# 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.mlmodel
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/
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
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlmodel # 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 platform
import subprocess
import time
import warnings
from copy import deepcopy
from pathlib import Path
import torch
from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import C2f, Detect, Segment
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, __version__, callbacks, colorstr,
get_default_args, yaml_save)
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
ARM64 = platform.machine() in ('arm64', 'aarch64')
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', '.mlmodel', 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], ]
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}')
return None, None
return outer_func
class Exporter:
"""
A class for exporting a model.
Attributes:
args (SimpleNamespace): Configuration for the exporter.
save_dir (Path): Directory to save results.
"""
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'
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 = flags # export booleans
# Load PyTorch model
self.device = select_device('cpu' if self.args.device is None else self.args.device)
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.'
# Checks
model.names = check_class_names(model.names)
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
if self.args.optimize:
assert self.device.type == 'cpu', '--optimize 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['yaml_file'])
if file.suffix == '.yaml':
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 k, m in model.named_modules():
if isinstance(m, (Detect, Segment)):
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
# 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) for x in y)
self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
trained_on = f'trained on {Path(self.args.data).name}' if self.args.data else '(untrained)'
description = f'Ultralytics {self.pretty_name} model {trained_on}'
self.metadata = {
'description': description,
'author': 'Ultralytics',
'license': 'AGPL-3.0 https://ultralytics.com/license',
'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.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: # 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], s_model = self.export_saved_model()
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self.export_pb(s_model)
if tflite:
f[7], _ = self.export_tflite(s_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()
# 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(' ', '')
data = f'data={self.args.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} {data}'
f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={self.args.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>=0.4.17', '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', 1: 'anchors'} # shape(1,25200,85)
dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
elif isinstance(self.model, DetectionModel):
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
torch.onnx.export(
self.model.cpu() if dynamic else self.model, # --dynamic 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>=2022.3') # 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_coreml(self, prefix=colorstr('CoreML:')):
"""YOLOv8 CoreML export."""
check_requirements('coremltools>=6.0')
import coremltools as ct # noqa
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
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:
# 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)
bits, mode = (8, 'kmeans_lut') 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
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
if self.args.nms and self.model.task == 'detect':
ct_model = self._pipeline_coreml(ct_model)
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()})
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>=8.0.0
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 model requires maximum --batch-size argument')
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."""
try:
import tensorflow as tf # noqa
except ImportError:
cuda = torch.cuda.is_available()
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>=1.7.7', 'sng4onnx>=1.0.1', 'onnxsim>=0.4.17', 'onnx_graphsurgeon>=0.3.26',
'tflite_support', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
cmds='--extra-index-url https://pypi.ngc.nvidia.com')
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
int8 = '-oiqt -qt per-tensor' if self.args.int8 else ''
cmd = f'onnx2tf -i {f_onnx} -o {f} -nuo --non_verbose {int8}'
LOGGER.info(f"\n{prefix} running '{cmd.strip()}'")
subprocess.run(cmd, shell=True)
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml
# Remove/rename TFLite models
if self.args.int8:
for file in f.rglob('*_dynamic_range_quant.tflite'):
file.rename(file.with_stem(file.stem.replace('_dynamic_range_quant', '_int8')))
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)
# Load saved_model
keras_model = tf.saved_model.load(f, tags=None, options=None)
return str(f), 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.split(), check=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 = 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}')
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model --output_node_names={outputs} {f_pb} {f}'
subprocess.run(cmd.split(), check=True)
# 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, prefix=colorstr('CoreML Pipeline:')):
"""YOLOv8 CoreML pipeline."""
import coremltools as ct # noqa
LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
batch_size, ch, 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)) # img(192 width, 320 height)
# img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection
out = model.predict({'image': img})
out0_shape = out[out0.name].shape
out1_shape = out[out1.name].shape
else: # linux and windows can not run model.predict(), get sizes from pytorch 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
na, nc = out0_shape
# na, nc = out0.type.multiArrayType.shape # number anchors, classes
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)
# 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)
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 iOS export."""
def __init__(self, model, im):
"""Initialize the iOSDetectModel class with a YOLO model and example image."""
super().__init__()
b, c, 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)
def export(cfg=DEFAULT_CFG):
"""Export a YOLOv model to a specific format."""
cfg.model = cfg.model or 'yolov8n.yaml'
cfg.format = cfg.format or 'torchscript'
from ultralytics import YOLO
model = YOLO(cfg.model)
model.export(**vars(cfg))
if __name__ == '__main__':
"""
CLI:
yolo mode=export model=yolov8n.yaml format=onnx
"""
export()