GenConViT/predict.py at main · truemediaorg/GenConViT · 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
import os
import argparse
import json
from time import perf_counter
from datetime import datetime
from model.pred_func import *


def vid(
    file_path, dataset=None, num_frames=15, net=None, fp16=False
):
    result = set_result()
    r = 0
    f = 0
    count = 0
    model = load_genconvit(net, fp16)

    if os.path.isfile(file_path):
        try:
            if is_video(file_path):
                result, accuracy, count, pred = predict(
                    file_path,
                    model,
                    fp16,
                    result,
                    num_frames,
                    net,
                    "uncategorized",
                    count,
                )
                f, r = (
                    f + 1, r) if "FAKE" == real_or_fake(pred[0]) else (f, r + 1)
                print(
                    f"Prediction: {pred[1]} {real_or_fake(pred[0])} \t\tFake: {f} Real: {r}"
                )
            else:
                print(
                    f"Invalid video file: {file_path}. Please provide a valid video file.")

        except Exception as e:
            print(f"An error occurred: {str(e)}")
    else:
        print(f"The file {file_path} does not exist.")

    return result


def gen_parser():
    parser = argparse.ArgumentParser("GenConViT prediction")
    parser.add_argument("--p", type=str, required=False,
                        default=None, help="video or image file path")
    parser.add_argument(
        "--f", type=int, default=15, help="number of frames to process for prediction"
    )
    parser.add_argument(
        "--d", type=str, default="other", help="dataset type, dfdc, faceforensics, timit, celeb"
    )
    parser.add_argument("--n", type=str, default="genconvit",
                        help="network ed or vae")
    parser.add_argument("--fp16", action="store_true",
                        help="half precision support")

    args = parser.parse_args()
    return args.p, args.d, args.f, args.n, args.fp16


def predict(
    vid,
    model,
    fp16,
    result,
    num_frames,
    net,
    klass,
    count=0,
    accuracy=-1,
    correct_label="unknown",
    compression=None,
):
    count += 1
    print(f"\n\n{str(count)} Loading... {vid}")

    df = df_face(vid, num_frames, net)  # extract face from the frames
    if fp16:
        df.half()
    y, y_val = (
        pred_vid(df, model)
        if len(df) >= 1
        else (torch.tensor(0).item(), torch.tensor(0.5).item())
    )
    result = store_result(
        result, os.path.basename(
            vid), y, y_val, klass, correct_label, compression
    )

    if accuracy > -1:
        if correct_label == real_or_fake(y):
            accuracy += 1
        print(
            f"\nPrediction: {y_val} {real_or_fake(y)} \t\t {accuracy}/{count} {accuracy/count}"
        )

    return result, accuracy, count, [y, y_val]


def main():
    start_time = perf_counter()
    file_path, dataset, num_frames, net, fp16 = gen_parser()
    result = vid(file_path, dataset, num_frames, net, fp16)

    curr_time = datetime.now().strftime("%B_%d_%Y_%H_%M_%S")
    file_name = os.path.basename(file_path).split('.')[0]
    file_path = os.path.join(
        "result", f"prediction_{file_name}_{dataset}_{net}_{curr_time}.json")

    with open(file_path, "w") as f:
        json.dump(result, f)
    end_time = perf_counter()
    print("\n\n--- %s seconds ---" % (end_time - start_time))


if __name__ == "__main__":
    main()