t data = np.frombuffer(t.raw_data, np.float32).reshape(*t.dims) print(data.shape) print(data) output: "352" name: "Constant_218" op_type: "Constant" attribute { name: "value" type: TENSOR t { dims: 1 dims: 3 dims: 80 dims: 80 dims: 2 data_type: 1 raw_data: "\000\...
输出：0: ONNX_DATA_TYPE_UNDEFINED 1: ONNX_DATA_TYPE_FLOAT 2: ONNX_DATA_TYPE_UINT8 3: ONNX_DATA_TYPE_INT8 4: ONNX_DATA_TYPE_UINT16 5: ONNX_DATA_TYPE_INT16 6: ONNX_DATA_TYPE_INT32 7: ONNX_DATA_TYPE_INT64 8: ONNX_DATA_TYPE_STRING 9: ONNX_DATA_TYPE_BOOL10: ONNX_DATA...
op_type: "Div" doc_string: "" domain: "" } name: "Extracted from {CNTKGraph}" initializer { data_type: 1 float_data: 127.5 name: "Constant339" } initializer { data_type: 1 float_data: 255.0 name: "Constant343" } input { name: "Input3" type { tensor_type { elem_type: 1 sh...
optional TensorProto.DataType elem_type = 1; optional TensorShapeProto shape = 2; 维数大小的空列表[]是有效的张量shape,表示零维(标量)值。零维张量不同于未知维数的张量,它由张量记录中缺少的“shape”属性来表示。当值类型(包括节点输入)中缺少shape属性时,它指示相应的运行时值可以具有任何shape。本小节...
fromskl2onnx.common.data_typesimportFloatTensorType, Int64TensorType, DoubleTensorTypedefconvert_dataframe_schema(df, drop=None, batch_axis=False):inputs = [] nrows =Noneifbatch_axiselse1fork, vinzip(df.columns, df.dtypes):ifdropisnotNoneandkindrop:continueifv =='int64': t = Int64TensorType...
fromskl2onnx.common.data_typesimportFloatTensorType, Int64TensorType, DoubleTensorTypedefconvert_dataframe_schema(df, drop=None, batch_axis=False):inputs = [] nrows =Noneifbatch_axiselse1fork, vinzip(df.columns, df.dtypes):ifdropisnotNoneandkindrop:continueifv =='int64': t = Int64TensorType...
2,Loading an ONNX Model with External Data 【默认加载模型方式】如果外部数据(external data)和模型文件在同一个目录下,仅使用onnx.load()即可加载模型,方法见上小节。 如果外部数据(external data)和模型文件不在同一个目录下,在使用onnx_load()函数后还需使用load_external_data_for_model()函数指定外部数...
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op_type: "Reshape" } node { input: "fc_0.tmp_0" input: "fc_0.b_0" output: "fc_0.tmp_1" op_type: "Add" attribute { name: "axis" i: 1 type: INT } attribute { name: "broadcast" i: 1 type: INT } } name: "fit_a_line" initializer { dims: 1 data_type: FLOAT float...
data = inputs[0]print("inputs-: ",type(data), data.dtype)print("axis: ", self.axis) res = np.argmax(data, axis=self.axis)# shape = data.shape# print("shape: ", shape)# res = np.random.randint(0, shape[self.axis], tuple(self.dim), dtype=np.longlong)print(res, res.shap...