# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
Format | `format=argument` | Model
--- | --- | ---
PyTorch | - | yolov8n.pt
TorchScript | `torchscript` | yolov8n.torchscript
ONNX | `onnx` | yolov8n.onnx
OpenVINO | `openvino` | yolov8n_openvino_model/
TensorRT | `engine` | yolov8n.engine
CoreML | `coreml` | yolov8n.mlpackage
TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
TensorFlow GraphDef | `pb` | yolov8n.pb
TensorFlow Lite | `tflite` | yolov8n.tflite
TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov8n_web_model/
PaddlePaddle | `paddle` | yolov8n_paddle_model/
ncnn | `ncnn` | yolov8n_ncnn_model/
Requirements:
$ pip install "ultralytics[export]"
Python:
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
results = model.export(format='onnx')
CLI:
$ yolo mode=export model=yolov8n.pt format=onnx
Inference:
$ yolo predict model=yolov8n.pt # PyTorch
yolov8n.torchscript # TorchScript
yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlpackage # CoreML (macOS-only)
yolov8n_saved_model # TensorFlow SavedModel
yolov8n.pb # TensorFlow GraphDef
yolov8n.tflite # TensorFlow Lite
yolov8n_edgetpu.tflite # TensorFlow Edge TPU
yolov8n_paddle_model # PaddlePaddle
TensorFlow.js:
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
$ npm install
$ ln -s ../../yolov5/yolov8n_web_model public/yolov8n_web_model
$ npm start
"""
import json
import os
import shutil
import subprocess
import time
import warnings
from copy import deepcopy
from datetime import datetime
from pathlib import Path
import torch
from ultralytics.cfg import get_cfg
from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, WINDOWS, __version__, callbacks,
colorstr, get_default_args, yaml_save)
from ultralytics.utils.checks import check_imgsz, check_requirements, check_version
from ultralytics.utils.downloads import attempt_download_asset, get_github_assets
from ultralytics.utils.files import file_size, spaces_in_path
from ultralytics.utils.ops import Profile
from ultralytics.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
def export_formats():
"""YOLOv8 export formats."""
import pandas
x = [
['PyTorch', '-', '.pt', True, True],
['TorchScript', 'torchscript', '.torchscript', True, True],
['ONNX', 'onnx', '.onnx', True, True],
['OpenVINO', 'openvino', '_openvino_model', True, False],
['TensorRT', 'engine', '.engine', False, True],
['CoreML', 'coreml', '.mlpackage', True, False],
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
['TensorFlow GraphDef', 'pb', '.pb', True, True],
['TensorFlow Lite', 'tflite', '.tflite', True, False],
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', True, False],
['TensorFlow.js', 'tfjs', '_web_model', True, False],
['PaddlePaddle', 'paddle', '_paddle_model', True, True],
['ncnn', 'ncnn', '_ncnn_model', True, True], ]
return pandas.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
def gd_outputs(gd):
"""TensorFlow GraphDef model output node names."""
name_list, input_list = [], []
for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
name_list.append(node.name)
input_list.extend(node.input)
return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
def try_export(inner_func):
"""YOLOv8 export decorator, i..e @try_export."""
inner_args = get_default_args(inner_func)
def outer_func(*args, **kwargs):
"""Export a model."""
prefix = inner_args['prefix']
try:
with Profile() as dt:
f, model = inner_func(*args, **kwargs)
LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as '{f}' ({file_size(f):.1f} MB)")
return f, model
except Exception as e:
LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
raise e
return outer_func
class Exporter:
"""
A class for exporting a model.
Attributes:
args (SimpleNamespace): Configuration for the exporter.
callbacks (list, optional): List of callback functions. Defaults to None.
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""
Initializes the Exporter class.
Args:
cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
overrides (dict, optional): Configuration overrides. Defaults to None.
_callbacks (list, optional): List of callback functions. Defaults to None.
