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.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+basic_ref_en.wav filter=lfs diff=lfs merge=lfs -text
+basic_ref_zh.wav filter=lfs diff=lfs merge=lfs -text
+F5_Transformer_opset19.rknn filter=lfs diff=lfs merge=lfs -text
+generated_audio-rknn2.wav filter=lfs diff=lfs merge=lfs -text

F5-TTS-ONNX-Inference-rknn2.py

@@ -0,0 +1,253 @@
+# import ztu_ort_enable_debug
+import re
+import time
+import jieba
+import torch
+import ztu_somemodelruntime_rknnlite2 as rknnort
+import onnxruntime
+import soundfile as sf
+import numpy as np
+from pydub import AudioSegment
+from pypinyin import lazy_pinyin, Style
+from tqdm import tqdm
+
+vocab_path           = "vocab.txt"                             # The F5-TTS model vocab download path.     URL: https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_v1_Base
+onnx_model_A         = "F5_Preprocess.onnx"                     # The exported onnx model path.
+onnx_model_B         = "F5_Transformer_opset19.onnx"                    # The exported onnx model path.
+onnx_model_C         = "F5_Decode.onnx"                         # The exported onnx model path.
+generated_audio      = "./generated_audio-rknn2.wav"
+test_in_english = False
+
+if test_in_english:
+    reference_audio  = "basic_ref_en.wav"
+    ref_text         = "Some call me nature, others call me mother nature."
+    gen_text         = "Some call me Dake, others call me QQ."
+else:
+    reference_audio  = "basic_ref_zh.wav"        # The reference audio path.
+    ref_text         = "对，这就是我，万人敬仰的太乙真人。"                                               # The ASR result of reference audio.
+    gen_text         = "哇，这个辣鸡芯片居然也能运行模型。"                                               # The target TTS.
+
+
+# RANDOM_SEED = 9527                                        # Set seed to reproduce the generated audio
+NFE_STEP = 32                                             # F5-TTS model setting, 0~31
+FUSE_NFE = 1                                              # Maintain the same values as the exported model.
+SPEED = 1.0                                               # Set for talking speed. Only works with dynamic_axes=True
+MODEL_SAMPLE_RATE = 24000                                 # Do not modify it.
+HOP_LENGTH = 256                                          # It affects the generated audio length and speech speed.
+
+
+
+with open(vocab_path, "r", encoding="utf-8") as f:
+    vocab_char_map = {}
+    for i, char in enumerate(f):
+        vocab_char_map[char[:-1]] = i
+vocab_size = len(vocab_char_map)
+
+
+# From the official code
+def convert_char_to_pinyin(text_list, polyphone=True):
+    if jieba.dt.initialized is False:
+        jieba.default_logger.setLevel(50)  # CRITICAL
+        jieba.initialize()
+
+    final_text_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return (
+            "\u3100" <= c <= "\u9fff"  # common chinese characters
+        )
+
+    for text in text_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_text_list.append(char_list)
+    return final_text_list
+
+
+# From the official code
+def list_str_to_idx(
+    text,#: list[str] | list[list[str]],
+    vocab_char_map,#: dict[str, int],  # {char: idx}
+    padding_value=-1
+):
+    get_idx = vocab_char_map.get
+    list_idx_tensors = [torch.tensor([get_idx(c, 0) for c in t], dtype=torch.int32) for t in text]
+    text = torch.nn.utils.rnn.pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+def normalize_to_int16(audio):
+    max_val = np.max(np.abs(audio))
+    scaling_factor = 32767.0 / max_val if max_val > 0 else 1.0
+    return (audio * float(scaling_factor)).astype(np.int16)
+
+ort_session_A = onnxruntime.InferenceSession(onnx_model_A, providers=['CPUExecutionProvider'], provider_options=None)
+model_type = ort_session_A._inputs_meta[0].type
+in_name_A = ort_session_A.get_inputs()
+out_name_A = ort_session_A.get_outputs()
+in_name_A0 = in_name_A[0].name
+in_name_A1 = in_name_A[1].name
+in_name_A2 = in_name_A[2].name
+out_name_A0 = out_name_A[0].name
+out_name_A1 = out_name_A[1].name
+out_name_A2 = out_name_A[2].name
+out_name_A3 = out_name_A[3].name
+out_name_A4 = out_name_A[4].name
+out_name_A5 = out_name_A[5].name
+out_name_A6 = out_name_A[6].name
+out_name_A7 = out_name_A[7].name
+
+
+ort_session_B = rknnort.InferenceSession(onnx_model_B)
+
+in_name_B = ort_session_B.get_inputs()
+out_name_B = ort_session_B.get_outputs()
+in_name_B0 = in_name_B[0].name
+in_name_B1 = in_name_B[1].name
+in_name_B2 = in_name_B[2].name
+in_name_B3 = in_name_B[3].name
+in_name_B4 = in_name_B[4].name
+in_name_B5 = in_name_B[5].name
+in_name_B6 = in_name_B[6].name
+in_name_B7 = in_name_B[7].name
+out_name_B0 = out_name_B[0].name
