API Reference

Mobilint SDK qb Compiler v1.2 MCS002-EN

Classes | Functions | Variables

API Reference

Mobilint qb Compiler API provides functions for compiling models from various framework(onnx, pytorch, transformers, etc.) to MXQ format. More...

Classes
class	qbcompiler.frontend.Model_Dict
	Wrapper around the Mobilint compiler to support compilation and inference workflows. More...
class	qbcompiler.calibration.preprocess.GetNpy
	Load NumPy data from an in-memory array or a .npy file. More...
class	qbcompiler.calibration.preprocess.NpyNormalize
class	qbcompiler.calibration.preprocess.NpyPad
	Pad NumPy arrays to a desired shape. More...
class	qbcompiler.calibration.preprocess.GetImage
	Retrieve an image tensor from a file path or NumPy array using OpenCV. More...
class	qbcompiler.calibration.preprocess.Pad
	Pad images to a target shape or to meet a size divisor. More...
class	qbcompiler.calibration.preprocess.Normalize
	Apply mean/std normalization to images. More...
class	qbcompiler.calibration.preprocess.ResizeTorch
class	qbcompiler.calibration.preprocess.Resize
	Resize images with optional aspect-ratio preservation. More...
class	qbcompiler.calibration.preprocess.CenterCrop
	Perform a centered crop on the image. More...
class	qbcompiler.calibration.preprocess.SetOrder
class	qbcompiler.calibration.preprocess.preproc_builder
	Build a calibration preprocessing pipeline from a YAML or dict configuration. More...
class	GetText
	Extract text strings from raw inputs. More...
class	TokenizeToEmbedding
	Convert tokenized text into embeddings using a Hugging Face model. More...
class	YoloPre
	Apply YOLO-style resize, padding, and normalization to images. More...

