File size: 7,867 Bytes
92894b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# YOLOv5 🚀 by Ultralytics, AGPL-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], batch_size=batch_size, 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)
|