+out_name_B1 = out_name_B[1].name
+
+ort_session_C = onnxruntime.InferenceSession(onnx_model_C, providers=['CPUExecutionProvider'], provider_options=None)
+in_name_C = ort_session_C.get_inputs()
+out_name_C = ort_session_C.get_outputs()
+in_name_C0 = in_name_C[0].name
+in_name_C1 = in_name_C[1].name
+out_name_C0 = out_name_C[0].name
+
+# Load the input audio
+print(f"\nReference Audio: {reference_audio}")
+audio = np.array(AudioSegment.from_file(reference_audio).set_channels(1).set_frame_rate(MODEL_SAMPLE_RATE).get_array_of_samples(), dtype=np.float32)
+audio = normalize_to_int16(audio) # Normalization should happen on the final output, not here
+audio_len = len(audio)
+audio = audio.reshape(1, 1, -1)
+
+zh_pause_punc = r"。，、；：？！"
+ref_text_len = len(ref_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, ref_text))
+gen_text_len = len(gen_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, gen_text))
+ref_audio_len = audio_len // HOP_LENGTH + 1
+
+
+original_max_duration_val = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / SPEED)
+fixed_max_duration_val = 1536 # The hardcoded duration
+speed_adjustment_ratio = fixed_max_duration_val / original_max_duration_val
+
+print(f"Original estimated duration: {original_max_duration_val} frames")
+print(f"Fixed duration: {fixed_max_duration_val} frames")
+print(f"Speed adjustment ratio: {speed_adjustment_ratio:.3f}")
+
+# Force max_duration to the fixed value for the model input
+max_duration = np.array([fixed_max_duration_val], dtype=np.int64)
+
+print(f"zt: hack: force max_duration to {fixed_max_duration_val}")
+gen_text = convert_char_to_pinyin([ref_text + gen_text])
+text_ids = list_str_to_idx(gen_text, vocab_char_map).numpy()
+time_step = np.array([0], dtype=np.int32)
+
+
+device_type = None
+
+print("\n\nRun F5-TTS.")
+start_count = time.time()
+noise, rope_cos_q, rope_sin_q, rope_cos_k, rope_sin_k, cat_mel_text, cat_mel_text_drop, ref_signal_len = ort_session_A.run(
+        [out_name_A0, out_name_A1, out_name_A2, out_name_A3, out_name_A4, out_name_A5, out_name_A6, out_name_A7],
+        {
+            in_name_A0: audio,
+            in_name_A1: text_ids,
+            in_name_A2: max_duration
+        })
+end_count = time.time()
+print(f"\nPrepare input data time cost: {end_count - start_count:.3f} seconds")
+
+print("NFE_STEP: 0")
+for i in tqdm(range(0, NFE_STEP - 1, FUSE_NFE)):
+    noise, time_step = ort_session_B.run(
+        [out_name_B0, out_name_B1],
+        {
+            in_name_B0: noise,
+            in_name_B1: rope_cos_q,
+            in_name_B2: rope_sin_q,
+            in_name_B3: rope_cos_k,
+            in_name_B4: rope_sin_k,
+            in_name_B5: cat_mel_text,
+            in_name_B6: cat_mel_text_drop,
+            in_name_B7: time_step
+        })
+    print(f"NFE_STEP: {i + FUSE_NFE}")
+
+start_count = time.time()
+generated_signal = ort_session_C.run(
+        [out_name_C0],
+        {
+            in_name_C0: noise,
+            in_name_C1: ref_signal_len
+        })[0]
+end_count = time.time()
+print(f"Decode time cost: {end_count - start_count:.3f} seconds")
+
+# Adjust speed of the generated audio
+print("\nAdjusting audio speed...")
+# Flatten signal and ensure it's float32 before normalization
+generated_signal_flat = generated_signal.flatten().astype(np.float32)
+
+# Normalize the generated signal to int16 for pydub
+generated_signal_int16 = normalize_to_int16(generated_signal_flat)
+
+# Create an AudioSegment from the int16 numpy array
+try:
+    segment = AudioSegment(
+        generated_signal_int16.tobytes(),
+        frame_rate=MODEL_SAMPLE_RATE,
+        sample_width=generated_signal_int16.dtype.itemsize,
+        channels=1
+    )
+
+    # Apply speed adjustment
+    # Note: speedup > 1.0 means faster, < 1.0 means slower
+    if speed_adjustment_ratio != 1.0:
+         print(f"Applying speed change with factor: {speed_adjustment_ratio:.3f}")
+         adjusted_segment = segment.speedup(playback_speed=speed_adjustment_ratio)
+    else:
+         print("No speed adjustment needed.")
+         adjusted_segment = segment # No adjustment needed
+
+    # Save the adjusted audio using pydub's export
+    adjusted_segment.export(generated_audio, format="wav")
+    print(f"\nSpeed-adjusted audio saved to: {generated_audio}")
+
+except Exception as e:
+    print(f"Error during audio speed adjustment or saving: {e}")
+    print("Saving original audio instead.")
+    # Fallback to saving the original audio if adjustment fails
+    sf.write(generated_audio, generated_signal_flat, MODEL_SAMPLE_RATE, format='WAVEX')
+
+print(f"\nAudio generation and processing is complete.\n\nONNXRuntime Time Cost in Seconds:\n{end_count - start_count:.3f}")