Functions
	qbcompiler.frontend.mxq_compile (model, str target_device, Union[str, List[str]] calib_data_path=_UNSET, int save_subgraph_type=0, output_subgraph_path="", Union[str, List[str]] save_path=_UNSET, backend="onnx", feed_dict=None, dynamic_axes=None, in_dformats=None, yolo_decode_include=False, exclude_first_subgraph=False, device=_UNSET, inference_scheme=_UNSET, use_random_calib=_UNSET, cpu_offload=_UNSET, optimize_option=_UNSET, buffer_mode=_UNSET, input_shape_dict=_UNSET, force_npu_input_reposition=_UNSET, force_npu_output_reposition=_UNSET, image_channels=_UNSET, split_blocks=_UNSET, split_parts=_UNSET, Optional[str] config_preset=None, Optional[CompileConfig] compile_config=None, Optional[ResourceManagementConfig] resource_management_config=None, Optional[CalibrationConfig] calibration_config=None, Optional[BitConfig] bit_config=None, Optional[LlmConfig] llm_config=None, Optional[OptqConfig] optq_config=None, Optional[ModConfig] mod_config=None, Optional[EquivalentTransformationConfig] equivalent_transformation_config=None, Optional[SearchWeightScaleConfig] search_weight_scale_config=None, Optional[SaveSampleConfig] save_sample_config=None, Optional[Uint8InputConfig] uint8_input_config=None, Optional[PreprocessingConfig] preprocessing_config=None, **kwargs)
	Compile a model into a Mobilint eXeCUtable (MXQ) package for execution on Mobilint NPUs.
	qbcompiler.frontend.mblt_compile (str|Any model, str mblt_save_path, str target_device, backend="onnx", device="cpu", feed_dict=None, dynamic_axes=None, in_dformats=None, yolo_decode_include=False, cpu_offload=False, exclude_first_subgraph=False, **kwargs)
	Export a model to the Mobilint .mblt format without producing an MXQ package.
None	qbcompiler.frontend.mxq_compile_V2 (model, str target_device, Union[str, List[str]] calib_data_path=_UNSET, int save_subgraph_type=0, output_subgraph_path="", Union[str, List[str]] save_path=_UNSET, backend="onnx", feed_dict=None, dynamic_axes=None, in_dformats=None, yolo_decode_include=False, exclude_first_subgraph=False, device=_UNSET, inference_scheme=_UNSET, use_random_calib=_UNSET, cpu_offload=_UNSET, optimize_option=_UNSET, buffer_mode=_UNSET, input_shape_dict=_UNSET, force_npu_input_reposition=_UNSET, force_npu_output_reposition=_UNSET, image_channels=_UNSET, split_blocks=_UNSET, split_parts=_UNSET, Optional[str] config_preset=None, Optional[CompileConfig|str] compile_config=None, Optional[ResourceManagementConfig] resource_management_config=None, Optional[CalibrationConfig] calibration_config=None, Optional[BitConfig] bit_config=None, Optional[LlmConfig] llm_config=None, Optional[OptqConfig] optq_config=None, Optional[ModConfig] mod_config=None, Optional[EquivalentTransformationConfig] equivalent_transformation_config=None, Optional[SearchWeightScaleConfig] search_weight_scale_config=None, Optional[SaveSampleConfig] save_sample_config=None, Optional[Uint8InputConfig] uint8_input_config=None, Optional[PreprocessingConfig] preprocessing_config=None, **kwargs)
	V2 compile/quantize entry point backed only by ConfigManager resolution.
None	qbcompiler.frontend.mblt_compile_V2 (str|object model, str target_device, str mblt_save_path, backend="onnx", object device=_UNSET, feed_dict=None, dynamic_axes=None, in_dformats=None, yolo_decode_include=False, object cpu_offload=_UNSET, exclude_first_subgraph=False, Optional[str] config_preset=None, Optional[CompileConfig|str] compile_config=None, Optional[str] compilation_mode=None, **kwargs)
	V2 compile-to-mblt entry point backed only by ConfigManager resolution.
None	qbcompiler.frontend.mblt_compile_with_callback_V2 (str model, str target_device, str mblt_save_path, str backend="onnx", Optional[str] device=None, Optional[bool] cpu_offload=None, Optional[str] config_preset=None, Optional[CompileConfig|str] compile_config=None, Optional[str] compilation_mode=None, *, Callable[[int, str], None] progress_callback)
	V2 compile-to-mblt entry point with progress callbacks.
None	qbcompiler.frontend.mxq_compile_with_callback_V2 (str model, str target_device, str save_path, str backend="onnx", Optional[str] device=None, Optional[Union[str, List[str]]] calib_data_path=None, Optional[bool] use_random_calib=None, Optional[str] config_preset=None, Optional[CompileConfig|str] compile_config=None, *, Callable[[int, str], None] progress_callback)
	V2 compile/quantize entry point with progress callbacks.
	qbcompiler.frontend.run_mblt_model (str path_to_mblt, bool save_all_outputs=True, Optional[str] inference_output_path=None, Optional[int] target_subgraph=None)
	qbcompiler.calibration.make_calib_data.get_yaml (str yaml_path)
	qbcompiler.calibration.make_calib_data.make_calib_man (pre_ftn, str data_dir, str save_dir, str save_name, str anno_json=None, str file_format="%012d.jpg", int max_size=-1, bool remove_npy=False, int seed=2023, bool save_calib_msg=False, str msg_path=None)
	From images and a user-provided preprocessing function, generate calibration NumPy files and a manifest.
str	qbcompiler.calibration.make_calib_data.make_calib (Union[str, dict] args_pre, str data_dir, str save_dir, str save_name=None, str anno_json=None, str file_format="%012d.jpg", int max_size=-1, bool remove_npy=False, int seed=2023, bool save_calib_msg=False, str msg_path=None)
	Create calibration NumPy files and a manifest using a YAML or dict preprocessing configuration.
List[str]	qbcompiler.calibration.make_calib_data.make_calib_llm (Union[str, dict] args_pre, str dataset_name, Union[List[str], str] subset_list, str text_field, str split, str save_dir, int max_calib=512, Optional[str] save_prefix=None)
	Generate calibration embeddings for LLM models using Hugging Face datasets.
	qbcompiler.calibration.preprocess.preprocess_fnc (fnc, Datatype="Image")

