🐛 Describe the bug When the devisor is -1 and input is a large negative integer, torch.floor_divide will throw a floating point exception. import torch input = torch.tensor([[-9223372036854775808]]) other = torch.tensor([-1]) torch.floor...
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...
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...
.floor(向下取整).ceil(向上取整) .round(四舍五入) .trunc(整数部分).frac(小数部分) torch.clamp gradient clipping (min) (min,max) AI检测代码解析 >>> grad=torch.rand(2,3)*15 >>> grad.max() tensor(11.2428) >>> grad.median()
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 ...
torch.floor_divide 是 支持fp16,fp32,uint8,int8,int16,int32,int64 torch.fmod 是 支持fp16,fp32,uint8,int8,int32,int64 torch.gradient 是 支持bf16,fp16,fp32,int8,int16,int32,int64 torch.ldexp 是 支持fp16,fp64,complex64 torch.lerp 是 支持fp16,fp32 torch.log 是 ...
return torch.floor_divide(other, self) Output: tensor(1, dtype=torch.int32) The return value of a scripted wrap_div, is an integer while Eager mode returns a tensor. @gmagogsfm,@eellison: Since 5 is an int, we implicitly convert the tensor to an int. Is this expected behavior?
torch.Tensor.floor_divide_ Supported 129 torch.Tensor.fmod Supported 130 torch.Tensor.fmod_ Supported 131 torch.Tensor.frac Supported 132 torch.Tensor.frac_ Supported 133 torch.Tensor.gather Supported 134 torch.Tensor.ge Supported 135 torch.Tensor.ge_ ...
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]: ...