# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
Format | `export.py --include` | Model
--- | --- | ---
PyTorch | - | yolov5s.pt
TorchScript | `torchscript` | yolov5s.torchscript
ONNX | `onnx` | yolov5s.onnx
OpenVINO | `openvino` | yolov5s_openvino_model/
TensorRT | `engine` | yolov5s.engine
CoreML | `coreml` | yolov5s.mlmodel
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
TensorFlow GraphDef | `pb` | yolov5s.pb
TensorFlow Lite | `tflite` | yolov5s.tflite
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov5s_web_model/
PaddlePaddle | `paddle` | yolov5s_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
Usage:
$ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
Inference:
$ python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_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/yolov5s_web_model public/yolov5s_web_model
$ npm start
"""
import argparse
import contextlib
import json
import os
import platform
import re
import subprocess
import sys
import time
import warnings
from pathlib import Path
import pandas as pd
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
if platform.system() != "Windows":
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.experimental import attempt_load
from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
from utils.dataloaders import LoadImages
from utils.general import (
LOGGER,
Profile,
check_dataset,
check_img_size,
check_requirements,
check_version,
check_yaml,
colorstr,
file_size,
get_default_args,
print_args,
url2file,
yaml_save,
)
from utils.torch_utils import select_device, smart_inference_mode
MACOS = platform.system() == "Darwin" # macOS environment
class iOSModel(torch.nn.Module):
"""An iOS-compatible wrapper for YOLOv5 models that normalizes input images based on their dimensions."""
def __init__(self, model, im):
"""
Initializes an iOS compatible model with normalization based on image dimensions.
Args:
model (torch.nn.Module): The PyTorch model to be adapted for iOS compatibility.
im (torch.Tensor): An input tensor representing a batch of images with shape (B, C, H, W).
Returns:
None: This method does not return any value.
Notes:
This initializer configures normalization based on the input image dimensions, which is critical for
ensuring the model's compatibility and proper functionality on iOS devices. The normalization step
involves dividing by the image width if the image is square; otherwise, additional conditions might apply.
"""
super().__init__()
b, c, h, w = im.shape # batch, channel, height, width
self.model = model
self.nc = model.nc # number of classes
if w == h:
self.normalize = 1.0 / w
else:
self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller)
# np = model(im)[0].shape[1] # number of points
# self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger)
def forward(self, x):
"""
Run a forward pass on the input tensor, returning class confidences and normalized coordinates.
Args:
x (torch.Tensor): Input tensor containing the image data with shape (batch, channels, height, width).
Returns:
torch.Tensor: Concatenated tensor with normalized coordinates (xywh), confidence scores (conf),
and class probabilities (cls), having shape (N, 4 + 1 + C), where N is the number of predictions,
and C is the number of classes.
Examples:
```python
model = iOSModel(pretrained_model, input_image)
output = model.forward(torch_input_tensor)
```
"""
xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)
return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)
def export_formats():
r"""
Returns a DataFrame of supported YOLOv5 model export formats and their properties.
Returns:
pandas.DataFrame: A DataFrame containing supported export formats and their properties. The DataFrame
includes columns for format name, CLI argument suffix, file extension or directory name, and boolean flags
indicating if the export format supports training and detection.
Examples:
```python
formats = export_formats()
print(f"Supported export formats:\n{formats}")
```
Notes:
The DataFrame contains the following columns:
- Format: The name of the model format (e.g., PyTorch, TorchScript, ONNX, etc.).
- Include Argument: The argument to use with the export script to include this format.
- File Suffix: File extension or directory name associated with the format.
- Supports Training: Whether the format supports training.
- Supports Detection: Whether the format supports detection.
"""
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", ".mlpackage", 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):
"""
Log success or failure, execution time, and file size for YOLOv5 model export functions wrapped with @try_export.
Args:
inner_func (Callable): The model export function to be wrapped by the decorator.
Returns:
Callable: The wrapped function that logs execution details. When executed, this wrapper function returns either:
- Tuple (str | torch.nn.Module): On success — the file path of the exported model and the model instance.
- Tuple (None, None): On failure — None values indicating export failure.
Examples:
```python
@try_export
def export_onnx(model, filepath):
# implementation here
pass
exported_file, exported_model = export_onnx(yolo_model, 'path/to/save/model.onnx')
```
Notes:
For additional requirements and model export formats, refer to the
[Ultralytics YOLOv5 GitHub repository](https://github.com/ultralytics/ultralytics).
"""
inner_args = get_default_args(inner_func)
def outer_func(*args, **kwargs):
"""Logs success/failure and execution details of model export functions wrapped with @try_export decorator."""
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
@try_export
def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")):
"""
Export a YOLOv5 model to the TorchScript format.