Variables
dict	qbcompiler.calibration.preprocess.DTYPE_GETDATA_MAP = {"Image": "GetImage", "Npy": "GetNpy", "LLM": "GetText"}

Detailed Description

Mobilint qb Compiler API provides functions for compiling models from various framework(onnx, pytorch, transformers, etc.) to MXQ format.

Function Documentation

◆ mxq_compile()

qbcompiler.frontend.mxq_compile	(		model,
		str	target_device,
		Union[str, List[str]]	calib_data_path = _UNSET,
		int	save_subgraph_type = 0,
			output_subgraph_path = "",
		Union[str, List[str]]	save_path = _UNSET,
			backend = "onnx",
			feed_dict = None,
			dynamic_axes = None,
			in_dformats = None,
			yolo_decode_include = False,
			exclude_first_subgraph = False,
			device = _UNSET,
			inference_scheme = _UNSET,
			use_random_calib = _UNSET,
			cpu_offload = _UNSET,
			optimize_option = _UNSET,
			buffer_mode = _UNSET,
			input_shape_dict = _UNSET,
			force_npu_input_reposition = _UNSET,
			force_npu_output_reposition = _UNSET,
			image_channels = _UNSET,
			split_blocks = _UNSET,
			split_parts = _UNSET,
		Optional[str]	config_preset = None,
		Optional[CompileConfig]	compile_config = None,
		Optional[ResourceManagementConfig]	resource_management_config = None,
		Optional[CalibrationConfig]	calibration_config = None,
		Optional[BitConfig]	bit_config = None,
		Optional[LlmConfig]	llm_config = None,
		Optional[OptqConfig]	optq_config = None,
		Optional[ModConfig]	mod_config = None,
		Optional[EquivalentTransformationConfig]	equivalent_transformation_config = None,
		Optional[SearchWeightScaleConfig]	search_weight_scale_config = None,
		Optional[SaveSampleConfig]	save_sample_config = None,
		Optional[Uint8InputConfig]	uint8_input_config = None,
		Optional[PreprocessingConfig]	preprocessing_config = None,
		**	kwargs )

Compile a model into a Mobilint eXeCUtable (MXQ) package for execution on Mobilint NPUs.

When no explicit value is provided for a parameter marked with _UNSET, the default from CompileConfig is used. This allows a compile_config file or object to supply the value without being overridden by function-level defaults.

Configuration is resolved in priority order (highest to lowest):

1. Explicitly passed function arguments
2. Individual sub-config objects (calibration_config, llm_config, etc.)
3. kwargs partial overrides (quantization_method, weight_dtype, etc.)
4. compile_config (CompileConfig object or JSON/YAML file) or config_preset
5. CompileConfig field defaults

