| import os.path | |
| import re | |
| import torch | |
| import time | |
| import tempfile | |
| import streamlit as st | |
| from training.zoo.classifiers import DeepFakeClassifier | |
| from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video_set | |
| def load_model(): | |
| path = 'weights/final_999_DeepFakeClassifier_tf_efficientnet_b7_ns_0_23' | |
| model = DeepFakeClassifier(encoder="tf_efficientnet_b7_ns") | |
| print("loading state dict {}".format(path)) | |
| checkpoint = torch.load(path, map_location="cpu") | |
| state_dict = checkpoint.get("state_dict", checkpoint) | |
| model.load_state_dict( | |
| {re.sub("^module.", "", k): v for k, v in state_dict.items()}, | |
| strict=True) | |
| model.eval() | |
| del checkpoint | |
| return model | |
| def write_bytesio_to_file(filename, bytesio): | |
| with open(filename, "wb") as outfile: | |
| outfile.write(bytesio.getbuffer()) | |
| def load_video(): | |
| uploaded_file = st.file_uploader(label='Pick a video (mp4) file to test') | |
| if uploaded_file is not None: | |
| video_data = uploaded_file.getvalue() | |
| tfile = tempfile.NamedTemporaryFile(delete=False) | |
| tfile.write(video_data) | |
| return tfile.name | |
| else: | |
| return None | |
| def inference(model, test_video): | |
| frames_per_video = 32 | |
| video_reader = VideoReader() | |
| video_read_fn = lambda x: video_reader.read_frames( | |
| x, num_frames=frames_per_video) | |
| face_extractor = FaceExtractor(video_read_fn) | |
| input_size = 380 | |
| strategy = confident_strategy | |
| test_videos = [test_video] | |
| print("Predicting {} videos".format(len(test_videos))) | |
| models = [model] | |
| predictions = predict_on_video_set(face_extractor=face_extractor, | |
| input_size=input_size, models=models, | |
| strategy=strategy, | |
| frames_per_video=frames_per_video, | |
| videos=test_videos, | |
| num_workers=6, test_dir="test_video") | |
| st.write("Prediction: ", predictions[0]) | |
| def main(): | |
| st.title('Deepfake video inference demo') | |
| model = load_model() | |
| video_data_path = load_video() | |
| if video_data_path is not None and os.path.exists(video_data_path): | |
| st.video(video_data_path) | |
| result = st.button('Run on video') | |
| if result: | |
| st.write("Inference on video...") | |
| stime = time.time() | |
| inference(model, video_data_path) | |
| st.write("Elapsed time: ", time.time() - stime, " seconds") | |
| if __name__ == '__main__': | |
| main() | |