HSC_VAD/motion_classifier_training.py at master · shengyangsun/HSC_VAD · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
import os
import argparse
import numpy as np
import torch
from torch.nn import functional as F
from utils.load_dataset import regressor_dataset
from models.HSCModel import HSCModel
from models.MemoryBank import MemoryBank
from models.DecoderModel import DecoderModel
from models.Regressor import Regressor
from utils.utils import get_CE_loss, set_seeds, gaussian_filter, eval, calc_l2_error, min_max_norm, get_n_clips
from tqdm import tqdm

def parser_arg():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', type=str, default='DC_Main')
    parser.add_argument('--dataset', type=str, default='ShanghaiTech', help="[ShanghaiTech/Avenue/UCSD_ped2]")
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--segment_len', type=int, default=16)
    parser.add_argument('--gpu', type=str, default='0')
    parser.add_argument('--num_workers', type=int, default=4)
    parser.add_argument('--inter_epoch', type=int, default=1)

    parser.add_argument('--lambda_contrastive', type=float, default=0.2)
    parser.add_argument('--MB_momentum', type=float, default=0.9)
    parser.add_argument('--lr', type=float, default=0.01)
    parser.add_argument('--weight_decay', type=float, default=0.0001)
    parser.add_argument('--temporature', type=float, default=0.5)
    parser.add_argument('--batch_size', type=int, default=256)
    parser.add_argument('--epochs', type=int, default=200)
    parser.add_argument('--appear_input_dim', type=int, default=2614)
    parser.add_argument('--motion_input_dim', type=int, default=2102)
    parser.add_argument('--appear_feature_dim', type=int, default=1024)
    parser.add_argument('--motion_feature_dim', type=int, default=512)
    parser.add_argument('--background_feature_dim', type=int, default=1590)
    parser.add_argument('--encoder_output_dim', type=int, default=1280)
    parser.add_argument('--num_cluster', type=int, default=23)
    parser.add_argument('--model_weight_init', action="store_true", help="init the model")


    parser.add_argument('--train_dataset_path', type=str, default="")
    parser.add_argument('--test_dataset_path', type=str, default= "")
    parser.add_argument('--test_mask_dir', type=str, default="")
    parser.add_argument('--model_saved_dir', type=str, default="")

    args = parser.parse_args()
    return args


def get_CE_loss(positive_outputs, negative_outputs):
    loss = torch.mean(-torch.log(positive_outputs+1e-10)-torch.log(1-negative_outputs+1e-10))
    return loss


def train(args):

    with open('config/'+args.dataset+'.yaml', encoding='utf-8') as config_file:
        data = yaml.load(config_file, Loader=yaml.FullLoader)

    args.test_dataset_path = data['test_dataset_path']
    args.test_mask_dir = data['test_mask_dir']
    gaussian_len = data['gaussian_len']
    gaussian_sigma = data['gaussian_sigma']
    n_clips_dict = get_n_clips(args.dataset)

    train_dataset = regressor_dataset(args.train_dataset_path)
    dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers, drop_last=True)

    test_dataset = np.load(args.test_dataset_path, allow_pickle=True).tolist()
    appear_feats_dict = test_dataset['appear_feats']
    motion_feats_dict = test_dataset['motion_feats']
    filter_2d = gaussian_filter(np.arange(1, gaussian_len), gaussian_sigma)
    padding_size = len(filter_2d) // 2

    model = Regressor(input_feature_dim=args.motion_input_dim)
    model = model.cuda().train()

    optimizer = torch.optim.Adam([{"params": model.parameters(), "lr": args.lr}],
                                 weight_decay=args.weight_decay)
    MSELoss = torch.nn.MSELoss()

    best_test_auc = 0

    for epoch in range(args.epochs):
        iter_count = 0
        for positive_feats, negative_feats in dataloader:
            positive_feats = positive_feats.view([-1, args.motion_input_dim]).cuda().float()
            negative_feats = negative_feats.view([-1, args.motion_input_dim]).cuda().float()

            positive_outputs = model(positive_feats)
            negative_outputs = model(negative_feats)
            loss = get_CE_loss(positive_outputs, negative_outputs)
            print('[{}/{}]: loss {:.4f}, '.format(iter_count, epoch, loss))
            iter_count += 1

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        if epoch % args.inter_epoch == 0:
            appear_scores_list = []
            motion_scores_list = []
            label_list = []
            model = model.eval()
            with torch.no_grad():
                for video_name in tqdm(appear_feats_dict.keys()):
                    n_clips = n_clips_dict[video_name]
                    gt_path = os.path.join(args.test_mask_dir, video_name + ".npy")
                    if os.path.exists(gt_path):
                        gt = np.load(gt_path, allow_pickle=True)
                    else:
                        gt = np.zeros((n_clips * args.segment_len), dtype=np.float32)
                    motion_frames_scores = np.zeros((n_clips * args.segment_len), dtype=np.float32)

                    scores_list = []
                    for clip_i in motion_feats_dict[video_name].keys():
                        error_clip = 0
                        for cls_id in motion_feats_dict[video_name][clip_i].keys():
                            feats = torch.from_numpy(motion_feats_dict[video_name][clip_i][cls_id]).cuda()
                            score_output = model(feats)
                            error_clip = max(error_clip, torch.max(score_output).cpu().item())
                        motion_frames_scores[clip_i * args.segment_len:(clip_i + 1) * args.segment_len] = error_clip
                    motion_scores_list.extend(motion_frames_scores.tolist())
                    label_list.extend(gt[:n_clips * args.segment_len])

            motion_scores = np.array(motion_scores_list)
            temp = np.concatenate((motion_scores[:padding_size], motion_scores, motion_scores[-padding_size:]))
            motion_scores = np.correlate(temp, filter_2d, 'valid')
            frame_scores_list = motion_scores.tolist()

            auc_test = eval(frame_scores_list, label_list, None)
            print("test auc = ", auc_test)
            if auc_test > best_test_auc:
                best_test_auc = auc_test
                torch.save(model.state_dict(), os.path.join(args.model_saved_dir, args.tip+"MA_epoch_" + str(epoch) + "_" + str(best_test_auc)))
                print ("saved finished.")
                np.save("/data/ssy/code/HSC_VAD/data/"+args.dataset+"_MA.npy", frame_scores_list)
            model = model.train()
        dataloader.dataset.shuffle_keys()

if __name__ == '__main__':
    args = parser_arg()
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    set_seeds(args.seed)
    train(args)