Parameters

model	string or model instance. Model path. When using backend="onnx", this should be the path to an ONNX model file. When backend is "torchscript", provide a TorchScript module; when "torch", provide a standard PyTorch model. For backend="tf", pass the directory that contains the TensorFlow SavedModel graph and assets. For backend="tflite", pass the TF Lite model path.
calib_data_path	string or list of strings. Path(s) to the calibration dataset. Accepts either a text/json file that lists NumPy files or a directory that contains the pre-processed NumPy files.
save_subgraph_type	int. Controls optional MBLT subgraph exports: 0 disables exports; 1 saves only the graph structure; 2 saves graph structure plus weights; 3 saves the graph structure split into multiple subgraphs; 4 saves both structure and weights split into multiple subgraphs. Defaults to 0.
output_subgraph_path	string. Destination path for the exported .mblt file when save_subgraph_type is 1–4. The resulting file can be used for visualization. Defaults to "".
save_path	string or list of strings. Output MXQ filename(s). When omitted, defaults to "{model_name}.mxq" derived from the model path basename.
backend	string. Framework used to generate the Mobilint IR. Must be one of "onnx", "tf", "tflite", "torchscript", or "torch". Defaults to "onnx". When model is a path to an already-compiled .mblt file (the output of mblt_compile()), it is compiled directly with MXQ regardless of the backend value, and the file is validated beforehand; it must be a runnable artifact produced by mblt_compile() rather than a save_subgraph_type preview export.
target_device	string. Target NPU device for parser/compiler configuration (for example "aries-rb"). Required by execution paths that parse or quantize a model.
device	string. Compilation and inference device: "cpu" or "gpu". When omitted, uses CompileConfig default.
feed_dict	dict. Example input tensors for shape inference and inference validation.
dynamic_axes	dict. Marks model axes as dynamic. Keys are input names and values map dimension indices to aliases (for example {"input": {2: "seq_len"}}).
in_dformats	dict. Describes input tensor data formats.
inference_scheme	string. NPU inference scheme. One of "single", "multi", "global", "global4", or "global8". When omitted, uses CompileConfig default.
yolo_decode_include	bool. Determines whether YOLO decode runs on NPU. Defaults to False.
use_random_calib	bool. Generates random calibration data to validate model compilability. When omitted, uses CompileConfig default.
cpu_offload	bool. Enables CPU offloading during NPU inference. When omitted, uses CompileConfig default.
optimize_option	int. Compiler optimization strategy selector. When omitted, uses CompileConfig default.
buffer_mode	int. Buffer serialization mode: 0 uses a naive buffer, 1 uses an mmap-backed buffer. When omitted, uses CompileConfig default.
input_shape_dict	dict. Multi-shape compilation specification for STT/TTS models only (e.g., Conformer-CTC, MeloTTS). The dynamic axis is defined with respect to each input tensor's shape. During compilation, the same axis is varied across all model inputs using the provided values. Currently, only a single entry key ("multi_shape0") is supported. Example: {"multi_shape0": {"axis": 2, "values": [100, 200, 300]}}
force_npu_input_reposition	bool. Force input reposition operations to run on NPU instead of CPU. When omitted, uses CompileConfig default.
force_npu_output_reposition	bool. Force output reposition operations to run on NPU instead of CPU. When omitted, uses CompileConfig default.
image_channels	int. Number of image channels (0 for auto-detect). When omitted, uses CompileConfig default.
split_blocks	list of int. Multi-MXQ split points by transformer block index. Only supported for LLM models. When omitted, uses CompileConfig default.
split_parts	int. Evenly split transformer blocks into N MXQ parts. Only supported for LLM models. When omitted, uses CompileConfig default.
exclude_first_subgraph	bool. Applies only when CPU offloading is enabled: exclude the first subgraph from the final graph if it is unsupported. Defaults to False.
config_preset	string. Name of a built-in configuration preset. When provided, loads the preset via CompileConfig.from_preset(). Defaults to None (no preset). Available presets: "classification": Image classification models (ResNet, EfficientNet, ViT, etc.) "detection": Object detection models (YOLO, SSD, DETR, etc.) "classification_torchvision": Torchvision classification models with standard preprocessing "yolo_640": YOLO detection models with 640x640 letterbox preprocessing "yolo_1280": YOLO detection models with 1280x1280 letterbox preprocessing "llm": Large Language Models (LLaMA, Qwen, Gemma, etc.) "llm_fast": LLM with faster compilation (less accuracy optimization) "vision_transformer": Vision Transformer models (ViT, DeiT, Swin, etc.) "multimodal": Multimodal models (CLIP, BLIP, LLaVA, etc.)
compile_config	CompileConfig or string. CompileConfig object or path to JSON/YAML configuration file containing all compilation settings (resourceManagement, calibration, bit, optq, mod, llm, etc.).
resource_management_config	ResourceManagementConfig. Resource management configuration object.
calibration_config	CalibrationConfig. Calibration configuration object.
bit_config	BitConfig. Bit configuration object for quantization precision settings.
llm_config	LlmConfig. LLM configuration object.
optq_config	OptqConfig. OPTQ (Optimal Brain Quantization) configuration object.
mod_config	ModConfig. MOD (Metric-based Optimization and Distillation) configuration object.
equivalent_transformation_config	EquivalentTransformationConfig. Configuration for equivalent transformations like SmoothQuant.
search_weight_scale_config	SearchWeightScaleConfig. Configuration for weight scale search.
save_sample_config	SaveSampleConfig. Configuration for sample data generation and saving.
uint8_input_config	Uint8InputConfig. Configuration for uint8 input handling.
preprocessing_config	PreprocessingConfig. Preprocessing pipeline configuration.
kwargs	dict. Additional compiler arguments. Supports partial config overrides such as quantization_method, quantization_mode, percentile, weight_dtype, ram_usage, max_sequence_length, etc. Also accepts deprecated parameters (quantization_config, advanced_quantization_config, input_process_config, save_sample, sample_dtype) which will emit DeprecationWarning.

