最后,它对图像和target进行transform变换并返回。 这其中重要的地方是reader/parser解析器,其在ImageDataset实例化时被设置 reader/Parser解析器 reader解析器,之前的版本叫parser解析器,东西是一个东西 reader由create_reader方法自动设置。 reader在根目录中查找所有图像和目标 reader设置一个class_to_idx字典,从类映射...
MultiProcessingDataLoaderIter继承的是BaseDataLoaderIter,开始初始化,然后Dataloader进行初始化,然后进入 next __()方法 随机生成索引,进而生成batch,最后调用 _get_data() 方法得到data。idx, data = self._get_data(), data = self.data_queue.get(timeout=timeout) ...
1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts. 2. Start workers to work on the dicts. Each worker will: * Map each metadata dict into another format to be consumed by the model. * Batch them by simply putting dicts into a list. T...
self.class_dict = json.load(f) self.transforms = transforms # 获取数据集大小 def __len__(self): return len(self.xml_list) # 根据传入的索引值获取数据信息 def __getitem__(self, idx): # 读取xml文件 xml_path = self.xml_list[idx] with open(xml_path) as fid: xml_str = fid.read...
(self.classes,1): self.class_dict[class_name] = number self.transforms = transforms def __len__(self): return len(self.xml_list) def __getitem__(self, idx): # read xml xml_path = self.xml_list[idx] with open(xml_path) as fid: xml_str = fid.read() xml = etree.fromstring...
(callable, optional): Optional transform to be applied on a sample. """ self.landmarks_frame = pd.read_csv(csv_file) self.root_dir = root_dir self.transform = transform def __len__(self): return len(self.landmarks_frame) def __getitem__(self,idx): #获得指定索引图片的路径 img_...
(:,frame_idx) = rsum; micro_doppler(:,frame_idx) = vsum; end % to generate range-time maps init_frame = init_frame + max_frame; FRAME_SET = 1:init_frame; axis xy; subplot(121); rt_show = [rt_show,db(abs(range_plane))/2]; imagesc(FRAME_SET*100/1e3,RANGE_AXIS(end/2+1...
tile_in_d = idx_map[idx] break return token, tile_in, choice, fulu_part, tile_in_d class GamePlayer: def __init__(self, ranks_and_dans, json_dct: dict, engine: Engine, persprctive=-1): ranks, dans = ranks_and_dans ...
- idx (int): 样本的索引。 返回: - sample (dict): 包含图像和标签的字典。 """img_name = os.path.join(self.data_dir, self.file_list[idx]) image = Image.open(img_name)ifself.transform: image = self.transform(image)# 假设你有一个标签文件或其他方式来获取样本标签label = self.get_labe...
解析成DatasetFromList DatasetFromList(dataset_dict)函数定义在detectron2/data/common.py中,它其实就是一个torch.utils.data.Dataset类,其源码如下 class DatasetFromList(data.Dataset): """ Wrap a list to a torch Dataset. It produces elements of the list as data. """ def __init__(self, lst...