Args:
model (torch.nn.Module): The YOLOv5 model to be exported.
im (torch.Tensor): Example input tensor to be used for tracing the TorchScript model.
file (Path): File path where the exported TorchScript model will be saved.
optimize (bool): If True, applies optimizations for mobile deployment.
prefix (str): Optional prefix for log messages. Default is 'TorchScript:'.
Returns:
(str | None, torch.jit.ScriptModule | None): A tuple containing the file path of the exported model
(as a string) and the TorchScript model (as a torch.jit.ScriptModule). If the export fails, both elements
of the tuple will be None.
Notes:
- This function uses tracing to create the TorchScript model.
- Metadata, including the input image shape, model stride, and class names, is saved in an extra file (`config.txt`)
within the TorchScript model package.
- For mobile optimization, refer to the PyTorch tutorial: https://pytorch.org/tutorials/recipes/mobile_interpreter.html
Example:
```python
from pathlib import Path
import torch
from models.experimental import attempt_load
from utils.torch_utils import select_device
# Load model
weights = 'yolov5s.pt'
device = select_device('')
model = attempt_load(weights, device=device)
# Example input tensor
im = torch.zeros(1, 3, 640, 640).to(device)
# Export model
file = Path('yolov5s.torchscript')
export_torchscript(model, im, file, optimize=False)
```
"""
LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...")
f = file.with_suffix(".torchscript")
ts = torch.jit.trace(model, im, strict=False)
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap()
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")):
"""
Export a YOLOv5 model to ONNX format with dynamic axes support and optional model simplification.
Args:
model (torch.nn.Module): The YOLOv5 model to be exported.
im (torch.Tensor): A sample input tensor for model tracing, usually the shape is (1, 3, height, width).
file (pathlib.Path | str): The output file path where the ONNX model will be saved.
opset (int): The ONNX opset version to use for export.
dynamic (bool): If True, enables dynamic axes for batch, height, and width dimensions.
simplify (bool): If True, applies ONNX model simplification for optimization.
prefix (str): A prefix string for logging messages, defaults to 'ONNX:'.
Returns:
tuple[pathlib.Path | str, None]: The path to the saved ONNX model file and None (consistent with decorator).
Raises:
ImportError: If required libraries for export (e.g., 'onnx', 'onnx-simplifier') are not installed.
AssertionError: If the simplification check fails.
Notes:
The required packages for this function can be installed via:
```
pip install onnx onnx-simplifier onnxruntime onnxruntime-gpu
```
Example:
```python
from pathlib import Path
import torch
from models.experimental import attempt_load
from utils.torch_utils import select_device
# Load model
weights = 'yolov5s.pt'
device = select_device('')
model = attempt_load(weights, map_location=device)
# Example input tensor
im = torch.zeros(1, 3, 640, 640).to(device)
# Export model
file_path = Path('yolov5s.onnx')
export_onnx(model, im, file_path, opset=12, dynamic=True, simplify=True)
```
"""
check_requirements("onnx>=1.12.0")
import onnx
LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...")
f = str(file.with_suffix(".onnx"))
output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"]
if dynamic:
dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640)
if isinstance(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(model, DetectionModel):
dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85)
torch.onnx.export(
model.cpu() if dynamic else model, # --dynamic only compatible with cpu
im.cpu() if dynamic else im,
f,
verbose=False,
opset_version=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(model.stride)), "names": 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 simplify:
try:
cuda = torch.cuda.is_available()
check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnxslim"))
import onnxslim
LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...")
model_onnx = onnxslim.slim(model_onnx)
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(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")):
"""
Export a YOLOv5 model to OpenVINO format with optional FP16 and INT8 quantization.
Args:
file (Path): Path to the output file where the OpenVINO model will be saved.
metadata (dict): Dictionary including model metadata such as names and strides.
half (bool): If True, export the model with FP16 precision.
int8 (bool): If True, export the model with INT8 quantization.
data (str): Path to the dataset YAML file required for INT8 quantization.
prefix (str): Prefix string for logging purposes (default is "OpenVINO:").
Returns:
(str, openvino.runtime.Model | None): The OpenVINO model file path and openvino.runtime.Model object if export is
successful; otherwise, None.
Notes:
- Requires `openvino-dev` package version 2023.0 or higher. Install with:
`$ pip install openvino-dev>=2023.0`
- For INT8 quantization, also requires `nncf` library version 2.5.0 or higher. Install with:
`$ pip install nncf>=2.5.0`
Examples:
```python
from pathlib import Path
from ultralytics import YOLOv5
model = YOLOv5('yolov5s.pt')
export_openvino(Path('yolov5s.onnx'), metadata={'names': model.names, 'stride': model.stride}, half=True,
int8=False, data='data.yaml')
```
This will export the YOLOv5 model to OpenVINO with FP16 precision but without INT8 quantization, saving it to
the specified file path.
