# Ultralytics YOLO 🚀, GPL-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 -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
Python:
from ultralytics import YOLO
model = YOLO('yolov8n.yaml')
results = model.export(format='onnx')
CLI:
$ yolo mode=export model=yolov8n.pt format=onnx
Inference:
$ python detect.py --weights 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 contextlib
import json
import os
import platform
import re
import subprocess
import time
import warnings
from collections import defaultdict
from copy import deepcopy
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import ultralytics
from ultralytics.nn.modules import Detect, Segment
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, guess_model_task
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
from ultralytics.yolo.data.utils import check_det_dataset
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
MACOS = platform.system() == 'Darwin' # macOS environment
def export_formats():
# YOLOv8 export formats
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', False, False],
['TensorFlow.js', 'tfjs', '_web_model', False, False], ['PaddlePaddle', 'paddle', '_paddle_model', True, True]]
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
def try_export(inner_func):
# YOLOv8 export decorator, i..e @try_export
inner_args = get_default_args(inner_func)
def outer_func(*args, **kwargs):
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:
"""
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):
"""
Initializes the Exporter class.
Args:
cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_cfg(cfg, overrides)
self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
@smart_inference_mode()
def __call__(self, model=None):
self.run_callbacks("on_export_start")
t = time.time()
format = self.args.format.lower() # to lowercase
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]
assert sum(flags), f'ERROR: Invalid 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:
if self.device.type == 'cpu' and not coreml and not xml:
LOGGER.info('half=True only compatible with GPU or CoreML export, i.e. use device=0 or format=coreml')
self.args.half = False
assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
# Checks
# if self.args.batch == model.args['batch_size']: # user has not modified training batch_size
self.args.batch = 1
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'
# 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
y = None
for _ in range(2):
y = model(im) # dry runs
if self.args.half and not coreml and not xml:
im, model = im.half(), model.half() # to FP16
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
LOGGER.info(
f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
# 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 (x.shape for x in y)
self.metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
# 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
raise NotImplementedError('YOLOv8 TensorFlow export support is still under development. '
'Please consider contributing to the effort if you have TF expertise. Thank you!')
assert not isinstance(model, ClassificationModel), 'ClassificationModel TF exports not yet supported.'
nms = False
f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
agnostic_nms=self.args.agnostic_nms or tfjs)
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self._export_pb(s_model)
if tflite or edgetpu:
f[7], _ = self._export_tflite(s_model,
int8=self.args.int8 or edgetpu,
data=self.args.data,
nms=nms,
agnostic_nms=self.args.agnostic_nms)
if edgetpu:
f[8], _ = self._export_edgetpu()
self._add_tflite_metadata(f[8] or f[7], num_outputs=len(s_model.outputs))
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):
task = guess_model_task(model)
s = "-WARNING ⚠️ not yet supported for YOLOv8 exported models"
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nPredict: yolo task={task} mode=predict model={f[-1]} {s}"
f"\nValidate: yolo task={task} mode=val model={f[-1]} {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)
d = {"shape": self.im.shape, "stride": int(max(self.model.stride)), "names": self.model.names}
extra_files = {'config.txt': json.dumps(d)} # 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
check_requirements('onnx>=1.12.0')
import onnx # noqa
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__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=self.args.opset,
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
# Metadata
d = {'stride': int(max(self.model.stride)), 'names': self.model.names}
for k, v in d.items():
meta = model_onnx.metadata_props.add()
meta.key, meta.value = k, str(v)
onnx.save(model_onnx, f)
# Simplify
if self.args.simplify:
try:
check_requirements('onnxsim')
import onnxsim
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
subprocess.run(f'onnxsim {f} {f}', shell=True)
except Exception as e:
LOGGER.info(f'{prefix} simplifier failure: {e}')
return f, model_onnx
@try_export
def _export_openvino(self, prefix=colorstr('OpenVINO:')):
# YOLOv8 OpenVINO export
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie # noqa
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
f_onnx = self.file.with_suffix('.onnx')
cmd = f"mo --input_model {f_onnx} --output_dir {f} {'--compress_to_fp16' * self.args.half}"
subprocess.run(cmd.split(), check=True, env=os.environ) # export
yaml_save(Path(f) / self.file.with_suffix('.yaml').name, 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) / self.file.with_suffix('.yaml').name, 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
class iOSModel(torch.nn.Module):
# Wrap an Ultralytics YOLO model for iOS export
def __init__(self, model, im):
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):
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)
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])])
bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
if MACOS: # quantization only supported on macOS
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
else:
LOGGER.info(f'{prefix} quantization only supported on macOS, skipping...')
