@tensorrt_converter('torch.nn.functional.log_softmax')defconvert_logSoftmax(ctx):input=ctx.method_args[0]input_trt=add_missing_trt_tensors(ctx.network,[input])[0]output=ctx.method_return# get dims from args or kwargsif'dim'inctx.method_kwargs:dim=ctx.method_kwargs['dim']eliflen(ctx....
import torch # create a tensor of type float32 x = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32) # convert it to float64 using .to() y = x.to(torch.float64) print(y) 在模型类型转换方面,Pytorch也提供了非常灵活的函数。例如,可以使用torch.onnx.export()函数将PyTorch模型转换为ONN...
# 返回tensor变量 def image_loader(image_name): image = Image.open(image_name).convert('RGB') image = loader(image).unsqueeze(0) return image.to(device, torch.float) # 输入PIL格式图片 # 返回tensor变量 def PIL_to_tensor(image): image = loader(image).unsqueeze(0) return image.to(device...
pic (PIL Image or numpy.ndarray): Image to be converted to tensor. Returns: Tensor: Converted image. """ return F.to_tensor(pic) def __repr__(self): return self.__class__.__name__ + '()' class ToTensor(object): """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor....
再另外介绍一个取数字的函数:item() ,该函数把tensor和numpy的数转化为数的类型。例如,type(a[0])和type(b[0])分别为tensor和numpy,用item()就可以转化为int或float。当要把训练结果写入到excel保存时,只能时python的原生数字形式,该函数就发挥作用了。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ...
(2) 张量和numpy之间的类型转换 numpy转张量:使用tf.convert_to_tensor() 张量转numpy:由Session.run或eval返回的任何张量都是NumPy数组。 (3)tensorflow好像不存在什么gpu张量和cpu张量类型 如果有什么错误还请指出,有什么遗漏的还请补充,会进行相应的修改。
input_var = Variable(torch.FloatTensor(input_tensor)) # get PyTorch ResNet18 model model_to_transfer = FullyConvolutionalResnet18(pretrained=pretrained_resnet) model_to_transfer.eval() # convert PyTorch model to Keras model = pytorch_to_keras( ...
在GraphProto中包含了四个关键的repeated数组,分别是node(NodeProto类型),input(ValueInfoProto类型),output(ValueInfoProto类型)和initializer(TensorProto类型),其中node中存放着模型中的所有计算节点,input中存放着模型所有的输入节点,output存放着模型所有的输出节点,initializer存放着模型所有的权重;那么节点与节点之间的...
# Convert the pytorch tensor to a numpy array:numpy_tensor = pytorch_tensor.numpy()print("type: ", type(numpy_tensor), " and size: ", numpy_tensor.shape) # Convert the numpy array to Pytorch Tensor:print("type: ", type(torch.Tensor(numpy_tensor)), " and size: ", torch.Tensor(...
image_numpy = image_tensor.cpu().float().numpy()# convert it into a numpy arrayifimage_numpy.shape[0] ==1:# grayscale to RGBimage_numpy = np.tile(image_numpy, (3,1,1))foriinrange(len(mean)):# 反标准化,乘以方差,加上均值image_numpy[i] = image_numpy[i] * std[i] + mean[...