python:Tensor运算符__getitem__支持和验证[:,,]

[miaobyte]

[:,,]运算符的切片用法，需要进一步支持

[miaobyte]

pytorch 的tensor的getitem()是从c++层面重写，从而避开了TypeError: 'NoneType' object is not subscriptable的问题

def deepx_getitem():
    from deepx  import newtensor
    t=newtensor((2,3,4)).full_(1)
    t2=t[None, :, None]
    t2.print()
def torch_getitem():
    import torch
    t=torch.full((2,3,4),1)
    t2=t[None, :, None]
    print(t2)
if __name__ == "__main__":
    deepx_getitem()
    torch_getitem()

而deepx的py是目前纯python

后期再考虑支持这个[None,:]的特性

[miaobyte]

经过tinygrad启发，实际上可以传递none作为参数

class Tensor:
    def __init__(self, data):
        # 假设 data 是嵌套 list 或 numpy ndarray
        import numpy as np
        self.data = np.array(data)
    
    def __getitem__(self, idx):
        # 支持 None, int, slice, tuple 等
        # 重点：遇到 None 时，插入新轴
        import numpy as np

        if not isinstance(idx, tuple):
            idx = (idx,)
        
        # 统计原始索引和 None 的位置，组装成新的索引
        new_idx = []
        expand_axes = []
        for i, ix in enumerate(idx):
            if ix is None:
                expand_axes.append(len(new_idx))
            else:
                new_idx.append(ix)
        
        # 先索引
        result = self.data[tuple(new_idx)]
        # 再插入新轴
        for ax in expand_axes:
            result = np.expand_dims(result, axis=ax)
        
        # 返回新 Tensor
        ret = Tensor(result)
        return ret
    
    @property
    def shape(self):
        return self.data.shape

    def __repr__(self):
        return f"Tensor(shape={self.shape}, data=\n{self.data})"


# 测试代码
t = Tensor([[1, 2, 3], [4, 5, 6]])
print("原始shape:", t.shape)           # (2, 3)

t2 = t[None]
print("t[None].shape:", t2.shape)      # (1, 2, 3)

t3 = t[:, None]
print("t[:, None].shape:", t3.shape)   # (2, 1, 3)

t4 = t[None, :, None]
print("t[None, :, None].shape:", t4.shape) # (1, 2, 1, 3)

[miaobyte]

#74