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- """
- 验证白盒水印提取效果
- """
- import os
- import cv2
- import time
- import torch
- import argparse
- import albumentations
- from tool import secret_func
- from tool.training_embedding import Embedding
- from model.layer import deploy
- # -------------------------------------------------------------------------------------------------------------------- #
- parser = argparse.ArgumentParser(description='|pt模型白盒水印提取|')
- parser.add_argument('--model_path', default='best.pt', type=str, help='|pt模型位置|')
- parser.add_argument('--key_path', default='./checkpoints/Alexnet/white_box_embed/x_random.pt', type=str,
- help='|白盒模型投影矩阵位置|')
- parser.add_argument('--data_path',
- default='/home/yhsun/classification-main/dataset/CIFAR-10/train_cifar10_JPG/airplane', type=str,
- help='|图片文件夹位置|')
- parser.add_argument('--input_size', default=32, type=int, help='|模型输入图片大小|')
- parser.add_argument('--normalization', default='sigmoid', type=str,
- help='|选择sigmoid或softmax归一化,单类别一定要选sigmoid|')
- parser.add_argument('--batch', default=1, type=int, help='|输入图片批量|')
- parser.add_argument('--device', default='cuda', type=str, help='|推理设备|')
- parser.add_argument('--num_worker', default=0, type=int, help='|CPU处理数据的进程数,0只有一个主进程,一般为0、2、4、8|')
- parser.add_argument('--float16', default=False, type=bool, help='|推理数据类型,要支持float16的GPU,False时为float32|')
- args, _ = parser.parse_known_args() # 防止传入参数冲突,替代args = parser.parse_args()
- # -------------------------------------------------------------------------------------------------------------------- #
- assert os.path.exists(args.model_path), f'! model_path不存在:{args.model_path} !'
- assert os.path.exists(args.data_path), f'! data_path不存在:{args.data_path} !'
- if args.float16:
- assert torch.cuda.is_available(), 'cuda不可用,因此无法使用float16'
- # -------------------------------------------------------------------------------------------------------------------- #
- def predict_pt(args):
- # 加载模型
- model_dict = torch.load(args.model_path, map_location='cpu')
- model = model_dict['model']
- # 初始化白盒水印编码器
- # key_path = './checkpoints/Alexnet/white_box_embed/x_random.pt' # 保存投影矩阵位置
- embeder = Embedding(model=model.to(args.device), code='', key_path=args.key_path, train=False, device=args.device)
- code = embeder.test()
- print(f'code:{code}')
- if secret_func.verify_secret(code):
- print('模型水印验证成功')
- else:
- print('模型水印验证失败')
- # 检测模型预测指标
- model = deploy(model, args.normalization)
- model.half().eval().to(args.device) if args.float16 else model.float().eval().to(args.device)
- epoch = model_dict['epoch_finished']
- m_ap = round(model_dict['standard'], 4)
- print(f'| 模型加载成功:{args.model_path} | epoch:{epoch} | m_ap:{m_ap}|')
- # 推理
- image_dir = sorted(os.listdir(args.data_path))
- start_time = time.time()
- with torch.no_grad():
- dataloader = torch.utils.data.DataLoader(torch_dataset(image_dir), batch_size=args.batch,
- shuffle=False, drop_last=False, pin_memory=False,
- num_workers=args.num_worker)
- result = []
- for item, batch in enumerate(dataloader):
- batch = batch.to(args.device)
- pred_batch = model(batch).detach().cpu()
- result.extend(pred_batch.tolist())
- for i in range(len(result)):
- result[i] = [round(result[i][_], 2) for _ in range(len(result[i]))]
- print(f'| {image_dir[i]}:{result[i]} |')
- end_time = time.time()
- print('| 数据:{} 批量:{} 每张耗时:{:.4f} |'.format(len(image_dir), args.batch,
- (end_time - start_time) / len(image_dir)))
- class torch_dataset(torch.utils.data.Dataset):
- def __init__(self, image_dir):
- self.image_dir = image_dir
- self.transform = albumentations.Compose([
- albumentations.LongestMaxSize(args.input_size),
- albumentations.PadIfNeeded(min_height=args.input_size, min_width=args.input_size,
- border_mode=cv2.BORDER_CONSTANT, value=(128, 128, 128))])
- def __len__(self):
- return len(self.image_dir)
- def __getitem__(self, index):
- image = cv2.imread(args.data_path + '/' + self.image_dir[index]) # 读取图片
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 转为RGB通道
- image = self.transform(image=image)['image'] # 缩放和填充图片(归一化、调维度在模型中完成)
- image = torch.tensor(image, dtype=torch.float16 if args.float16 else torch.float32)
- return image
- if __name__ == '__main__':
- predict_pt(args)
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