# F5隐写方法的模拟实现 def f5_hide(image_data, secret_message): # 量化矩阵 QUANTIZATION_MATRIX = np.array([[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]]) # 将秘密信息转换为二进制字符串 secret_bits = ''.join(format(ord(char), '08b') for char in secret_message) # 转换为YUV色彩空间 yuv_image = cv2.cvtColor(image_data, cv2.COLOR_BGR2YCrCb) y_channel, u_channel, v_channel = cv2.split(yuv_image) # 对Y通道进行分块DCT变换 h, w = y_channel.shape block_size = 8 bit_idx = 0 # 为了增加安全性，打乱DCT块的处理顺序（洗牌过程） blocks = [(i, j) for i in range(0, h, block_size) for j in range(0, w, block_size)] random.shuffle(blocks) for (i, j) in blocks: block = y_channel[i:i+block_size, j:j+block_size] if block.shape == (block_size, block_size): dct_block = cv2.dct(block.astype(np.float32)) dct_block = np.round(dct_block / QUANTIZATION_MATRIX) # 嵌入秘密信息 for x in range(block_size): for y in range(block_size): # 跳过直流系数（即dct_block[0, 0]） if x == 0 and y == 0: continue if bit_idx < len(secret_bits) and dct_block[x, y] != 0: # 使用伴随比特翻转的方法嵌入秘密信息 if (int(dct_block[x, y]) % 2) != int(secret_bits[bit_idx]): if dct_block[x, y] > 0: dct_block[x, y] -= 1 else: dct_block[x, y] += 1 bit_idx += 1 if bit_idx >= len(secret_bits): break # 反量化并进行逆DCT dct_block = dct_block * QUANTIZATION_MATRIX block = cv2.idct(dct_block) y_channel[i:i+block_size, j:j+block_size] = block # 合并Y、U、V通道 yuv_image = cv2.merge([y_channel, u_channel, v_channel]) encoded_image_data = cv2.cvtColor(yuv_image, cv2.COLOR_YCrCb2BGR) return encoded_image_data将这个转换为c++代码void f5EmbedColorImage(cv::Mat& image, const std::string& secret_message)