Returns

None.

Using configuration

There are three ways to configure quantization settings:

1. Load all settings from a JSON/YAML config file:
   from qbcompiler import mxq_compile

   mxq_compile(

   model="path/to/model.onnx",

   target_device="aries-rb",

   calib_data_path="path/to/calib",

   compile_config="path/to/config.json", # or config.yaml

   device="gpu",

   )
2. Pass individual sub-config objects:
   from qbcompiler import mxq_compile

   from qbcompiler.configs import (

   ResourceManagementConfig,

   CalibrationConfig,

   BitConfig,

   OptqConfig,

   ModConfig,

   LlmConfig,

   )

   resource_mgmt = ResourceManagementConfig(weight_dtype="float32")

   calib_cfg = CalibrationConfig(method=1, mode=1)

   bit_cfg = BitConfig(...)

   optq_cfg = OptqConfig(apply=True)

   mod_cfg = ModConfig(apply=False)

   llm_cfg = LlmConfig(apply=True)

   mxq_compile(

   model="path/to/model.onnx",

   target_device="aries-rb",

   calib_data_path="path/to/calib",

   resource_management_config=resource_mgmt,

   calibration_config=calib_cfg,

   bit_config=bit_cfg,

   optq_config=optq_cfg,

   mod_config=mod_cfg,

   llm_config=llm_cfg,

   device="gpu",

   )
3. Automatically applied partial configuration overrides:
   from qbcompiler import mxq_compile

   mxq_compile(

   model="path/to/model.onnx",

   target_device="aries-rb",

   calib_data_path="path/to/calib",

   quantization_method=1, # per channel quantization

   quantization_mode=1, # max percentile quantization

   percentile=0.999, # percentile value for max percentile quantization

   quantization_output=0, # per layer quantization for the output layer

   device="gpu",

   )
   Please refer to the mxq_compile function and the quantization configuration section for the meaning of the quantization-related numeric values.

Compiling models with custom inputs (ONNX/Torch/TensorFlow)

Provide NumPy inputs whenever the model omits shape information so that qbcompiler can infer unknown dimensions and data formats.

from qbcompiler import mxq_compile

import numpy as np

example_input = {

"input_node_name_1": np.random.randn(1, 3, 224, 224).astype(np.float32),

"input_node_name_2": np.random.randn(1, 8, 224, 224).astype(np.float32),

"input_node_name_3": np.random.randn(1, 4, 56, 56).astype(np.float32),

}

in_dformats = {

"input_node_name_1": "NCHW",

"input_node_name_2": "NCHW",

"input_node_name_3": "NCHW",

}

onnx_model_path = "path/to/your/model.onnx"

mxq_compile(

model=onnx_model_path,

target_device="aries-rb",

feed_dict=example_input,

in_dformats=in_dformats,

backend="onnx",

compile_config="path/to/config.json",

)

Definition at line 543 of file frontend.py.