"""
check_requirements("openvino-dev>=2023.0") # 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(file).replace(file.suffix, f"_{'int8_' if int8 else ''}openvino_model{os.sep}")
f_onnx = file.with_suffix(".onnx")
f_ov = str(Path(f) / file.with_suffix(".xml").name)
ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half) # export
if int8:
check_requirements("nncf>=2.5.0") # requires at least version 2.5.0 to use the post-training quantization
import nncf
import numpy as np
from utils.dataloaders import create_dataloader
def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4):
"""Generates a DataLoader for model training or validation based on the given YAML dataset configuration."""
data_yaml = check_yaml(yaml_path)
data = check_dataset(data_yaml)
dataloader = create_dataloader(
data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers
)[0]
return dataloader
# noqa: F811
def transform_fn(data_item):
"""
Quantization transform function.
Extracts and preprocess input data from dataloader item for quantization.
Args:
data_item: Tuple with data item produced by DataLoader during iteration
Returns:
input_tensor: Input data for quantization
"""
assert data_item[0].dtype == torch.uint8, "input image must be uint8 for the quantization preprocessing"
img = data_item[0].numpy().astype(np.float32) # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
return np.expand_dims(img, 0) if img.ndim == 3 else img
ds = gen_dataloader(data)
quantization_dataset = nncf.Dataset(ds, transform_fn)
ov_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
ov.serialize(ov_model, f_ov) # save
yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml
return f, None
@try_export
def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")):
"""
Export a YOLOv5 PyTorch model to PaddlePaddle format using X2Paddle, saving the converted model and metadata.
Args:
model (torch.nn.Module): The YOLOv5 model to be exported.
im (torch.Tensor): Input tensor used for model tracing during export.
file (pathlib.Path): Path to the source file to be converted.
metadata (dict): Additional metadata to be saved alongside the model.
prefix (str): Prefix for logging information.
Returns:
tuple (str, None): A tuple where the first element is the path to the saved PaddlePaddle model, and the
second element is None.
Examples:
```python
from pathlib import Path
import torch
# Assume 'model' is a pre-trained YOLOv5 model and 'im' is an example input tensor
model = ... # Load your model here
im = torch.randn((1, 3, 640, 640)) # Dummy input tensor for tracing
file = Path("yolov5s.pt")
metadata = {"stride": 32, "names": ["person", "bicycle", "car", "motorbike"]}
export_paddle(model=model, im=im, file=file, metadata=metadata)
```
Notes:
Ensure that `paddlepaddle` and `x2paddle` are installed, as these are required for the export function. You can
install them via pip:
```
$ pip install paddlepaddle x2paddle
```
"""
check_requirements(("paddlepaddle>=3.0.0", "x2paddle"))
import x2paddle
from x2paddle.convert import pytorch2paddle
LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...")
f = str(file).replace(".pt", f"_paddle_model{os.sep}")
pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im]) # export
yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml
return f, None
@try_export
def export_coreml(model, im, file, int8, half, nms, mlmodel, prefix=colorstr("CoreML:")):
"""
Export a YOLOv5 model to CoreML format with optional NMS, INT8, and FP16 support.
Args:
model (torch.nn.Module): The YOLOv5 model to be exported.
im (torch.Tensor): Example input tensor to trace the model.
file (pathlib.Path): Path object where the CoreML model will be saved.
int8 (bool): Flag indicating whether to use INT8 quantization (default is False).
half (bool): Flag indicating whether to use FP16 quantization (default is False).
nms (bool): Flag indicating whether to include Non-Maximum Suppression (default is False).
mlmodel (bool): Flag indicating whether to export as older *.mlmodel format (default is False).
prefix (str): Prefix string for logging purposes (default is 'CoreML:').
Returns:
tuple[pathlib.Path | None, None]: The path to the saved CoreML model file, or (None, None) if there is an error.
Notes:
The exported CoreML model will be saved with a .mlmodel extension.
Quantization is supported only on macOS.
Example:
```python
from pathlib import Path
import torch
from models.yolo import Model
model = Model(cfg, ch=3, nc=80)
im = torch.randn(1, 3, 640, 640)
file = Path("yolov5s_coreml")
export_coreml(model, im, file, int8=False, half=False, nms=True, mlmodel=False)
```
"""
check_requirements("coremltools")
import coremltools as ct
LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...")
if mlmodel:
f = file.with_suffix(".mlmodel")
convert_to = "neuralnetwork"
precision = None
else:
f = file.with_suffix(".mlpackage")
convert_to = "mlprogram"
precision = ct.precision.FLOAT16 if half else ct.precision.FLOAT32
if nms:
model = iOSModel(model, im)
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
ct_model = ct.convert(
ts,
inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])],
convert_to=convert_to,
compute_precision=precision,
)
bits, mode = (8, "kmeans") if int8 else (16, "linear") if half else (32, None)
if bits < 32:
if mlmodel:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", category=DeprecationWarning
) # suppress numpy==1.20 float warning, fixed in coremltools==7.0
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
elif bits == 8:
op_config = ct.optimize.coreml.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512)
config = ct.optimize.coreml.OptimizationConfig(global_config=op_config)
ct_model = ct.optimize.coreml.palettize_weights(ct_model, config)
ct_model.save(f)
return f, ct_model
@try_export
def export_engine(
model, im, file, half, dynamic, simplify, workspace=4, verbose=False, cache="", prefix=colorstr("TensorRT:")
):
"""
Export a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0.
