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import os |
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import torch |
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import torchvision.transforms as transforms |
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import torchvision.transforms.functional as TF |
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from torchvision.io import read_video |
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import torch.utils.data |
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import numpy as np |
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from sklearn.metrics import average_precision_score, precision_recall_curve, accuracy_score |
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import pickle |
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from tqdm import tqdm |
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from datetime import datetime |
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from copy import deepcopy |
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from dataset_paths import DATASET_PATHS |
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import random |
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from datasets import create_test_dataloader |
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from utils.logger import create_logger |
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import options |
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from networks.validator import Validator |
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def get_model(): |
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val_opt = options.TestOptions().parse(print_options=False) |
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output_dir=os.path.join(val_opt.output, val_opt.name) |
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os.makedirs(output_dir, exist_ok=True) |
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print(f"working...") |
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model = Validator(val_opt) |
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model.load_state_dict(val_opt.ckpt) |
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print("ckpt loaded!") |
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return model |
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def detect_video(video_path, model): |
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frames, _, _ = read_video(str(video_path), pts_unit='sec') |
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frames = frames[:16] |
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frames = frames.permute(0, 3, 1, 2) |
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video_frames = torch.cat([model.clip_model.preprocess(TF.to_pil_image(frame)).unsqueeze(0) for frame in frames]) |
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with torch.no_grad(): |
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model.set_input([torch.as_tensor(video_frames), torch.tensor([0])]) |
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pred = model.model(model.input).view(-1).unsqueeze(1).sigmoid() |
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return pred[0].item() |
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if __name__ == '__main__': |
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video_path = '../../dataset/MSRVTT/videos/all/video1.mp4' |
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model = get_model() |
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pred = detect_video(video_path, model) |
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if pred > 0.5: |
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print(f"Fake: {pred*100:.2f}%") |
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else: |
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print(f"Real: {(1-pred)*100:.2f}%") |
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