numpy转tensorflow的tensor import numpy as np import tensorflow as tf a = np.array([[1,2,3],[4,5,6],[4,9,2],[3,6,4]]) b=tf.convert_to_tensor(a) #转换语句 print(type(b)) #输出为<class 'tensorflow.python.framework.ops.Ea
@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....
(optional) assign a name to output tensor and layer. thescopeargument of handler is unique. Tips to write TensorRT-compatible modules inputs and outputs of net.forward can't be dict. tensorrt don't support type cast for now. all data should be float. avoid to use operations such as "to...
Convert the data into a torch.Tensor. If the data is already a Tensor with the same dtype and device, no copy will be performed, otherwise a new Tensor will be returned with computational graph retained if data Tensor has requires_grad=True. Similarly, if the data is an ndarray of the ...
tensor除法会使输出结果的精度高一级,可能会导致后面计算类型不匹配,如float32 / float32 = float64。在上面的代码中,torch.equal(img_convert_to_tensor1, img_convert_to_tensor2)是等于False的。Tensor默认的dtype是float32,所以当Tensor的类型为float32时,打印Tensor是不会显示的。
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。
y = tf.convert_to_tensor(y, dtype=tf.int32) print(x.shape, y.shape, x.dtype, y.dtype) #(60000, 28, 28) (60000,) <dtype: 'float32'> <dtype: 'int32'> print(tf.reduce_min(x), tf.reduce_max(x)) #tf.Tensor(0.0, shape=(), dtype=float32) tf.Tensor(1.0, shape=(), ...
def convert_tensor(input_array, target_type, device=None): """ 将输入的数据类型转换为目标类型,支持 CuPy、NumPy 和 PyTorch Tensor 之间的转换。 对于CuPy 到 PyTorch 的转换,使用 DLPack 进行高效转换。 参数: input_array: 输入的数组,可以是 CuPy 数组、NumPy 数组或 PyTorch Tensor ...
# Convert to PyTorch tensors X_train=torch.tensor(X_train, dtype=torch.float32) y_train=torch.tensor(y_train, dtype=torch.float32).view(-1, 1) X_test=torch.tensor(X_test, dtype=torch.float32) y_test=torch.tensor(y_test, dtype=torch.float32).view(-1, 1) ...
# 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,...