◆ mblt_compile()

qbcompiler.frontend.mblt_compile	(	str | Any	model,
		str	mblt_save_path,
		str	target_device,
			backend = "onnx",
			device = "cpu",
			feed_dict = None,
			dynamic_axes = None,
			in_dformats = None,
			yolo_decode_include = False,
			cpu_offload = False,
			exclude_first_subgraph = False,
		**	kwargs )

Export a model to the Mobilint .mblt format without producing an MXQ package.

Parameters

model	string or model instance. Source model or path to compile.
mblt_save_path	string. Output path for the .mblt artifact.
backend	string. Framework identifier ("onnx", "tf", "tflite", "torchscript", or "torch"). Defaults to "onnx".
device	string. Compilation device ("cpu" or "gpu"). Defaults to "cpu".
feed_dict	dict. Example inputs used for shape inference.
dynamic_axes	dict. Declares dynamic axes per input name.
in_dformats	dict. Input dataformat metadata.
yolo_decode_include	bool. Runs YOLO decode on NPU when True.
cpu_offload	bool. Enables CPU offloading for unsupported groups.
exclude_first_subgraph	bool. Applies only when CPU offloading is enabled: exclude the first subgraph from the final graph if it is unsupported.
kwargs	dict. Additional arguments forwarded to the compiler.

Returns

None.

Definition at line 932 of file frontend.py.

◆ mxq_compile_V2()

None qbcompiler.frontend.mxq_compile_V2	(		model,
		str	target_device,
		Union[str, List[str]]	calib_data_path = _UNSET,
		int	save_subgraph_type = 0,
			output_subgraph_path = "",
		Union[str, List[str]]	save_path = _UNSET,
			backend = "onnx",
			feed_dict = None,
			dynamic_axes = None,
			in_dformats = None,
			yolo_decode_include = False,
			exclude_first_subgraph = False,
			device = _UNSET,
			inference_scheme = _UNSET,
			use_random_calib = _UNSET,
			cpu_offload = _UNSET,
			optimize_option = _UNSET,
			buffer_mode = _UNSET,
			input_shape_dict = _UNSET,
			force_npu_input_reposition = _UNSET,
			force_npu_output_reposition = _UNSET,
			image_channels = _UNSET,
			split_blocks = _UNSET,
			split_parts = _UNSET,
		Optional[str]	config_preset = None,
		Optional[CompileConfig | str]	compile_config = None,
		Optional[ResourceManagementConfig]	resource_management_config = None,
		Optional[CalibrationConfig]	calibration_config = None,
		Optional[BitConfig]	bit_config = None,
		Optional[LlmConfig]	llm_config = None,
		Optional[OptqConfig]	optq_config = None,
		Optional[ModConfig]	mod_config = None,
		Optional[EquivalentTransformationConfig]	equivalent_transformation_config = None,
		Optional[SearchWeightScaleConfig]	search_weight_scale_config = None,
		Optional[SaveSampleConfig]	save_sample_config = None,
		Optional[Uint8InputConfig]	uint8_input_config = None,
		Optional[PreprocessingConfig]	preprocessing_config = None,
		**	kwargs )

V2 compile/quantize entry point backed only by ConfigManager resolution.

Definition at line 1003 of file frontend.py.

