unsqueezed_tensor = tensor.unsqueeze(0) # Adds a dimension at index 0 print(unsqueezed_tensor) 数学运算 torch.add(x, y): 加法 对两个张量进行逐元素加法运算。 import torch x = torch.tensor([1, 2, 3]) y = torch.tensor([4, 5, 6]) result = torch.add(x, y) print(result) torch...
# Stride is the gap between one element to the next one # in a dimension. print(x.stride()) # (3072, 1024, 32, 1)# Convert the tensor to NHWC in memory x2 = x.to(memory_format=torch.channels_last) print(x2.shape)# (10, 3, 32,...
AI代码解释 #classRegress_Loss(torch.nn.Module):def__init__(self):super(Regress_Loss,self).__init__()defforward(self,x,y):y_shape=y.size()[1]x_added_dim=x.unsqueeze(1)x_stacked_along_dimension1=x_added_dim.repeat(1,NUM_WORDS,1)diff=torch.sum((y-x_stacked_along_dimension1)**...
本教程的数据摘自Kaggle,该数据最初由Intel在analytics-vidhya上发布,以举办图像分类挑战赛。 https://www.kaggle.com/puneet6060/intel-image-classification 关于数据集 该数据包含大约65,000幅大小为150x150的25,000张图像。 {‘buildings’ : 0,‘forest’ : 1,‘glacier’ : 2,‘mountain’ : 3,‘sea’ ...
PyTorch最好的资料是官方文档。本文是PyTorch常用代码段,在参考资料[1](张皓:PyTorch Cookbook)的基础上做了一些修补,方便使用时查阅。 1 『基本配置』 导入包和版本查询 import torchimport torch.nn as nnimport torchvisionprint(torch.__version__)print(torch.version.cuda)prin...
defpredict(image,model,device,detection_threshold):# transform the image to tensorimage=transform(image).to(device)image=image.unsqueeze(0)# add a batch dimensionoutputs=model(image)# get the predictions on the image# print the results individually# print(f"BOXES: {outputs[0]['boxes']}")# ...
signal: (Tensor) Input tensor to be convolved with the kernel. kernel: (Tensor) Convolution kernel. bias: (Optional, Tensor) Bias tensor to add to the output. padding: (int) Number of zero samples to pad the input on the last dimension. Returns: (Tensor) Convolved tensor...
add_(self, other, *args, **kwargs) align_as(self, other) align_to(self, *args, **kwargs) all(self, dim=None, keepdim=False) allclose(self, other, rtol=1, *args, **kwargs) amax(self, dim=None, keepdim=False) amin(self, dim=None, keepdim=False) ...
`~torch.utils.data.DataLoader` by default constructs a indexsampler that yields integral indices. To make it work with a map-styledataset with non-integral indices/keys, a custom sampler must be provided."""def __getitem__(self, index) -> T_co:raise NotImplementedErrordef __add__(self,...
open(img_path) plt.imshow(img) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) ...