1 系统环境
硬件环境(Ascend/GPU/CPU): Ascend/GPU/CPU
MindSpore版本: mindspore=1.7.0
执行模式(PyNative/ Graph):不限
Python版本: Python=3.9.13
操作系统平台: 不限
2 报错信息
2.1 问题描述
运行一下代码,mindspore推理报错
2.2 脚本代码(代码格式,可上传附件)
3 根因分析
******此处由用户补充详细的定位过程******
4 解决方案
******此处由用户填写******
包含文字方案和最终脚本代码
请将正确的脚本打包并上传附件
1 系统环境
硬件环境(Ascend/GPU/CPU): Ascend/GPU/CPU
MindSpore版本: mindspore=1.7.0
执行模式(PyNative/ Graph):不限
Python版本: Python=3.9.13
操作系统平台: 不限
2 报错信息
2.1 问题描述
运行一下代码,mindspore推理报错
[CRITICAL] PARSER(3727058,7f213f7cd740,python):2023-04-27-17:58:18.788.462 [mindspore/ccsrc/pipeline/jit/parse/function_block.cc:257] HandleBuiltinNamespaceInfo] The name 'LTM' is not defined, or not supported in graph mode. Traceback (most recent call last): File "testtest.py", line 21, in <module> outs = nets(inps) File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/nn/cell.py", line 586, in __call__ out = self.compile_and_run(*args) File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/nn/cell.py", line 964, in compile_and_run self.compile(*inputs) File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/nn/cell.py", line 937, in compile _cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode) File "/home/ma-user/anaconda3/envs/tmp/lib/python3.7/site-packages/mindspore/common/api.py", line 1006, in compile result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode()) NameError: mindspore/ccsrc/pipeline/jit/parse/function_block.cc:257 HandleBuiltinNamespaceInfo] The name 'LTM' is not defined, or not supported in graph mode. # In file testtest.py(13) LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)]2.2 脚本代码(代码格式,可上传附件)
import numpy as np import mindspore as ms import mindspore.nn as nn import mindspore.context as context class model(nn.Cell): def __init__(self,): super().__init__() def construct(self, LTM): k = LTM.shape[2] LTM_ = [LTM[:, :i].sum(2) for i in range(k + 1)] return LTM_ if __name__ == '__main__': context.set_context(mode=context.GRAPH_MODE, device_target='GPU', save_graphs=False) inps = ms.Tensor(np.random.random((3, 8, 256, 256))) nets = model() outs = nets(inps) import pdb; pdb.set_trace() print(outs.shape)3 根因分析
******此处由用户补充详细的定位过程******
4 解决方案
******此处由用户填写******
包含文字方案和最终脚本代码
请将正确的脚本打包并上传附件