TextDetection/benchmarks.py

# 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)