Args:
model (torch.nn.Module): YOLOv5 model to be exported.
im (torch.Tensor): Input tensor of shape (B, C, H, W).
file (pathlib.Path): Path to save the exported model.
half (bool): Set to True to export with FP16 precision.
dynamic (bool): Set to True to enable dynamic input shapes.
simplify (bool): Set to True to simplify the model during export.
workspace (int): Workspace size in GB (default is 4).
verbose (bool): Set to True for verbose logging output.
cache (str): Path to save the TensorRT timing cache.
prefix (str): Log message prefix.
Returns:
(pathlib.Path, None): Tuple containing the path to the exported model and None.
Raises:
AssertionError: If executed on CPU instead of GPU.
RuntimeError: If there is a failure in parsing the ONNX file.
Example:
```python
from ultralytics import YOLOv5
import torch
from pathlib import Path
model = YOLOv5('yolov5s.pt') # Load a pre-trained YOLOv5 model
input_tensor = torch.randn(1, 3, 640, 640).cuda() # example input tensor on GPU
export_path = Path('yolov5s.engine') # export destination
export_engine(model.model, input_tensor, export_path, half=True, dynamic=True, simplify=True, workspace=8, verbose=True)
```
"""
assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`"
try:
import tensorrt as trt
except Exception:
if platform.system() == "Linux":
check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com")
import tensorrt as trt
if trt.__version__[0] == "7": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
grid = model.model[-1].anchor_grid
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
model.model[-1].anchor_grid = grid
else: # TensorRT >= 8
check_version(trt.__version__, "8.0.0", hard=True) # require tensorrt>=8.0.0
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
onnx = file.with_suffix(".onnx")
LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
is_trt10 = int(trt.__version__.split(".")[0]) >= 10 # is TensorRT >= 10
assert onnx.exists(), f"failed to export ONNX file: {onnx}"
f = 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()
if is_trt10:
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)
else: # TensorRT versions 7, 8
config.max_workspace_size = workspace * 1 << 30
if cache: # enable timing cache
Path(cache).parent.mkdir(parents=True, exist_ok=True)
buf = Path(cache).read_bytes() if Path(cache).exists() else b""
timing_cache = config.create_timing_cache(buf)
config.set_timing_cache(timing_cache, ignore_mismatch=True)
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 dynamic:
if im.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, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
config.add_optimization_profile(profile)
LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}")
if builder.platform_has_fast_fp16 and half:
config.set_flag(trt.BuilderFlag.FP16)
build = builder.build_serialized_network if is_trt10 else builder.build_engine
with build(network, config) as engine, open(f, "wb") as t:
t.write(engine if is_trt10 else engine.serialize())
if cache: # save timing cache
with open(cache, "wb") as c:
c.write(config.get_timing_cache().serialize())
return f, None
@try_export
def export_saved_model(
model,
im,
file,
dynamic,
tf_nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
keras=False,
prefix=colorstr("TensorFlow SavedModel:"),
):
"""
Export a YOLOv5 model to the TensorFlow SavedModel format, supporting dynamic axes and non-maximum suppression
(NMS).
Args:
model (torch.nn.Module): The PyTorch model to convert.
im (torch.Tensor): Sample input tensor with shape (B, C, H, W) for tracing.
file (pathlib.Path): File path to save the exported model.
dynamic (bool): Flag to indicate whether dynamic axes should be used.
tf_nms (bool, optional): Enable TensorFlow non-maximum suppression (NMS). Default is False.
agnostic_nms (bool, optional): Enable class-agnostic NMS. Default is False.
topk_per_class (int, optional): Top K detections per class to keep before applying NMS. Default is 100.
topk_all (int, optional): Top K detections across all classes to keep before applying NMS. Default is 100.
iou_thres (float, optional): IoU threshold for NMS. Default is 0.45.
conf_thres (float, optional): Confidence threshold for detections. Default is 0.25.
keras (bool, optional): Save the model in Keras format if True. Default is False.
prefix (str, optional): Prefix for logging messages. Default is "TensorFlow SavedModel:".