F5_Decode.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a16fea891beb4889b47e5987b80c841bb996beaccc1d1fe27a1f9323a089a6da
+size 62550703

F5_Preprocess.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e71d3ed14e90ba3fc1e83560512e86a771e25b6f0be7789b2cf53a5c9ba5617d
+size 68549853

F5_Transformer_opset19.rknn

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+version https://git-lfs.github.com/spec/v1
+oid sha256:461b116017297289f5cd7ac28725e916dc06a8ea11d2354046a1d83fe0da9092
+size 757947208

basic_ref_en.wav

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+oid sha256:b0e22048e72414fcc1e6b6342e47a774d748a195ed34e4a5b3fcf416707f2b71
+size 256018

basic_ref_zh.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96724a113240d1f82c6ded1334122f0176b96c9226ccd3c919e625bcfd2a3ede
+size 324558

convert_F5_Transformer_opset19.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+# ztu_somemodelruntime_rknn2: F5_Transformer_opset19
+
+from rknn.api import RKNN
+import os
+import numpy as np
+
+def main():
+    # 创建RKNN实例
+    rknn = RKNN(verbose=True)
+
+    # ONNX模型路径
+    ONNX_MODEL = "F5_Transformer_opset19.onnx"
+    # 输出RKNN模型路径
+    RKNN_MODEL = "F5_Transformer_opset19.rknn"
+
+    # 配置参数
+    print("--> Config model")
+    ret = rknn.config(target_platform="rk3588",
+                      dynamic_input=None,
+                      disable_rules=[])
+    if ret != 0:
+        print('Config model failed!')
+        exit(ret)
+
+    # 加载ONNX模型
+    print("--> Loading model")
+    ret = rknn.load_onnx(model=ONNX_MODEL,
+                         inputs=['noise', 'rope_cos_q', 'rope_sin_q', 'rope_cos_k', 'rope_sin_k', 'cat_mel_text', 'cat_mel_text_drop', 'time_step.1'],
+                         input_size_list=[[1, 1536, 100], [2, 16, 1536, 64], [2, 16, 1536, 64], [2, 16, 64, 1536], [2, 16, 64, 1536], [1, 1536, 612], [1, 1536, 612], [1]])
+    if ret != 0:
+        print('Load model failed!')
+        exit(ret)
+
+    # 构建模型
+    print("--> Building model")
+    ret = rknn.build(do_quantization=False)
+    if ret != 0:
+        print('Build model failed!')
+        exit(ret)
+
+    # 导出RKNN模型
+    print("--> Export RKNN model")
+    ret = rknn.export_rknn(RKNN_MODEL)
+    if ret != 0:
+        print('Export RKNN model failed!')
+        exit(ret)
+
+    print(f'Done! The converted RKNN model has been saved to: ' + RKNN_MODEL)
+    rknn.release()
+
+if __name__ == '__main__':
+    main()