◆ mblt_compile_V2()

None qbcompiler.frontend.mblt_compile_V2	(	str | object	model,
		str	target_device,
		str	mblt_save_path,
			backend = "onnx",
		object	device = _UNSET,
			feed_dict = None,
			dynamic_axes = None,
			in_dformats = None,
			yolo_decode_include = False,
		object	cpu_offload = _UNSET,
			exclude_first_subgraph = False,
		Optional[str]	config_preset = None,
		Optional[CompileConfig | str]	compile_config = None,
		Optional[str]	compilation_mode = None,
		**	kwargs )

V2 compile-to-mblt entry point backed only by ConfigManager resolution.

compilation_mode is an internal knob: one of {"release", "dev", "debug"} or None (default) to defer to the MBLT_APP_ENV environment variable. When set it overrides the env and drives the parser's inference_validation / device_alloc / log_level cascade — the same preset as MBLT_APP_ENV. Not surfaced in the CLI on purpose; intended for in-process scripts and tests that want scoped dev-mode validation without mutating process-wide environment.

Definition at line 1088 of file frontend.py.

◆ mblt_compile_with_callback_V2()

None qbcompiler.frontend.mblt_compile_with_callback_V2	(	str	model,
		str	target_device,
		str	mblt_save_path,
		str	backend = "onnx",
		Optional[str]	device = None,
		Optional[bool]	cpu_offload = None,
		Optional[str]	config_preset = None,
		Optional[CompileConfig | str]	compile_config = None,
		Optional[str]	compilation_mode = None,
		*	,
		Callable[[int, str], None]	progress_callback )

V2 compile-to-mblt entry point with progress callbacks.

Only forwards explicitly-set values so mblt_compile_V2's _UNSET defaults (and the merged CompileConfig behind them) apply when the caller omits a flag.

Definition at line 1137 of file frontend.py.

◆ mxq_compile_with_callback_V2()

None qbcompiler.frontend.mxq_compile_with_callback_V2	(	str	model,
		str	target_device,
		str	save_path,
		str	backend = "onnx",
		Optional[str]	device = None,
		Optional[Union[str, List[str]]]	calib_data_path = None,
		Optional[bool]	use_random_calib = None,
		Optional[str]	config_preset = None,
		Optional[CompileConfig | str]	compile_config = None,
		*	,
		Callable[[int, str], None]	progress_callback )

V2 compile/quantize entry point with progress callbacks.

Definition at line 1179 of file frontend.py.

◆ run_mblt_model()

qbcompiler.frontend.run_mblt_model	(	str	path_to_mblt,
		bool	save_all_outputs = True,
		Optional[str]	inference_output_path = None,
		Optional[int]	target_subgraph = None )

Definition at line 1221 of file frontend.py.

◆ get_yaml()

qbcompiler.calibration.make_calib_data.get_yaml

(

str

yaml_path

)

Definition at line 29 of file make_calib_data.py.

◆ make_calib_man()

qbcompiler.calibration.make_calib_data.make_calib_man	(		pre_ftn,
		str	data_dir,
		str	save_dir,
		str	save_name,
		str	anno_json = None,
		str	file_format = "%012d.jpg",
		int	max_size = -1,
		bool	remove_npy = False,
		int	seed = 2023,
		bool	save_calib_msg = False,
		str	msg_path = None )

From images and a user-provided preprocessing function, generate calibration NumPy files and a manifest.

Parameters

pre_ftn	Callable. Pre-processing function that accepts an image path and returns a NumPy array.
data_dir	string. Directory containing the raw calibration images.
save_dir	string. Directory in which to store the generated NumPy files and metadata text file.
save_name	string. Basename for the saved assets. NumPy files are written to {save_dir}/{save_name}_npy and the manifest to {save_dir}/{save_name}.txt. Defaults to the basename of data_dir when omitted.
anno_json	string. Optional COCO-style annotation file. When provided, samples are randomly selected while maintaining class balance.
file_format	string. Filename format that uses image_idx (for example "%012d.jpg").
max_size	int. Maximum number of calibration samples. A value of -1 disables the limit.
remove_npy	bool. Remove existing NumPy outputs before generating new data.
seed	int. Random seed applied to sampling and class-balanced selection. Defaults to 2023.
save_calib_msg	bool. Persist the calibration metadata as an MSGpack file in addition to the manifest.
msg_path	string. Explicit MSGpack output path. When omitted, the path is auto-generated by dataset name and sample count.