"""
self.args = get_cfg(cfg, overrides)
self.callbacks = _callbacks or callbacks.get_default_callbacks()
callbacks.add_integration_callbacks(self)
@smart_inference_mode()
def __call__(self, model=None):
"""Returns list of exported files/dirs after running callbacks."""
self.run_callbacks('on_export_start')
t = time.time()
format = self.args.format.lower() # to lowercase
if format in ('tensorrt', 'trt'): # 'engine' aliases
format = 'engine'
if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios'): # 'coreml' aliases
format = 'coreml'
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]
if sum(flags) != 1:
raise ValueError(f"Invalid export format='{format}'. Valid formats are {fmts}")
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, ncnn = flags # export booleans
# Device
if format == 'engine' and self.args.device is None:
LOGGER.warning('WARNING ⚠️ TensorRT requires GPU export, automatically assigning device=0')
self.args.device = '0'
self.device = select_device('cpu' if self.args.device is None else self.args.device)
# Checks
model.names = check_class_names(model.names)
if self.args.half and onnx and self.device.type == 'cpu':
LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')
self.args.half = False
assert not self.args.dynamic, 'half=True not compatible with dynamic=True, i.e. use only one.'
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
if self.args.optimize:
assert not ncnn, "optimize=True not compatible with format='ncnn', i.e. use optimize=False"
assert self.device.type == 'cpu', "optimize=True not compatible with cuda devices, i.e. use device='cpu'"
if edgetpu and not LINUX:
raise SystemError('Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler/')
# Input
im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
file = Path(
getattr(model, 'pt_path', None) or getattr(model, 'yaml_file', None) or model.yaml.get('yaml_file', ''))
if file.suffix in ('.yaml', '.yml'):
file = Path(file.name)
# Update model
model = deepcopy(model).to(self.device)
for p in model.parameters():
p.requires_grad = False
model.eval()
model.float()
model = model.fuse()
for m in model.modules():
if isinstance(m, (Detect, RTDETRDecoder)): # Segment and Pose use Detect base class
m.dynamic = self.args.dynamic
m.export = True
m.format = self.args.format
elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
# EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
m.forward = m.forward_split
y = None
for _ in range(2):
y = model(im) # dry runs
if self.args.half and (engine or onnx) and self.device.type != 'cpu':
im, model = im.half(), model.half() # to FP16
# Filter warnings
warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
warnings.filterwarnings('ignore', category=UserWarning) # suppress shape prim::Constant missing ONNX warning
warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress CoreML np.bool deprecation warning
# Assign
self.im = im
self.model = model
self.file = file
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else \
tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
data = model.args['data'] if hasattr(model, 'args') and isinstance(model.args, dict) else ''
description = f'Ultralytics {self.pretty_name} model {f"trained on {data}" if data else ""}'
self.metadata = {
'description': description,
'author': 'Ultralytics',
'license': 'AGPL-3.0 https://ultralytics.com/license',
'date': datetime.now().isoformat(),
'version': __version__,
'stride': int(max(model.stride)),
'task': model.task,
'batch': self.args.batch,
'imgsz': self.imgsz,
'names': model.names} # model metadata
if model.task == 'pose':
self.metadata['kpt_shape'] = model.model[-1].kpt_shape
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and "
f'output shape(s) {self.output_shape} ({file_size(file):.1f} MB)')
# Exports
f = [''] * len(fmts) # exported filenames
if jit or ncnn: # TorchScript
f[0], _ = self.export_torchscript()
if engine: # TensorRT required before ONNX
f[1], _ = self.export_engine()
if onnx or xml: # OpenVINO requires ONNX
f[2], _ = self.export_onnx()
if xml: # OpenVINO
f[3], _ = self.export_openvino()
if coreml: # CoreML
f[4], _ = self.export_coreml()
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
self.args.int8 |= edgetpu
f[5], keras_model = self.export_saved_model()
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self.export_pb(keras_model=keras_model)
if tflite:
f[7], _ = self.export_tflite(keras_model=keras_model, nms=False, agnostic_nms=self.args.agnostic_nms)
if edgetpu:
f[8], _ = self.export_edgetpu(tflite_model=Path(f[5]) / f'{self.file.stem}_full_integer_quant.tflite')
if tfjs:
f[9], _ = self.export_tfjs()
if paddle: # PaddlePaddle
f[10], _ = self.export_paddle()
if ncnn: # ncnn
f[11], _ = self.export_ncnn()
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
f = str(Path(f[-1]))
square = self.imgsz[0] == self.imgsz[1]
s = '' if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " \
f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(' ', '')
predict_data = f'data={data}' if model.task == 'segment' and format == 'pb' else ''
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {predict_data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {s}'
f'\nVisualize: https://netron.app')
self.run_callbacks('on_export_end')
return f # return list of exported files/dirs
@try_export
def export_torchscript(self, prefix=colorstr('TorchScript:')):
"""YOLOv8 TorchScript model export."""
