T.CenterCrop(224), T.ConvertImageDtype(torch.float), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ) def forward(self, x: torch.Tensor) -> torch.Tensor: with torch.no_grad(): x = self.transforms(x) y_pred = self.resnet18(x) return y_pred.argmax(dim=1) 1. 2....
dtype) 运行结果如下 torch.int32 torch.int64 tensor([1., 2., 3.]) torch.float32 torch.float64 方式-2 import torch a = torch.tensor([1,2,3],dtype=torch.int32) print(a) a = torch.tensor([1,2,3],dtype=torch.int64) print(a) a = torch.tensor([1,2,3],dtype=torch.float32)...
🐛 Describe the bug Hi there, I ran the following code on CPU or GPU, and observed that torch.tensor([0.01], dtype=torch.float16) * torch.tensor(65536, dtype=torch.float32) returns INF. The second scalar operand (torch.tensor(65536, dtype...
fromtorch2trt.torch2trtimport*@tensorrt_converter('torch.zeros')defconvert_zeros(ctx):size=ctx.method_argsoutput=ctx.method_returnkwargs=ctx.method_kwargsdtype=kwargs.get('dtype')zeros_tensor=torch.zeros(*size,dtype=dtype)output._trt=add_trt_constant(ctx.network,zeros_tensor) 再运行convert.py...
# Torch Code: torch.Tensor((1,2,3,4)) #output: #tensor([1., 2., 3., 4.]) # PaddlePaddle Code: paddle.to_tensor((1,2,3,4)) # 全部为整数 #output: #Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, # [1, 2, 3, 4]) paddle.to_tensor((1,2,3,...
np.transpose(),torch.permute(),tensor.permute() 在完成两个维度转换时效果一样,只不过transpose是对np操作,permute是对tensor操作; transpose每次只能换两个维度,两种写法,参数顺序无所谓; permute每次可以交换多个维度,但所有的维度也必须都写上,参数顺序表示交换结果是原值的哪个维度,只有一种写法。
tensor = np.zeros((8,8,8,8), dtype=np.float32)forx, y, u, vinitertools.product(range(8), repeat=4): tensor[x, y, u, v] = np.cos((2* u +1) * x * np.pi /16) * np.cos( (2* v +1) * y * np.pi /16)# result = 0.25 * torch.tensordot(image, torch.as_tensor...
self.quant=torch.quantization.QuantStub()self.conv=torch.nn.Conv2d(1,1,1)self.relu=torch.nn.ReLU()# DeQuantStub converts tensors from quantized to floating point self.dequant=torch.quantization.DeQuantStub()defforward(self,x):# 自己指定开始量化的层 ...
正如jasonharper所指出的，你可以使用torch.is_floating_point来检查你的Tensor：
t=torch.tensor( [],dtype=storage.dtype,device=storage._untyped_storage.device)returnt.set_(storage._untyped_storage,storage_offset,size,stride)mode=_get_current_dispatch_mode()ifisinstance(mode,FakeTensorMode):# Create a real tensor and then convert it to FakeTensor.# We cannot directly create...