78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
import os
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import glob
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from torchvision import transforms
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from torch.utils.data import DataLoader, Dataset
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from PIL import Image
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from core.const import mode, label_name, input_size, batch_size
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label_dict = {}
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for idx, name in enumerate(label_name):
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label_dict[name] = idx
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def default_loader(path):
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return Image.open(path).convert('RGB')
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train_transform = transforms.Compose([
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transforms.Resize(input_size),
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transforms.CenterCrop(input_size),
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transforms.RandomHorizontalFlip(p=0.5), # 50%的概率（p=0.5）水平翻转图片
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transforms.RandomRotation(10), # 轻微旋转
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transforms.ColorJitter(brightness=0.1, contrast=0.1),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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test_transform = transforms.Compose([
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transforms.Resize(input_size),
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transforms.CenterCrop(input_size),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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class MyDataset(Dataset):
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def __init__(self, im_list, transform=None, loader=default_loader):
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super(MyDataset, self).__init__()
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imgs = []
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for im_item in im_list:
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im_label_name = os.path.basename(os.path.dirname(im_item))
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imgs.append([im_item, label_dict[im_label_name]])
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self.imgs = imgs
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self.transform = transform
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self.loader = loader
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def __getitem__(self, index):
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im_path, im_label = self.imgs[index]
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im_data = self.loader(im_path)
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if self.transform is not None:
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im_data = self.transform(im_data)
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return im_data, im_label
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def __len__(self):
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return len(self.imgs)
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dataset_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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im_train_list = glob.glob(os.path.join(dataset_root, "dataset", mode, "train", "*", "*.jpg"))
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im_test_list = glob.glob(os.path.join(dataset_root, "dataset", mode, "test", "*", "*.jpg"))
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train_dataset = MyDataset(im_train_list, transform=train_transform)
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test_dataset = MyDataset(im_test_list, transform=test_transform)
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print("train_dataset", len(train_dataset))
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print("test_dataset", len(test_dataset))
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train_dataloader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
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test_dataloader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False, num_workers=4) |