helper.make_operatorsetid("ai.onnx", 11) ] # producer主要是保持和pytorch一致 model = helper.make_model(graph, opset_imports=opset, producer_name="pytorch", producer_version="1.9") onnx.save_model(model, "my.onnx") print(model) print("Done.!") 1. 2. 3. 4. 5. 6. 7. 8. 9....
# 需要导入模块: import onnx [as 别名]# 或者: from onnx importhelper[as 别名]deftest_reshape():input_data = np.arange(2560).reshape(16,4,4,10) reshape_node = onnx.helper.make_node('Reshape', inputs=['x'], outputs=['y'], shape=(256,10)) expected_output = input_data.reshape...
helper.make_node('AveragePool', inputs=['x'], outputs=['y'], kernel_shape=(2, 2), strides=(2, 2)) y = np.array([[13.5, 15.5], [21.5, 23.5]], dtype=np.float32).reshape(1, 1, 2, 2) ng_results = run_node(node, [x]) assert np.array_equal(ng_results, [y]) node ...
另外我们要修改模型node[1]的输入,因为之前node[1]的input是原先的input[0]它的name为onnx::Reshape_0。因此我们新建一个Reshape节点。 reshape_node = helper.make_node( 'Reshape', # 节点类型 inputs=['input',"/Constant_output_0"], # 输入张量列表 outputs=['/Reshape_output_0'] # 输出张量列表 ...
node = onnx.helper.make_node( "Momentum", inputs=["R", "T", "X", "G", "V"], outputs=["X_new", "V_new"], norm_coefficient=norm_coefficient, alpha=alpha, beta=beta, mode="nesterov", domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, ...
PreShapeNodeElimination 这个pass 非常奇怪,删除 shape 前面的 cast 算子? 构建了一个 graph,测试了下,最后的融合结果不太符合逻辑。 import onnx from onnx import helper from onnx import TensorProto import onnxruntime as ort M = 16 N = 16 # 创建一个输入张量 X X = helper.make_tensor_value_...
add_body(1, "{:15} = helper.make_node('Reshape', inputs=['{}', '{}'], outputs=['{}'], name='{}')".format( IR_node.variable_name, self.parent_variable_name(IR_node), IR_node.variable_name + '_shape', IR_node.variable_name, IR_node.variable_name)) self.nodes.append(...
Variables from mapping.py will be deprecated and become private implementation details. Please use public functions to get corresponding types from helper.py instead:#4554 Installation notice You can upgrade to the latest release usingpip install onnx --upgradeor build from source following the READ...
🐛 Describe the bug I encounter this error when converting a pytorch model to onnx. I am trying to convolve with specific weights and in groups. I narrowed down the piece of code creating the problem shown below. import torch class Filter...
这篇文章从多个角度探索了ONNX,从ONNX的导出到ONNX和Caffe的对比,以及使用ONNX遭遇的困难以及一些解决办法,另外还介绍了ONNXRuntime以及如何基于ONNXRuntime来调试ONNX模型等,后续也会继续结合ONNX做一些探索性工作。 0x0. 前言 这一节我将主要从盘点ONNX模型部署有哪些常见问题,以及针对这些问题提出一些解决方法...