torch.compile 是加速 PyTorch 代码的最新方法! torch.compile 通过 JIT 将 PyTorch 代码编译成优化的内核,使 PyTorch 代码运行得更快,大部分过程仅需修改一行代码。 本篇文章主要介绍下torch.compile的基本用法,并展示了 torch.compile 相对于以前的 PyTorch 编译器解决方案(例如 TorchScript 和 FX Tracing)的优势。
示例12: create_dct ▲点赞 5▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importcos[as 别名]defcreate_dct( n_mfcc: int, n_mels: int, norm: Optional[str] )-> Tensor:r"""Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``), normalized dependin...
# 需要导入模块: import torch [as 别名]# 或者: from torch importirfft[as 别名]defidct(X, norm=None):""" The inverse to DCT-II, which is a scaled Discrete Cosine Transform, Type III Our definition of idct is that idct(dct(x)) == x For the meaning of the parameter `norm`, see...
开发者ID:wangleiphy,项目名称:tensorgrad,代码行数:27,代码来源:measure.py 示例2: idct_8x8 ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importtensordot[as 别名]defidct_8x8(image):alpha = np.array([1./ np.sqrt(2)] + [1] *7) alpha = torch.FloatTensor(np....
def_transform(self, x):bands = self.dct_transform.frequency_decomposition( x, self.factors, axis=-1) norms = [torch.norm(b, dim=-1, keepdim=True)forbinbands] bands = [b / (n +1e-8)for(b, n)inzip(bands, norms)] fine = torch.cat(bands, dim=-1) ...