if device: input_array = input_array.to(device) return cp.from_dlpack(torch.utils.dlpack.to_dlpack(input_array)) # return cp.asarray(input_array.cpu().numpy()) elif target_type == 'numpy': return input_array.numpy() elif target_type == 'torch': if device: input_array = input_ar...
import randomfrom torch_geometric.utils import to_networkximport networkx as nxdef convert_to_networkx(graph, n_sample=None): g = to_networkx(graph, node_attrs=["x"]) y = graph.y.numpy() if n_sample is not None: sampled_nodes = random.sample(g.nodes, n_sample) g =...
#定义批量测试defbenchmark(model, input_shape=(1024,1,224,224), dtype='fp32', nwarmup=50, nruns=10000): input_data = torch.randn(input_shape)#这里采用随机数不是真实图片input_data = input_data.to("cuda")ifdtype=='fp16': input_data = input_data.half()print("Warm up ...")with...
其中的 N 是一个数字,指代维度. 在 NumPy 中,数组是由 numpy.ndarray 类来实现的,它是 NumPy 的核心数据结构。 而Python 中的列表,其实也可以达到与 NumPy 数组相同的功能,但它们又有差异,做个对比你就能体会到 NumPy 数组的特点了。 1.Python中的列表可以动态地改变,而NumPy数组是不可以的,它在创建时就有...
import numpy as np a = np.ones(5) b = torch.from_numpy(a) np.add(a, 1, out=a) print(a) print(b) out: [2. 2. 2. 2. 2.] tensor([2., 2., 2., 2., 2.], dtype=torch.float64) 当然还有能在GPU上运算的CUDA tensors 先判断cuda有没有安装好: torch.cuda.is_available()...
Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1)[0, 1)[0,1) . torch.rand_like(input) is equivalent to torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device). ...
import numpy as np from matplotlib import pyplot as plt # 1. 定义数据 x = torch.rand([50,1]) y = x*3 + 0.8 #2 .定义模型 class Lr(nn.Module): def __init__(self): super(Lr,self).__init__() self.linear = nn.Linear(1,1) ...
2.6 从numpy创建Tensor# Torch code: x = torch.from_numpy(x).float() # PaddlePaddle code x = paddle.to_tensor(x).astype(np.float32) In [7] import paddle x=paddle.to_tensor([1,2,3,4,5,6,7,8,9,10,11,12]) sample_lst=[0,5,7,11] x[sample_lst] Tensor(shape=[4], dtype...
torch.from_numpy()函数的作用是什么? torch.from_numpy()如何将NumPy数组转换为张量? 使用torch.from_numpy()转换后的张量与原始NumPy数组共享内存吗? 简单说一下,就是torch.from_numpy()方法把数组转换成张量,且二者共享内存,对张量进行修改比如重新赋值,那么原始数组也会相应发生改变。
from_numpy(a) >>> t tensor([ 1, 2, 3]) >>> t[0] = -1 >>> a array([-1, 2, 3]) torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)→ Tensor Returns a tensor filled with the scalar value 0, with the shape defined by the ...