import tkinter as tk from tkinter import filedialog, messagebox from PIL import Image, ImageTk import cv2 import numpy as np from scipy.fftpack import dct, idct import threading class DCTCompressorApp: def init(self, root): self.root = root self.root.title("DCT图像压缩工具") self.root.geometry("1100x850") # 图像变量 self.original_image = None self.compressed_image = None self.residual_image = None self.tk_images = [] # 防止图像被垃圾回收 self.filename = None self.compression_ratio = 1.0 # DCT参数 self.quality = tk.IntVar(value=50) # 压缩质量 (1-100) self.block_size = tk.IntVar(value=8) # DCT块大小 self.show_dct = tk.BooleanVar(value=False) # 是否显示DCT系数 # 创建界面 self.create_widgets() def create_widgets(self): # 菜单栏 menubar = tk.Menu(self.root) filemenu = tk.Menu(menubar, tearoff=0) filemenu.add_command(label="打开", command=self.open_image) filemenu.add_command(label="保存压缩图", command=self.save_compressed) filemenu.add_command(label="保存残差图", command=self.save_residual) filemenu.add_separator() filemenu.add_command(label="退出", command=self.root.quit) menubar.add_cascade(label="文件", menu=filemenu) self.root.config(menu=menubar) # 控制面板 control_frame = tk.Frame(self.root) control_frame.pack(side=tk.LEFT, fill=tk.Y, padx=10, pady=10) # 压缩参数 param_frame = tk.LabelFrame(control_frame, text="DCT压缩参数", padx=5, pady=5) param_frame.pack(fill=tk.X, padx=5, pady=5) tk.Label(param_frame, text="压缩质量:").pack(anchor=tk.W) tk.Scale(param_frame, from_=1, to=100, orient=tk.HORIZONTAL, variable=self.quality).pack(fill=tk.X) tk.Label(param_frame, text="块大小:").pack(anchor=tk.W) tk.Scale(param_frame, from_=4, to=32, resolution=4, orient=tk.HORIZONTAL, variable=self.block_size).pack(fill=tk.X) tk.Checkbutton(param_frame, text="显示DCT系数", variable=self.show_dct).pack(anchor=tk.W) # 操作按钮 btn_frame = tk.Frame(control_frame) btn_frame.pack(fill=tk.X, padx=5, pady=10) tk.Button(btn_frame, text="执行DCT压缩", command=self.compress_image).pack(side=tk.TOP, fill=tk.X, pady=5) tk.Button(btn_frame, text="重置图像", command=self.reset_image).pack(side=tk.TOP, fill=tk.X, pady=5) # 信息显示 info_frame = tk.LabelFrame(control_frame, text="压缩信息", padx=5, pady=5) info_frame.pack(fill=tk.X, padx=5, pady=5) self.info_label = tk.Label(info_frame, text="压缩比: -\nPSNR: - dB", justify=tk.LEFT) self.info_label.pack() # 图像显示区域 display_frame = tk.Frame(self.root) display_frame.pack(side=tk.RIGHT, expand=True, fill=tk.BOTH, padx=10, pady=10) # 原始图像 orig_frame = tk.LabelFrame(display_frame, text="原始图像", padx=5, pady=5) orig_frame.pack(side=tk.LEFT, expand=True, fill=tk.BOTH) self.orig_canvas = tk.Canvas(orig_frame, bg="#f0f0f0", bd=0, highlightthickness=0) self.orig_canvas.pack(expand=True, fill=tk.BOTH) # 压缩图像 comp_frame = tk.LabelFrame(display_frame, text="压缩图像", padx=5, pady=5) comp_frame.pack(side=tk.LEFT, expand=True, fill=tk.BOTH) self.comp_canvas = tk.Canvas(comp_frame, bg="#f0f0f0", bd=0, highlightthickness=0) self.comp_canvas.pack(expand=True, fill=tk.BOTH) # 残差图像 res_frame = tk.LabelFrame(display_frame, text="残差图像", padx=5, pady=5) res_frame.pack(side=tk.LEFT, expand=True, fill=tk.BOTH) self.res_canvas = tk.Canvas(res_frame, bg="#f0f0f0", bd=0, highlightthickness=0) self.res_canvas.pack(expand=True, fill=tk.BOTH) def open_image(self): filetypes = [("图像文件", ".bmp;.png;.jpg;.jpeg"), ("所有文件", ".")] filename = filedialog.askopenfilename(filetypes=filetypes) if filename: try: self.filename = filename self.original_image = Image.open(filename).convert("RGB") self.compressed_image = None self.residual_image = None self.display_images() self.update_info() except Exception as e: messagebox.showerror("错误", f"无法打开图像:\n{str(e)}") def save_compressed(self): if self.compressed_image: filetypes = [("PNG", ".png"), ("JPEG", ".jpg"), ("BMP", ".bmp")] filename = filedialog.asksaveasfilename(defaultextension=".png", filetypes=filetypes) if filename: try: self.compressed_image.save(filename) messagebox.showinfo("保存成功", "压缩图像已保存") except Exception as e: messagebox.showerror("错误", f"保存失败:\n{str(e)}") def save_residual(self): if self.residual_image is not None: filetypes = [("PNG", ".png"), ("BMP", ".bmp")] filename = filedialog.asksaveasfilename(defaultextension=".png", filetypes=filetypes) if filename: try: # 将残差图像转换为可视化的灰度图 residual = cv2.normalize(self.residual_image, None, 0, 255, cv2.NORM_MINMAX) Image.fromarray(residual.astype(np.uint8)).save(filename) messagebox.showinfo("保存成功", "残差图像已保存") except Exception as e: messagebox.showerror("错误", f"保存失败:\n{str(e)}") def compress_image(self): if not self.original_image: return def process(): try: # 转换为OpenCV格式 (BGR) img_cv = cv2.cvtColor(np.array(self.original_image), cv2.COLOR_RGB2BGR) height, width = img_cv.shape[:2] # 转换为YCrCb色彩空间 (类似JPEG) img_yuv = cv2.cvtColor(img_cv, cv2.COLOR_BGR2YCrCb) # 获取块大小 block_size = self.block_size.get() # 创建量化矩阵 (基于JPEG标准调整) Q = self.create_quantization_matrix(block_size, self.quality.get()) # 对每个通道进行处理 compressed_channels = [] dct_blocks = [] # 声明在外部循环，以便后续使用 for ch in range(3): channel = img_yuv[:, :, ch].astype(np.float32) - 128 # 中心化 # 分块DCT ch_dct_blocks = [] for i in range(0, height, block_size): for j in range(0, width, block_size): block = channel[i:i + block_size, j:j + block_size] if block.shape[0] < block_size or block.shape[1] < block_size: # 边缘块填充 padded = np.zeros((block_size, block_size), dtype=np.float32) padded[:block.shape[0], :block.shape[1]] = block block = padded # DCT变换 dct_block = dct(dct(block.T, norm='ortho').T, norm='ortho') ch_dct_blocks.append((i, j, dct_block)) # 存储当前通道的DCT块 dct_blocks.append(ch_dct_blocks) # 量化和反量化 for ch in range(3): channel = img_yuv[:, :, ch].astype(np.float32) - 128 rec_channel = np.zeros_like(channel) ch_dct_blocks = dct_blocks[ch] # 获取当前通道的DCT块 for i, j, dct_block in ch_dct_blocks: # 量化 quantized = np.round(dct_block / Q) # 反量化 dequantized = quantized Q # 逆DCT idct_block = idct(idct(dequantized.T, norm='ortho').T, norm='ortho') # 存储重建块 block_h = min(block_size, height - i) block_w = min(block_size, width - j) rec_channel[i:i + block_h, j:j + block_w] = idct_block[:block_h, :block_w] + 128 compressed_channels.append(rec_channel) # 合并通道并转换回RGB compressed_yuv = np.stack(compressed_channels, axis=-1) compressed_yuv = np.clip(compressed_yuv, 0, 255).astype(np.uint8) compressed_bgr = cv2.cvtColor(compressed_yuv, cv2.COLOR_YCrCb2BGR) self.compressed_image = Image.fromarray(cv2.cvtColor(compressed_bgr, cv2.COLOR_BGR2RGB)) # 计算残差 original_array = np.array(self.original_image).astype(np.float32) compressed_array = np.array(self.compressed_image).astype(np.float32) # 确保尺寸完全一致 min_h = min(original_array.shape[0], compressed_array.shape[0]) min_w = min(original_array.shape[1], compressed_array.shape[1]) original_array = original_array[:min_h, :min_w, :] compressed_array = compressed_array[:min_h, :min_w, :] # 计算残差 self.residual_image = np.abs(original_array - compressed_array).mean(axis=2) # 转灰度残差 # 确保残差图像尺寸有效 if self.residual_image.size == 0 or self.residual_image.shape[0] <= 0 or self.residual_image.shape[1] <= 0: messagebox.showerror("残差计算错误", "无法生成有效的残差图像，请尝试其他图像或参数") return # 计算压缩比 original_size = width * height * 24 # 24 bits per pixel (RGB) nonzero_count = sum(np.count_nonzero(np.round(dct_block / Q)) for ch in range(3) for _, _, dct_block in dct_blocks[ch]) compressed_size = nonzero_count * (8 + 8) # 假设每个非零系数用8位存值+8位存位置 self.compression_ratio = original_size / max(1, compressed_size) # 更新显示 self.display_images() self.update_info() except Exception as e: messagebox.showerror("处理错误", f"DCT压缩失败:\n{str(e)}") threading.Thread(target=process).start() def create_quantization_matrix(self, size, quality): """创建量化矩阵 (基于JPEG标准调整)""" # 基础矩阵 (JPEG亮度量化表扩展) base_Q = np.array([ [16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99] ], dtype=np.float32) # 调整到指定大小 Q = cv2.resize(base_Q, (size, size), interpolation=cv2.INTER_LINEAR) # 根据质量调整量化步长 scale = 1.0 if quality < 50: scale = 5000 / quality else: scale = 200 - 2 * quality Q = np.floor((Q * scale + 50) / 100) Q = np.clip(Q, 1, 255) return Q def calculate_psnr(self, original, compressed): """计算峰值信噪比 (PSNR)""" mse = np.mean((original - compressed) ** 2) if mse == 0: return float('inf') max_pixel = 255.0 return 20 * np.log10(max_pixel / np.sqrt(mse)) def display_images(self): """显示所有图像""" self.tk_images = [] # 清除旧引用 # 显示原始图像 if self.original_image: self.display_image(self.original_image, self.orig_canvas, "原始图像") # 显示压缩图像 if self.compressed_image: self.display_image(self.compressed_image, self.comp_canvas, "压缩图像") # 显示残差图像 if self.residual_image is not None: # 确保残差图像尺寸有效 if self.residual_image.size == 0 or self.residual_image.shape[0] <= 0 or self.residual_image.shape[1] <= 0: messagebox.showwarning("残差警告", "残差图像尺寸无效，跳过显示") return # 将残差归一化到0-255范围 residual = cv2.normalize(self.residual_image, None, 0, 255, cv2.NORM_MINMAX) residual_img = Image.fromarray(residual.astype(np.uint8)) self.display_image(residual_img, self.res_canvas, "残差图像") def display_image(self, image, canvas, title): """在指定画布上显示图像""" try: # 检查图像宽高是否有效 img_width, img_height = image.size if img_width <= 0 or img_height <= 0: raise ValueError(f"{title} 宽高无效: {img_width}x{img_height}") canvas_width = canvas.winfo_width() canvas_height = canvas.winfo_height() if canvas_width <= 1 or canvas_height <= 1: return # 计算保持比例的缩放 ratio = min( (canvas_width - 20) / img_width, (canvas_height - 20) / img_height ) new_size = (int(img_width * ratio), int(img_height * ratio)) # 高质量缩放 resized_img = image.resize(new_size, Image.LANCZOS) tk_image = ImageTk.PhotoImage(resized_img) # 存储引用防止被回收 self.tk_images.append(tk_image) # 更新画布 canvas.delete("all") canvas.create_image( canvas_width // 2, canvas_height // 2, anchor=tk.CENTER, image=tk_image ) except Exception as e: messagebox.showerror("显示错误", f"显示{title}失败:\n{str(e)}") def update_info(self): """更新压缩信息""" if self.compressed_image and self.original_image: # 计算PSNR original = np.array(self.original_image).astype(np.float32) compressed = np.array(self.compressed_image).astype(np.float32) min_h = min(original.shape[0], compressed.shape[0]) min_w = min(original.shape[1], compressed.shape[1]) original = original[:min_h, :min_w, :] compressed = compressed[:min_h, :min_w, :] psnr = self.calculate_psnr(original, compressed) info_text = (f"压缩比: {self.compression_ratio:.2f}:1\n" f"PSNR: {psnr:.2f} dB\n" f"块大小: {self.block_size.get()}x{self.block_size.get()}\n" f"质量: {self.quality.get()}/100") self.info_label.config(text=info_text) else: self.info_label.config(text="压缩比: -\nPSNR: - dB") def reset_image(self): """重置为原始图像""" if self.original_image: self.compressed_image = None self.residual_image = None self.display_images() self.update_info() if name == "main": root = tk.Tk() app = DCTCompressorApp(root) root.mainloop() "height and width must be > 0" 错误

例如，用户提供的引用[2]中的代码片段，使用了cv2.imread(function.basic.READ_IMAGE)，如果READ_IMAGE路径有问题，input_image会是None，导致后续处理出错。接下来，需要确保在压缩前调整图像尺寸为8的倍数。因为...