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
f = self.file.with_suffix('.torchscript')
ts = torch.jit.trace(self.model, self.im, strict=False)
extra_files = {'config.txt': json.dumps(self.metadata)} # torch._C.ExtraFilesMap()
if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
LOGGER.info(f'{prefix} optimizing for mobile...')
from torch.utils.mobile_optimizer import optimize_for_mobile
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
else:
ts.save(str(f), _extra_files=extra_files)
return f, None
@try_export
def export_onnx(self, prefix=colorstr('ONNX:')):
"""YOLOv8 ONNX export."""
requirements = ['onnx>=1.12.0']
if self.args.simplify:
requirements += ['onnxsim>=0.4.33', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
check_requirements(requirements)
import onnx # noqa
opset_version = self.args.opset or get_latest_opset()
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__} opset {opset_version}...')
f = str(self.file.with_suffix('.onnx'))
output_names = ['output0', 'output1'] if isinstance(self.model, SegmentationModel) else ['output0']
dynamic = self.args.dynamic
if dynamic:
dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
if isinstance(self.model, SegmentationModel):
dynamic['output0'] = {0: 'batch', 2: 'anchors'} # shape(1, 116, 8400)
dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
elif isinstance(self.model, DetectionModel):
dynamic['output0'] = {0: 'batch', 2: 'anchors'} # shape(1, 84, 8400)
torch.onnx.export(
self.model.cpu() if dynamic else self.model, # dynamic=True only compatible with cpu
self.im.cpu() if dynamic else self.im,
f,
verbose=False,
opset_version=opset_version,
do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
input_names=['images'],
output_names=output_names,
dynamic_axes=dynamic or None)
# Checks
model_onnx = onnx.load(f) # load onnx model
# onnx.checker.check_model(model_onnx) # check onnx model
# Simplify
if self.args.simplify:
try:
import onnxsim
LOGGER.info(f'{prefix} simplifying with onnxsim {onnxsim.__version__}...')
# subprocess.run(f'onnxsim "{f}" "{f}"', shell=True)
model_onnx, check = onnxsim.simplify(model_onnx)
assert check, 'Simplified ONNX model could not be validated'
except Exception as e:
LOGGER.info(f'{prefix} simplifier failure: {e}')
# Metadata
for k, v in self.metadata.items():
meta = model_onnx.metadata_props.add()
meta.key, meta.value = k, str(v)
onnx.save(model_onnx, f)
return f, model_onnx
@try_export
def export_openvino(self, prefix=colorstr('OpenVINO:')):
"""YOLOv8 OpenVINO export."""
check_requirements('openvino-dev>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.runtime as ov # noqa
from openvino.tools import mo # noqa
LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
f_onnx = self.file.with_suffix('.onnx')
f_ov = str(Path(f) / self.file.with_suffix('.xml').name)
ov_model = mo.convert_model(f_onnx,
model_name=self.pretty_name,
framework='onnx',
compress_to_fp16=self.args.half) # export
# Set RT info
ov_model.set_rt_info('YOLOv8', ['model_info', 'model_type'])
ov_model.set_rt_info(True, ['model_info', 'reverse_input_channels'])
ov_model.set_rt_info(114, ['model_info', 'pad_value'])
ov_model.set_rt_info([255.0], ['model_info', 'scale_values'])
ov_model.set_rt_info(self.args.iou, ['model_info', 'iou_threshold'])
ov_model.set_rt_info([v.replace(' ', '_') for k, v in sorted(self.model.names.items())],
['model_info', 'labels'])
if self.model.task != 'classify':
ov_model.set_rt_info('fit_to_window_letterbox', ['model_info', 'resize_type'])
ov.serialize(ov_model, f_ov) # save
yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
return f, None
@try_export
def export_paddle(self, prefix=colorstr('PaddlePaddle:')):
"""YOLOv8 Paddle export."""
