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from typing import Any, Optional, List
import time
import tempfile
import statistics
import gradio
import DeepFakeAI.globals
from DeepFakeAI import wording
from DeepFakeAI.capturer import get_video_frame_total
from DeepFakeAI.core import conditional_process
from DeepFakeAI.uis.typing import Update
from DeepFakeAI.utilities import normalize_output_path, clear_temp
BENCHMARK_RESULT_DATAFRAME : Optional[gradio.Dataframe] = None
BENCHMARK_CYCLES_SLIDER : Optional[gradio.Button] = None
BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
BENCHMARK_CLEAR_BUTTON : Optional[gradio.Button] = None
def render() -> None:
global BENCHMARK_RESULT_DATAFRAME
global BENCHMARK_CYCLES_SLIDER
global BENCHMARK_START_BUTTON
global BENCHMARK_CLEAR_BUTTON
with gradio.Box():
BENCHMARK_RESULT_DATAFRAME = gradio.Dataframe(
label = wording.get('benchmark_result_dataframe_label'),
headers =
[
'target_path',
'benchmark_cycles',
'average_run',
'fastest_run',
'slowest_run',
'relative_fps'
],
col_count = (6, 'fixed'),
row_count = (7, 'fixed'),
datatype =
[
'str',
'number',
'number',
'number',
'number',
'number'
]
)
BENCHMARK_CYCLES_SLIDER = gradio.Slider(
label = wording.get('benchmark_cycles_slider_label'),
minimum = 1,
step = 1,
value = 3,
maximum = 10
)
with gradio.Row():
BENCHMARK_START_BUTTON = gradio.Button(wording.get('start_button_label'))
BENCHMARK_CLEAR_BUTTON = gradio.Button(wording.get('clear_button_label'))
def listen() -> None:
BENCHMARK_START_BUTTON.click(update, inputs = BENCHMARK_CYCLES_SLIDER, outputs = BENCHMARK_RESULT_DATAFRAME)
BENCHMARK_CLEAR_BUTTON.click(clear, outputs = BENCHMARK_RESULT_DATAFRAME)
def update(benchmark_cycles : int) -> Update:
DeepFakeAI.globals.source_path = '.assets/examples/source.jpg'
target_paths =\
[
'.assets/examples/target-240p.mp4',
'.assets/examples/target-360p.mp4',
'.assets/examples/target-540p.mp4',
'.assets/examples/target-720p.mp4',
'.assets/examples/target-1080p.mp4',
'.assets/examples/target-1440p.mp4',
'.assets/examples/target-2160p.mp4'
]
value = [ benchmark(target_path, benchmark_cycles) for target_path in target_paths ]
return gradio.update(value = value)
def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
process_times = []
total_fps = 0.0
for i in range(benchmark_cycles + 1):
DeepFakeAI.globals.target_path = target_path
DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, tempfile.gettempdir())
video_frame_total = get_video_frame_total(DeepFakeAI.globals.target_path)
start_time = time.perf_counter()
conditional_process()
end_time = time.perf_counter()
process_time = end_time - start_time
fps = video_frame_total / process_time
if i > 0:
process_times.append(process_time)
total_fps += fps
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\
[
DeepFakeAI.globals.target_path,
benchmark_cycles,
average_run,
fastest_run,
slowest_run,
relative_fps
]
def clear() -> Update:
if DeepFakeAI.globals.target_path:
clear_temp(DeepFakeAI.globals.target_path)
return gradio.update(value = None)
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