from mmdet.datasets import build_dataset 这行代码的作用是从mmdet库的datasets子模块中导入build_dataset函数。 调用build_dataset函数: build_dataset函数通常接受一个配置字典(config dictionary)作为输入,这个配置字典包含了数据集的所有必要信息,例如数据集的类型、数据根目录、注解文件路径等。以下是一个示例代码...
This is from the MMDetection V3.0.0rc4 Release mmdet/datasets/builder.py (https://github.com/open-mmlab/mmdetection/releases/tag/v3.0.0rc4): def build_dataset(cfg, default_args=None): from mmengine.dataset import ClassBalancedDataset from .dataset_wrappers import MultiImageMixDataset if cfg['...
1. 安装datasets库 在终端中运行以下命令来安装datasets库: ```bash pip install datasets ``` 2. 从datasets模块中导入load_dataset方法 在你的Python脚本或Jupyter笔记本中,使用以下代码导入load_dataset方法: ```python from datasets import load_dataset ``` 这一步将允许你使用load_dataset方法来加载数据集。
File "Meg_dataset/tools/train.py", line 15, in from mmdet3d.datasets import build_dataset File "/data/bevfusion/mmdet3d/datasets/init.py", line 4, in from .custom_3d import * File "/data/bevfusion/mmdet3d/datasets/custom_3d.py", line 10, in from ..core.bbox import get_box_type...
from datasets import load_dataset dataset = load_dataset("squad", split="train") dataset.features {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None), 'context': Value(dtype='string', id=None...
from datasets import load_dataset datasets = load_dataset('cail2018') print(datasets) # 查看数据的结构 下面是打印出来看到的数据结构,整个数据集划分成了多个数据子集,包含train,valid以及test集。每个arrow_dataset都有多少条数据,以及这些数据的feature是什么。
datasets, weights=None, seed=None, stop_on_empty_dataset=False) 参数 datasets具有兼容结构的tf.data.Dataset对象的非空列表。 weights(可选。)len(datasets)浮点值的列表或张量,其中weights[i]表示从datasets[i]或tf.data.Dataset对象中采样的概率,其中每个元素都是这样的列表。默认为跨datasets的均匀分布。
(),normalize,])iftest:dataset=datasets.CIFAR100(root=data_dir,train=False,download=True,transform=transform,)data_loader=torch.utils.data.DataLoader(dataset,batch_size=batch_size,shuffle=shuffle)returndata_loader# load the datasettrain_dataset=datasets.CIFAR100(root=data_dir,train=True,download=True...
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Build scalable, structured, high-performance PyTorch models with Lightning and log them with W&B. # This script needs these libraries to be installed: # torch, torchvision, pytorch_lightning import wandb import os from torch import optim, nn, utils from torchvision.datasets import MNIST from torc...