import tensorflow as tf import numpy as np import cv2 import os import json from tqdm import tqdm class ObjectRecognitionDeployer: def init(self, model_path, class_labels): """ 初始化部署器 :param model_path: 模型文件路径 (Keras或TFLite) :param class_labels: 类别标签列表 """ self.class_labels = class_labels self.model_path = model_path self.interpreter = None self.input_details = None self.output_details = None # 根据模型类型加载 if model_path.endswith('.tflite'): self.load_tflite_model(model_path) else: self.model = tf.keras.models.load_model(model_path) self.input_shape = self.model.input_shape[1:3] def load_tflite_model(self, model_path): """加载并配置TFLite模型""" # 加载模型 self.interpreter = tf.lite.Interpreter(model_path=model_path) self.interpreter.allocate_tensors() # 获取输入输出详细信息 self.input_details = self.interpreter.get_input_details() self.output_details = self.interpreter.get_output_details() # 保存输入形状 self.input_shape = tuple(self.input_details[0]['shape'][1:3]) # 安全地打印模型元数据 self.print_model_metadata(model_path) def print_model_metadata(self, model_path): """安全地打印TFLite模型元数据""" try: from tflite_support import metadata displayer = metadata.MetadataDisplayer.with_model_file(model_path) print("--- 模型元数据 ---") print(displayer.get_metadata_json()) print("--- 关联文件 ---") print(displayer.get_packed_associated_file_list()) except (ImportError, ValueError) as e: print(f"警告: 无法获取模型元数据 - {str(e)}") print("使用输入/输出详细信息代替:") print(f"输入: {self.input_details}") print(f"输出: {self.output_details}") def preprocess_image(self, image, input_size, input_dtype=np.float32): """ 预处理图像 :param image: 输入图像 (numpy数组或文件路径) :param input_size: 模型输入尺寸 (height, width) :param input_dtype: 期望的输入数据类型 :return: 预处理后的图像张量 """ if isinstance(image, str): if not os.path.exists(image): raise FileNotFoundError(f"图像文件不存在: {image}") img = cv2.imread(image) if img is None: raise ValueError(f"无法读取图像: {image}") else: img = image # 调整尺寸和颜色空间 img = cv2.resize(img, (input_size[1], input_size[0])) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 根据数据类型进行归一化 if input_dtype == np.uint8: img = img.astype(np.uint8) # 量化模型使用uint8 else: # 浮点模型使用float32 img = img.astype(np.float32) / 255.0 # 添加批次维度 img = np.expand_dims(img, axis=0) return img def predict(self, image): """ 执行预测 :param image: 输入图像 (numpy数组或文件路径) :return: 预测结果 (类别名称, 置信度) """ if self.interpreter is not None: # TFLite模型推理 return self.predict_tflite(image) else: # Keras模型推理 return self.predict_keras(image) def predict_keras(self, image): """使用Keras模型预测""" # 预处理 img = self.preprocess_image(image, self.input_shape, np.float32) # 预测 predictions = self.model.predict(img, verbose=0)[0] class_idx = np.argmax(predictions) confidence = predictions[class_idx] class_name = self.class_labels[class_idx] return class_name, confidence def predict_tflite(self, image): """使用TFLite模型预测""" # 获取输入数据类型 input_dtype = self.input_details[0]['dtype'] # 预处理 img = self.preprocess_image(image, self.input_shape, input_dtype) # 设置输入张量 self.interpreter.set_tensor(self.input_details[0]['index'], img) # 执行推理 self.interpreter.invoke() # 获取输出 output_data = self.interpreter.get_tensor(self.output_details[0]['index']) predictions = output_data[0] # 解析结果 class_idx = np.argmax(predictions) confidence = predictions[class_idx] # 如果输出是量化数据，需要反量化 if self.output_details[0]['dtype'] == np.uint8: # 反量化输出 scale, zero_point = self.output_details[0]['quantization'] confidence = scale * (confidence - zero_point) class_name = self.class_labels[class_idx] return class_name, confidence def benchmark(self, image, runs=100): """ 模型性能基准测试 :param image: 测试图像 :param runs: 运行次数 :return: 平均推理时间(ms), 内存占用(MB) """ # 预热运行 self.predict(image) # 计时测试 start_time = tf.timestamp() for _ in range(runs): self.predict(image) end_time = tf.timestamp() avg_time_ms = (end_time - start_time).numpy() * 1000 / runs # 内存占用 if self.interpreter: # 计算输入张量内存占用 input_size = self.input_details[0]['shape'] dtype_size = np.dtype(self.input_details[0]['dtype']).itemsize mem_usage = np.prod(input_size) * dtype_size / (1024 * 1024) else: # 估算Keras模型内存 mem_usage = self.model.count_params() * 4 / (1024 * 1024) # 假设32位浮点数 return avg_time_ms, mem_usage def create_metadata(self, output_path): """ 创建并保存模型元数据文件 :param output_path: 元数据文件输出路径 """ metadata = { "model_type": "tflite" if self.model_path.endswith('.tflite') else "keras", "class_labels": self.class_labels, "input_size": self.input_shape, "input_dtype": str(self.input_details[0]['dtype']) if self.interpreter else "float32", "quantization": None } if self.interpreter and self.input_details[0]['dtype'] == np.uint8: metadata["quantization"] = { "input_scale": float(self.input_details[0]['quantization'][0]), "input_zero_point": int(self.input_details[0]['quantization'][1]), "output_scale": float(self.output_details[0]['quantization'][0]), "output_zero_point": int(self.output_details[0]['quantization'][1]) } with open(output_path, 'w') as f: json.dump(metadata, f, indent=4) return metadata def convert_to_tflite_with_metadata(self, output_path, quantize=False, representative_data_dir=None): """ 将Keras模型转换为TFLite格式并添加元数据 :param output_path: 输出TFLite文件路径 :param quantize: 是否进行量化 :param representative_data_dir: 代表性数据集目录 """ if not self.model_path.endswith(('.keras', '.h5')): raise ValueError("需要Keras模型格式进行转换") # 加载Keras模型 keras_model = tf.keras.models.load_model(self.model_path) # 创建转换器 converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) if quantize: # 量化配置 converter.optimizations = [tf.lite.Optimize.DEFAULT] # 设置代表性数据集生成器 converter.representative_dataset = lambda: self.representative_dataset( representative_data_dir, input_size=self.input_shape ) # 设置输入输出类型 converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 # 转换模型 tflite_model = converter.convert() # 保存模型 with open(output_path, 'wb') as f: f.write(tflite_model) print(f"TFLite模型已保存到: {output_path}") # 添加元数据 self.add_tflite_metadata(output_path) return output_path def representative_dataset(self, data_dir=None, input_size=(224, 224), num_samples=100): """ 生成代表性数据集用于量化 :param data_dir: 真实数据目录 :param input_size: 输入尺寸 (height, width) :param num_samples: 样本数量 """ # 优先使用真实数据 if data_dir and os.path.exists(data_dir): image_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg'))] # 限制样本数量 image_files = image_files[:min(len(image_files), num_samples)] print(f"使用 {len(image_files)} 张真实图像进行量化校准") for img_path in tqdm(image_files, desc="量化校准"): try: # 读取并预处理图像 img = cv2.imread(img_path) if img is None: continue img = cv2.resize(img, (input_size[1], input_size[0])) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = img.astype(np.float32) / 255.0 # 转换为float32并归一化 img = np.expand_dims(img, axis=0) yield [img] except Exception as e: print(f"处理图像 {img_path} 时出错: {str(e)}") else: # 使用随机数据作为备选 print(f"使用随机数据生成 {num_samples} 个样本进行量化校准") for _ in range(num_samples): # 生成随机图像，归一化到[0,1]范围，使用float32类型 data = np.random.rand(1, input_size[0], input_size[1], 3).astype(np.float32) yield [data] def add_tflite_metadata(self, model_path): """为TFLite模型添加元数据""" # 创建标签文件 labels_path = os.path.join(os.path.dirname(model_path), "labels.txt") with open(labels_path, 'w') as f: for label in self.class_labels: f.write(f"{label}\n") # 创建元数据 metadata_path = os.path.join(os.path.dirname(model_path), "metadata.json") self.create_metadata(metadata_path) print(f"元数据已创建: {metadata_path}") print(f"标签文件已创建: {labels_path}") # 使用示例 if name == "main": # 类别标签 CLASS_LABELS = ['book', 'cup', 'glasses', 'phone', 'shoe'] # 初始化部署器 deployer = ObjectRecognitionDeployer( model_path='optimized_model.keras', class_labels=CLASS_LABELS ) # 转换为带元数据的TFLite格式 tflite_path = 'model_quantized.tflite' # 使用真实数据目录进行量化校准 REPRESENTATIVE_DATA_DIR = 'path/to/representative_dataset' # 替换为实际路径 deployer.convert_to_tflite_with_metadata( tflite_path, quantize=True, representative_data_dir=REPRESENTATIVE_DATA_DIR ) # 重新加载带元数据的模型 tflite_deployer = ObjectRecognitionDeployer( model_path=tflite_path, class_labels=CLASS_LABELS ) # 测试预测 test_image = 'test_image.jpg' class_name, confidence = tflite_deployer.predict(test_image) print(f"预测结果: {class_name}, 置信度: {confidence:.2f}") # 性能测试 avg_time, mem_usage = tflite_deployer.benchmark(test_image) print(f"平均推理时间: {avg_time:.2f} ms") print(f"内存占用: {mem_usage:.2f} MB") # 创建元数据文件 metadata = deployer.create_metadata('model_metadata.json') print("模型元数据:", json.dumps(metadata, indent=4)) import tensorflow as tf import numpy as np import