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))

# 创建一个包含所有花的列表，列表中每个元素都是一种花的类别索引
flower_list = train_dataset.class_to_idx

# 将key和value互换位置，将花的类别索引转换为花的名称
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
json_str = json.dumps(cla_dict, indent=4)

# 将json字符串写入文件class_indices.json中
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)

# 设置批大小为16，设置dataloader的worker数量，确保不超过CPU核心数和批大小
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))

# 创建训练集dataloader
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)

# 创建验证集dataset和dataloader
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)

# 打印训练集和验证集的图片数量
print("using {} images for training, {} images for validation.".format(train_num, val_num))