from typing import Any, Optional, List, Dict, Generator import time import tempfile import statistics import gradio import facefusion.globals from facefusion import wording from facefusion.face_analyser import get_face_analyser from facefusion.face_store import clear_static_faces from facefusion.processors.frame.core import get_frame_processors_modules from facefusion.vision import count_video_frame_total from facefusion.core import limit_resources, conditional_process from facefusion.normalizer import normalize_output_path from facefusion.filesystem import clear_temp from facefusion.uis.core import get_ui_component BENCHMARK_RESULTS_DATAFRAME : Optional[gradio.Dataframe] = None BENCHMARK_START_BUTTON : Optional[gradio.Button] = None BENCHMARK_CLEAR_BUTTON : Optional[gradio.Button] = None BENCHMARKS : Dict[str, str] =\ { '240p': '.assets/examples/target-240p.mp4', '360p': '.assets/examples/target-360p.mp4', '540p': '.assets/examples/target-540p.mp4', '720p': '.assets/examples/target-720p.mp4', '1080p': '.assets/examples/target-1080p.mp4', '1440p': '.assets/examples/target-1440p.mp4', '2160p': '.assets/examples/target-2160p.mp4' } def render() -> None: global BENCHMARK_RESULTS_DATAFRAME global BENCHMARK_START_BUTTON global BENCHMARK_CLEAR_BUTTON BENCHMARK_RESULTS_DATAFRAME = gradio.Dataframe( label = wording.get('benchmark_results_dataframe_label'), headers = [ 'target_path', 'benchmark_cycles', 'average_run', 'fastest_run', 'slowest_run', 'relative_fps' ], datatype = [ 'str', 'number', 'number', 'number', 'number', 'number' ] ) BENCHMARK_START_BUTTON = gradio.Button( value = wording.get('start_button_label'), variant = 'primary', size = 'sm' ) BENCHMARK_CLEAR_BUTTON = gradio.Button( value = wording.get('clear_button_label'), size = 'sm' ) def listen() -> None: benchmark_runs_checkbox_group = get_ui_component('benchmark_runs_checkbox_group') benchmark_cycles_slider = get_ui_component('benchmark_cycles_slider') if benchmark_runs_checkbox_group and benchmark_cycles_slider: BENCHMARK_START_BUTTON.click(start, inputs = [ benchmark_runs_checkbox_group, benchmark_cycles_slider ], outputs = BENCHMARK_RESULTS_DATAFRAME) BENCHMARK_CLEAR_BUTTON.click(clear, outputs = BENCHMARK_RESULTS_DATAFRAME) def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]: facefusion.globals.source_paths = [ '.assets/examples/source.jpg' ] target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ] benchmark_results = [] if target_paths: pre_process() for target_path in target_paths: benchmark_results.append(benchmark(target_path, benchmark_cycles)) yield benchmark_results post_process() def pre_process() -> None: limit_resources() get_face_analyser() for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors): frame_processor_module.get_frame_processor() def post_process() -> None: clear_static_faces() def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]: process_times = [] total_fps = 0.0 for i in range(benchmark_cycles): facefusion.globals.target_path = target_path facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, tempfile.gettempdir()) video_frame_total = count_video_frame_total(facefusion.globals.target_path) start_time = time.perf_counter() conditional_process() end_time = time.perf_counter() process_time = end_time - start_time total_fps += video_frame_total / process_time process_times.append(process_time) average_run = round(statistics.mean(process_times), 2) fastest_run = round(min(process_times), 2) slowest_run = round(max(process_times), 2) relative_fps = round(total_fps / benchmark_cycles, 2) return\ [ facefusion.globals.target_path, benchmark_cycles, average_run, fastest_run, slowest_run, relative_fps ] def clear() -> gradio.Dataframe: if facefusion.globals.target_path: clear_temp(facefusion.globals.target_path) return gradio.Dataframe(value = None)