54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
from torch.utils.data import Dataset, DataLoader, RandomSampler
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import torchvision.transforms as transforms
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import torch
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class ListDataset(Dataset):
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def __init__(self, data_list, labels_list, status):
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self.data = data_list
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self.labels = labels_list
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self.status = status
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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sample = self.data[idx]
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if self.status:
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from Image_Process.Image_Generator import Image_generator
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ImageGenerator = Image_generator("", "", 12)
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Transform = ImageGenerator.Generator_Content(5)
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sample = Transform(sample)
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label = self.labels[idx]
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return sample, label
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class Training_Precesses:
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def __init__(self, Training_Datas, Training_Labels, Testing_Datas, Testing_Labels):
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self.Training_Datas = Training_Datas
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self.Training_Labels = Training_Labels
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self.Testing_Datas = Testing_Datas
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self.Testing_Labels = Testing_Labels
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pass
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def Total_Data_Combine_To_DataLoader(self, Batch_Size):
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Training_Dataset = self.Convert_Data_To_DataSet(self.Training_Datas, self.Training_Labels)
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Testing_Dataset = self.Convert_Data_To_DataSet(self.Testing_Datas, self.Testing_Labels)
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Training_DataLoader = DataLoader(dataset = Training_Dataset, batch_size = Batch_Size, num_workers = 0, pin_memory=True, shuffle = True)
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Testing_DataLoader = DataLoader(dataset = Testing_Dataset, batch_size = 1, num_workers = 0, pin_memory=True, shuffle = True)
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return Training_DataLoader, Testing_DataLoader
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def Convert_Data_To_DataSet(self, Datas : list, Labels : list, status : bool = True):
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seed = 42 # 設定任意整數作為種子
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# 產生隨機種子產生器
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generator = torch.Generator()
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generator.manual_seed(seed)
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# 創建 Dataset
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list_dataset = ListDataset(Datas, Labels, status)
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# sampler = RandomSampler(list_dataset, generator = generator) # 創建Sampler
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return list_dataset
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