cv2 import os import json from tqdm import tqdm class ObjectRecognitionDeployer: def init(self, model_path, class_labels): """ 初始化部署器 :param model_path: 模型文件路径 (Keras或TFLite) :param class_labels: 类别标签列表 """ self.class_labels = class_labels self.model_path = model_path self.interpreter = None self.input_details = None self.output_details = None # 根据模型类型加载 if model_path.endswith('.tflite'): self.load_tflite_model(model_path) else: self.model = tf.keras.models.load_model(model_path) self.input_shape = self.model.input_shape[1:3] def load_tflite_model(self, model_path): """加载并配置TFLite模型""" # 加载模型 self.interpreter = tf.lite.Interpreter(model_path=model_path) self.interpreter.allocate_tensors() # 获取输入输出详细信息 self.input_details = self.interpreter.get_input_details() self.output_details = self.interpreter.get_output_details() # 保存输入形状 self.input_shape = tuple(self.input_details[0]['shape'][1:3]) # 安全地打印模型元数据 self.print_model_metadata(model_path) def print_model_metadata(self, model_path): """安全地打印TFLite模型元数据""" try: from tflite_support import metadata displayer = metadata.MetadataDisplayer.with_model_file(model_path) print("--- 模型元数据 ---") print(displayer.get_metadata_json()) print("--- 关联文件 ---") print(displayer.get_packed_associated_file_list()) except (ImportError, ValueError) as e: print(f"警告: 无法获取模型元数据 - {str(e)}") print("使用输入/输出详细信息代替:") print(f"输入: {self.input_details}") print(f"输出: {self.output_details}") def preprocess_image(self, image, input_size, input_dtype=np.float32): """ 预处理图像 :param image: 输入图像 (numpy数组或文件路径) :param input_size: 模型输入尺寸 (height, width) :param input_dtype: 期望的输入数据类型 :return: 预处理后的图像张量 """ if isinstance(image, str): if not os.path.exists(image): raise FileNotFoundError(f"图像文件不存在: {image}") img = cv2.imread(image) if img is None: raise ValueError(f"无法读取图像: {image}") else: img = image # 调整尺寸和颜色空间 img = cv2.resize(img, (input_size[1], input_size[0])) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 根据数据类型进行归一化 if input_dtype == np.uint8: img = img.astype(np.uint8) # 量化模型使用uint8 else: # 浮点模型使用float32 img = img.astype(np.float32) / 255.0 # 添加批次维度 img = np.expand_dims(img, axis=0) return img def predict(self, image): """ 执行预测 :param image: 输入图像 (numpy数组或文件路径) :return: 预测结果 (类别名称, 置信度) """ if self.interpreter is not None: # TFLite模型推理 return self.predict_tflite(image) else: # Keras模型推理 return self.predict_keras(image) def predict_keras(self, image): """使用Keras模型预测""" # 预处理 img = self.preprocess_image(image, self.input_shape, np.float32) # 预测 predictions = self.model.predict(img, verbose=0)[0] class_idx = np.argmax(predictions) confidence = predictions[class_idx] class_name = self.class_labels[class_idx] return class_name, confidence def predict_tflite(self, image): """使用TFLite模型预测""" # 获取输入数据类型 input_dtype = self.input_details[0]['dtype'] # 预处理 img = self.preprocess_image(image, self.input_shape, input_dtype) # 设置输入张量 self.interpreter.set_tensor(self.input_details[0]['index'], img) # 执行推理 self.interpreter.invoke() # 获取输出 output_data = self.interpreter.get_tensor(self.output_details[0]['index']) predictions = output_data[0] # 解析结果 class_idx = np.argmax(predictions) confidence = predictions[class_idx] # 如果输出是量化数据，需要反量化 if self.output_details[0]['dtype'] == np.uint8: # 反量化输出 scale, zero_point = self.output_details[0]['quantization'] confidence = scale * (confidence - zero_point) class_name = self.class_labels[class_idx] return class_name, confidence def benchmark(self, image, runs=100): """ 模型性能基准测试 :param image: 测试图像 :param runs: 运行次数 :return: 平均推理时间(ms), 内存占用(MB) """ # 预热运行 self.predict(image) # 计时测试 start_time = tf.timestamp() for _ in range(runs): self.predict(image) end_time = tf.timestamp() avg_time_ms = (end_time - start_time).numpy() * 1000 / runs # 内存占用 if self.interpreter: # 计算输入张量内存占用 input_size = self.input_details[0]['shape'] dtype_size = np.dtype(self.input_details[0]['dtype']).itemsize mem_usage = np.prod(input_size) * dtype_size / (1024 * 1024) else: # 估算Keras模型内存 mem_usage = self.model.count_params() * 4 / (1024 * 1024) # 假设32位浮点数 return avg_time_ms, mem_usage def create_metadata(self, output_path): """ 创建并保存模型元数据文件 :param output_path: 元数据文件输出路径 """ metadata = { "model_type": "tflite" if self.model_path.endswith('.tflite') else "keras", "class_labels": self.class_labels, "input_size": self.input_shape, "input_dtype": str(self.input_details[0]['dtype']) if self.interpreter else "float32", "quantization": None } if self.interpreter and self.input_details[0]['dtype'] == np.uint8: metadata["quantization"] = { "input_scale": float(self.input_details[0]['quantization'][0]), "input_zero_point": int(self.input_details[0]['quantization'][1]), "output_scale": float(self.output_details[0]['quantization'][0]), "output_zero_point": int(self.output_details[0]['quantization'][1]) } with open(output_path, 'w') as f: json.dump(metadata, f, indent=4) return metadata def convert_to_tflite_with_metadata(self, output_path, quantize=False, representative_data_dir=None): """ 将Keras模型转换为TFLite格式并添加元数据 :param output_path: 输出TFLite文件路径 :param quantize: 是否进行量化 :param representative_data_dir: 代表性数据集目录 """ if not self.model_path.endswith(('.keras', '.h5')): raise ValueError("需要Keras模型格式进行转换") # 加载Keras模型 keras_model = tf.keras.models.load_model(self.model_path) # 创建转换器 converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) if quantize: # 量化配置 converter.optimizations = [tf.lite.Optimize.DEFAULT] # 设置代表性数据集生成器 converter.representative_dataset = lambda: self.representative_dataset( representative_data_dir, input_size=self.input_shape ) # 设置输入输出类型 converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 # 转换模型 tflite_model = converter.convert() # 保存模型 with open(output_path, 'wb') as f: f.write(tflite_model) print(f"TFLite模型已保存到: {output_path}") # 添加元数据 self.add_tflite_metadata(output_path) return output_path def representative_dataset(self, data_dir=None, input_size=(224, 224), num_samples=100): """ 生成代表性数据集用于量化 :param data_dir: 真实数据目录 :param input_size: 输入尺寸 (height, width) :param num_samples: 样本数量 """ # 优先使用真实数据 if data_dir and os.path.exists(data_dir): image_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg'))] # 限制样本数量 image_files = image_files[:min(len(image_files), num_samples)] print(f"使用 {len(image_files)} 张真实图像进行量化校准") for img_path in tqdm(image_files, desc="量化校准"): try: # 读取并预处理图像 img = cv2.imread(img_path) if img is None: continue img = cv2.resize(img, (input_size[1], input_size[0])) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = img.astype(np.float32) / 255.0 # 转换为float32并归一化 img = np.expand_dims(img, axis=0) yield [img] except Exception as e: print(f"处理图像 {img_path} 时出错: {str(e)}") else: # 使用随机数据作为备选 print(f"使用随机数据生成 {num_samples} 个样本进行量化校准") for _ in range(num_samples): # 生成随机图像，归一化到[0,1]范围，使用float32类型 data = np.random.rand(1, input_size[0], input_size[1], 3).astype(np.float32) yield [data] def add_tflite_metadata(self, model_path): """为TFLite模型添加元数据""" # 创建标签文件 labels_path = os.path.join(os.path.dirname(model_path), "labels.txt") with open(labels_path, 'w') as f: for label in self.class_labels: f.write(f"{label}\n") # 创建元数据 metadata_path = os.path.join(os.path.dirname(model_path), "metadata.json") self.create_metadata(metadata_path) print(f"元数据已创建: {metadata_path}") print(f"标签文件已创建: {labels_path}") # 使用示例 if name == "main": # 类别标签 CLASS_LABELS = ['book', 'cup', 'glasses', 'phone', 'shoe'] # 初始化部署器 deployer = ObjectRecognitionDeployer( model_path='optimized_model.keras', class_labels=CLASS_LABELS ) # 转换为带元数据的TFLite格式 tflite_path = 'model_quantized.tflite' # 使用真实数据目录进行量化校准 REPRESENTATIVE_DATA_DIR = 'path/to/representative_dataset' # 替换为实际路径 deployer.convert_to_tflite_with_metadata( tflite_path, quantize=True, representative_data_dir=REPRESENTATIVE_DATA_DIR ) # 重新加载带元数据的模型 tflite_deployer = ObjectRecognitionDeployer( model_path=tflite_path, class_labels=CLASS_LABELS ) # 测试预测 test_image = 'test_image.jpg' class_name, confidence = tflite_deployer.predict(test_image) print(f"预测结果: {class_name}, 置信度: {confidence:.2f}") # 性能测试 avg_time, mem_usage = tflite_deployer.benchmark(test_image) print(f"平均推理时间: {avg_time:.2f} ms") print(f"内存占用: {mem_usage:.2f} MB") # 创建元数据文件 metadata = deployer.create_metadata('model_metadata.json') print("模型元数据:", json.dumps(metadata, indent=4)) 上述代码我已经成功执行，并且我的ObjectRecognitionDeployer类路径导入代码是from 计算机视觉.test2 import ObjectRecognitionDeployer

