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Saeed Khosravi
2025-11-08 19:15:39 +01:00
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import json
from pathlib import Path
import torch
from ultralytics.utils import IS_JETSON, LOGGER
from .imx import torch2imx # noqa
def torch2onnx(
torch_model: torch.nn.Module,
im: torch.Tensor,
onnx_file: str,
opset: int = 14,
input_names: list[str] = ["images"],
output_names: list[str] = ["output0"],
dynamic: bool | dict = False,
) -> None:
"""
Export a PyTorch model to ONNX format.
Args:
torch_model (torch.nn.Module): The PyTorch model to export.
im (torch.Tensor): Example input tensor for the model.
onnx_file (str): Path to save the exported ONNX file.
opset (int): ONNX opset version to use for export.
input_names (list[str]): List of input tensor names.
output_names (list[str]): List of output tensor names.
dynamic (bool | dict, optional): Whether to enable dynamic axes.
Notes:
Setting `do_constant_folding=True` may cause issues with DNN inference for torch>=1.12.
"""
torch.onnx.export(
torch_model,
im,
onnx_file,
verbose=False,
opset_version=opset,
do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic or None,
)
def onnx2engine(
onnx_file: str,
engine_file: str | None = None,
workspace: int | None = None,
half: bool = False,
int8: bool = False,
dynamic: bool = False,
shape: tuple[int, int, int, int] = (1, 3, 640, 640),
dla: int | None = None,
dataset=None,
metadata: dict | None = None,
verbose: bool = False,
prefix: str = "",
) -> None:
"""
Export a YOLO model to TensorRT engine format.
Args:
onnx_file (str): Path to the ONNX file to be converted.
engine_file (str, optional): Path to save the generated TensorRT engine file.
workspace (int, optional): Workspace size in GB for TensorRT.
half (bool, optional): Enable FP16 precision.
int8 (bool, optional): Enable INT8 precision.
dynamic (bool, optional): Enable dynamic input shapes.
shape (tuple[int, int, int, int], optional): Input shape (batch, channels, height, width).
dla (int, optional): DLA core to use (Jetson devices only).
dataset (ultralytics.data.build.InfiniteDataLoader, optional): Dataset for INT8 calibration.
metadata (dict, optional): Metadata to include in the engine file.
verbose (bool, optional): Enable verbose logging.
prefix (str, optional): Prefix for log messages.
Raises:
ValueError: If DLA is enabled on non-Jetson devices or required precision is not set.
RuntimeError: If the ONNX file cannot be parsed.
Notes:
TensorRT version compatibility is handled for workspace size and engine building.
INT8 calibration requires a dataset and generates a calibration cache.
Metadata is serialized and written to the engine file if provided.
"""
import tensorrt as trt # noqa
engine_file = engine_file or Path(onnx_file).with_suffix(".engine")
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE
# Engine builder
builder = trt.Builder(logger)
config = builder.create_builder_config()
workspace_bytes = int((workspace or 0) * (1 << 30))
is_trt10 = int(trt.__version__.split(".", 1)[0]) >= 10 # is TensorRT >= 10
if is_trt10 and workspace_bytes > 0:
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace_bytes)
elif workspace_bytes > 0: # TensorRT versions 7, 8
config.max_workspace_size = workspace_bytes
flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
network = builder.create_network(flag)
half = builder.platform_has_fast_fp16 and half
int8 = builder.platform_has_fast_int8 and int8
# Optionally switch to DLA if enabled
if dla is not None:
if not IS_JETSON:
raise ValueError("DLA is only available on NVIDIA Jetson devices")
LOGGER.info(f"{prefix} enabling DLA on core {dla}...")
if not half and not int8:
raise ValueError(
"DLA requires either 'half=True' (FP16) or 'int8=True' (INT8) to be enabled. Please enable one of them and try again."
)
config.default_device_type = trt.DeviceType.DLA
config.DLA_core = int(dla)
config.set_flag(trt.BuilderFlag.GPU_FALLBACK)
# Read ONNX file
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(onnx_file):
raise RuntimeError(f"failed to load ONNX file: {onnx_file}")
# Network inputs
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:
profile = builder.create_optimization_profile()
min_shape = (1, shape[1], 32, 32) # minimum input shape
max_shape = (*shape[:2], *(int(max(2, workspace or 2) * d) for d in shape[2:])) # max input shape
for inp in inputs:
profile.set_shape(inp.name, min=min_shape, opt=shape, max=max_shape)
config.add_optimization_profile(profile)
if int8:
config.set_calibration_profile(profile)
LOGGER.info(f"{prefix} building {'INT8' if int8 else 'FP' + ('16' if half else '32')} engine as {engine_file}")
if int8:
config.set_flag(trt.BuilderFlag.INT8)
config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED
class EngineCalibrator(trt.IInt8Calibrator):
"""
Custom INT8 calibrator for TensorRT engine optimization.