Returns:
tuple[str, tf.keras.Model | None]: A tuple containing the path to the saved model folder and the Keras model instance,
or None if TensorFlow export fails.
Notes:
- The method supports TensorFlow versions up to 2.15.1.
- TensorFlow NMS may not be supported in older TensorFlow versions.
- If the TensorFlow version exceeds 2.13.1, it might cause issues when exporting to TFLite.
Refer to: https://github.com/ultralytics/yolov5/issues/12489
Example:
```python
model, im = ... # Initialize your PyTorch model and input tensor
export_saved_model(model, im, Path("yolov5_saved_model"), dynamic=True)
```
"""
# YOLOv5 TensorFlow SavedModel export
try:
import tensorflow as tf
except Exception:
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}<=2.15.1")
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
from models.tf import TFModel
LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
if tf.__version__ > "2.13.1":
helper_url = "https://github.com/ultralytics/yolov5/issues/12489"
LOGGER.info(
f"WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}"
) # handling issue https://github.com/ultralytics/yolov5/issues/12489
f = str(file).replace(".pt", "_saved_model")
batch_size, ch, *imgsz = list(im.shape) # BCHW
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
outputs = tf_model.predict(inputs, tf_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 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 tf_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(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")):
"""
Export YOLOv5 model to TensorFlow GraphDef (*.pb) format.
Args:
keras_model (tf.keras.Model): The Keras model to be converted.
file (Path): The output file path where the GraphDef will be saved.
prefix (str): Optional prefix string; defaults to a colored string indicating TensorFlow GraphDef export status.
Returns:
Tuple[Path, None]: The file path where the GraphDef model was saved and a None placeholder.
Notes:
For more details, refer to the guide on frozen graphs: https://github.com/leimao/Frozen_Graph_TensorFlow
Example:
```python
from pathlib import Path
keras_model = ... # assume an existing Keras model
file = Path("model.pb")
export_pb(keras_model, file)
```
"""
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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(
keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:")
):
# YOLOv5 TensorFlow Lite export
"""
Export a YOLOv5 model to TensorFlow Lite format with optional INT8 quantization and NMS support.
Args:
keras_model (tf.keras.Model): The Keras model to be exported.
im (torch.Tensor): An input image tensor for normalization and model tracing.
file (Path): The file path to save the TensorFlow Lite model.
int8 (bool): Enables INT8 quantization if True.
per_tensor (bool): If True, disables per-channel quantization.
data (str): Path to the dataset for representative dataset generation in INT8 quantization.
nms (bool): Enables Non-Maximum Suppression (NMS) if True.
agnostic_nms (bool): Enables class-agnostic NMS if True.
prefix (str): Prefix for log messages.
Returns:
(str | None, tflite.Model | None): The file path of the exported TFLite model and the TFLite model instance, or None
if the export failed.
Example:
```python
from pathlib import Path
import torch
import tensorflow as tf
# Load a Keras model wrapping a YOLOv5 model
keras_model = tf.keras.models.load_model('path/to/keras_model.h5')
# Example input tensor
im = torch.zeros(1, 3, 640, 640)
# Export the model
export_tflite(keras_model, im, Path('model.tflite'), int8=True, per_tensor=False, data='data/coco.yaml',
nms=True, agnostic_nms=False)
```
Notes:
- Ensure TensorFlow and TensorFlow Lite dependencies are installed.
- INT8 quantization requires a representative dataset to achieve optimal accuracy.
- TensorFlow Lite models are suitable for efficient inference on mobile and edge devices.
"""
import tensorflow as tf
LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
batch_size, ch, *imgsz = list(im.shape) # BCHW
f = str(file).replace(".pt", "-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:
from models.tf import representative_dataset_gen
dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False)
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=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
if per_tensor:
converter._experimental_disable_per_channel = True
f = str(file).replace(".pt", "-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(file, prefix=colorstr("Edge TPU:")):
"""
Exports a YOLOv5 model to Edge TPU compatible TFLite format; requires Linux and Edge TPU compiler.
Args:
file (Path): Path to the YOLOv5 model file to be exported (.pt format).
prefix (str, optional): Prefix for logging messages. Defaults to colorstr("Edge TPU:").
Returns:
tuple[Path, None]: Path to the exported Edge TPU compatible TFLite model, None.
Raises:
AssertionError: If the system is not Linux.
subprocess.CalledProcessError: If any subprocess call to install or run the Edge TPU compiler fails.
Notes:
To use this function, ensure you have the Edge TPU compiler installed on your Linux system. You can find
installation instructions here: https://coral.ai/docs/edgetpu/compiler/.
Example:
```python
from pathlib import Path
file = Path('yolov5s.pt')
export_edgetpu(file)
```
"""
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 2>&1", 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(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model
f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model
subprocess.run(
[
"edgetpu_compiler",
"-s",
"-d",
"-k",
"10",
"--out_dir",
str(file.parent),
f_tfl,
],
check=True,
)
return f, None
@try_export
def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")):
"""
Convert a YOLOv5 model to TensorFlow.js format with optional uint8 quantization.
