错误: divide by zero encountered in divide 形状不匹配且无法广播:如果两个tensor的形状不匹配,且无法通过广播机制使它们匹配,那么在执行除法运算时会抛出一个RuntimeError。 python x = torch.tensor([1.0, 2.0, 3.0]) y = torch.tensor([[1.0], [2.0]]) try: result = x / y except RuntimeError...
div() == divide() digamma() erf() erfc() erfinv() exp() exp2() expm1() fake_quantize_per_channel_affine() fake_quantize_per_tensor_affine() fix() == trunc() float_power() floor() floor_divide() fmod() frac() imag() ldexp() lerp() lgamma() log() log10() log1p() log...
运行程序,就可以看到所有的函数、方法 import torch s = dir(torch) for i in s: print(i) 1. 2. 3. 4. 输出有一千多个结果 AVG AggregationType AnyType Argument ArgumentSpec BFloat16Storage BFloat16Tensor BenchmarkConfig BenchmarkExecutionStats Block BoolStorage BoolTensor BoolType BufferDict Byte...
coo张量可用的tensor成员函数(经实测,csr也有一些可以用,比如dim()) add()add_()addmm()addmm_()any()asin()asin_()arcsin()arcsin_()bmm()clone()deg2rad()deg2rad_()detach()detach_()dim()div()div_()floor_divide()floor_divide_()get_device()index_select()isnan()log1p()log1p_()mm()mul...
Add/minus/multiply/divide Matmul(矩阵式相乘) Pow Sqrt/rsqrt Round basic(+ - * / add sub mul div) 建议直接使用运算符 AI检测代码解析 >>> a=torch.rand(3,4) >>> b=torch.rand(4) #broadingcast机制 >>> a+b tensor([[0.2349, 1.7635, 1.4385, 0.5826], ...
Add/minus/multiply/divide Matmul(矩阵式相乘) Pow Sqrt/rsqrt Round basic(+ - * / add sub mul div) 建议直接使用运算符 view code matmul Torch.mm(only for 2d 不推荐) Torch.matmul(推荐) @ 注意:①*是element-wise,对应元素相乘;②.matmul是矩阵相乘 ...
return floor_divide(a, b) else: raise ValueError(f"div does not support the rounding_mode={rounding_mode} argument") Collaborator mruberry May 23, 2024 Nice error message mruberry reviewed May 23, 2024 View reviewed changes thunder/tests/opinfos.py pytest.mark.xfail, "test_core_...
out.true_divide_(count) else: out.div_(count, rounding_mode='floor') return out def scatter_min( src: torch.Tensor, index: torch.Tensor, dim: int = -1, out: Optional[torch.Tensor] = None, dim_size: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]: ...
true_divide(x, y).shape, y.shape) # Regression test for https://github.com/pytorch/pytorch/issues/115066 self.assertEqual(torch.mul(x, y).shape, y.shape) # Regression test for https://github.com/pytorch/pytorch/issues/113037 self.assertEqual(torch.div(x, y, rounding_mod...
torch.div 是 支持bf16,fp16,fp32,uint8,int8,int16,int32,int64,bool torch.divide 是 支持bf16,fp16,fp32,uint8,int8,int16,int32,int64,bool torch.erf 是 支持fp16,fp32,int64,bool torch.erfc 是 支持fp16,fp32,int64,bool torch.erfinv 是 支持fp16,fp32,uint8,int8,int...