convert_opset.py

@@ -0,0 +1,41 @@
+import onnx
+from onnx import version_converter, helper
+
+def convert_opset(input_model_path: str, output_model_path: str, target_opset: int):
+    """
+    使用 onnx.version_converter 安全地转换 ONNX 模型的 Opset 版本。
+
+    Args:
+        input_model_path (str): 输入 ONNX 模型文件路径。
+        output_model_path (str): 输出转换后 ONNX 模型文件路径。
+        target_opset (int): 目标 Opset 版本号。
+    """
+    try:
+        # 1. 加载模型
+        original_model = onnx.load(input_model_path)
+        print(f"原始 Opset Import: {original_model.opset_import}")
+
+        # 2. 使用 version_converter 进行转换
+        # 它会尝试将算子适配到目标 Opset
+        converted_model = version_converter.convert_version(original_model, target_opset)
+
+        # 3. 检查转换后的模型
+        onnx.checker.check_model(converted_model)
+        print("ONNX Checker 检查转换后的模型通过。")
+
+        # 4. 保存转换后的模型
+        onnx.save(converted_model, output_model_path)
+        print(f"模型已安全转换为 Opset {target_opset} 并保存到: {output_model_path}")
+        print(f"转换后的 Opset Import: {converted_model.opset_import}")
+
+    except ValueError as e:
+         print(f"转换失败：模型包含无法转换到 Opset {target_opset} 的算子。错误: {e}")
+    except Exception as e:
+        print(f"处理模型时发生错误: {e}")
+
+# --- 使用示例 ---
+input_model = "F5_Transformer.onnx"
+output_model_converted = "F5_Transformer_opset19.onnx"
+target_version = 19
+
+convert_opset(input_model, output_model_converted, target_version)

generated_audio-rknn2.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fabd2f11b0d6f631f9768b5b90d8a844a4f2bba27656de5ca687a381060b49d7
+size 383708

vocab.txt

@@ -0,0 +1,2545 @@
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+)
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+=
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+퀴
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+턴
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+품
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+프
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+피
+필
+하
+학
+한
+할
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+합
+항
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+햇
+했
+행
+허
+험
+형
+혜
+호
+혼
+홀
+화
+회
+획
+후
+휴
+흐
+흔
+희
+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶

ztu_somemodelruntime_rknnlite2.py

@@ -0,0 +1,569 @@
+# 模块级常量和函数
+from rknnlite.api import RKNNLite
+import numpy as np
+import os
+import warnings
+import logging
+from typing import List, Dict, Union, Optional
+
+try:
+    import onnxruntime as ort
+    HAS_ORT = True
+except ImportError:
+    HAS_ORT = False
+    warnings.warn("onnxruntime未安装,只能使用RKNN后端", ImportWarning)
+
+# 配置日志
+logger = logging.getLogger("somemodelruntime_rknnlite2")
+logger.setLevel(logging.ERROR)  # 默认只输出错误信息
+if not logger.handlers:
+    handler = logging.StreamHandler()
+    handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+    logger.addHandler(handler)
+
+# ONNX Runtime日志级别到Python logging级别的映射
+_LOGGING_LEVEL_MAP = {
+    0: logging.DEBUG,    # Verbose
+    1: logging.INFO,     # Info
+    2: logging.WARNING,  # Warning
+    3: logging.ERROR,    # Error
+    4: logging.CRITICAL  # Fatal
+}
+
+# 检查环境变量中的日志级别设置
+try:
+    env_log_level = os.getenv('ZTU_MODELRT_RKNNL2_LOG_LEVEL')
+    if env_log_level is not None:
+        log_level = int(env_log_level)
+        if log_level in _LOGGING_LEVEL_MAP:
+            logger.setLevel(_LOGGING_LEVEL_MAP[log_level])
+            logger.info(f"从环境变量设置日志级别: {log_level}")
+        else:
+            logger.warning(f"环境变量ZTU_MODELRT_RKNNL2_LOG_LEVEL的值无效: {log_level}, 应该是0-4之间的整数")
+except ValueError:
+    logger.warning(f"环境变量ZTU_MODELRT_RKNNL2_LOG_LEVEL的值无效: {env_log_level}, 应该是0-4之间的整数")
+
+
+def set_default_logger_severity(level: int) -> None:
+    """
+    Sets the default logging severity. 0:Verbose, 1:Info, 2:Warning, 3:Error, 4:Fatal
+    
+    Args:
+        level: 日志级别(0-4)
+    """
+    if level not in _LOGGING_LEVEL_MAP:
+        raise ValueError(f"无效的日志级别: {level}, 应该是0-4之间的整数")
+    logger.setLevel(_LOGGING_LEVEL_MAP[level])
+
+def set_default_logger_verbosity(level: int) -> None:
+    """
+    Sets the default logging verbosity level. To activate the verbose log, 
+    you need to set the default logging severity to 0:Verbose level.
+    
+    Args:
+        level: 日志级别(0-4)
+    """
+    set_default_logger_severity(level)
+
+# RKNN tensor type到numpy dtype的映射
+RKNN_DTYPE_MAP = {
+    0: np.float32,  # RKNN_TENSOR_FLOAT32
+    1: np.float16,  # RKNN_TENSOR_FLOAT16
+    2: np.int8,     # RKNN_TENSOR_INT8
+    3: np.uint8,    # RKNN_TENSOR_UINT8
+    4: np.int16,    # RKNN_TENSOR_INT16
+    5: np.uint16,   # RKNN_TENSOR_UINT16
+    6: np.int32,    # RKNN_TENSOR_INT32
+    7: np.uint32,   # RKNN_TENSOR_UINT32
+    8: np.int64,    # RKNN_TENSOR_INT64
+    9: bool,        # RKNN_TENSOR_BOOL
+    10: np.int8,    # RKNN_TENSOR_INT4 (用int8表示)
+}
+
+def get_available_providers() -> List[str]:
+    """
+    获取可用的设备提供者列表(为保持接口兼容性的占位函数)
+    
+    Returns:
+        list: 可用的设备提供者列表,总是返回["CPUExecutionProvider", "somemodelruntime_rknnlite2_ExecutionProvider"]
+    """
+    return ["CPUExecutionProvider", "somemodelruntime_rknnlite2_ExecutionProvider"]
+
+
+def get_device() -> str:
+    """
+    获取当前设备
+
+    Returns:
+        str: 当前设备
+    """
+    return "RKNN2"
+
+def get_version_info() -> Dict[str, str]:
+    """
+    获取版本信息
+    
+    Returns:
+        dict: 包含API和驱动版本信息的字典
+    """
+    runtime = RKNNLite()
+    version = runtime.get_sdk_version()
+    return {
+        "api_version": version.split('\n')[2].split(': ')[1].split(' ')[0],
+        "driver_version": version.split('\n')[3].split(': ')[1]
+    }
+
+class IOTensor:
+    """输入/输出张量的信息封装类"""
+    def __init__(self, name, shape, type=None):
+        self.name = name.decode() if isinstance(name, bytes) else name
+        self.shape = shape
+        self.type = type
+
+    def __str__(self):
+        return f"IOTensor(name='{self.name}', shape={self.shape}, type={self.type})"
+
+class SessionOptions:
+    """会话选项类"""
+    def __init__(self):
+        self.enable_profiling = False  # 是否使用性能分析
+        self.intra_op_num_threads = 1  # 设置RKNN的线程数, 对应rknn的core_mask
+        self.log_severity_level = -1 # 另一个设置日志级别的参数
+        self.log_verbosity_level = -1 # 另一个设置日志级别的参数
+
+
+class InferenceSession:
+    """
+    RKNNLite运行时封装类,API风格类似ONNX Runtime
+    """
+
+    def __new__(cls, model_path: str, sess_options: Optional[SessionOptions] = None, **kwargs):
+        processed_path = InferenceSession._process_model_path(model_path, sess_options)
+        if isinstance(processed_path, str) and processed_path.lower().endswith('.onnx'):
+            logger.info("使用ONNX Runtime加载模型")
+            if not HAS_ORT:
+                raise RuntimeError("未安装onnxruntime,无法加载ONNX模型")