check_requirements(('paddlepaddle', 'x2paddle'))
import x2paddle # noqa
from x2paddle.convert import pytorch2paddle # noqa
LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_paddle_model{os.sep}')
pytorch2paddle(module=self.model, save_dir=f, jit_type='trace', input_examples=[self.im]) # export
yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
return f, None
@try_export
def export_ncnn(self, prefix=colorstr('ncnn:')):
"""
YOLOv8 ncnn export using PNNX https://github.com/pnnx/pnnx.
"""
check_requirements('git+https://github.com/Tencent/ncnn.git' if ARM64 else 'ncnn') # requires ncnn
import ncnn # noqa
LOGGER.info(f'\n{prefix} starting export with ncnn {ncnn.__version__}...')
f = Path(str(self.file).replace(self.file.suffix, f'_ncnn_model{os.sep}'))
f_ts = self.file.with_suffix('.torchscript')
pnnx_filename = 'pnnx.exe' if WINDOWS else 'pnnx'
if Path(pnnx_filename).is_file():
pnnx = pnnx_filename
elif (ROOT / pnnx_filename).is_file():
pnnx = ROOT / pnnx_filename
else:
LOGGER.warning(
f'{prefix} WARNING ⚠️ PNNX not found. Attempting to download binary file from '
'https://github.com/pnnx/pnnx/.\nNote PNNX Binary file must be placed in current working directory '
f'or in {ROOT}. See PNNX repo for full installation instructions.')
_, assets = get_github_assets(repo='pnnx/pnnx', retry=True)
system = 'macos' if MACOS else 'ubuntu' if LINUX else 'windows' # operating system
asset = [x for x in assets if system in x][0] if assets else \
f'https://github.com/pnnx/pnnx/releases/download/20230816/pnnx-20230816-{system}.zip' # fallback
asset = attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
unzip_dir = Path(asset).with_suffix('')
pnnx = ROOT / pnnx_filename # new location
(unzip_dir / pnnx_filename).rename(pnnx) # move binary to ROOT
shutil.rmtree(unzip_dir) # delete unzip dir
Path(asset).unlink() # delete zip
pnnx.chmod(0o777) # set read, write, and execute permissions for everyone
use_ncnn = True
ncnn_args = [
f'ncnnparam={f / "model.ncnn.param"}',
f'ncnnbin={f / "model.ncnn.bin"}',
f'ncnnpy={f / "model_ncnn.py"}', ] if use_ncnn else []
use_pnnx = False
pnnx_args = [
f'pnnxparam={f / "model.pnnx.param"}',
f'pnnxbin={f / "model.pnnx.bin"}',
f'pnnxpy={f / "model_pnnx.py"}',
f'pnnxonnx={f / "model.pnnx.onnx"}', ] if use_pnnx else []
cmd = [
str(pnnx),
str(f_ts),
*ncnn_args,
*pnnx_args,
f'fp16={int(self.args.half)}',
f'device={self.device.type}',
f'inputshape="{[self.args.batch, 3, *self.imgsz]}"', ]
f.mkdir(exist_ok=True) # make ncnn_model directory
LOGGER.info(f"{prefix} running '{' '.join(cmd)}'")
subprocess.run(cmd, check=True)
for f_debug in 'debug.bin', 'debug.param', 'debug2.bin', 'debug2.param': # remove debug files
Path(f_debug).unlink(missing_ok=True)
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml
return str(f), None
@try_export
def export_coreml(self, prefix=colorstr('CoreML:')):
"""YOLOv8 CoreML export."""
