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Add imgsz check and improve logs for benchmarks (#18917)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin 2025-01-29 02:15:52 +08:00 • committed by GitHub
parent 765facc5d7
commit 813511a232
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1 changed files with 8 additions and 3 deletions
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11
ultralytics/utils/benchmarks.py
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@ -42,7 +42,7 @@ from ultralytics import YOLO, YOLOWorld
from ultralytics.cfg import TASK2DATA, TASK2METRIC
from ultralytics.engine.exporter import export_formats
from ultralytics.utils import ARM64, ASSETS, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo, is_rockchip
from ultralytics.utils.checks import IS_PYTHON_3_12, check_imgsz, check_requirements, check_yolo, is_rockchip
from ultralytics.utils.downloads import safe_download
from ultralytics.utils.files import file_size
from ultralytics.utils.torch_utils import get_cpu_info, select_device
@ -80,6 +80,9 @@ def benchmark(
>>> from ultralytics.utils.benchmarks import benchmark
>>> benchmark(model="yolo11n.pt", imgsz=640)
"""
imgsz = check_imgsz(imgsz)
assert imgsz[0] == imgsz[1] if isinstance(imgsz, list) else True, "benchmark() only supports square imgsz."
import pandas as pd # scope for faster 'import ultralytics'
pd.options.display.max_columns = 10
@ -148,7 +151,7 @@ def benchmark(
assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML
if i in {13}:
assert not is_end2end, "End-to-end torch.topk operation is not supported for NCNN prediction yet"
exported_model.predict(ASSETS / "bus.jpg", imgsz=imgsz, device=device, half=half)
exported_model.predict(ASSETS / "bus.jpg", imgsz=imgsz, device=device, half=half, verbose=False)
# Validate
data = data or TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
@ -170,8 +173,10 @@ def benchmark(
df = pd.DataFrame(y, columns=["Format", "Status❔", "Size (MB)", key, "Inference time (ms/im)", "FPS"])
name = model.model_name
s = f"\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n"
s = f"\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df.fillna('-')}\n"
LOGGER.info(s)
LOGGER.info("Status legends:")
LOGGER.info("✅ - Benchmark passed | ❎ - Export passed but validation failed | ❌️ - Export failed")
with open("benchmarks.log", "a", errors="ignore", encoding="utf-8") as f:
f.write(s)