+            return ort.InferenceSession(processed_path, sess_options=sess_options, **kwargs)
+        else:
+            # 如果不是 ONNX 模型，则调用父类的 __new__ 创建 InferenceSession 实例
+            instance = super().__new__(cls)
+            # 保存处理后的路径
+            instance._processed_path = processed_path
+            return instance
+
+    def __init__(self, model_path: str, sess_options: Optional[SessionOptions] = None, **kwargs):
+        """
+        初始化运行时并加载模型
+        
+        Args:
+            model_path: 模型文件路径(.rknn或.onnx)
+            sess_options: 会话选项
+            **kwargs: 其他初始化参数
+        """
+        options = sess_options or SessionOptions()
+
+        # 只在未设置环境变量时使用SessionOptions中的日志级别
+        if os.getenv('ZTU_MODELRT_RKNNL2_LOG_LEVEL') is None:
+            if options.log_severity_level != -1:
+                set_default_logger_severity(options.log_severity_level)
+            if options.log_verbosity_level != -1:
+                set_default_logger_verbosity(options.log_verbosity_level)
+            
+        # 使用__new__中处理好的路径
+        model_path = getattr(self, '_processed_path', model_path)
+        if isinstance(model_path, str) and model_path.lower().endswith('.onnx'):
+            # 避免重复加载 ONNX 模型
+            return
+
+        # ... 现有的 RKNN 模型加载和初始化代码 ...
+        self.model_path = model_path
+        if not os.path.exists(self.model_path):
+            logger.error(f"模型文件不存在: {self.model_path}")
+            raise FileNotFoundError(f"模型文件不存在: {self.model_path}")
+
+        self.runtime = RKNNLite(verbose=options.enable_profiling)
+
+        logger.debug(f"正在加载模型: {self.model_path}")
+        ret = self.runtime.load_rknn(self.model_path)
+        if ret != 0:
+            logger.error(f"加载RKNN模型失败: {self.model_path}")
+            raise RuntimeError(f'加载RKNN模型失败: {self.model_path}')
+        logger.debug("模型加载成功")
+
+
+        if options.intra_op_num_threads == 1:
+            core_mask = RKNNLite.NPU_CORE_AUTO
+        elif options.intra_op_num_threads == 2:
+            core_mask = RKNNLite.NPU_CORE_0_1
+        elif options.intra_op_num_threads == 3:
+            core_mask = RKNNLite.NPU_CORE_0_1_2
+        else:
+            raise ValueError(f"intra_op_num_threads的值无效: {options.intra_op_num_threads}, 只能是1,2或3")
+
+        logger.debug("正在初始化运行时环境")
+        ret = self.runtime.init_runtime(core_mask=core_mask)
+        if ret != 0:
+            logger.error("初始化运行时环境失败")
+            raise RuntimeError('初始化运行时环境失败')
+        logger.debug("运行时环境初始化成功")
+
+        self._init_io_info()
+        self.options = options
+
+    def get_performance_info(self) -> Dict[str, float]:
+        """
+        获取性能信息
+        
+        Returns:
+            dict: 包含性能信息的字典
+        """
+        if not self.options.perf_debug:
+            raise RuntimeError("性能分析未启用,请在SessionOptions中设置perf_debug=True")
+            
+        perf = self.runtime.rknn_runtime.get_run_perf()
+        return {
+            "run_duration": perf.run_duration / 1000.0  # 转换为毫秒
+        }
+
+    def set_core_mask(self, core_mask: int) -> None:
+        """
+        设置NPU核心使用模式
+        
+        Args:
+            core_mask: NPU核心掩码,使用NPU_CORE_*常量
+        """
+        ret = self.runtime.rknn_runtime.set_core_mask(core_mask)
+        if ret != 0:
+            raise RuntimeError("设置NPU核心模式失败")
+
+    @staticmethod
+    def _process_model_path(model_path, sess_options):
+        """
+        处理模型路径,支持.onnx和.rknn文件
+        
+        Args:
+            model_path: 模型文件路径
+        """
+        # 如果是ONNX文件,检查是否需要自动加载RKNN
+        if model_path.lower().endswith('.onnx'):
+            logger.info("检测到ONNX模型文件")
+            
+            # 获取需要跳过自动加载的模型列表
+            skip_models = os.getenv('ZTU_MODELRT_RKNNL2_SKIP', '').strip()
+            if skip_models:
+                skip_list = [m.strip() for m in skip_models.split(',')]
+                # 获取模型文件名(不含路径)用于匹配
+                model_name = os.path.basename(model_path)
+                if model_name.lower() in [m.lower() for m in skip_list]:
+                    logger.info(f"模型{model_name}在跳过列表中,将使用ONNX Runtime")
+                    return model_path
+            
+            # 构造RKNN文件路径
+            rknn_path = os.path.splitext(model_path)[0] + '.rknn'
+            if os.path.exists(rknn_path):
+                logger.info(f"找到对应的RKNN模型,将使用RKNN: {rknn_path}")
+                return rknn_path
+            else:
+                logger.info("未找到对应的RKNN模型,将使用ONNX Runtime")
