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# Ultralytics YOLO 🚀, GPL-3.0 license
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"""
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Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
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Format | `format=argument` | Model
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--- | --- | ---
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PyTorch | - | yolov8n.pt
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TorchScript | `torchscript` | yolov8n.torchscript
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ONNX | `onnx` | yolov8n.onnx
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OpenVINO | `openvino` | yolov8n_openvino_model/
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TensorRT | `engine` | yolov8n.engine
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CoreML | `coreml` | yolov8n.mlmodel
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TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
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TensorFlow GraphDef | `pb` | yolov8n.pb
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TensorFlow Lite | `tflite` | yolov8n.tflite
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TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
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TensorFlow.js | `tfjs` | yolov8n_web_model/
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PaddlePaddle | `paddle` | yolov8n_paddle_model/
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Requirements:
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$ pip install ultralytics[export]
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Python:
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from ultralytics import YOLO
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model = YOLO('yolov8n.pt')
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results = model.export(format='onnx')
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CLI:
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$ yolo mode=export model=yolov8n.pt format=onnx
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Inference:
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$ yolo predict model=yolov8n.pt # PyTorch
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yolov8n.torchscript # TorchScript
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yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov8n_openvino_model # OpenVINO
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yolov8n.engine # TensorRT
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yolov8n.mlmodel # CoreML (macOS-only)
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yolov8n_saved_model # TensorFlow SavedModel
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yolov8n.pb # TensorFlow GraphDef
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yolov8n.tflite # TensorFlow Lite
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yolov8n_edgetpu.tflite # TensorFlow Edge TPU
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yolov8n_paddle_model # PaddlePaddle
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TensorFlow.js:
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$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
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$ npm install
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$ ln -s ../../yolov5/yolov8n_web_model public/yolov8n_web_model
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$ npm start
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"""
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import json
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import os
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import platform
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import subprocess
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import time
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import warnings
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from collections import defaultdict
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from copy import deepcopy
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import torch
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from ultralytics.nn.autobackend import check_class_names
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from ultralytics.nn.modules import C2f, Detect, Segment
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from ultralytics.nn.tasks import DetectionModel, SegmentationModel
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
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from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD, check_det_dataset
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from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, __version__, callbacks, colorstr,
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get_default_args, yaml_save)
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from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
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from ultralytics.yolo.utils.files import file_size
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from ultralytics.yolo.utils.ops import Profile
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from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
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ARM64 = platform.machine() in ('arm64', 'aarch64')
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def export_formats():
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# YOLOv8 export formats
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x = [
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['PyTorch', '-', '.pt', True, True],
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['TorchScript', 'torchscript', '.torchscript', True, True],
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['ONNX', 'onnx', '.onnx', True, True],
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['OpenVINO', 'openvino', '_openvino_model', True, False],
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['TensorRT', 'engine', '.engine', False, True],
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['CoreML', 'coreml', '.mlmodel', True, False],
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['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
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['TensorFlow GraphDef', 'pb', '.pb', True, True],
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['TensorFlow Lite', 'tflite', '.tflite', True, False],
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['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', True, False],
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['TensorFlow.js', 'tfjs', '_web_model', True, False],
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['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
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return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
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EXPORT_FORMATS_LIST = list(export_formats()['Argument'][1:])
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EXPORT_FORMATS_TABLE = str(export_formats())
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def gd_outputs(gd):
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# TensorFlow GraphDef model output node names
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name_list, input_list = [], []
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for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
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name_list.append(node.name)
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input_list.extend(node.input)
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return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
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def try_export(inner_func):
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# YOLOv8 export decorator, i..e @try_export
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inner_args = get_default_args(inner_func)
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def outer_func(*args, **kwargs):
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prefix = inner_args['prefix']
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try:
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with Profile() as dt:
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f, model = inner_func(*args, **kwargs)
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LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
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return f, model
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except Exception as e:
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LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
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return None, None
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return outer_func
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class Exporter:
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"""
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Exporter
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A class for exporting a model.
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Attributes:
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args (SimpleNamespace): Configuration for the exporter.
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save_dir (Path): Directory to save results.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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"""
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Initializes the Exporter class.