mlmodel = self.args.format.lower() == 'mlmodel' # legacy *.mlmodel export format requested
check_requirements('coremltools>=6.0,<=6.2' if mlmodel else 'coremltools>=7.0.b1')
import coremltools as ct # noqa
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel' if mlmodel else '.mlpackage')
if f.is_dir():
shutil.rmtree(f)
bias = [0.0, 0.0, 0.0]
scale = 1 / 255
classifier_config = None
if self.model.task == 'classify':
classifier_config = ct.ClassifierConfig(list(self.model.names.values())) if self.args.nms else None
model = self.model
elif self.model.task == 'detect':
model = IOSDetectModel(self.model, self.im) if self.args.nms else self.model
else:
if self.args.nms:
LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is only available for Detect models like 'yolov8n.pt'.")
# TODO CoreML Segment and Pose model pipelining
model = self.model
ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts,
inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
classifier_config=classifier_config,
convert_to='neuralnetwork' if mlmodel else 'mlprogram')
bits, mode = (8, 'kmeans') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
if 'kmeans' in mode:
check_requirements('scikit-learn') # scikit-learn package required for k-means quantization
if mlmodel:
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
elif bits == 8: # mlprogram already quantized to FP16
import coremltools.optimize.coreml as cto
op_config = cto.OpPalettizerConfig(mode='kmeans', nbits=bits, weight_threshold=512)
config = cto.OptimizationConfig(global_config=op_config)
ct_model = cto.palettize_weights(ct_model, config=config)
if self.args.nms and self.model.task == 'detect':
if mlmodel:
import platform
# coremltools<=6.2 NMS export requires Python<3.11
check_version(platform.python_version(), '<3.11', name='Python ', hard=True)
weights_dir = None
else:
ct_model.save(str(f)) # save otherwise weights_dir does not exist
weights_dir = str(f / 'Data/com.apple.CoreML/weights')
ct_model = self._pipeline_coreml(ct_model, weights_dir=weights_dir)
m = self.metadata # metadata dict
ct_model.short_description = m.pop('description')
ct_model.author = m.pop('author')
ct_model.license = m.pop('license')
ct_model.version = m.pop('version')
ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()})
try:
ct_model.save(str(f)) # save *.mlpackage
except Exception as e:
LOGGER.warning(
f'{prefix} WARNING ⚠️ CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. '
f'Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928.')
f = f.with_suffix('.mlmodel')
ct_model.save(str(f))
return f, ct_model
@try_export
def export_engine(self, prefix=colorstr('TensorRT:')):
"""YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt."""
assert self.im.device.type != 'cpu', "export running on CPU but must be on GPU, i.e. use 'device=0'"
try:
import tensorrt as trt # noqa
except ImportError:
if LINUX:
check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
import tensorrt as trt # noqa
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
self.args.simplify = True
f_onnx, _ = self.export_onnx()
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
assert Path(f_onnx).exists(), f'failed to export ONNX file: {f_onnx}'
f = self.file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if self.args.verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE
builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = self.args.workspace * 1 << 30
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(f_onnx):
raise RuntimeError(f'failed to load ONNX file: {f_onnx}')
inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
for inp in inputs:
LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
for out in outputs:
LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
if self.args.dynamic:
shape = self.im.shape
if shape[0] <= 1:
LOGGER.warning(f"{prefix} WARNING ⚠️ 'dynamic=True' model requires max batch size, i.e. 'batch=16'")
profile = builder.create_optimization_profile()
for inp in inputs:
profile.set_shape(inp.name, (1, *shape[1:]), (max(1, shape[0] // 2), *shape[1:]), shape)
config.add_optimization_profile(profile)
LOGGER.info(
f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and self.args.half else 32} engine as {f}')
if builder.platform_has_fast_fp16 and self.args.half:
config.set_flag(trt.BuilderFlag.FP16)
# Write file
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
# Metadata
meta = json.dumps(self.metadata)
t.write(len(meta).to_bytes(4, byteorder='little', signed=True))
t.write(meta.encode())
# Model
t.write(engine.serialize())
return f, None
@try_export
def export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):
"""YOLOv8 TensorFlow SavedModel export."""
