0: _API_MODULE 1: __internal__ 2: __operators__ 3: _api 4: _compat 5: _current_module 6: _distutils 7: _fi 8: _initializers 9: _inspect 10: _ll 11: _logging 12: _losses 13: _metrics 14: _module_util 15: _optimi
PyTorch also overloads a range of arithmetic and logical operators: z = -x # z = torch.neg(x) z = x + y # z = torch.add(x, y) z = x - y # z = torch.sub(x, y) z = x * y # z = torch.mul(x, y) z = x / y # z = torch.div(x, y) z = x // y # ...
# 使用PIL的Image.open()方法打开图片，然后将其转换为NumPy数组。# 将NumPy数组转换为PyTorch张量（Tens...
torch.library.triton_opoffers a standard way of creating custom operators that are backed by user-defined triton kernels. When users turn user-defined triton kernels into custom operators,torch.library.triton_opallowstorch.compileto peek into the implementation, enablingtorch.compileto optimize the tri...
1.构建和训练神经网络 PyTorch 提供了构建和训练各种神经网络所需的所有基本构建块，包括卷积神经网络（...
Comparison Operators a == b a != b a < b a > b a <= b a >= b Logical Operators a and b a or b not b Subscripts t[0] t[-1] t[0:2] t[1:] t[:1] t[:] t[0, 1] t[0, 1:2] t[0, :1] t[-1, 1:, 0] t[1:, -1, 0] t[i:j, i] ...
Comparison Operators a == b a != b a < b a > b a <= b a >= b Logical Operators a and b a or b not b Subscripts t[0] t[-1] t[0:2] t[1:] t[:1] t[:] t[0, 1] t[0, 1:2] t[0, :1] t[-1, 1:, 0] t[1:, -1, 0] t[i:j, i] ...
logical_and_npu_ 69 logical_and.out logical_and_out_npu 70 logical_or logical_or_npu 71 logical_or_ logical_or_npu_ 72 logical_or.out logical_or_out_npu 73 blackman_window blackman_window_npu 74 blackman_window.periodic blackman_window_npu 75 bmm bmm_npu 76 bmm...
Microsoft’s new tool makes it possible to use your own GPU to work with popular machine learning platforms.
A brief taxonomy of PyTorch operators by shape behavior I've recently been working on a revamp of how we specify tensor shape formulas in PyTorch. As part of this process, I classified every single operator in PyTorch by its shaping behavior; yes, that's all 1364 of them (this includes ...