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Args:
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cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_cfg(cfg, overrides)
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
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callbacks.add_integration_callbacks(self)
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@smart_inference_mode()
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def __call__(self, model=None):
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self.run_callbacks('on_export_start')
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t = time.time()
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format = self.args.format.lower() # to lowercase
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if format in {'tensorrt', 'trt'}: # engine aliases
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format = 'engine'
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fmts = tuple(export_formats()['Argument'][1:]) # available export formats
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flags = [x == format for x in fmts]
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if sum(flags) != 1:
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raise ValueError(f"Invalid export format='{format}'. Valid formats are {fmts}")
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jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
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# Load PyTorch model
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self.device = select_device('cpu' if self.args.device is None else self.args.device)
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if self.args.half and onnx and self.device.type == 'cpu':
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LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')
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self.args.half = False
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assert not self.args.dynamic, 'half=True not compatible with dynamic=True, i.e. use only one.'
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# Checks
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model.names = check_class_names(model.names)
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self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
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if self.args.optimize:
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assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
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if edgetpu and not LINUX:
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raise SystemError('Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler/')
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# Input
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im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
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file = Path(getattr(model, 'pt_path', None) or getattr(model, 'yaml_file', None) or model.yaml['yaml_file'])
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if file.suffix == '.yaml':
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file = Path(file.name)
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# Update model
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model = deepcopy(model).to(self.device)
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for p in model.parameters():
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p.requires_grad = False
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model.eval()
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model.float()
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model = model.fuse()
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for k, m in model.named_modules():
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if isinstance(m, (Detect, Segment)):
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m.dynamic = self.args.dynamic
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m.export = True
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m.format = self.args.format
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elif isinstance(m, C2f) and not edgetpu:
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# EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
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m.forward = m.forward_split
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y = None
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for _ in range(2):
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y = model(im) # dry runs
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if self.args.half and (engine or onnx) and self.device.type != 'cpu':
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im, model = im.half(), model.half() # to FP16
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# Warnings
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warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
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warnings.filterwarnings('ignore', category=UserWarning) # suppress shape prim::Constant missing ONNX warning
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warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress CoreML np.bool deprecation warning
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# Assign
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self.im = im
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self.model = model
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self.file = file
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self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
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self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
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description = f'Ultralytics {self.pretty_name} model ' + f'trained on {Path(self.args.data).name}' \
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if self.args.data else '(untrained)'
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self.metadata = {
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'description': description,
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'author': 'Ultralytics',
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'license': 'GPL-3.0 https://ultralytics.com/license',
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'version': __version__,
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'stride': int(max(model.stride)),
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'task': model.task,
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'batch': self.args.batch,
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'imgsz': self.imgsz,
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'names': model.names} # model metadata
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} BCHW and "
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f'output shape(s) {self.output_shape} ({file_size(file):.1f} MB)')
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# Exports
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f = [''] * len(fmts) # exported filenames
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if jit: # TorchScript
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f[0], _ = self._export_torchscript()
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if engine: # TensorRT required before ONNX
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f[1], _ = self._export_engine()
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if onnx or xml: # OpenVINO requires ONNX
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f[2], _ = self._export_onnx()
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if xml: # OpenVINO
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f[3], _ = self._export_openvino()
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if coreml: # CoreML
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f[4], _ = self._export_coreml()
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if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
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self.args.int8 |= edgetpu
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f[5], s_model = self._export_saved_model()
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if pb or tfjs: # pb prerequisite to tfjs
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f[6], _ = self._export_pb(s_model)
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if tflite:
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f[7], _ = self._export_tflite(s_model, nms=False, agnostic_nms=self.args.agnostic_nms)
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if edgetpu:
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f[8], _ = self._export_edgetpu(tflite_model=str(
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Path(f[5]) / (self.file.stem + '_full_integer_quant.tflite'))) # int8 in/out
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if tfjs:
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f[9], _ = self._export_tfjs()
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if paddle: # PaddlePaddle
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f[10], _ = self._export_paddle()
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# Finish
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f = [str(x) for x in f if x] # filter out '' and None
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if any(f):
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f = str(Path(f[-1]))
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square = self.imgsz[0] == self.imgsz[1]
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s = '' if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " \
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f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
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imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(' ', '')
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data = f'data={self.args.data}' if model.task == 'segment' and format == 'pb' else ''
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LOGGER.info(
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f'\nExport complete ({time.time() - t:.1f}s)'
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {data}'
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f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={self.args.data} {s}'
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f'\nVisualize: https://netron.app')
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self.run_callbacks('on_export_end')
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return f # return list of exported files/dirs
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@try_export
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def _export_torchscript(self, prefix=colorstr('TorchScript:')):
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# YOLOv8 TorchScript model export
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = self.file.with_suffix('.torchscript')
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ts = torch.jit.trace(self.model, self.im, strict=False)
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extra_files = {'config.txt': json.dumps(self.metadata)} # torch._C.ExtraFilesMap()
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if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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LOGGER.info(f'{prefix} optimizing for mobile...')