cuda = torch.cuda.is_available()
try:
import tensorflow as tf # noqa
except ImportError:
check_requirements(f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if cuda else '-cpu'}")
import tensorflow as tf # noqa
check_requirements(
('onnx', 'onnx2tf>=1.15.4', 'sng4onnx>=1.0.1', 'onnxsim>=0.4.33', 'onnx_graphsurgeon>=0.3.26',
'tflite_support', 'onnxruntime-gpu' if cuda else 'onnxruntime'),
cmds='--extra-index-url https://pypi.ngc.nvidia.com') # onnx_graphsurgeon only on NVIDIA
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
if f.is_dir():
import shutil
shutil.rmtree(f) # delete output folder
# Export to ONNX
self.args.simplify = True
f_onnx, _ = self.export_onnx()
# Export to TF
tmp_file = f / 'tmp_tflite_int8_calibration_images.npy' # int8 calibration images file
if self.args.int8:
verbosity = '--verbosity info'
if self.args.data:
import numpy as np
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
# Generate calibration data for integer quantization
LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
data = check_det_dataset(self.args.data)
dataset = YOLODataset(data['val'], data=data, imgsz=self.imgsz[0], augment=False)
images = []
n_images = 100 # maximum number of images
for n, batch in enumerate(dataset):
if n >= n_images:
break
im = batch['img'].permute(1, 2, 0)[None] # list to nparray, CHW to BHWC
images.append(im)
f.mkdir()
images = torch.cat(images, 0).float()
# mean = images.view(-1, 3).mean(0) # imagenet mean [123.675, 116.28, 103.53]
# std = images.view(-1, 3).std(0) # imagenet std [58.395, 57.12, 57.375]
np.save(str(tmp_file), images.numpy()) # BHWC
int8 = f'-oiqt -qt per-tensor -cind images "{tmp_file}" "[[[[0, 0, 0]]]]" "[[[[255, 255, 255]]]]"'
else:
int8 = '-oiqt -qt per-tensor'
else:
verbosity = '--non_verbose'
int8 = ''
cmd = f'onnx2tf -i "{f_onnx}" -o "{f}" -nuo {verbosity} {int8}'.strip()
LOGGER.info(f"{prefix} running '{cmd}'")
subprocess.run(cmd, shell=True)
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml
# Remove/rename TFLite models
if self.args.int8:
tmp_file.unlink(missing_ok=True)
for file in f.rglob('*_dynamic_range_quant.tflite'):
file.rename(file.with_name(file.stem.replace('_dynamic_range_quant', '_int8') + file.suffix))
for file in f.rglob('*_integer_quant_with_int16_act.tflite'):
file.unlink() # delete extra fp16 activation TFLite files
# Add TFLite metadata
for file in f.rglob('*.tflite'):
f.unlink() if 'quant_with_int16_act.tflite' in str(f) else self._add_tflite_metadata(file)
return str(f), tf.saved_model.load(f, tags=None, options=None) # load saved_model as Keras model
@try_export
def export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
"""YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow."""
import tensorflow as tf # noqa
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = self.file.with_suffix('.pb')
m = tf.function(lambda x: keras_model(x)) # full model
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
frozen_func = convert_variables_to_constants_v2(m)
frozen_func.graph.as_graph_def()
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
return f, None
@try_export
def export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
"""YOLOv8 TensorFlow Lite export."""
import tensorflow as tf # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
saved_model = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
if self.args.int8:
f = saved_model / f'{self.file.stem}_int8.tflite' # fp32 in/out
elif self.args.half:
f = saved_model / f'{self.file.stem}_float16.tflite' # fp32 in/out
else:
f = saved_model / f'{self.file.stem}_float32.tflite'
return str(f), None
@try_export
def export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
"""YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/."""