if self.args.nms:
ct_model = self._pipeline_coreml(ct_model)
ct_model.save(str(f))
return f, ct_model
@try_export
def _export_engine(self, workspace=4, verbose=False, 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 platform.system() == '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._export_onnx()
onnx = self.file.with_suffix('.onnx')
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
f = self.file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE
builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = 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(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {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)
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
t.write(engine.serialize())
return f, None
@try_export
def _export_saved_model(self,
nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv8 TensorFlow SavedModel export
try:
import tensorflow as tf # noqa
except ImportError:
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
import tensorflow as tf # noqa
check_requirements(("onnx", "onnx2tf", "sng4onnx", "onnxsim", "onnx_graphsurgeon"),
cmds="--extra-index-url https://pypi.ngc.nvidia.com ")
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = str(self.file).replace(self.file.suffix, '_saved_model')
# Export to ONNX
self._export_onnx()
onnx = self.file.with_suffix('.onnx')
# Export to TF SavedModel
subprocess.run(f'onnx2tf -i {onnx} --output_signaturedefs -o {f}', shell=True)
# Load saved_model
keras_model = tf.saved_model.load(f, tags=None, options=None)
return f, keras_model
@try_export
def _export_saved_model_OLD(self,
nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv8 TensorFlow SavedModel export
try:
import tensorflow as tf # noqa
except ImportError:
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
import tensorflow as tf # noqa
# from models.tf import TFModel
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 = str(self.file).replace(self.file.suffix, '_saved_model')
batch_size, ch, *imgsz = list(self.im.shape) # BCHW
tf_models = None # TODO: no TF modules available
tf_model = tf_models.TFModel(cfg=self.model.yaml, model=self.model.cpu(), nc=self.model.nc, imgsz=imgsz)
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
_ = tf_model.predict(im, nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if self.args.dynamic else batch_size)
outputs = tf_model.predict(inputs, nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
keras_model.trainable = False
keras_model.summary()
if self.args.keras:
keras_model.save(f, save_format='tf')
else:
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
m = tf.function(lambda x: keras_model(x)) # full model
m = m.get_concrete_function(spec)
frozen_func = convert_variables_to_constants_v2(m)
tfm = tf.Module()
tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if nms else frozen_func(x), [spec])
tfm.__call__(im)
tf.saved_model.save(tfm,
f,
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
if check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
return f, keras_model
@try_export
def _export_pb(self, keras_model, file, 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 = 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, int8, data, 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__}...')
batch_size, ch, *imgsz = list(self.im.shape) # BCHW
f = str(self.file).replace(self.file.suffix, '-fp16.tflite')
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
converter.target_spec.supported_types = [tf.float16]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
if int8:
def representative_dataset_gen(dataset, n_images=100):
# Dataset generator for use with converter.representative_dataset, returns a generator of np arrays
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
im = np.transpose(img, [1, 2, 0])
im = np.expand_dims(im, axis=0).astype(np.float32)
im /= 255
yield [im]
if n >= n_images:
break
dataset = LoadImages(check_det_dataset(check_yaml(data))['train'], imgsz=imgsz, auto=False)
converter.representative_dataset = lambda: representative_dataset_gen(dataset, n_images=100)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.target_spec.supported_types = []
converter.inference_input_type = tf.uint8 # or tf.int8
converter.inference_output_type = tf.uint8 # or tf.int8
converter.experimental_new_quantizer = True
f = str(self.file).replace(self.file.suffix, '-int8.tflite')
if nms or agnostic_nms:
converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
tflite_model = converter.convert()
open(f, "wb").write(tflite_model)
return f, None
@try_export
def _export_edgetpu(self, prefix=colorstr('Edge TPU:')):
# YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
cmd = 'edgetpu_compiler --version'
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
if subprocess.run(f'{cmd} >/dev/null', 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" | ' # no comma
'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(self.file).replace(self.file.suffix, '-int8_edgetpu.tflite') # Edge TPU model
f_tfl = str(self.file).replace(self.file.suffix, '-int8.tflite') # TFLite model
cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {self.file.parent} {f_tfl}"
subprocess.run(cmd.split(), check=True)
return f, None
@try_export
def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
# YOLOv8 TensorFlow.js export
check_requirements('tensorflowjs')
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
f_json = Path(f) / 'model.json' # *.json path
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
subprocess.run(cmd.split())
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)
return f, None
def _add_tflite_metadata(self, file, num_outputs):
# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
with contextlib.suppress(ImportError):
# check_requirements('tflite_support')
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
tmp_file = Path('/tmp/meta.txt')
with open(tmp_file, 'w') as meta_f:
meta_f.write(str(self.metadata))
model_meta = _metadata_fb.ModelMetadataT()
label_file = _metadata_fb.AssociatedFileT()
label_file.name = tmp_file.name
model_meta.associatedFiles = [label_file]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
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(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[1], self.output_shape[2] - 5 # (3780, 80)
out1_shape = self.output_shape[1], 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.versionString = f'Ultralytics YOLOv{ultralytics.__version__}'
pipeline.spec.description.metadata.shortDescription = f'Ultralytics {self.pretty_name} CoreML model'
pipeline.spec.description.metadata.author = 'Ultralytics (https://ultralytics.com)'
pipeline.spec.description.metadata.license = 'GPL-3.0 license (https://ultralytics.com/license)'
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 run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)
def export(cfg=DEFAULT_CFG):
cfg.model = cfg.model or "yolov8n.yaml"
cfg.format = cfg.format or "torchscript"
# exporter = Exporter(cfg)
#
# model = None
# if isinstance(cfg.model, (str, Path)):
# if Path(cfg.model).suffix == '.yaml':
# model = DetectionModel(cfg.model)
# elif Path(cfg.model).suffix == '.pt':
# model = attempt_load_weights(cfg.model, fuse=True)
# else:
# TypeError(f'Unsupported model type {cfg.model}')
# exporter(model=model)
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()