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from torch.utils.mobile_optimizer import optimize_for_mobile
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optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
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else:
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ts.save(str(f), _extra_files=extra_files)
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return f, None
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@try_export
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def _export_onnx(self, prefix=colorstr('ONNX:')):
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# YOLOv8 ONNX export
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requirements = ['onnx>=1.12.0']
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if self.args.simplify:
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requirements += ['onnxsim>=0.4.17', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
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check_requirements(requirements)
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import onnx # noqa
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LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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f = str(self.file.with_suffix('.onnx'))
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output_names = ['output0', 'output1'] if isinstance(self.model, SegmentationModel) else ['output0']
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dynamic = self.args.dynamic
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if dynamic:
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dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
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if isinstance(self.model, SegmentationModel):
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dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
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elif isinstance(self.model, DetectionModel):
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dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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torch.onnx.export(
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self.model.cpu() if dynamic else self.model, # --dynamic only compatible with cpu
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self.im.cpu() if dynamic else self.im,
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f,
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verbose=False,
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opset_version=self.args.opset or get_latest_opset(),
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do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
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input_names=['images'],
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output_names=output_names,
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dynamic_axes=dynamic or None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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# onnx.checker.check_model(model_onnx) # check onnx model
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# Simplify
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if self.args.simplify:
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try:
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import onnxsim
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LOGGER.info(f'{prefix} simplifying with onnxsim {onnxsim.__version__}...')
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# subprocess.run(f'onnxsim {f} {f}', shell=True)
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model_onnx, check = onnxsim.simplify(model_onnx)
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assert check, 'Simplified ONNX model could not be validated'
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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# Metadata
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for k, v in self.metadata.items():
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meta = model_onnx.metadata_props.add()
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meta.key, meta.value = k, str(v)
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onnx.save(model_onnx, f)
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return f, model_onnx
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@try_export
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def _export_openvino(self, prefix=colorstr('OpenVINO:')):
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# YOLOv8 OpenVINO export
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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
|
|
|
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
|
|
|
|
|
|
class iOSDetectModel(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')
|
|
|
|
|
|
bias = [0.0, 0.0, 0.0]
|
|
|
scale = 1 / 255
|
|
|
classifier_config = None
|
|
|
if self.model.task == 'classify':
|
|
|
bias = [-x for x in IMAGENET_MEAN]
|
|
|
scale = 1 / 255 / (sum(IMAGENET_STD) / 3)
|
|
|
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
|
|
|
elif self.model.task == 'segment':
|
|
|
# TODO CoreML Segmentation 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, 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 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 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(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}')
|
|
|
subprocess.run(cmd, shell=True)
|
|
|
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml
|
|
|
|
|
|
# Add TFLite metadata
|
|
|
for file in f.rglob('*.tflite'):
|
|
|
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 / (self.file.stem + 'yolov8n_integer_quant.tflite') # fp32 in/out
|
|
|
elif self.args.half:
|
|
|
f = saved_model / (self.file.stem + '_float16.tflite')
|
|
|
else:
|
|
|
f = saved_model / (self.file.stem + '_float32.tflite')
|
|
|
return str(f), None # noqa
|
|
|
|
|
|
# OLD VERSION BELOW ---------------------------------------------------------------
|
|
|
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 self.args.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(self.args.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, 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}'
|
|
|
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()
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|
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 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()
|