LOGGER.warning(f'{prefix} WARNING ⚠️ Edge TPU known bug https://github.com/ultralytics/ultralytics/issues/1185')
cmd = 'edgetpu_compiler --version'
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
assert LINUX, f'export only supported on Linux. See {help_url}'
if subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True).returncode != 0:
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
for c in (
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
f = str(tflite_model).replace('.tflite', '_edgetpu.tflite') # Edge TPU model
cmd = f'edgetpu_compiler -s -d -k 10 --out_dir "{Path(f).parent}" "{tflite_model}"'
LOGGER.info(f"{prefix} running '{cmd}'")
subprocess.run(cmd, shell=True)
self._add_tflite_metadata(f)
return f, None
@try_export
def export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
"""YOLOv8 TensorFlow.js export."""
check_requirements('tensorflowjs')
import tensorflow as tf
import tensorflowjs as tfjs # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
f = str(self.file).replace(self.file.suffix, '_web_model') # js dir
f_pb = str(self.file.with_suffix('.pb')) # *.pb path
gd = tf.Graph().as_graph_def() # TF GraphDef
with open(f_pb, 'rb') as file:
gd.ParseFromString(file.read())
outputs = ','.join(gd_outputs(gd))
LOGGER.info(f'\n{prefix} output node names: {outputs}')
with spaces_in_path(f_pb) as fpb_, spaces_in_path(f) as f_: # exporter can not handle spaces in path
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model --output_node_names={outputs} "{fpb_}" "{f_}"'
LOGGER.info(f"{prefix} running '{cmd}'")
subprocess.run(cmd, shell=True)
if ' ' in str(f):
LOGGER.warning(f"{prefix} WARNING ⚠️ your model may not work correctly with spaces in path '{f}'.")
# f_json = Path(f) / 'model.json' # *.json path
# with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
# subst = re.sub(
# r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
# r'"Identity.?.?": {"name": "Identity.?.?"}, '
# r'"Identity.?.?": {"name": "Identity.?.?"}, '
# r'"Identity.?.?": {"name": "Identity.?.?"}}}',
# r'{"outputs": {"Identity": {"name": "Identity"}, '
# r'"Identity_1": {"name": "Identity_1"}, '
# r'"Identity_2": {"name": "Identity_2"}, '
# r'"Identity_3": {"name": "Identity_3"}}}',
# f_json.read_text(),
# )
# j.write(subst)
yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
return f, None
def _add_tflite_metadata(self, file):
"""Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata."""
from tflite_support import flatbuffers # noqa
from tflite_support import metadata as _metadata # noqa
from tflite_support import metadata_schema_py_generated as _metadata_fb # noqa
# Create model info
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.metadata['description']
model_meta.version = self.metadata['version']
model_meta.author = self.metadata['author']
model_meta.license = self.metadata['license']
# Label file
tmp_file = Path(file).parent / 'temp_meta.txt'
with open(tmp_file, 'w') as f:
f.write(str(self.metadata))
label_file = _metadata_fb.AssociatedFileT()
label_file.name = tmp_file.name
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
# Create input info
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = 'image'
input_meta.description = 'Input image to be detected.'
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = _metadata_fb.ColorSpaceType.RGB
input_meta.content.contentPropertiesType = _metadata_fb.ContentProperties.ImageProperties
# Create output info
output1 = _metadata_fb.TensorMetadataT()
output1.name = 'output'
output1.description = 'Coordinates of detected objects, class labels, and confidence score'
output1.associatedFiles = [label_file]
if self.model.task == 'segment':
output2 = _metadata_fb.TensorMetadataT()
output2.name = 'output'
output2.description = 'Mask protos'
output2.associatedFiles = [label_file]
# Create subgraph info
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output1, output2] if self.model.task == 'segment' else [output1]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
populator = _metadata.MetadataPopulator.with_model_file(str(file))
populator.load_metadata_buffer(metadata_buf)
populator.load_associated_files([str(tmp_file)])
populator.populate()
tmp_file.unlink()
def _pipeline_coreml(self, model, weights_dir=None, prefix=colorstr('CoreML Pipeline:')):
"""YOLOv8 CoreML pipeline."""