This calibrator provides the necessary interface for TensorRT to perform INT8 quantization calibration
using a dataset. It handles batch generation, caching, and calibration algorithm selection.
Attributes:
dataset: Dataset for calibration.
data_iter: Iterator over the calibration dataset.
algo (trt.CalibrationAlgoType): Calibration algorithm type.
batch (int): Batch size for calibration.
cache (Path): Path to save the calibration cache.
Methods:
get_algorithm: Get the calibration algorithm to use.
get_batch_size: Get the batch size to use for calibration.
get_batch: Get the next batch to use for calibration.
read_calibration_cache: Use existing cache instead of calibrating again.
write_calibration_cache: Write calibration cache to disk.
"""
def __init__(
self,
dataset, # ultralytics.data.build.InfiniteDataLoader
cache: str = "",
) -> None:
"""Initialize the INT8 calibrator with dataset and cache path."""
trt.IInt8Calibrator.__init__(self)
self.dataset = dataset
self.data_iter = iter(dataset)
self.algo = (
trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2 # DLA quantization needs ENTROPY_CALIBRATION_2
if dla is not None
else trt.CalibrationAlgoType.MINMAX_CALIBRATION
)
self.batch = dataset.batch_size
self.cache = Path(cache)
def get_algorithm(self) -> trt.CalibrationAlgoType:
"""Get the calibration algorithm to use."""
return self.algo
def get_batch_size(self) -> int:
"""Get the batch size to use for calibration."""
return self.batch or 1
def get_batch(self, names) -> list[int] | None:
"""Get the next batch to use for calibration, as a list of device memory pointers."""
try:
im0s = next(self.data_iter)["img"] / 255.0
im0s = im0s.to("cuda") if im0s.device.type == "cpu" else im0s
return [int(im0s.data_ptr())]
except StopIteration:
# Return None to signal to TensorRT there is no calibration data remaining
return None
def read_calibration_cache(self) -> bytes | None:
"""Use existing cache instead of calibrating again, otherwise, implicitly return None."""
if self.cache.exists() and self.cache.suffix == ".cache":
return self.cache.read_bytes()
def write_calibration_cache(self, cache: bytes) -> None:
"""Write calibration cache to disk."""
_ = self.cache.write_bytes(cache)
# Load dataset w/ builder (for batching) and calibrate
config.int8_calibrator = EngineCalibrator(
dataset=dataset,
cache=str(Path(onnx_file).with_suffix(".cache")),
)
elif half:
config.set_flag(trt.BuilderFlag.FP16)
# Write file
build = builder.build_serialized_network if is_trt10 else builder.build_engine
with build(network, config) as engine, open(engine_file, "wb") as t:
# Metadata
if metadata is not None:
meta = json.dumps(metadata)
t.write(len(meta).to_bytes(4, byteorder="little", signed=True))
t.write(meta.encode())
# Model
t.write(engine if is_trt10 else engine.serialize())

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import subprocess
import types
from pathlib import Path
import torch
from ultralytics.nn.modules import Detect, Pose
from ultralytics.utils import LOGGER
from ultralytics.utils.tal import make_anchors
from ultralytics.utils.torch_utils import copy_attr
class FXModel(torch.nn.Module):
"""
A custom model class for torch.fx compatibility.
This class extends `torch.nn.Module` and is designed to ensure compatibility with torch.fx for tracing and graph
manipulation. It copies attributes from an existing model and explicitly sets the model attribute to ensure proper
copying.
Attributes:
model (nn.Module): The original model's layers.
"""
def __init__(self, model, imgsz=(640, 640)):
"""
Initialize the FXModel.
Args:
model (nn.Module): The original model to wrap for torch.fx compatibility.
imgsz (tuple[int, int]): The input image size (height, width). Default is (640, 640).
"""
super().__init__()
copy_attr(self, model)
# Explicitly set `model` since `copy_attr` somehow does not copy it.
self.model = model.model
self.imgsz = imgsz
def forward(self, x):
"""
Forward pass through the model.
