|
|
# YOLOv5 🚀 by Ultralytics, 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.new('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 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, attempt_load_weights |
|
|
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, colorstr, get_default_args |
|
|
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml |
|
|
from ultralytics.yolo.utils.files import file_size, increment_path, yaml_save |
|
|
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: |
|
|
cfg (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) |
|
|
project = self.args.project or f"runs/{self.args.task}" |
|
|
name = self.args.name or "exp" # hardcode mode as export doesn't require it |
|
|
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok) |
|
|
self.save_dir.mkdir(parents=True, exist_ok=True) |
|
|
|
|
|
@smart_inference_mode() |
|
|
def __call__(self, model=None): |
|
|
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(self.args.device) |
|
|
if self.args.half: |
|
|
if self.device.type == 'cpu' or 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 |
|
|
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 |
|
|
self.args.batch_size = 1 # TODO: resolve this issue, default 16 not fit for export |
|
|
im = torch.zeros(self.args.batch_size, 3, *self.imgsz).to(self.device) |
|
|
file = Path(getattr(model, 'yaml_file', None) or Path(model.yaml['yaml_file']).name) |
|
|
|
|
|
# Update model |
|
|
model = deepcopy(model) |
|
|
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) |
|
|
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 |
|
|
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") |
|
|
return f # return list of exported files/dirs |
|
|
|
|
|
@try_export |
|
|
def _export_torchscript(self, prefix=colorstr('TorchScript:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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: |
|
|
cuda = torch.cuda.is_available() |
|
|
check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1')) |
|
|
import onnxsim # noqa |
|
|
|
|
|
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') |
|
|
model_onnx, check = onnxsim.simplify(model_onnx) |
|
|
assert check, 'assert check failed' |
|
|
onnx.save(model_onnx, f) |
|
|
except Exception as e: |
|
|
LOGGER.info(f'{prefix} simplifier failure: {e}') |
|
|
return f, model_onnx |
|
|
|
|
|
@try_export |
|
|
def _export_openvino(self, prefix=colorstr('OpenVINO:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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) 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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:')): |
|
|
# YOLOv5 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 |
|
|
|
|
|
|
|
|
@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) |
|
|
else: |
|
|
TypeError(f'Unsupported model type {cfg.model}') |
|
|
exporter(model=model) |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
""" |
|
|
CLI: |
|
|
yolo mode=export model=yolov8n.yaml format=onnx |
|
|
""" |
|
|
export()
|
|
|
|
