133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
import hashlib
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import os
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import statistics
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import tempfile
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from time import perf_counter
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from typing import Any, Dict, Generator, List, Optional
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import gradio
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from facefusion import state_manager, wording
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from facefusion.core import conditional_process
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from facefusion.filesystem import get_file_extension, is_video
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from facefusion.memory import limit_system_memory
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from facefusion.uis.core import get_ui_component
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from facefusion.vision import count_video_frame_total, detect_video_fps, detect_video_resolution, pack_resolution
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BENCHMARK_BENCHMARKS_DATAFRAME : Optional[gradio.Dataframe] = None
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BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
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BENCHMARKS : Dict[str, str] =\
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{
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'240p': '.assets/examples/target-240p.mp4',
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'360p': '.assets/examples/target-360p.mp4',
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'540p': '.assets/examples/target-540p.mp4',
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'720p': '.assets/examples/target-720p.mp4',
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'1080p': '.assets/examples/target-1080p.mp4',
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'1440p': '.assets/examples/target-1440p.mp4',
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'2160p': '.assets/examples/target-2160p.mp4'
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}
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def render() -> None:
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global BENCHMARK_BENCHMARKS_DATAFRAME
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global BENCHMARK_START_BUTTON
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BENCHMARK_BENCHMARKS_DATAFRAME = gradio.Dataframe(
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headers =
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[
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'target_path',
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'benchmark_cycles',
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'average_run',
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'fastest_run',
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'slowest_run',
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'relative_fps'
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],
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datatype =
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[
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'str',
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'number',
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'number',
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'number',
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'number',
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'number'
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],
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show_label = False
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)
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BENCHMARK_START_BUTTON = gradio.Button(
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value = wording.get('uis.start_button'),
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variant = 'primary',
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size = 'sm'
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)
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def listen() -> None:
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benchmark_runs_checkbox_group = get_ui_component('benchmark_runs_checkbox_group')
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benchmark_cycles_slider = get_ui_component('benchmark_cycles_slider')
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if benchmark_runs_checkbox_group and benchmark_cycles_slider:
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BENCHMARK_START_BUTTON.click(start, inputs = [ benchmark_runs_checkbox_group, benchmark_cycles_slider ], outputs = BENCHMARK_BENCHMARKS_DATAFRAME)
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def suggest_output_path(target_path : str) -> Optional[str]:
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if is_video(target_path):
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target_file_extension = get_file_extension(target_path)
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return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_file_extension)
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return None
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def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
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state_manager.init_item('source_paths', [ '.assets/examples/source.jpg', '.assets/examples/source.mp3' ])
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state_manager.init_item('face_landmarker_score', 0)
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state_manager.init_item('temp_frame_format', 'bmp')
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state_manager.init_item('output_audio_volume', 0)
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state_manager.init_item('output_video_preset', 'ultrafast')
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state_manager.sync_item('execution_providers')
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state_manager.sync_item('execution_thread_count')
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state_manager.sync_item('execution_queue_count')
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state_manager.sync_item('system_memory_limit')
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benchmark_results = []
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target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
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if target_paths:
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pre_process()
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for target_path in target_paths:
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state_manager.init_item('target_path', target_path)
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state_manager.init_item('output_path', suggest_output_path(state_manager.get_item('target_path')))
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benchmark_results.append(benchmark(benchmark_cycles))
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yield benchmark_results
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def pre_process() -> None:
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system_memory_limit = state_manager.get_item('system_memory_limit')
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if system_memory_limit and system_memory_limit > 0:
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limit_system_memory(system_memory_limit)
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def benchmark(benchmark_cycles : int) -> List[Any]:
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process_times = []
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video_frame_total = count_video_frame_total(state_manager.get_item('target_path'))
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output_video_resolution = detect_video_resolution(state_manager.get_item('target_path'))
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state_manager.init_item('output_video_resolution', pack_resolution(output_video_resolution))
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state_manager.init_item('output_video_fps', detect_video_fps(state_manager.get_item('target_path')))
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conditional_process()
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for index in range(benchmark_cycles):
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start_time = perf_counter()
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conditional_process()
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end_time = perf_counter()
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process_times.append(end_time - start_time)
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average_run = round(statistics.mean(process_times), 2)
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fastest_run = round(min(process_times), 2)
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slowest_run = round(max(process_times), 2)
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relative_fps = round(video_frame_total * benchmark_cycles / sum(process_times), 2)
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return\
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[
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state_manager.get_item('target_path'),
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benchmark_cycles,
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average_run,
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fastest_run,
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slowest_run,
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relative_fps
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]
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