requires_grad:是否需要计算梯度,默认为False。 torch.empty创建的张量是未初始化的,即其值是随机的,取决于内存中的内容。因此,如果需要具有特定值的张量,应该使用其他函数进行初始化,如torch.zeros、torch.ones或torch.rand。 以下是一个示例代码,展示了如何使用torch.empty创建一个张量并计算其值: ...
batch_size: int, batch_indices: Tensor)-> Tuple[Tensor, Tensor]:cross_entropies = torch.empty(batch_size, dtype=torch.float, device=anchor_objectnesses.device) smooth_l1_losses = torch.empty(batch_size, dtype=torch.float, device=anchor_transformers.device)forbatch_indexinrange(batch_size): ...
torch.empty在生成随机数时效率较高，适用于初始化某些不需要特定值的变量。而torch.zeros则精确地创建了一个全零张量，适用于需要明确初始值的场景。因此，根据具体需求选择合适的函数，可以避免潜在的错误，提高代码的稳定性和计算效率。
import torch x = torch.empty(5, 3) # 生成空的矩阵 print(x) x = torch.rand(5, 4) # 生成随机矩阵 print(x) x = torch.zeros(5, 4, dtype=torch.long) # 生成空矩阵 print(x) x = torch.tensor([5, 3]) # 将列表转换维tensor类型 print(x) x = x.new_ones([5, 3], dtype=...
import torch x = torch.empty(5, 3) # ⽣成空的矩阵 print(x)x = torch.rand(5, 4) # ⽣成随机矩阵 print(x)x = torch.zeros(5, 4, dtype=torch.long) # ⽣成空矩阵 print(x)x = torch.tensor([5, 3]) # 将列表转换维tensor类型 print(x)x = x.new_ones([5, 3], dtype=...
在三维重建中，标定是很重要的一环，而在所有标定中，单目相机标定是最基础的，对于新手而言，跑通了一...
🐛 Bug Playing around with torch 1.2.0 seems to output the wrong tensor when calling empty(5,3) To Reproduce Steps to reproduce the behavior: >>> import torch >>> x = torch.empty(5, 3) >>> print(x) tensor([[1.8361e+25, 1.4603e-19, 1.6795e...
📚 Documentation The example presented for torch.empty_like seems wrong: >>> torch.empty((2,3), dtype=torch.int64) tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], [ 7.5751e+18, 7.1428e+18, 7.5955e+18]]) I guess the desided example would be...
update(self.kwargs) for _key in self.keys: out = torch.empty_like(data[_key]) for _i in range(data[_key].shape[1]): out[:, _i] = self.augment_fn(data[_key][:, _i], out=out[:, _i], **kwargs) data[_key] = out return data else: return super().forward(**data) ...
torch.cuda.empty_cache() 显存就进行了释放,为679MB。 更改代码: importtorchimporttimeimportosimportfunctools#os.environ["CUDA_VISIBLE_DEVICES"] = "3"device='cuda:0'shape_= (4, 1024, 512, 512)#4GB#dummy_tensor_4 = torch.randn(120, 3, 512, 512).float().to(device) # 120*3*512*512...