This method performs the forward pass through the model, handling the dependencies between layers and saving
intermediate outputs.
Args:
x (torch.Tensor): The input tensor to the model.
Returns:
(torch.Tensor): The output tensor from the model.
"""
y = [] # outputs
for m in self.model:
if m.f != -1: # if not from previous layer
# from earlier layers
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]
if isinstance(m, Detect):
m._inference = types.MethodType(_inference, m) # bind method to Detect
m.anchors, m.strides = (
x.transpose(0, 1)
for x in make_anchors(
torch.cat([s / m.stride.unsqueeze(-1) for s in self.imgsz], dim=1), m.stride, 0.5
)
)
if type(m) is Pose:
m.forward = types.MethodType(pose_forward, m) # bind method to Detect
x = m(x) # run
y.append(x) # save output
return x
def _inference(self, x: list[torch.Tensor]) -> tuple[torch.Tensor]:
"""Decode boxes and cls scores for imx object detection."""
x_cat = torch.cat([xi.view(x[0].shape[0], self.no, -1) for xi in x], 2)
box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
return dbox.transpose(1, 2), cls.sigmoid().permute(0, 2, 1)
def pose_forward(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Forward pass for imx pose estimation, including keypoint decoding."""
bs = x[0].shape[0] # batch size
kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1) # (bs, 17*3, h*w)
x = Detect.forward(self, x)
pred_kpt = self.kpts_decode(bs, kpt)
return (*x, pred_kpt.permute(0, 2, 1))
class NMSWrapper(torch.nn.Module):
"""Wrap PyTorch Module with multiclass_nms layer from sony_custom_layers."""
def __init__(
self,
model: torch.nn.Module,
score_threshold: float = 0.001,
iou_threshold: float = 0.7,
max_detections: int = 300,
task: str = "detect",
):
"""
Initialize NMSWrapper with PyTorch Module and NMS parameters.
Args:
model (torch.nn.Module): Model instance.
score_threshold (float): Score threshold for non-maximum suppression.
iou_threshold (float): Intersection over union threshold for non-maximum suppression.
max_detections (int): The number of detections to return.
task (str): Task type, either 'detect' or 'pose'.
"""
super().__init__()
self.model = model
self.score_threshold = score_threshold
self.iou_threshold = iou_threshold
self.max_detections = max_detections
self.task = task
def forward(self, images):
"""Forward pass with model inference and NMS post-processing."""
from sony_custom_layers.pytorch import multiclass_nms_with_indices
# model inference
outputs = self.model(images)
boxes, scores = outputs[0], outputs[1]
nms_outputs = multiclass_nms_with_indices(
boxes=boxes,
scores=scores,
score_threshold=self.score_threshold,
iou_threshold=self.iou_threshold,
max_detections=self.max_detections,
)
if self.task == "pose":
kpts = outputs[2] # (bs, max_detections, kpts 17*3)
out_kpts = torch.gather(kpts, 1, nms_outputs.indices.unsqueeze(-1).expand(-1, -1, kpts.size(-1)))
return nms_outputs.boxes, nms_outputs.scores, nms_outputs.labels, out_kpts
return nms_outputs.boxes, nms_outputs.scores, nms_outputs.labels, nms_outputs.n_valid
def torch2imx(
model: torch.nn.Module,
file: Path | str,
conf: float,
iou: float,
max_det: int,
metadata: dict | None = None,
gptq: bool = False,
dataset=None,
prefix: str = "",
):
"""
Export YOLO model to IMX format for deployment on Sony IMX500 devices.
This function quantizes a YOLO model using Model Compression Toolkit (MCT) and exports it
to IMX format compatible with Sony IMX500 edge devices. It supports both YOLOv8n and YOLO11n
models for detection and pose estimation tasks.
Args:
model (torch.nn.Module): The YOLO model to export. Must be YOLOv8n or YOLO11n.
file (Path | str): Output file path for the exported model.
conf (float): Confidence threshold for NMS post-processing.
iou (float): IoU threshold for NMS post-processing.
max_det (int): Maximum number of detections to return.
metadata (dict | None, optional): Metadata to embed in the ONNX model. Defaults to None.
gptq (bool, optional): Whether to use Gradient-Based Post Training Quantization.
If False, uses standard Post Training Quantization. Defaults to False.
dataset (optional): Representative dataset for quantization calibration. Defaults to None.
prefix (str, optional): Logging prefix string. Defaults to "".
Returns:
f (Path): Path to the exported IMX model directory
Raises:
ValueError: If the model is not a supported YOLOv8n or YOLO11n variant.