+                return model_path
+            
+        return model_path
+        
+    def _convert_nhwc_to_nchw(self, shape):
+        """将NHWC格式的shape转换为NCHW格式"""
+        if len(shape) == 4:
+            # NHWC -> NCHW
+            n, h, w, c = shape
+            return [n, c, h, w]
+        return shape
+        
+    def _init_io_info(self):
+        """初始化模型的输入输出信息"""
+        runtime = self.runtime.rknn_runtime
+        
+        # 获取输入输出数量
+        n_input, n_output = runtime.get_in_out_num()
+        
+        # 获取输入信息
+        self.input_tensors = []
+        for i in range(n_input):
+            attr = runtime.get_tensor_attr(i)
+            shape = [attr.dims[j] for j in range(attr.n_dims)]
+            # 对四维输入进行NHWC到NCHW的转换
+            shape = self._convert_nhwc_to_nchw(shape)
+            # 获取dtype
+            dtype = RKNN_DTYPE_MAP.get(attr.type, None)
+            tensor = IOTensor(attr.name, shape, dtype)
+            self.input_tensors.append(tensor)
+            
+        # 获取输出信息
+        self.output_tensors = []
+        for i in range(n_output):
+            attr = runtime.get_tensor_attr(i, is_output=True)
+            shape = runtime.get_output_shape(i)
+            # 获取dtype
+            dtype = RKNN_DTYPE_MAP.get(attr.type, None)
+            tensor = IOTensor(attr.name, shape, dtype)
+            self.output_tensors.append(tensor)
+        
+    def get_inputs(self):
+        """
+        获取模型输入信息
+        
+        Returns:
+            list: 包含输入信息的列表
+        """
+        return self.input_tensors
+        
+    def get_outputs(self):
+        """
+        获取模型输出信息
+        
+        Returns:
+            list: 包含输出信息的列表
+        """
+        return self.output_tensors
+        
+    def run(self, output_names=None, input_feed=None, data_format="nchw", **kwargs):
+        """
+        执行模型推理
+        
+        Args:
+            output_names: 输出节点名称列表,指定需要返回哪些输出
+            input_feed: 输入数据字典或列表
+            data_format: 输入数据格式,"nchw"或"nhwc"
+            **kwargs: 其他运行时参数
+            
+        Returns:
+            list: 模型输出结果列表,如果指定了output_names则只返回指定的输出
+        """
+        if input_feed is None:
+            logger.error("input_feed不能为None")
+            raise ValueError("input_feed不能为None")
+            
+        # 准备输入数据
+        if isinstance(input_feed, dict):
+            # 如果是字典,按照模型输入顺序排列
+            inputs = []
+            input_map = {tensor.name: i for i, tensor in enumerate(self.input_tensors)}
+            for tensor in self.input_tensors:
+                if tensor.name not in input_feed:
+                    raise ValueError(f"缺少输入: {tensor.name}")
+                inputs.append(input_feed[tensor.name])
+        elif isinstance(input_feed, (list, tuple)):
+            # 如果是列表,确保长度匹配
+            if len(input_feed) != len(self.input_tensors):
+                raise ValueError(f"输入数量不匹配: 期望{len(self.input_tensors)}, 实际{len(input_feed)}")
+            inputs = list(input_feed)
+        else:
+            logger.error("input_feed必须是字典或列表类型")
+            raise ValueError("input_feed必须是字典或列表类型")
+            
+        # 执行推理
+        try:
+            logger.debug("开始执行推理")
+            all_outputs = self.runtime.inference(inputs=inputs, data_format=data_format)
+            
+            # 如果没有指定output_names,返回所有输出
+            if output_names is None:
+                return all_outputs
+                
+            # 获取指定的输出
+            output_map = {tensor.name: i for i, tensor in enumerate(self.output_tensors)}
+            selected_outputs = []
+            for name in output_names:
+                if name not in output_map:
+                    raise ValueError(f"未找到输出节点: {name}")
+                selected_outputs.append(all_outputs[output_map[name]])
+                    
+            return selected_outputs
+            
+        except Exception as e:
+            logger.error(f"推理执行失败: {str(e)}")
+            raise RuntimeError(f"推理执行失败: {str(e)}")
+        
+    def close(self):
+        """
+        关闭会话,释放资源
+        """
+        if self.runtime is not None:
+            logger.info("正在释放运行时资源")
+            self.runtime.release()
+            self.runtime = None
+            
+    def __enter__(self):
+        return self
+        
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+    def end_profiling(self) -> Optional[str]:
+        """
+        结束性能分析的存根方法
+        
+        Returns:
+            Optional[str]: None
+        """
