# Ultralytics YOLO 🚀, GPL-3.0 license """ Export a YOLOv5 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 hydra 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 from ultralytics.yolo.configs import get_config from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages from ultralytics.yolo.data.utils import check_dataset from ultralytics.yolo.utils import DEFAULT_CONFIG, 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 guess_task_from_head, select_device, smart_inference_mode MACOS = platform.system() == 'Darwin' # macOS environment def export_formats(): # YOLOv5 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): # YOLOv5 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 (OmegaConf): Configuration for the exporter. save_dir (Path): Directory to save results. """ def __init__(self, config=DEFAULT_CONFIG, overrides=None): """ Initializes the Exporter class. Args: config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG. overrides (dict, optional): Configuration overrides. Defaults to None. """ if overrides is None: overrides = {} self.args = get_config(config, 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: 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 = 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: 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_task_from_head(model.yaml["head"][-1][-2]) 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} --data_type {'FP16' if self.args.half else 'FP32'}" 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. `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_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) @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) def export(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(**cfg) if __name__ == "__main__": """ CLI: yolo mode=export model=yolov8n.yaml format=onnx """ export()