Example:
>>> from ultralytics import YOLO
>>> model = YOLO("yolo11n.pt")
>>> path, _ = export_imx(model, "model.imx", conf=0.25, iou=0.45, max_det=300)
Note:
- Requires model_compression_toolkit, onnx, edgemdt_tpc, and sony_custom_layers packages
- Only supports YOLOv8n and YOLO11n models (detection and pose tasks)
- Output includes quantized ONNX model, IMX binary, and labels.txt file
"""
import model_compression_toolkit as mct
import onnx
from edgemdt_tpc import get_target_platform_capabilities
LOGGER.info(f"\n{prefix} starting export with model_compression_toolkit {mct.__version__}...")
def representative_dataset_gen(dataloader=dataset):
for batch in dataloader:
img = batch["img"]
img = img / 255.0
yield [img]
tpc = get_target_platform_capabilities(tpc_version="4.0", device_type="imx500")
bit_cfg = mct.core.BitWidthConfig()
if "C2PSA" in model.__str__(): # YOLO11
if model.task == "detect":
layer_names = ["sub", "mul_2", "add_14", "cat_21"]
weights_memory = 2585350.2439
n_layers = 238 # 238 layers for fused YOLO11n
elif model.task == "pose":
layer_names = ["sub", "mul_2", "add_14", "cat_22", "cat_23", "mul_4", "add_15"]
weights_memory = 2437771.67
n_layers = 257 # 257 layers for fused YOLO11n-pose
else: # YOLOv8
if model.task == "detect":
layer_names = ["sub", "mul", "add_6", "cat_17"]
weights_memory = 2550540.8
n_layers = 168 # 168 layers for fused YOLOv8n
elif model.task == "pose":
layer_names = ["add_7", "mul_2", "cat_19", "mul", "sub", "add_6", "cat_18"]
weights_memory = 2482451.85
n_layers = 187 # 187 layers for fused YOLO11n-pose
# Check if the model has the expected number of layers
if len(list(model.modules())) != n_layers:
raise ValueError("IMX export only supported for YOLOv8n and YOLO11n models.")
for layer_name in layer_names:
bit_cfg.set_manual_activation_bit_width([mct.core.common.network_editors.NodeNameFilter(layer_name)], 16)
config = mct.core.CoreConfig(
mixed_precision_config=mct.core.MixedPrecisionQuantizationConfig(num_of_images=10),
quantization_config=mct.core.QuantizationConfig(concat_threshold_update=True),
bit_width_config=bit_cfg,
)
resource_utilization = mct.core.ResourceUtilization(weights_memory=weights_memory)
quant_model = (
mct.gptq.pytorch_gradient_post_training_quantization( # Perform Gradient-Based Post Training Quantization
model=model,
representative_data_gen=representative_dataset_gen,
target_resource_utilization=resource_utilization,
gptq_config=mct.gptq.get_pytorch_gptq_config(
n_epochs=1000, use_hessian_based_weights=False, use_hessian_sample_attention=False
),
core_config=config,
target_platform_capabilities=tpc,
)[0]
if gptq
else mct.ptq.pytorch_post_training_quantization( # Perform post training quantization
in_module=model,
representative_data_gen=representative_dataset_gen,
target_resource_utilization=resource_utilization,
core_config=config,
target_platform_capabilities=tpc,
)[0]
)
quant_model = NMSWrapper(
model=quant_model,
score_threshold=conf or 0.001,
iou_threshold=iou,
max_detections=max_det,
task=model.task,
)
f = Path(str(file).replace(file.suffix, "_imx_model"))
f.mkdir(exist_ok=True)
onnx_model = f / Path(str(file.name).replace(file.suffix, "_imx.onnx")) # js dir
mct.exporter.pytorch_export_model(
model=quant_model, save_model_path=onnx_model, repr_dataset=representative_dataset_gen
)
model_onnx = onnx.load(onnx_model) # load onnx model
for k, v in metadata.items():
meta = model_onnx.metadata_props.add()
meta.key, meta.value = k, str(v)
onnx.save(model_onnx, onnx_model)
subprocess.run(
["imxconv-pt", "-i", str(onnx_model), "-o", str(f), "--no-input-persistency", "--overwrite-output"],
check=True,
)
# Needed for imx models.
with open(f / "labels.txt", "w", encoding="utf-8") as file:
file.writelines([f"{name}\n" for _, name in model.names.items()])
return f