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benchmarks.py
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benchmarks.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 benchmarks on all supported export formats
Format | `export.py --include` | Model
--- | --- | ---
PyTorch | - | yolov5s.pt
TorchScript | `torchscript` | yolov5s.torchscript
ONNX | `onnx` | yolov5s.onnx
OpenVINO | `openvino` | yolov5s_openvino_model/
TensorRT | `engine` | yolov5s.engine
CoreML | `coreml` | yolov5s.mlmodel
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
TensorFlow GraphDef | `pb` | yolov5s.pb
TensorFlow Lite | `tflite` | yolov5s.tflite
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov5s_web_model/
Requirements:
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
Usage:
$ python benchmarks.py --weights yolov5s.pt --img 640
"""
import argparse
import platform
import sys
import time
from pathlib import Path
import pandas as pd
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd()) # relative
import export
from models.experimental import attempt_load
from models.yolo import SegmentationModel
from segment.val import run as val_seg
from utils import notebook_init
from utils.general import LOGGER, check_yaml, file_size, print_args
from utils.torch_utils import select_device
from val import run as val_det
def run(
weights=ROOT / 'yolov5s.pt', # weights path
imgsz=640, # inference size (pixels)
batch_size=1, # batch size
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
half=False, # use FP16 half-precision inference
test=False, # test exports only
pt_only=False, # test PyTorch only
hard_fail=False, # throw error on benchmark failure
):
y, t = [], time.time()
device = select_device(device)
model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc.
for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)
try:
assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
if 'cpu' in device.type:
assert cpu, 'inference not supported on CPU'
if 'cuda' in device.type:
assert gpu, 'inference not supported on GPU'
# Export
if f == '-':
w = weights # PyTorch format
else:
w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others
assert suffix in str(w), 'export failed'
# Validate
if model_type == SegmentationModel:
result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
else: # DetectionModel:
result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))
speed = result[2][1] # times (preprocess, inference, postprocess)
y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference
except Exception as e:
if hard_fail:
assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}')
y.append([name, None, None, None]) # mAP, t_inference
if pt_only and i == 0:
break # break after PyTorch
# Print results
LOGGER.info('\n')
parse_opt()
notebook_init() # print system info
c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', '']
py = pd.DataFrame(y, columns=c)
LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)')
LOGGER.info(str(py if map else py.iloc[:, :2]))
if hard_fail and isinstance(hard_fail, str):
metrics = py['mAP50-95'].array # values to compare to floor
floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}'
return py
def test(
weights=ROOT / 'yolov5s.pt', # weights path
imgsz=640, # inference size (pixels)
batch_size=1, # batch size
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
half=False, # use FP16 half-precision inference
test=False, # test exports only
pt_only=False, # test PyTorch only
hard_fail=False, # throw error on benchmark failure
):
y, t = [], time.time()
device = select_device(device)
for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
try:
w = weights if f == '-' else \
export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights
assert suffix in str(w), 'export failed'
y.append([name, True])
except Exception:
y.append([name, False]) # mAP, t_inference
# Print results
LOGGER.info('\n')
parse_opt()
notebook_init() # print system info
py = pd.DataFrame(y, columns=['Format', 'Export'])
LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)')
LOGGER.info(str(py))
return py
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--test', action='store_true', help='test exports only')
parser.add_argument('--pt-only', action='store_true', help='test PyTorch only')
parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric')
opt = parser.parse_args()
opt.data = check_yaml(opt.data) # check YAML
print_args(vars(opt))
return opt
def main(opt):
test(**vars(opt)) if opt.test else run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)