AttributeError: Tensor[Int64] object has no attribute: asnumpy

[Hanshize]

1 系统环境

硬件环境(Ascend/GPU/CPU): Ascend910
MindSpore版本: mindspore=1.10.1
执行模式（PyNative/ Graph）:Graph
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

在图模式+Ascend下h_s = int(H_start[idx_H].asnumpy())报错AttributeError: Tensor[Int64] object has no attribute: asnumpy。
具体环境为mindspore-acsend=1.10.1，硬件环境为acsend 910系列

2.2 脚本代码

import mindspore.numpy as np  
from mindspore import dtype as mstype  
from mindspore import ops, Tensor, context, nn  
    
class AdaptiveAvgPool2d(nn.Cell):  
    def __init__(self, output_size):  
        """Initialize AdaptiveAvgPool2d."""  
        super(AdaptiveAvgPool2d, self).__init__()  
        self.output_size = output_size  
    
    def adaptive_avgpool2d(self, inputs):  
        """ NCHW """  
        H = self.output_size[0]  
        W = self.output_size[1]  
    
        H_start = ops.Cast()(np.arange(start=0, stop=H, dtype=mstype.float32) * (inputs.shape[-2] / H), mstype.int64)  
        H_end = ops.Cast()(np.ceil(((np.arange(start=0, stop=H, dtype=mstype.float32)+1) * (inputs.shape[-2] / H))), mstype.int64)  
    
        W_start = ops.Cast()(np.arange(start=0, stop=W, dtype=mstype.float32) * (inputs.shape[-1] / W), mstype.int64)  
        W_end = ops.Cast()(np.ceil(((np.arange(start=0, stop=W, dtype=mstype.float32)+1) * (inputs.shape[-1] / W))), mstype.int64)  
    
        pooled2 = []  
        for idx_H in range(H):  
            pooled1 = []  
            for idx_W in range(W):  
                h_s = int(H_start[idx_H].asnumpy())  
                h_e = int(H_end[idx_H].asnumpy())  
                w_s = int(W_start[idx_W].asnumpy())  
                w_e = int(W_end[idx_W].asnumpy())  
                res = inputs[:, :, h_s:h_e, w_s:w_e]  
                pooled1.append(ops.ReduceMean(keep_dims=True)(res, (-2,-1)))  
            pooled1 = ops.Concat(-1)(pooled1)  
            pooled2.append(pooled1)  
        pooled2 = ops.Concat(-2)(pooled2)  
    
        return pooled2  
    
    def construct(self, x):  
        x = self.adaptive_avgpool2d(x)  
        return x  
    
a=AdaptiveAvgPool2d((3,3))  
x=Tensor(np.randn(3,12,12,12))  
print(a(x))

3 根因分析

图模式下asnumpy()的操作不支持, 使用最新2.0以上版本就可以在图模式下支持asnumpy()了.

4 解决方案

从代码上看,H_start 没必要从numpy 转Tensor再转成numpy, 所以去掉转换的过程.

import mindspore.numpy as np  
from mindspore import dtype as mstype  
from mindspore import ops, Tensor, context, nn  
    
class AdaptiveAvgPool2d(nn.Cell):  
    def __init__(self, output_size):  
        super(AdaptiveAvgPool2d, self).__init__()  
        self.output_size = output_size  
    
    def adaptive_avgpool2d(self, inputs):  
        """ NCHW """  
        H = self.output_size[0]  
        W = self.output_size[1]  
    
        H_start = np.arange(start=0, stop=H, dtype=mstype.float32) * (inputs.shape[-2] / H)  
        H_end = np.ceil(((np.arange(start=0, stop=H, dtype=mstype.float32) + 1) * (inputs.shape[-2] / H)))  
    
        W_start = np.arange(start=0, stop=W, dtype=mstype.float32) * (inputs.shape[-1] / W)  
        W_end = np.ceil(((np.arange(start=0, stop=W, dtype=mstype.float32) + 1) * (inputs.shape[-1] / W)))  
    
        H_start = H_start.astype(np.int64)  
        H_end= H_end.astype(np.int64)  
        W_start= W_start.astype(np.int64)  
        W_end= W_end.astype(np.int64)  
        pooled2 = []  
        for idx_H in range(H):  
            pooled1 = []  
            for idx_W in range(W):  
                h_s = H_start[idx_H]  
                h_e = H_end[idx_H]  
                w_s = W_start[idx_W]  
                w_e = W_end[idx_W]  
                res = inputs[:, :, h_s:h_e, w_s:w_e]  
                pooled1.append(ops.ReduceMean(keep_dims=True)(res, (-2,-1)))  
            pooled1 = ops.Concat(-1)(pooled1)  
            pooled2.append(pooled1)  
        pooled2 = ops.Concat(-2)(pooled2)  
    
        return pooled2  
    
    def construct(self, x):  
        x = self.adaptive_avgpool2d(x)  
        return x  
    
a=AdaptiveAvgPool2d((3,3))  
x=Tensor(np.randn(3,12,12,12))  
print(a(x))