importmath fromPILimportImage, ImageOps from torch.optimimportSGD, Adam, lr_scheduler from torch.autogradimportVariable from torch.utils.dataimportDataLoader from torchvision.transformsimportResize from torchvision.transformsimportToTensor, ToPILImage from datasetimportcityscapes from datasetimportidd_lite imports...
userByUsernameLoader- DataLoader 接收 usernames,通过batchQueryLoader查询数据库得到 user IDs,再调用userByIDLoader返回 user 对象。 其他DataLoaders 调用 batchQueryLoader,以类似的调用组合,保证了数据库活动是分批进行的,从而降低延迟。同时由于userByUsernameLoader获取到 IDs 后再调用userByIDLoader,userByIDLoader就...
# 需要导入模块: from ansible.parsing.dataloader import DataLoader [as 别名]# 或者: from ansible.parsing.dataloader.DataLoader importload_from_file[as 别名]defmain(self, path):data_dir = self.conf['data_dir'] loader = DataLoader() full_path="%s/%s"% (data_dir, path)ifos.path.isfile("%...
train_data = TripletData(PATH_TRAIN, train_transforms) val_data = TripletData(PATH_VALID, val_transforms) train_loader = torch.utils.data.DataLoader(dataset = train_data, batch_size=32, shuffle=True, num_workers=4) val_loader = torch.utils.data.DataLoader(dataset = val_data, batch_size=3...
from torch.utils.data import DataLoader from model import Yolov1 from dataset import VOCDataset from utils import ( non_max_suppression, mean_average_precision, intersection_over_union, cellboxes_to_boxes, get_bboxes, plot_image, save_checkpoint, ...
weighted_loader = DataLoader(dataset, batch_size=2, sampler=weighted_sampler) # 使用BatchSampler将样本索引分成多个批次 batch_sampler = torch.utils.data.sampler.BatchSampler(SequentialSampler(dataset), batch_size=2, drop_last=False) batch_loader = DataLoader(dataset, batch_sampler=batch_sampler) ...
데이터 로더 - Data Loader The length of each time series is different. Therefore,collate_fnindataset/dataset.pyimplements the basic collate function.collate_fnis used as thecollate_fnargument oftorch.utils.data.DataLoader. fromdataset.datasetimportcollate_fnfromtorch.utils.dataimportDataLoader...
from ansible.parsing.dataloader import DataLoader from ansible.inventory.manager import InventoryManager if __name__ == '__main__': inventory_file_name = 'my.inventory' data_loader = DataLoader() inventory = InventoryManager(loader = data_loader, sources=[inventory_file_name]) print(invento...
import dataset from darbnet53_module import * from torch import nn weight_path= 'darknet_params/net597.pt' myDataset = dataset.MyDataset() train_loader = torch.utils.data.DataLoader(myDataset, batch_size=5, shuffle=True) net = Darknet53().cuda() ...
DataLoader(dataset, batch_size=2, sampler=weighted_sampler)# 使用BatchSampler将样本索引分成多个批次batch_sampler = torch.utils.data.sampler.BatchSampler(SequentialSampler(dataset), batch_size=2, drop_last=False)batch_loader = DataLoader(dataset, batch_sampler=batch_sampler)# 遍历DataLoader,输出每个...