Args:
file (Path): Path to the YOLOv5 model file to be converted, typically having a ".pt" or ".onnx" extension.
int8 (bool): If True, applies uint8 quantization during the conversion process.
prefix (str): Optional prefix for logging messages, default is 'TensorFlow.js:' with color formatting.
Returns:
(str, None): Tuple containing the output directory path as a string and None.
Notes:
- This function requires the `tensorflowjs` package. Install it using:
```shell
pip install tensorflowjs
```
- The converted TensorFlow.js model will be saved in a directory with the "_web_model" suffix appended to the original file name.
- The conversion involves running shell commands that invoke the TensorFlow.js converter tool.
Example:
```python
from pathlib import Path
file = Path('yolov5.onnx')
export_tfjs(file, int8=False)
```
"""
check_requirements("tensorflowjs")
import tensorflowjs as tfjs
LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...")
f = str(file).replace(".pt", "_web_model") # js dir
f_pb = file.with_suffix(".pb") # *.pb path
f_json = f"{f}/model.json" # *.json path
args = [
"tensorflowjs_converter",
"--input_format=tf_frozen_model",
"--quantize_uint8" if int8 else "",
"--output_node_names=Identity,Identity_1,Identity_2,Identity_3",
str(f_pb),
f,
]
subprocess.run([arg for arg in args if arg], check=True)
json = Path(f_json).read_text()
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"}}}',
json,
)
j.write(subst)
return f, None
def add_tflite_metadata(file, metadata, num_outputs):
"""
Adds metadata to a TensorFlow Lite (TFLite) model file, supporting multiple outputs according to TensorFlow
guidelines.
Args:
file (str): Path to the TFLite model file to which metadata will be added.
metadata (dict): Metadata information to be added to the model, structured as required by the TFLite metadata schema.
Common keys include "name", "description", "version", "author", and "license".
num_outputs (int): Number of output tensors the model has, used to configure the metadata properly.
Returns:
None
Example:
```python
metadata = {
"name": "yolov5",
"description": "YOLOv5 object detection model",
"version": "1.0",
"author": "Ultralytics",
"license": "Apache License 2.0"
}
add_tflite_metadata("model.tflite", metadata, num_outputs=4)
```
Note:
TFLite metadata can include information such as model name, version, author, and other relevant details.
For more details on the structure of the metadata, refer to TensorFlow Lite
[metadata guidelines](https://ai.google.dev/edge/litert/models/metadata).
"""
with contextlib.suppress(ImportError):
# check_requirements('tflite_support')
from tflite_support import flatbuffers
from tflite_support import metadata as _metadata
from tflite_support import metadata_schema_py_generated as _metadata_fb
tmp_file = Path("/tmp/meta.txt")
with open(tmp_file, "w") as meta_f:
meta_f.write(str(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(model, im, file, names, y, mlmodel, prefix=colorstr("CoreML Pipeline:")):
"""
Convert a PyTorch YOLOv5 model to CoreML format with Non-Maximum Suppression (NMS), handling different input/output
shapes, and saving the model.
Args:
model (torch.nn.Module): The YOLOv5 PyTorch model to be converted.
im (torch.Tensor): Example input tensor with shape (N, C, H, W), where N is the batch size, C is the number of channels,
H is the height, and W is the width.
file (Path): Path to save the converted CoreML model.
names (dict[int, str]): Dictionary mapping class indices to class names.
y (torch.Tensor): Output tensor from the PyTorch model's forward pass.
mlmodel (bool): Flag indicating whether to export as older *.mlmodel format (default is False).
prefix (str): Custom prefix for logging messages.
Returns:
(Path): Path to the saved CoreML model (.mlmodel).
Raises:
AssertionError: If the number of class names does not match the number of classes in the model.
Notes:
- This function requires `coremltools` to be installed.
- Running this function on a non-macOS environment might not support some features.
- Flexible input shapes and additional NMS options can be customized within the function.
