torch.Tensor是torch.empty和torch.tensor之间的一种混合,但是,当传入数据时,torch.Tensor使用全局默认dtype(FloatTensor),torch.tensor从数据中推断数据类型。 torch.tensor(1)返回一个固定值1,而torch.Tensor(1)返回一个大小为1的张量,其是随机初始化的值 t1 = torch.Tensor(1) t2 = torch.tensor(1) print(...
import torch import torch._C as _C from torch._namedtensor_internals import update_names, check_serializing_named_tensor, resolve_ellipsis from torch._namedtensor_internals import unzip_namedshape, single_ellipsis_index, is_ellipsis from collections import OrderedDict import torch.utils.hooks as hooks...
in particular when compiling the user-written Triton kernels. I'm not sure whyequal_to_1was not part of this so far: maybe due irrelevance of this for Inductor-generated Triton kernels (as 1 would be inlined anyway, not passed as an argument)?
equivalent = torch.equal(simple_tensor_conv2d, output)assertinput_dim ==4assertoutput_dim ==2assertnotequivalent 开发者ID:rdevon,项目名称:cortex,代码行数:22,代码来源:test_convnets.py 示例5: test_apply_nonlinearity ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch impor...
torch.vtensor<[4,6,8,2],f32> -> tensor<4x6x8x2xf32> // CHECK-DAG: %[[FALSE:.*]] = torch.constant.bool false // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index...
clip_grad_norm_(params, 1.0) exp_grad_norm = torch.full((15,), fill_value=2.).norm() self.assertTrue(torch.is_tensor(grad_norm)) self.assertEqual(grad_norm, exp_grad_norm) grad_norm = utils.clip_grad_norm_(params, 1.0) self.assertAlmostEqual(grad_norm, torch.tensor(1.0)) ...
shape[-2:]) print(norm(X)) # check Y = torch.zeros_like(X) for i in range(X.shape[0]): Y[i] = (X[i] - X[i].mean()) / torch.sqrt(X[i].var(unbiased=False) + 1e-5) print(Y) print(torch.max(torch.abs(norm(X) - Y))) # torch.Size([2, 2, 3]) # tensor([...
tensor([feature.segment_ids], dtype=torch.long).to(self.device) with torch.no_grad(): logits = self.model(input_ids, segment_ids, input_masks, labels=None) logits = torch.sigmoid(logits) preds = (logits > 0.4).float() preds_numpy = preds.cpu().nonzero().squeeze().numpy() # ...
您的代码中有一个小错误，它会导致所有问题:在内部循环中不增加new_idx。因此，您可以将直接从dataset中提取的元素与self.concat_datasets中相应数据集的第一个元素进行比较。
to_dense() update_jacobians(x_tensor_dense, x_idx, d_tensor, d_idx, is_mkldnn=True) else: # Use .data here to get around the version check x_tensor = x_tensor.data for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])): update_jacobians(x_tensor...