现在，用户希望将之前的TkinterGUI应用转换为PyQt5，并且已经提供了模型部署器的实现。在之前的PyQt5GUI代码中，我们假设用户已经有一个ObjectRecognitionDeployer类。现在用户已经确认了导入路径。因此，我们需要将...

class QuantLinear(nn.Module): """ Quantized Module that can perform quantized convolution or normal convolution. To activate quantization, please use set_quant_state function. """ def init( self, org_module: nn.Linear, weight_quant_params: dict = {}, act_quant_params: dict = {}, disable_input_quant=False, ): super().init() self.fwd_kwargs = dict() self.fwd_func = F.linear self.register_buffer('weight',org_module.weight) if org_module.bias is not None: self.register_buffer('bias',org_module.bias) else: self.bias = None self.in_features = org_module.in_features self.out_features = org_module.out_features # de-activate the quantized forward default self.use_weight_quant = False self.use_act_quant = False # initialize quantizer self.weight_quantizer = UniformAffineQuantizer(weight_quant_params,shape=org_module.weight.shape) if not disable_input_quant: self.act_quantizer = UniformAffineQuantizer(act_quant_params) else: self.act_quantizer = None self.disable_input_quant = disable_input_quant self.use_temporary_parameter = False def forward(self, input: torch.Tensor): if self.use_temporary_parameter: weight = self.temp_weight bias = self.temp_bias elif self.use_weight_quant: weight = self.weight_quantizer(self.weight) bias = self.bias else: weight = self.weight bias = self.bias if self.use_act_quant and not self.disable_input_quant: input = self.act_quantizer(input) out = self.fwd_func(input, weight, bias, **self.fwd_kwargs) return out def set_quant_state(self, weight_quant: bool = False, act_quant: bool = False): self.use_weight_quant = weight_quant self.use_act_quant = act_quant

我们正在分析一个量化线性层（QuantLinear）的PyTorch实现代码。这个模块用于在神经网络中执行量化的线性操作，也可以执行普通的线性操作。用户要求解释这段代码，并在最后提出相关问题。 ### 代码解释 1. **初始...

import tensorrt as trt import numpy as np import time import pycuda.driver as cuda import pycuda.autoinit # 自定义INT8校准器类 class MyCalibrator(trt.IInt8EntropyCalibrator2): def init(self, batch_size=1, input_shape=(1, 3, 224, 224), cache_file="calibration.cache"): trt.IInt8EntropyCalibrator2.init(self) self.batch_size = batch_size self.input_shape = input_shape self.cache_file = cache_file # 生成随机校准数据（实际应用中应使用真实数据集） self.data = np.random.randn(100, input_shape).astype(np.float32) self.current_index = 0 # 分配设备内存 self.device_input = cuda.mem_alloc(self.data.nbytes self.batch_size) def get_batch_size(self): return self.batch_size def get_batch(self, names): if self.current_index + self.batch_size > len(self.data): return None batch = self.data[self.current_index:self.current_index+self.batch_size] self.current_index += self.batch_size # 将数据复制到设备 cuda.memcpy_htod(self.device_input, batch.tobytes()) return [int(self.device_input)] def read_calibration_cache(self): # 如果存在缓存，跳校准 try: with open(self.cache_file, "rb") as f: return f.read() except FileNotFoundError: return None def write_calibration_cache(self, cache): with open(self.cache_file, "wb") as f: f.write(cache) # 创建简单网络（使用 add_pooling_nd 替代 add_pooling） def create_network(): logger = trt.Logger(trt.Logger.WARNING) builder = trt.Builder(logger) network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) # 定义输入 input_tensor = network.add_input("input", trt.DataType.FLOAT, (1, 3, 224, 224)) # 添加卷积层（模拟ResNet-18第一层） conv1 = network.add_convolution_nd( input=input_tensor, num_output_maps=64, kernel_shape=(7, 7), kernel=np.random.randn(64, 3, 7, 7).astype(np.float32).ravel(), bias=np.random.randn(64).astype(np.float32) ) # 设置步长和填充（直接赋值属性） conv1.stride_nd = [2, 2] conv1.padding_nd = [3, 3] # 添加池化层（使用 add_pooling_nd） window_shape = trt.Dims([3, 3]) # 修改：使用 trt.Dims pool1 = network.add_pooling_nd( input=conv1.get_output(0), type=trt.PoolingType.MAX, window_size=window_shape ) pool1.stride_nd = [2, 2] # 设置池化步长 # 展平输出为二维张量（N, CHW） flatten = network.add_shuffle(pool1.get_output(0)) output_shape = (1, 64 * 56 * 56) # 假设输入是 (1, 64, 56, 56) flatten.reshape_dims = tuple(output_shape) # 定义权重和偏置 kernel = np.random.randn(1000, 64 * 56 * 56).astype(np.float32).ravel() bias = np.random.randn(1000).astype(np.float32) # 添加常量张量作为权重和偏置 w = network.add_constant((1000, 64 * 56 * 56), kernel) b = network.add_constant((1000,), bias) # 矩阵乘法：[input] @ [weights.T] + [bias] matmul = network.add_matrix_multiply(flatten.get_output(0), trt.MatrixOperation.NONE, w.get_output(0), trt.MatrixOperation.TRANSPOSE) bias_add = network.add_elementwise(matmul.get_output(0), b.get_output(0), trt.ElementWiseOperation.SUM) # 标记输出 bias_add.get_output(0).name = "output" network.mark_output(bias_add.get_output(0)) return builder, network, logger # 主测试函数 def int8_perf_test(): # 创建网络 builder, network, logger = create_network() # 配置INT8量化 config = builder.create_builder_config() config.set_flag(trt.BuilderFlag.INT8) config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30) # 1GB工作空间 # 创建校准器并设置给配置 calib = MyCalibrator(batch_size=1, input_shape=(1, 3, 224, 224), cache_file="calibration.cache") config.int8_calibrator = calib # ✅ 正确方式：直接赋值属性 # 构建引擎（新API） engine = builder.build_engine(network, config) if not engine: print("Engine构建失败!") return # 创建执行上下文 context = engine.create_execution_context() # 准备输入输出缓冲区 input_shape = (1, 3, 224, 224) output_shape = (1, 1000) host_input = np.random.randn(input_shape).astype(np.float32) host_output = np.empty(output_shape, dtype=np.float32) # 分配设备内存 d_input = cuda.mem_alloc(host_input.nbytes) d_output = cuda.mem_alloc(host_output.nbytes) bindings = [int(d_input), int(d_output)] # 创建CUDA流 stream = cuda.Stream() # 预热 for _ in range(5): cuda.memcpy_htod_async(d_input, host_input, stream) context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) cuda.memcpy_dtoh_async(host_output, d_output, stream) stream.synchronize() # 正式测试 start = time.time() for _ in range(100): cuda.memcpy_htod_async(d_input, host_input, stream) context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) cuda.memcpy_dtoh_async(host_output, d_output, stream) stream.synchronize() total_time = time.time() - start # 计算INT8 TOPS（基于ResNet-18的3.9G MACs） # 注意：INT8操作数 = MACs × 2（乘加各算一次操作） ops = 3.9e9 2 # ResNet-18的INT8操作数 tops = (ops * 100) / (total_time * 1e12) # 100次推理 print(f"INT8 TOPS: {tops:.2f} TOPS") # 清理资源 del context del engine if name == "main": int8_perf_test() 报错 jp@jp-Super-Server:~/test$ python3 TensorRT_int8.py /home/jp/test/TensorRT_int8.py:113: DeprecationWarning: Use Deprecated in TensorRT 10.1. Superseded by explicit quantization. instead. config.int8_calibrator = calib # ✅ 正确方式：直接赋值属性 Traceback (most recent call last): File "/home/jp/test/TensorRT_int8.py", line 166, in <module> int8_perf_test() File "/home/jp/test/TensorRT_int8.py", line 116, in int8_perf_test engine = builder.build_engine(network, config) AttributeError: 'tensorrt_bindings.tensorrt.Builder' object has no attribute 'build_engine' 给出修改后的完整代码

