""" eval pretained model. """ import os import numpy as np from os.path import join import cv2 import random import datetime import time import yaml import pickle from tqdm import tqdm from copy import deepcopy from PIL import Image as pil_image from metrics.utils import get_test_metrics import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.nn.functional as F import torch.utils.data import torch.optim as optim from dataset.abstract_dataset import DeepfakeAbstractBaseDataset from trainer.trainer import Trainer from detectors import DETECTOR from metrics.base_metrics_class import Recorder from collections import defaultdict import argparse from logger import create_logger parser = argparse.ArgumentParser(description='Process some paths.') parser.add_argument('--detector_path', type=str, default='./training/config/detector/effort.yaml', help='path to detector YAML file') parser.add_argument("--test_dataset", nargs="+") parser.add_argument('--weights_path', type=str, default='./weights/effort_ckpt.pth') #parser.add_argument("--lmdb", action='store_true', default=False) args = parser.parse_args() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") on_2060 = "2060" in torch.cuda.get_device_name() def init_seed(config): if config['manualSeed'] is None: config['manualSeed'] = random.randint(1, 10000) random.seed(config['manualSeed']) torch.manual_seed(config['manualSeed']) if config['cuda']: torch.cuda.manual_seed_all(config['manualSeed']) def prepare_testing_data(config): def get_test_data_loader(config, test_name): # update the config dictionary with the specific testing dataset config = config.copy() # create a copy of config to avoid altering the original one config['test_dataset'] = test_name # specify the current test dataset test_set = DeepfakeAbstractBaseDataset( config=config, mode='test', ) test_data_loader = \ torch.utils.data.DataLoader( dataset=test_set, batch_size=config['test_batchSize'], shuffle=False, num_workers=int(config['workers']), collate_fn=test_set.collate_fn, drop_last=False ) return test_data_loader test_data_loaders = {} for one_test_name in config['test_dataset']: test_data_loaders[one_test_name] = get_test_data_loader(config, one_test_name) return test_data_loaders def choose_metric(config): metric_scoring = config['metric_scoring'] if metric_scoring not in ['eer', 'auc', 'acc', 'ap']: raise NotImplementedError('metric {} is not implemented'.format(metric_scoring)) return metric_scoring def test_one_dataset(model, data_loader): prediction_lists = [] feature_lists = [] label_lists = [] for i, data_dict in tqdm(enumerate(data_loader), total=len(data_loader)): # get data data, label, mask, landmark = \ data_dict['image'], data_dict['label'], data_dict['mask'], data_dict['landmark'] label = torch.where(data_dict['label'] != 0, 1, 0) # move data to GPU data_dict['image'], data_dict['label'] = data.to(device), label.to(device) if mask is not None: data_dict['mask'] = mask.to(device) if landmark is not None: data_dict['landmark'] = landmark.to(device) # model forward without considering gradient computation predictions = inference(model, data_dict) label_lists += list(data_dict['label'].cpu().detach().numpy()) prediction_lists += list(predictions['prob'].cpu().detach().numpy()) feature_lists += list(predictions['feat'].cpu().detach().numpy()) return np.array(prediction_lists), np.array(label_lists),np.array(feature_lists) def test_epoch(model, test_data_loaders): # set model to eval mode model.eval() # define test recorder metrics_all_datasets = {} # testing for all test data keys = test_data_loaders.keys() for key in keys: data_dict = test_data_loaders[key].dataset.data_dict # compute loss for each dataset predictions_nps, label_nps,feat_nps = test_one_dataset(model, test_data_loaders[key]) # compute metric for each dataset metric_one_dataset = get_test_metrics(y_pred=predictions_nps, y_true=label_nps, img_names=data_dict['image']) metrics_all_datasets[key] = metric_one_dataset # info for each dataset tqdm.write(f"dataset: {key}") for k, v in metric_one_dataset.items(): tqdm.write(f"{k}: {v}") return metrics_all_datasets @torch.no_grad() def inference(model, data_dict): predictions = model(data_dict, inference=True) return predictions def main(): # parse options and load config with open(args.detector_path, 'r') as f: config = yaml.safe_load(f) with open('./training/config/test_config.yaml', 'r') as f: config2 = yaml.safe_load(f) config.update(config2) if on_2060: config['lmdb_dir'] = r'I:\transform_2_lmdb' config['train_batchSize'] = 10 config['workers'] = 0 else: config['workers'] = 8 config['lmdb_dir'] = r'/mnt/chongqinggeminiceph1fs/geminicephfs/mm-base-vision/jikangcheng/data/LMDBs' weights_path = None # If arguments are provided, they will overwrite the yaml settings if args.test_dataset: config['test_dataset'] = args.test_dataset if args.weights_path: config['weights_path'] = args.weights_path weights_path = args.weights_path # init seed init_seed(config) # set cudnn benchmark if needed if config['cudnn']: cudnn.benchmark = True # prepare the testing data loader test_data_loaders = prepare_testing_data(config) # prepare the model (detector) model_class = DETECTOR[config['model_name']] model = model_class(config).to(device) total_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) print(f"Total number of trainable parameters in the model: {total_trainable_params}") epoch = 0 if weights_path: try: epoch = int(weights_path.split('/')[-1].split('.')[0].split('_')[2]) except: epoch = 0 ckpt = torch.load(weights_path, map_location=device) if 'state_dict' in ckpt: ckpt = ckpt['state_dict'] # # 加载模型的状态字典 # model_dict = model.state_dict() # new_ckpt={} # for key in ckpt.keys(): # # 替换键 # new_key = key.replace('common_encoder_f','student_encoder') # # 将旧的值复制到新的键下 # new_ckpt[new_key] = ckpt[key] # # 获取ckpt和model的key集合 # ckpt_keys = set(new_ckpt.keys()) # model_keys = set(model_dict.keys()) # # # 找出共同的key # common_keys = ckpt_keys & model_keys # print("Common keys:") # for key in common_keys: # print(key) # # # 找出只在ckpt中的key # ckpt_unique_keys = ckpt_keys - model_keys # print("\nKeys only in ckpt:") # for key in ckpt_unique_keys: # print(key) # # # 找出只在model中的key # model_unique_keys = model_keys - ckpt_keys # print("\nKeys only in model:") # for key in model_unique_keys: # print(key) # 创建一个新的字典，删除module前缀 new_weights = {} for key, value in ckpt.items(): new_key = key.replace('module.', '') # 删除module前缀 #new_key = 'backbone.' + new_key # 删除module前缀 # if 'base_model.' in new_key: # new_key = new_key.replace('base_model.', 'backbone.') # if 'classifier.' in new_key: # new_key = new_key.replace('classifier.', 'head.') new_weights[new_key] = value model.load_state_dict(new_weights, strict=True) print('===> Load checkpoint done!') else: print('Fail to load the pre-trained weights') # clip_rank_results = analyze_clip_effective_rank(model.backbone) # for layer_name, rank in clip_rank_results.items(): # print(f"Layer: {layer_name}, Effective Rank: {rank}") # start testing best_metric = test_epoch(model, test_data_loaders) print('===> Test Done!') if __name__ == '__main__': main()