Examples:
```python
from pathlib import Path
import torch
model = torch.load('yolov5s.pt') # Load YOLOv5 model
im = torch.zeros((1, 3, 640, 640)) # Example input tensor
names = {0: "person", 1: "bicycle", 2: "car", ...} # Define class names
y = model(im) # Perform forward pass to get model output
output_file = Path('yolov5s.mlmodel') # Convert to CoreML
pipeline_coreml(model, im, output_file, names, y)
```
"""
import coremltools as ct
from PIL import Image
f = file.with_suffix(".mlmodel") if mlmodel else file.with_suffix(".mlpackage")
print(f"{prefix} starting pipeline with coremltools {ct.__version__}...")
batch_size, ch, h, w = list(im.shape) # BCHW
t = time.time()
# YOLOv5 Output shapes
spec = model.get_spec()
out0, out1 = iter(spec.description.output)
if platform.system() == "Darwin":
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, out1_shape = out[out0.name].shape, out[out1.name].shape
else: # linux and windows can not run model.predict(), get sizes from pytorch output y
s = tuple(y[0].shape)
out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4)
# Checks
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
weights_dir = None
weights_dir = None if mlmodel else str(f / "Data/com.apple.CoreML/weights")
model = ct.models.MLModel(spec, weights_dir=weights_dir)
# 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 = "https://github.com/ultralytics/yolov5"
pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5"
pipeline.spec.description.metadata.author = "glenn.jocher@ultralytics.com"
pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE"
pipeline.spec.description.metadata.userDefined.update(
{
"classes": ",".join(names.values()),
"iou_threshold": str(nms.iouThreshold),
"confidence_threshold": str(nms.confidenceThreshold),
}
)
# Save the model
model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir)
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)"
model.save(f) # pipelined
print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)")
@smart_inference_mode()
def run(
data=ROOT / "data/coco128.yaml", # 'dataset.yaml path'
weights=ROOT / "yolov5s.pt", # weights path
imgsz=(640, 640), # image (height, width)
batch_size=1, # batch size
device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu
include=("torchscript", "onnx"), # include formats
half=False, # FP16 half-precision export
inplace=False, # set YOLOv5 Detect() inplace=True
keras=False, # use Keras
optimize=False, # TorchScript: optimize for mobile
int8=False, # CoreML/TF INT8 quantization
per_tensor=False, # TF per tensor quantization
dynamic=False, # ONNX/TF/TensorRT: dynamic axes
cache="", # TensorRT: timing cache path
simplify=False, # ONNX: simplify model
mlmodel=False, # CoreML: Export in *.mlmodel format
opset=12, # ONNX: opset version
verbose=False, # TensorRT: verbose log
workspace=4, # TensorRT: workspace size (GB)
nms=False, # TF: add NMS to model
agnostic_nms=False, # TF: add agnostic NMS to model
topk_per_class=100, # TF.js NMS: topk per class to keep
topk_all=100, # TF.js NMS: topk for all classes to keep
iou_thres=0.45, # TF.js NMS: IoU threshold
conf_thres=0.25, # TF.js NMS: confidence threshold
):
"""
Exports a YOLOv5 model to specified formats including ONNX, TensorRT, CoreML, and TensorFlow.
Args:
data (str | Path): Path to the dataset YAML configuration file. Default is 'data/coco128.yaml'.
weights (str | Path): Path to the pretrained model weights file. Default is 'yolov5s.pt'.
imgsz (tuple): Image size as (height, width). Default is (640, 640).
batch_size (int): Batch size for exporting the model. Default is 1.
device (str): Device to run the export on, e.g., '0' for GPU, 'cpu' for CPU. Default is 'cpu'.
include (tuple): Formats to include in the export. Default is ('torchscript', 'onnx').
half (bool): Flag to export model with FP16 half-precision. Default is False.
inplace (bool): Set the YOLOv5 Detect() module inplace=True. Default is False.
keras (bool): Flag to use Keras for TensorFlow SavedModel export. Default is False.
optimize (bool): Optimize TorchScript model for mobile deployment. Default is False.
int8 (bool): Apply INT8 quantization for CoreML or TensorFlow models. Default is False.
per_tensor (bool): Apply per tensor quantization for TensorFlow models. Default is False.
dynamic (bool): Enable dynamic axes for ONNX, TensorFlow, or TensorRT exports. Default is False.
cache (str): TensorRT timing cache path. Default is an empty string.
simplify (bool): Simplify the ONNX model during export. Default is False.
opset (int): ONNX opset version. Default is 12.
verbose (bool): Enable verbose logging for TensorRT export. Default is False.
workspace (int): TensorRT workspace size in GB. Default is 4.
nms (bool): Add non-maximum suppression (NMS) to the TensorFlow model. Default is False.
agnostic_nms (bool): Add class-agnostic NMS to the TensorFlow model. Default is False.
topk_per_class (int): Top-K boxes per class to keep for TensorFlow.js NMS. Default is 100.
topk_all (int): Top-K boxes for all classes to keep for TensorFlow.js NMS. Default is 100.
iou_thres (float): IoU threshold for NMS. Default is 0.45.
conf_thres (float): Confidence threshold for NMS. Default is 0.25.
mlmodel (bool): Flag to use *.mlmodel for CoreML export. Default is False.
Returns:
None
Notes:
- Model export is based on the specified formats in the 'include' argument.
- Be cautious of combinations where certain flags are mutually exclusive, such as `--half` and `--dynamic`.