实现方法现在，我们需要改为：pythonclassCalibrator(trt.IInt8EntropyCalibrator2):#...实现方法注意：基类改为trt.IInt8EntropyCalibrator2（在trt模块下直接可用）。同时，确保实现了所有必要的方法...

import os import torch import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, TrainingArguments, DataCollatorForLanguageModeling, BitsAndBytesConfig, Trainer ) from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training from datasets import load_dataset import logging import psutil import gc from datetime import datetime # === 配置区域 === MODEL_NAME = "/home/vipuser/ai_writer_project_final_with_fixed_output_ui/models/Yi-6B" DATASET_PATH = "./data/train_lora_formatted.jsonl" OUTPUT_DIR = "./yi6b-lora-optimized" DEVICE_MAP = "auto" # 使用自动设备映射 # 确保输出目录存在 os.makedirs(OUTPUT_DIR, exist_ok=True) # === 内存优化配置 === os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" # 减少内存碎片 torch.backends.cuda.cufft_plan_cache.clear() # 清理CUDA缓存 # === 增强的日志系统 === def setup_logging(output_dir): """配置日志系统，支持文件和TensorBoard""" logger = logging.getLogger(name) logger.setLevel(logging.INFO) # 文件日志处理器 file_handler = logging.FileHandler(os.path.join(output_dir, "training.log")) file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')) logger.addHandler(file_handler) # 控制台日志处理器 console_handler = logging.StreamHandler() console_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')) logger.addHandler(console_handler) # TensorBoard日志目录 tensorboard_log_dir = os.path.join(output_dir, "logs", datetime.now().strftime("%Y%m%d-%H%M%S")) os.makedirs(tensorboard_log_dir, exist_ok=True) # 安装TensorBoard回调 tb_writer = None try: from torch.utils.tensorboard import SummaryWriter tb_writer = SummaryWriter(log_dir=tensorboard_log_dir) logger.info(f"TensorBoard日志目录: {tensorboard_log_dir}") except ImportError: logger.warning("TensorBoard未安装，可视化功能不可用") return logger, tb_writer logger, tb_writer = setup_logging(OUTPUT_DIR) # === 量化配置 - 使用更高效的配置 === quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, ) # === 加载模型 === logger.info("加载预训练模型...") model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map=DEVICE_MAP, quantization_config=quant_config, torch_dtype=torch.bfloat16, trust_remote_code=True, attn_implementation="flash_attention_2" # 使用FlashAttention优化内存 ) # === 分词器处理 === logger.info("加载分词器...") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) tokenizer.padding_side = "right" if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id # === 准备模型训练 === model = prepare_model_for_kbit_training( model, use_gradient_checkpointing=True # 启用梯度检查点以节省内存 ) # === LoRA 配置 - 优化内存使用 === logger.info("配置LoRA...") lora_config = LoraConfig( r=64, # 降低rank以减少内存使用 lora_alpha=32, # 降低alpha值 target_modules=["q_proj", "v_proj"], # 减少目标模块 lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, lora_config) # 记录可训练参数 trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) total_params = sum(p.numel() for p in model.parameters()) logger.info(f"可训练参数: {trainable_params:,} / 总参数: {total_params:,} ({trainable_params / total_params:.2%})") # === 加载并预处理数据集 === logger.info("加载和预处理数据集...") dataset = load_dataset("json", data_files=DATASET_PATH, split="train") # 文本过滤函数 def is_valid_text(example): text = example.get("text", "") return text is not None and len(text.strip()) > 200 # 增加最小长度要求 dataset = dataset.filter(is_valid_text) logger.info(f"过滤后数据集大小: {len(dataset)} 条") # 动态填充的分词函数 - 节省内存 def tokenize_function(examples): tokenized = tokenizer( examples["text"], padding=True, # 使用动态填充 truncation=True, max_length=1024, # 降低上下文长度以减少内存使用 ) # 创建 labels - 因果语言建模需要 labels = input_ids tokenized["labels"] = tokenized["input_ids"].copy() return tokenized tokenized_dataset = dataset.map( tokenize_function, batched=True, remove_columns=["text"], batch_size=64, # 降低批处理大小以减少内存峰值 num_proc=4, # 减少进程数以降低内存开销 ) # === 数据整理器 === data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm=False # 因果语言建模 ) # === 训练参数 - 优化内存使用 === report_to_list = ["tensorboard"] if tb_writer else [] training_args = TrainingArguments( output_dir=OUTPUT_DIR, per_device_train_batch_size=4, # 大幅降低批次大小 gradient_accumulation_steps=4, # 增加梯度累积步数以保持有效批次大小 learning_rate=2e-5, num_train_epochs=3, logging_steps=50, save_strategy="steps", save_steps=500, bf16=True, optim="paged_adamw_32bit", report_to=report_to_list, warmup_ratio=0.05, gradient_checkpointing=True, # 启用梯度检查点 fp16=False, max_grad_norm=0.3, # 降低梯度裁剪阈值 remove_unused_columns=True, # 移除未使用的列以节省内存 dataloader_num_workers=4, # 减少数据加载工作线程 evaluation_strategy="steps", eval_steps=500, save_total_limit=2, # 减少保存的检查点数量 logging_dir=os.path.join(OUTPUT_DIR, "logs"), load_best_model_at_end=True, ddp_find_unused_parameters=False, logging_first_step=True, group_by_length=True, lr_scheduler_type="cosine", weight_decay=0.01, ) # === GPU监控工具 === def monitor_gpu(): """监控GPU使用情况""" if torch.cuda.is_available(): device = torch.device("cuda") mem_alloc = torch.cuda.memory_allocated(device) / 1024 3 mem_reserved = torch.cuda.memory_reserved(device) / 1024 3 mem_total = torch.cuda.get_device_properties(device).total_memory / 1024 ** 3 return { "allocated": f"{mem_alloc:.2f} GB", "reserved": f"{mem_reserved:.2f} GB", "total": f"{mem_total:.2f} GB", "utilization": f"{mem_alloc / mem_total * 100:.1f}%" } return {} # === 创建训练器 === eval_dataset = None if len(tokenized_dataset) > 100: eval_dataset = tokenized_dataset.select(range(100)) trainer = Trainer( model=model, tokenizer=tokenizer, args=training_args, train_dataset=tokenized_dataset, eval_dataset=eval_dataset, data_collator=data_collator, ) # === 训练前验证 === def validate_data_and_model(): """验证数据和模型是否准备好训练""" logger.info("\n=== 训练前验证 ===") # 检查样本格式 sample = tokenized_dataset[0] logger.info(f"样本键: {list(sample.keys())}") logger.info(f"input_ids 长度: {len(sample['input_ids'])}") # 创建单个样本测试批次 test_batch = data_collator([sample]) # 移动数据到设备 test_batch = {k: v.to(model.device) for k, v in test_batch.items()} # 前向传播测试 model.train() outputs = model(test_batch) loss_value = outputs.loss.item() logger.info(f"测试批次损失: {loss_value:.4f}") # 记录到TensorBoard if tb_writer: tb_writer.add_scalar("debug/test_loss", loss_value, 0) # 反向传播测试 outputs.loss.backward() logger.info("反向传播成功!") # 重置梯度 model.zero_grad() logger.info("验证完成，准备开始训练\n") # 记录初始GPU使用情况 gpu_status = monitor_gpu() logger.info(f"初始GPU状态: {gpu_status}") # 记录到TensorBoard if tb_writer: tb_writer.add_text("system/initial_gpu", str(gpu_status), 0) validate_data_and_model() # === 自定义回调 - 监控资源使用 === class ResourceMonitorCallback(transformers.TrainerCallback): def init(self, tb_writer=None): self.tb_writer = tb_writer self.start_time = datetime.now() self.last_log_time = datetime.now() def on_step_end(self, args, state, control, kwargs): current_time = datetime.now() time_diff = (current_time - self.last_log_time).total_seconds() # 每分钟记录一次资源使用情况 if time_diff > 60: self.last_log_time = current_time # GPU监控 gpu_status = monitor_gpu() logger.info(f"Step {state.global_step} - GPU状态: {gpu_status}") # CPU和内存监控 cpu_percent = psutil.cpu_percent() mem = psutil.virtual_memory() logger.info( f"CPU使用率: {cpu_percent}%, 内存使用: {mem.used / 1024 3:.2f}GB/{mem.total / 1024 3:.2f}GB") # 记录到TensorBoard if self.tb_writer: # GPU显存使用 if torch.cuda.is_available(): device = torch.device("cuda") mem_alloc = torch.cuda.memory_allocated(device) / 1024 3 self.tb_writer.add_scalar("system/gpu_mem", mem_alloc, state.global_step) # CPU使用率 self.tb_writer.add_scalar("system/cpu_usage", cpu_percent, state.global_step) # 系统内存使用 self.tb_writer.add_scalar("system/ram_usage", mem.used / 1024 3, state.global_step) def on_log(self, args, state, control, logs=None, kwargs): """记录训练指标到TensorBoard""" if self.tb_writer and logs is not None: for metric_name, metric_value in logs.items(): if "loss" in metric_name or "lr" in metric_name or "grad_norm" in metric_name: self.tb_writer.add_scalar(f"train/{metric_name}", metric_value, state.global_step) def on_train_end(self, args, state, control, kwargs): """训练结束时记录总时间""" training_time = datetime.now() - self.start_time logger.info(f"训练总时间: {training_time}") if self.tb_writer: self.tb_writer.add_text("system/total_time", str(training_time)) # 添加回调 trainer.add_callback(ResourceMonitorCallback(tb_writer=tb_writer)) # === 内存清理函数 === def clear_memory(): """清理内存和GPU缓存""" gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() logger.info("内存清理完成") # === 启动训练 === try: logger.info("开始训练...") # 分阶段训练以减少内存峰值 num_samples = len(tokenized_dataset) chunk_size = 1000 # 每次处理1000个样本 for i in range(0, num_samples, chunk_size): end_idx = min(i + chunk_size, num_samples) logger.info(f"训练样本 {i} 到 {end_idx - 1} / {num_samples}") # 创建子数据集 chunk_dataset = tokenized_dataset.select(range(i, end_idx)) # 更新训练器 trainer.train_dataset = chunk_dataset # 训练当前块 trainer.train() # 清理内存 clear_memory() # 保存训练指标 metrics = trainer.evaluate() trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) # 保存最佳模型 trainer.save_model(OUTPUT_DIR) tokenizer.save_pretrained(OUTPUT_DIR) logger.info(f"训练完成! 模型保存在: {OUTPUT_DIR}") # 记录最终指标到TensorBoard if tb_writer: for metric_name, metric_value in metrics.items(): tb_writer.add_scalar(f"final/{metric_name}", metric_value) tb_writer.close() except Exception as e: logger.error(f"训练出错: {e}") import traceback logger.error(traceback.format_exc()) # 尝试更小批量训练 logger.info("\n尝试更小批量训练...") small_dataset = tokenized_dataset.select(range(50)) trainer.train_dataset = small_dataset trainer.train() # 保存模型 trainer.save_model(f"{OUTPUT_DIR}_small") tokenizer.save_pretrained(f"{OUTPUT_DIR}_small") logger.info(f"小批量训练完成! 模型保存在: {OUTPUT_DIR}_small") # 记录错误到TensorBoard if tb_writer: tb_writer.add_text("error/exception", traceback.format_exc()) # 清理内存 clear_memory() # === 训练后验证 === def validate_final_model(): """验证训练后的模型""" logger.info("\n=== 训练后验证 ===") # 加载保存的模型 from peft import PeftModel # 仅加载基础模型配置 base_model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map=DEVICE_MAP, quantization_config=quant_config, torch_dtype=torch.bfloat16, trust_remote_code=True, load_in_4bit=True ) # 加载LoRA适配器 peft_model = PeftModel.from_pretrained(base_model, OUTPUT_DIR) # 不再合并LoRA权重，直接使用 peft_model 推理 peft_model.eval() # 测试生成 prompt = "中国的首都是" inputs = tokenizer(prompt, return_tensors="pt").to(peft_model.device) outputs = peft_model.generate( inputs, max_new_tokens=50, # 减少生成长度 temperature=0.7, top_p=0.9, repetition_penalty=1.2, do_sample=True ) generated = tokenizer.decode(outputs[0], skip_special_tokens=True) logger.info(f"提示: {prompt}") logger.info(f"生成结果: {generated}") # 记录到TensorBoard if tb_writer: tb_writer.add_text("validation/sample", f"提示: {prompt}\n生成: {generated}") # 更全面的测试 test_prompts = [ "人工智能的未来发展趋势是", "如何学习深度学习？", "写一个关于太空探索的短故事：" ] for i, test_prompt in enumerate(test_prompts): inputs = tokenizer(test_prompt, return_tensors="pt").to(peft_model.device) outputs = peft_model.generate( inputs, max_new_tokens=100, # 减少生成长度 temperature=0.7, top_p=0.9, repetition_penalty=1.2, do_sample=True ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) logger.info(f"\n提示: {test_prompt}\n生成: {generated_text}\n{'=' * 50}") # 记录到TensorBoard if tb_writer: tb_writer.add_text(f"validation/test_{i}", f"提示: {test_prompt}\n生成: {generated_text}") logger.info("验证完成") # 执行验证 validate_final_model() # 关闭TensorBoard写入器 if tb_writer: tb_writer.close() logger.info("TensorBoard日志已关闭")

我们遇到了在训练后验证阶段加载模型进行推理时出现的矩阵形状不匹配错误。错误信息为： RuntimeError: mat1 and mat2 shapes cannot be multiplied (3x4096 and 512x4096) 这个错误发生在尝试将两个矩阵相乘时，...

import logging import torch from datasets import load_dataset from peft import get_peft_model, LoraConfig, TaskType from transformers import ( AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, ) logging.basicConfig(level=logging.INFO) logger = logging.getLogger(name) class LoRATrainer: def init(self, config): self.config = config self.model_path = config['model_path'] self.train_file = config['train_file'] self.output_dir = config['output_dir'] self.max_steps = config.get('max_steps', 100) self._load_tokenizer() self._load_dataset() self._load_model() def _load_tokenizer(self): logger.info("[1] 加载 Tokenizer...") self.tokenizer = AutoTokenizer.from_pretrained( self.model_path, trust_remote_code=True ) if self.tokenizer.pad_token is None: self.tokenizer.pad_token = self.tokenizer.eos_token def _load_dataset(self): logger.info("[2] 加载并 Tokenize 数据...") raw_dataset = load_dataset("text", data_files={"train": self.train_file}) def tokenize_function(example): return self.tokenizer( example["text"], truncation=True, max_length=2048, # 移除padding padding=False ) self.tokenized_dataset = raw_dataset.map( tokenize_function, batched=True, remove_columns=["text"] )["train"] def _load_model(self): logger.info("[3] 加载模型 + LoRA 适配...") # 配置4-bit量化 self.model = AutoModelForCausalLM.from_pretrained( self.model_path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True ) lora_config = LoraConfig( r=8, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type=TaskType.CAUSAL_LM ) self.model = get_peft_model(self.model, lora_config) # 启用梯度检查点 self.model.gradient_checkpointing_enable() self.data_collator = DataCollatorForLanguageModeling( tokenizer=self.tokenizer, mlm=False, pad_to_multiple_of=8 # 提升GPU效率 ) def train(self): logger.info("[4] 开始 LoRA 微调训练...") training_args = TrainingArguments( output_dir=self.output_dir, per_device_train_batch_size=1, gradient_accumulation_steps=4, logging_steps=10, max_steps=self.max_steps, save_strategy="steps", save_steps=50, # 定期保存 report_to="none", fp16=True # 混合精度训练 ) trainer = Trainer( model=self.model, args=training_args, train_dataset=self.tokenized_dataset, tokenizer=self.tokenizer, data_collator=self.data_collator ) trainer.train() if name == "main": config = { "model_path": r"C:\Users\vipuser\Documents\ai_writer_project_final_with_fixed_output_ui\models\Yi-6B", "train_file": "./data/train_cleaned.txt", "output_dir": "./output/lora", "max_steps": 100 } trainer = LoRATrainer(config) trainer.train()