Returns

string. Path to the generated text file that lists the preprocessed NumPy samples.

Note

Calibration samples are written as float32 NumPy arrays derived from the pre_ftn output. When max_size is positive and the dataset is larger, a random subset is used. COCO annotations override this sampling to balance categories.

Definition at line 40 of file make_calib_data.py.

◆ make_calib()

str qbcompiler.calibration.make_calib_data.make_calib	(	Union[str, dict]	args_pre,
		str	data_dir,
		str	save_dir,
		str	save_name = None,
		str	anno_json = None,
		str	file_format = "%012d.jpg",
		int	max_size = -1,
		bool	remove_npy = False,
		int	seed = 2023,
		bool	save_calib_msg = False,
		str	msg_path = None )

Create calibration NumPy files and a manifest using a YAML or dict preprocessing configuration.

Parameters

args_pre	string or dict. Path to a YAML file or an in-memory dictionary describing the preprocessing pipeline. See the preproc_builder class for all supported operators and parameters.
data_dir	string. Directory of calibration images.
save_dir	string. Directory where the generated NumPy files and manifest text file are stored.
save_name	string. Optional basename for outputs. Defaults to the basename of data_dir.
anno_json	string. Optional COCO-format annotation file used to sample images with class balance.
file_format	string. Filename template built from image_idx (defaults to "%012d.jpg").
max_size	int. Maximum number of calibration samples to generate; -1 keeps all available items.
remove_npy	bool. Remove existing NumPy outputs before running preprocessing.
seed	int. Random seed used for sampling. Defaults to 2023.
save_calib_msg	bool. Save calibration metadata as MSGpack alongside NumPy and text outputs.
msg_path	string. Explicit MSGpack output path. Auto-generated from dataset name and sample count when omitted.

Returns

string. Path to the manifest text file that lists the generated NumPy samples.

Note

The preprocessing pipeline is constructed through preproc_builder and executed via make_calib_man. Use the YAML schema to combine primitives such as GetImage, Resize, Normalize, Pad, CenterCrop, and SetOrder in a Pre-Order list.

Definition at line 139 of file make_calib_data.py.

◆ make_calib_llm()

List[str] qbcompiler.calibration.make_calib_data.make_calib_llm	(	Union[str, dict]	args_pre,
		str	dataset_name,
		Union[List[str], str]	subset_list,
		str	text_field,
		str	split,
		str	save_dir,
		int	max_calib = 512,
		Optional[str]	save_prefix = None )

Generate calibration embeddings for LLM models using Hugging Face datasets.

Args: args_pre: YAML path or preprocessing configuration dictionary. Must set Datatype to "LLM". dataset_name: Name of the Hugging Face dataset to load. subset_list: List of dataset subsets (e.g. language splits) to iterate. split: HF dataset split to use. text_field: Key containing text in dataset entries. save_dir: Root directory where calibration samples are stored. max_calib: Maximum number of calibration samples per subset. save_prefix: Override prefix used in output directory naming. If None, derives from tokenizer directory name when available.

Returns: List of paths to generated calibration txt files, one per subset.

Definition at line 200 of file make_calib_data.py.

◆ preprocess_fnc()

qbcompiler.calibration.preprocess.preprocess_fnc	(		fnc,
			Datatype = "Image" )

Definition at line 28 of file preprocess.py.

Variable Documentation

◆ DTYPE_GETDATA_MAP

dict qbcompiler.calibration.preprocess.DTYPE_GETDATA_MAP = {"Image": "GetImage", "Npy": "GetNpy", "LLM": "GetText"}

Definition at line 24 of file preprocess.py.

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