+        warnings.warn("end_profiling()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return None
+        
+    def get_profiling_start_time_ns(self) -> int:
+        """
+        获取性能分析开始时间的存根方法
+        
+        Returns:
+            int: 0
+        """
+        warnings.warn("get_profiling_start_time_ns()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return 0
+        
+    def get_modelmeta(self) -> Dict[str, str]:
+        """
+        获取模型元数据的存根方法
+        
+        Returns:
+            Dict[str, str]: 空字典
+        """
+        warnings.warn("get_modelmeta()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {}
+        
+    def get_session_options(self) -> SessionOptions:
+        """
+        获取会话选项
+        
+        Returns:
+            SessionOptions: 当前会话选项
+        """
+        return self.options
+        
+    def get_providers(self) -> List[str]:
+        """
+        获取当前使用的providers的存根方法
+        
+        Returns:
+            List[str]: ["CPUExecutionProvider"]
+        """
+        warnings.warn("get_providers()是存根方法,始终返回CPUExecutionProvider", RuntimeWarning, stacklevel=2)
+        return ["CPUExecutionProvider"]
+        
+    def get_provider_options(self) -> Dict[str, Dict[str, str]]:
+        """
+        获取provider选项的存根方法
+        
+        Returns:
+            Dict[str, Dict[str, str]]: 空字典
+        """
+        warnings.warn("get_provider_options()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {} 
+
+    def get_session_config(self) -> Dict[str, str]:
+        """
+        获取会话配置的存根方法
+        
+        Returns:
+            Dict[str, str]: 空字典
+        """
+        warnings.warn("get_session_config()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {}
+
+    def get_session_state(self) -> Dict[str, str]:
+        """
+        获取会话状态的存根方法
+        
+        Returns:
+            Dict[str, str]: 空字典
+        """
+        warnings.warn("get_session_state()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {}
+
+    def set_session_config(self, config: Dict[str, str]) -> None:
+        """
+        设置会话配置的存根方法
+        
+        Args:
+            config: 会话配置字典
+        """
+        warnings.warn("set_session_config()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+
+    def get_memory_info(self) -> Dict[str, int]:
+        """
+        获取内存使用信息的存根方法
+        
+        Returns:
+            Dict[str, int]: 空字典
+        """
+        warnings.warn("get_memory_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {}
+
+    def set_memory_pattern(self, enable: bool) -> None:
+        """
+        设置内存模式的存根方法
+        
+        Args:
+            enable: 是否启用内存模式
+        """
+        warnings.warn("set_memory_pattern()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+
+    def disable_memory_pattern(self) -> None:
+        """
+        禁用内存模式的存根方法
+        """
+        warnings.warn("disable_memory_pattern()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+
+    def get_optimization_level(self) -> int:
+        """
+        获取优化级别的存根方法
+        
+        Returns:
+            int: 0
+        """
+        warnings.warn("get_optimization_level()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return 0
+
+    def set_optimization_level(self, level: int) -> None:
+        """
+        设置优化级别的存根方法
+        
+        Args:
+            level: 优化级别
+        """
+        warnings.warn("set_optimization_level()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+
+    def get_model_metadata(self) -> Dict[str, str]:
+        """
+        获取模型元数据的存根方法(与get_modelmeta不同的接口)
+        
+        Returns:
+            Dict[str, str]: 空字典
+        """
+        warnings.warn("get_model_metadata()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return {}
+
+    def get_model_path(self) -> str:
+        """
+        获取模型路径
+        
+        Returns:
+            str: 模型文件路径
+        """
+        return self.model_path
+
+    def get_input_type_info(self) -> List[Dict[str, str]]:
+        """
+        获取输入类型信息的存根方法
+        
+        Returns:
+            List[Dict[str, str]]: 空列表
+        """
+        warnings.warn("get_input_type_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return []
+
+    def get_output_type_info(self) -> List[Dict[str, str]]:
+        """
+        获取输出类型信息的存根方法
+        
+        Returns:
+            List[Dict[str, str]]: 空列表
+        """
+        warnings.warn("get_output_type_info()是存根方法,不提供实际功能", RuntimeWarning, stacklevel=2)
+        return []