import coremltools as ct # noqa
LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
_, _, h, w = list(self.im.shape) # BCHW
# Output shapes
spec = model.get_spec()
out0, out1 = iter(spec.description.output)
if MACOS:
from PIL import Image
img = Image.new('RGB', (w, h)) # w=192, h=320
out = model.predict({'image': img})
out0_shape = out[out0.name].shape # (3780, 80)
out1_shape = out[out1.name].shape # (3780, 4)
else: # linux and windows can not run model.predict(), get sizes from PyTorch model output y
out0_shape = self.output_shape[2], self.output_shape[1] - 4 # (3780, 80)
out1_shape = self.output_shape[2], 4 # (3780, 4)
# Checks
names = self.metadata['names']
nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
_, nc = out0_shape # number of anchors, number of classes
# _, nc = out0.type.multiArrayType.shape
assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
# Define output shapes (missing)
out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80)
out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4)
# spec.neuralNetwork.preprocessing[0].featureName = '0'
# Flexible input shapes
# from coremltools.models.neural_network import flexible_shape_utils
# s = [] # shapes
# s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
# s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width)
# flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
# r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges
# r.add_height_range((192, 640))
# r.add_width_range((192, 640))
# flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
# Print
# print(spec.description)
# Model from spec
model = ct.models.MLModel(spec, weights_dir=weights_dir)
# 3. Create NMS protobuf
nms_spec = ct.proto.Model_pb2.Model()
nms_spec.specificationVersion = 5
for i in range(2):
decoder_output = model._spec.description.output[i].SerializeToString()
nms_spec.description.input.add()
nms_spec.description.input[i].ParseFromString(decoder_output)
nms_spec.description.output.add()
nms_spec.description.output[i].ParseFromString(decoder_output)
nms_spec.description.output[0].name = 'confidence'
nms_spec.description.output[1].name = 'coordinates'
output_sizes = [nc, 4]
for i in range(2):
ma_type = nms_spec.description.output[i].type.multiArrayType
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[0].lowerBound = 0
ma_type.shapeRange.sizeRanges[0].upperBound = -1
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
del ma_type.shape[:]
nms = nms_spec.nonMaximumSuppression
nms.confidenceInputFeatureName = out0.name # 1x507x80
nms.coordinatesInputFeatureName = out1.name # 1x507x4
nms.confidenceOutputFeatureName = 'confidence'
nms.coordinatesOutputFeatureName = 'coordinates'
nms.iouThresholdInputFeatureName = 'iouThreshold'
nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
nms.iouThreshold = 0.45
nms.confidenceThreshold = 0.25
nms.pickTop.perClass = True
nms.stringClassLabels.vector.extend(names.values())
nms_model = ct.models.MLModel(nms_spec)
# 4. Pipeline models together
pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
('iouThreshold', ct.models.datatypes.Double()),
('confidenceThreshold', ct.models.datatypes.Double())],
output_features=['confidence', 'coordinates'])
pipeline.add_model(model)
pipeline.add_model(nms_model)
# Correct datatypes
pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
# Update metadata
pipeline.spec.specificationVersion = 5
pipeline.spec.description.metadata.userDefined.update({
'IoU threshold': str(nms.iouThreshold),
'Confidence threshold': str(nms.confidenceThreshold)})
# Save the model
model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir)
model.input_description['image'] = 'Input image'
model.input_description['iouThreshold'] = f'(optional) IOU threshold override (default: {nms.iouThreshold})'
model.input_description['confidenceThreshold'] = \
f'(optional) Confidence threshold override (default: {nms.confidenceThreshold})'
model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
LOGGER.info(f'{prefix} pipeline success')
return model
def add_callback(self, event: str, callback):
"""
Appends the given callback.
"""
self.callbacks[event].append(callback)
def run_callbacks(self, event: str):
"""Execute all callbacks for a given event."""
for callback in self.callbacks.get(event, []):
callback(self)
class IOSDetectModel(torch.nn.Module):
"""Wrap an Ultralytics YOLO model for Apple iOS CoreML export."""
def __init__(self, model, im):
"""Initialize the IOSDetectModel class with a YOLO model and example image."""
super().__init__()
_, _, h, w = im.shape # batch, channel, height, width
self.model = model
self.nc = len(model.names) # number of classes
if w == h:
self.normalize = 1.0 / w # scalar
else:
self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller)
def forward(self, x):
"""Normalize predictions of object detection model with input size-dependent factors."""
xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)