = numpy_helper.from_array(numpy_array) print('TensorProto:\n{}...
from onnx import load_tensor_from_string from onnx.numpy_helper import to_array # 读取序列化数据 data_path = 'ONNX/saves/saved_serialized_tensor.pb' # pb: Protocol Buffers with open(data_path, 'rb') as f: serialized_tensor = f.read() print(f"--- serialized_tensor ---\n" f"{t...
from onnximportnumpy_helper # Preprocessing:create a Numpy array numpy_array=numpy.array([[1.0,2.0,3.0],[4.0,5.0,6.0]],dtype=float)print('Original Numpy array:\n{}\n'.format(numpy_array))# Convert the Numpy array to a TensorProto tensor=numpy_helper.from_array(numpy_array)print('Tensor...
import numpy import onnx from onnx import numpy_helper # Preprocessing: create a Numpy array numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float) print('Original Numpy array:\n{}\n'.format(numpy_array)) # Convert the Numpy array to a TensorProto tensor = ...
Bump up IR_VERSION to 10 (#5860) Support Python 3.12 (#5743) Fix corner case where output size need to reduce by one in MaxPool (#5741) Bump Numpy minimal version to 1.20 (#5902) Fix endianness conversion in numpy_helper.to_array() (#5904) Add valueinfos field to FunctionProto (#...
import numpy as np weight = np.random.randn(36) X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 2, 4, 4]) W = helper.make_tensor('W', TensorProto.FLOAT, [2, 2, 3, 3], weight) B = helper.make_tensor('B', TensorProto.FLOAT, [2], [1.0, 2.0]) ...
protobuf.json_format.MessageToDict(model_proto) tensor_proto = model_proto.graph.initializer[0] onnx.numpy_helper.to_array(tensor_proto, np.float32) # Worked Well raw_data = d["graph"]["initializer"][0]["rawData"] np.frombuffer(raw_data.encode(), np.float32) # Failed...
conv_node = helper.make_node("Conv",['conv_input','conv_weight','conv_bias'],['conv_output'],name='conv') add_node = helper.make_node('Add',['conv_output','add_input'],['output'],name='add') 通过make_node构建节点,参数分别表示onnx算子,输入名称、...
[W:onnxruntime:, execution_frame.cc:806 VerifyOutputSizes] Expected shape from model of {} does not match actual shape of {1,1} for output linear_1.tmp_0_ReduceMin /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/onnx/numpy_helper.py:93: DeprecationWarning: `np....
引入了onnxruntime用于加载和运行ONNX模型,cv2用于图像处理,numpy用于数组操作,time用于计时,yaml用于读取标签文件。 2. 图像处理函数 resize_image_cv2函数 将输入图像按比例缩放到目标尺寸,同时填充背景使得图像保持原比例不变。 def resize_image_cv2(image, size): ih, iw, ic = image.shape w, h = size...