onnx_runner.py

from pathlib import Path

import cv2
import numpy as np
import onnxruntime as ort


def parse_input_shape_fmt(input_shape):
    """parse input shape is nchw or nhwc format.
    We assume c is smaller than h&w dimensions
    """
    assert len(input_shape) == 4

    c1, c2, c3 = input_shape[1:]

    if c1 < min(c2, c3):  # c1 is channel dimension
        return "nchw"
    elif c3 < min(c1, c2):  # c3 is channel dimension
        return "nhwc"
    else:
        raise ValueError(f"can not parse input format for shape: {input_shape}")


def preprocess(img_bgr: np.ndarray, input_shape_hw: tuple[int, int]):
    in_h, in_w = input_shape_hw

    resized_bgr = cv2.resize(img_bgr, (in_w, in_h), interpolation=cv2.INTER_LINEAR)
    resized_rgb = cv2.cvtColor(resized_bgr, cv2.COLOR_BGR2RGB)
    normed_rgb = (resized_rgb / 255.0 - 0.5) / 0.5  # norm 0~255 -> -1~1

    return normed_rgb


def postprocess(pred_3d: np.ndarray, pred_fmt: str, origin_hw: tuple[int, int]):
    de_normed_3d = (pred_3d * 0.5 + 0.5) * 255  # de-norm -1~1 -> 0~255

    if pred_fmt == "nchw":
        hwc = np.transpose(de_normed_3d, [1, 2, 0])  # chw -> hwc
    else:  # nhwc
        hwc = de_normed_3d  # unchanged

    pred_rgb = np.clip(hwc, 0, 255).astype(np.uint8)
    pred_bgr = cv2.cvtColor(pred_rgb, cv2.COLOR_RGB2BGR)

    if tuple(pred_bgr.shape[:2]) != tuple(origin_hw):
        pred_bgr = cv2.resize(pred_bgr, origin_hw[::-1], interpolation=cv2.INTER_LINEAR)

    return pred_bgr


class OnnxRunner:
    def __init__(self, onnx_path, latent_path=None, debug=False):
        if "CUDAExecutionProvider" in ort.get_available_providers():
            providers = ["CUDAExecutionProvider"]
        else:
            providers = ["CPUExecutionProvider"]

        ort_session = ort.InferenceSession(str(onnx_path), providers=providers)

        input0 = ort_session.get_inputs()[0]
        self.input_name = input0.name
        self.input_shape = tuple(input0.shape)
        self.input_format = parse_input_shape_fmt(input0.shape)
        self.ort_session = ort_session
        self.debug = debug

        if self.input_format == "nchw":
            self._in_h, self._in_w = self.input_shape[2:]
        else:  # nhwc
            self._in_h, self._in_w = self.input_shape[1:3]

        if len(ort_session.get_inputs()) == 2:
            latent_input = ort_session.get_inputs()[1]
            self.latent_input_name = latent_input.name
            if latent_path is not None and Path(latent_path).is_file():
                latent = np.load(str(latent_path))  # nchw format
                latent = np.transpose(latent, [0, 2, 3, 1])  # nchw -> nhwc
            else:
                rng = np.random.default_rng(seed=5122)
                latent = rng.standard_normal(latent_input.shape)
            self.latent = np.float32(latent)
        else:
            self.latent_input_name = None

        if debug:
            self._dbg_out_dir = Path(__file__).parent / "outputs"
            self._dbg_out_dir.mkdir(exist_ok=True, parents=True)

    def run(self, original_bgr: np.ndarray) -> np.ndarray:
        """Enhance given uint8 bgr image, and return enhanced uint8 bgr image."""
        assert original_bgr.dtype == np.uint8
        assert original_bgr.ndim == 3
        assert original_bgr.shape[2] == 3

        # =====================
        # preprocessing
        # =====================
        input_hwc = preprocess(original_bgr, (self._in_h, self._in_w))

        # =====================
        # inference
        # =====================
        if self.input_format == "nchw":
            input_3d = np.transpose(input_hwc, [2, 0, 1])  # hwc -> chw
        else:  # nhwc
            input_3d = input_hwc

        feed = {
            self.input_name: np.float32(input_3d[None, ...]),
        }
        if self.latent_input_name is not None:
            feed[self.latent_input_name] = self.latent

        outputs = self.ort_session.run(None, feed)

        pred_3d: np.ndarray = outputs[0][0]
        enhanced_bgr = postprocess(pred_3d, self.input_format, original_bgr.shape[:2])

        return enhanced_bgr