t = torch.full_like(ps[:, 5:], cn, device=device) t[range(n), tcls[i]] = cp loss_cls += BCEcls(ps[:, 5:], t) loss_obj += BCEobj(pi[..., 4], tobj) * balance[i] s = 3 / np loss_box *= h['giou'] * s loss_obj *= h['obj'] * s * (1.4 if np == ...
sigma_gaussian ** 2)) dist_diff_weight = torch.full_like(estDisp, dist) disp = self.disp_conv[index_disp_conv](estDisp) if index_disp_conv == 0: weight = dist_diff_weight * image_diff_weight fineDisp = disp * dist_diff_weight * image_diff_weight else: weight += dist_diff_...
Python torch.full() Examples The following are 30 code examples of torch.full(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all ...
ts = torch.full([batch_size, 1], t) estimated_noise = model(xt, ts) mean = reciprocal_sqrt_alphas[ts] * (xt - betas_over_sqrt_one_minus_alphas_cumprod[ts] * estimated_noise) if t > 0: z = torch.randn_like(xt) else:
Python torch.zeros_like实例讲解 posted @2023-08-22 11:16gbc阅读(27) 评论(0)收藏举报
所有版本pytorch下载地址:https://download.pytorch.org/whl/torch_stable.html pytorch成功安装后再次运行webui-user.bat,提示Running on local URL时成功,会自动打开浏览器(或手动打开127.0.0.1:7860),结果如下: ps:#后面再运行不需要再开dev-sidecar
viztracer --log_torch your_model.py Advanced Usage Trace Filter VizTracer can filter out the data you don't want to reduce overhead and keep info of a longer time period before you dump the log. Min Duration Max Stack Depth Include Files ...
utils.dlpack import to_dlpack import torch import triton_python_backend_utils as pb_utils class TritonPythonModel: def execute(self, requests): ... pytorch_tensor = torch.tensor([1, 2, 3], device='cuda') # Create a Python backend tensor from the DLPack encoding of a PyTorch # tensor....
torch.full和torch.full_like可以看作是torch.ones和torch.ones_like衍生出来的函数。 arange, range, linspace, logspace:arange、range和linspace用来构造公差数列。logspace用来构造指数的幂为等差数列的数组,他们的用法如下: torch.arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.stride...
w_hat_mul = torch.where(torch.isnan(r), torch.full_like(r, np.nan), w_hat) w_hatm = torch.where(torch.isnan(r), torch.full_like(r, 0), w_hat) ws = torch.sum(w_hatm, axis=1) num = torch.sum(~torch.isnan(r), axis=1) wn = ws/num w0 = torch.empty(w_hat.shape...