Example:
```python
run(
data="data/coco128.yaml",
weights="yolov5s.pt",
imgsz=(640, 640),
batch_size=1,
device="cpu",
include=("torchscript", "onnx"),
half=False,
inplace=False,
keras=False,
optimize=False,
int8=False,
per_tensor=False,
dynamic=False,
cache="",
simplify=False,
opset=12,
verbose=False,
mlmodel=False,
workspace=4,
nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
)
```
"""
t = time.time()
include = [x.lower() for x in include] # to lowercase
fmts = tuple(export_formats()["Argument"][1:]) # --include arguments
flags = [x in include for x in fmts]
assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}"
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights) # PyTorch weights
# Load PyTorch model
device = select_device(device)
if half:
assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0"
assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both"
model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model
# Checks
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
if optimize:
assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu"
# Input
gs = int(max(model.stride)) # grid size (max stride)
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
ch = next(model.parameters()).size(1) # require input image channels
im = torch.zeros(batch_size, ch, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
# Update model
model.eval()
for k, m in model.named_modules():
if isinstance(m, Detect):
m.inplace = inplace
m.dynamic = dynamic
m.export = True
for _ in range(2):
y = model(im) # dry runs
if 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
metadata = {"stride": int(max(model.stride)), "names": model.names} # model metadata
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
# Exports
f = [""] * len(fmts) # exported filenames
warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning) # suppress TracerWarning
if jit: # TorchScript
f[0], _ = export_torchscript(model, im, file, optimize)
if engine: # TensorRT required before ONNX
f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose, cache)
if onnx or xml: # OpenVINO requires ONNX
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
if xml: # OpenVINO
f[3], _ = export_openvino(file, metadata, half, int8, data)
if coreml: # CoreML
f[4], ct_model = export_coreml(model, im, file, int8, half, nms, mlmodel)
if nms:
pipeline_coreml(ct_model, im, file, model.names, y, mlmodel)
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type."
assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported."
f[5], s_model = export_saved_model(
model.cpu(),
im,
file,
dynamic,
tf_nms=nms or agnostic_nms or tfjs,
agnostic_nms=agnostic_nms or tfjs,
topk_per_class=topk_per_class,
topk_all=topk_all,
iou_thres=iou_thres,
conf_thres=conf_thres,
keras=keras,
)
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = export_pb(s_model, file)
if tflite or edgetpu:
f[7], _ = export_tflite(
s_model, im, file, int8 or edgetpu, per_tensor, data=data, nms=nms, agnostic_nms=agnostic_nms
)
if edgetpu:
f[8], _ = export_edgetpu(file)
add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
if tfjs:
f[9], _ = export_tfjs(file, int8)
if paddle: # PaddlePaddle
f[10], _ = export_paddle(model, im, file, metadata)
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type
det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)
dir = Path("segment" if seg else "classify" if cls else "")
h = "--half" if half else "" # --half FP16 inference arg
s = (
"# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference"
if cls
else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference"
if seg
else ""
)
LOGGER.info(
f"\nExport complete ({time.time() - t:.1f}s)"
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}"
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}"
f"\nVisualize: https://netron.app"
)
return f # return list of exported files/dirs
def parse_opt(known=False):
"""
Parse command-line options for YOLOv5 model export configurations.
Args:
known (bool): If True, uses `argparse.ArgumentParser.parse_known_args`; otherwise, uses `argparse.ArgumentParser.parse_args`.
Default is False.
Returns:
argparse.Namespace: Object containing parsed command-line arguments.
Example:
```python
opts = parse_opt()
print(opts.data)
print(opts.weights)
```
"""
parser = argparse.ArgumentParser()
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model.pt path(s)")
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)")
parser.add_argument("--batch-size", type=int, default=1, help="batch size")
parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
parser.add_argument("--half", action="store_true", help="FP16 half-precision export")
parser.add_argument("--inplace", action="store_true", help="set YOLOv5 Detect() inplace=True")
parser.add_argument("--keras", action="store_true", help="TF: use Keras")
parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile")
parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization")
parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization")
parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes")
parser.add_argument("--cache", type=str, default="", help="TensorRT: timing cache file path")
parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model")
parser.add_argument("--mlmodel", action="store_true", help="CoreML: Export in *.mlmodel format")
parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version")
parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log")
parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)")
parser.add_argument("--nms", action="store_true", help="TF: add NMS to model")
parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model")
parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep")
parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep")
parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold")
parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold")
parser.add_argument(
"--include",
nargs="+",
default=["torchscript"],
help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle",
)
opt = parser.parse_known_args()[0] if known else parser.parse_args()
print_args(vars(opt))
return opt
def main(opt):
"""Run(**vars(opt)) # Execute the run function with parsed options."""
for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)