为每句代码做注释:flower_list = train_dataset.class_to_idx cla_dict = dict((val, key) for key, val in flower_list.items()) json_str = json.dumps(cla_dict, indent=4) with open('class_indices.json', 'w') as json_file: json_file.write(json_str) batch_size = 16 nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) print('Using {} dataloader workers every process'.format(nw)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"), transform=data_transform["val"]) val_num = len(validate_dataset) validate_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=batch_size, shuffle=False, num_workers=0) print("using {} images for training, {} images for validation.".format(train_num, val_num))
时间: 2023-06-26 08:07:00 浏览: 80
# 定义一个包含花卉类别和对应索引的列表
flower_list = train_dataset.class_to_idx
# 将花卉类别和对应索引的列表转换为字典,并将键值对中的键和值进行调换
cla_dict = dict((val, key) for key, val in flower_list.items())
# 将字典转换为 JSON 格式的字符串,并使用缩进格式化输出
json_str = json.dumps(cla_dict, indent=4)
# 将 JSON 字符串写入到指定文件 'class_indices.json' 中
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
# 定义每个批次的样本数 batch_size 为 16,并设置数据加载器的多进程数为 nw
batch_size = 16
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
print('Using {} dataloader workers every process'.format(nw))
# 定义训练集数据加载器,每次读取一个批次的数据,打乱数据顺序,不使用多进程
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
# 定义验证集数据加载器,每次读取一个批次的数据,不打乱数据顺序,不使用多进程
validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"), transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
# 输出训练集和验证集数据的大小
print("using {} images for training, {} images for validation.".format(train_num, val_num))
阅读全文
相关推荐