需要实现以下步骤： 1. 数据加载：使用一个适合因果语言建模的数据集（例如，wikitext, 或者自定义的文本数据） 2. tokenizer配置：使用与Yi-6B模型对应的tokenizer 3. 4-bit量化：使用bitsandbytes库进行4-bit...

class UniformAffineQuantizer(nn.Module): def init( self, n_bits: int = 8, symmetric: bool = False, per_channel_axes=[], metric="minmax", dynamic=False, dynamic_method="per_cluster", group_size=None, shape=None, lwc=False, disable_zero_point=False, ): """ support cluster quantize dynamic_method support per_token and per_cluster """ super().init() self.symmetric = symmetric self.disable_zero_point = disable_zero_point assert 2 <= n_bits <= 16, "bitwidth not supported" self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 (n_bits - 1)) self.qmax = 2 (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 ** (n_bits) - 1 self.per_channel_axes = per_channel_axes self.metric = metric self.cluster_counts = None self.cluster_dim = None self.scale = None self.zero_point = None self.round_zero_point = None self.cached_xmin = None self.cached_xmax = None self.dynamic = dynamic self.dynamic_method = dynamic_method self.deficiency = 0 self.lwc = lwc init_value = 4. # inti value of learnable weight clipping if lwc: if group_size: dim1 = int(shape[0]math.ceil(shape[1]/group_size)) self.deficiency = shape[-1]%group_size if self.deficiency > 0: self.deficiency = group_size - self.deficiency assert self.symmetric # support for mlc-llm symmetric quantization else: dim1 = shape[0] self.upbound_factor = nn.Parameter(torch.ones((dim1,1))init_value) self.lowbound_factor = nn.Parameter(torch.ones((dim1,1))*init_value) self.sigmoid = nn.Sigmoid() self.enable = True self.group_size = group_size def change_n_bits(self, n_bits): self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 (n_bits - 1)) self.qmax = 2 (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 (n_bits) - 1 def fake_quant(self, x, scale, round_zero_point): if self.deficiency > 0: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) if self.group_size: assert len(x.shape)==2, "only support linear layer now" dim1, dim2 = x.shape x = x.reshape(-1, self.group_size) x_int = round_ste(x / scale) if round_zero_point is not None: x_int = x_int.add(round_zero_point) x_int = x_int.clamp(self.qmin, self.qmax) x_dequant = x_int if round_zero_point is not None: x_dequant = x_dequant.sub(round_zero_point) x_dequant = x_dequant.mul(scale) if self.group_size: x_dequant = x_dequant.reshape(dim1, dim2) if self.deficiency > 0: x_dequant = x_dequant[:,:-self.deficiency] return x_dequant def forward(self, x: torch.Tensor): if self.n_bits >= 16 or not self.enable: return x if self.metric == "fix0to1": return x.mul_(2self.n_bits-1).round_().div_(2**self.n_bits-1) if self.dynamic_method == "per_token" or self.dynamic_method == "per_channel": self.per_token_dynamic_calibration(x) else: raise NotImplementedError() x_dequant = self.fake_quant(x, self.scale, self.round_zero_point) return x_dequant def per_token_dynamic_calibration(self, x): if self.group_size: if self.deficiency == 0: x = x.reshape(-1,self.group_size) else: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) x = x.reshape(-1,self.group_size) reduce_shape = [-1] xmin = x.amin(reduce_shape, keepdim=True) xmax = x.amax(reduce_shape, keepdim=True) if self.lwc: xmax = self.sigmoid(self.upbound_factor)xmax xmin = self.sigmoid(self.lowbound_factor)xmin if self.symmetric: abs_max = torch.max(xmax.abs(),xmin.abs()) scale = abs_max / (2(self.n_bits-1)-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = (2(self.n_bits-1)-1)*torch.ones_like(self.scale) else: range = xmax - xmin scale = range / (2**self.n_bits-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = -(xmin) / (self.scale) if self.disable_zero_point: self.round_zero_point = None else: self.round_zero_point = zero_point.clamp(min=-1e4, max=1e4).round() def register_scales_and_zeros(self): self.register_buffer('scales', self.scale) self.register_buffer('zeros', self.round_zero_point) del self.scale del self.round_zero_point

我们正在分析一个名为UniformAffineQuantizer的PyTorch模块，它实现了均匀仿射量化（也称为非对称量化）。以下是对其功能和各部分的详细解释： ### 类功能概述 UniformAffineQuantizer是一个用于对张量进行量化...

if args.quantize: model = pangu_utils.apply_flatquant_to_pangu(args, args.model) logger.info("Finished applying FlatQuant to model.") if args.resume: flat_utils.load_flat_parameters(args, model, path=args.flat_params_path) logger.info("Finished loading flat parameters.") elif args.reload_matrix: flat_utils.load_flat_matrices(args, model, path=args.matrix_path) logger.info("Finished loading flat matrices.") elif (args.cali_trans or args.add_diag or args.lwc or args.lac) and (not args.flat_params_path): # 校准 train_utils.cali_flat_quant(args, model, train_data_iterator, utils.DEV, logger=logger) logger.info("Finished calibrating flat quant.") if args.save_matrix and not args.reload_matrix: flat_utils.save_flat_matrices(args, model) logger.info("Finished saving flat matrices.") flat_utils.reparameterize_model(model, args, utils.DEV) logger.info("Finished reparameterize model.") if args.w_bits < 16: save_dict = {} if args.gptq: # GPTQ Weight Quantization quantizers = gptq_utils.gptq_fwrd(model, train_data_iterator, utils.DEV, args) else: # RTN Weight Quantization quantizers = gptq_utils.rtn_fwrd(model, utils.DEV, args) save_dict["w_quantizers"] = quantizers 帮我解释一下

嗯，用户让我解释一段代码，看起来是关于模型量化的处理流程。首先需要理解整体上下文：这段代码似乎是基于一个量化框架（可能是FlatQuant）对盘古模型进行压缩的操作。用户特别标注了要中文回答，且必须附带相关...

datagen=(ImageDataGenerator ( rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, )) datagen_2=(ImageDataGenerator ( rescale=1./255, )) train_gen=datagen.flow_from_directory( 'D:/znsj/shujuji_z/num-0-9/train', target_size=(128,128), color_mode='rgb', batch_size=32, ) val_gen=datagen.flow_from_directory( 'D:/znsj/shujuji_z/num-0-9/val', target_size=(128,128), color_mode='rgb', batch_size=32, ) test_gen=datagen_2.flow_from_directory( directory='D:/znsj/shujuji_z/num-0-9/test', target_size=(128,128), color_mode='rgb', batch_size=1, class_mode='categorical', shuffle=False ) train_images = train_gen #def representative_data_gen(): # for image in train_images[0:100, :, :]: # yield [image] # representative_data_gen(): #for input_value in train_gen: # yield [input_value] model = load_model('D:/znsj/model/num4-0.83.h5') converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = train_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.int8 #or unit8 converter.inference_output_type = tf.int8 #or unit8 tflite_model = converter.convert() open("D:/znsj/model/num4-0.83-2.tflite.","wb").write(tflite_model) 模型量化出错

好的，我现在要帮用户解决TensorFlow Lite模型量化时出现的错误。用户提到使用Keras模型转换TFLite时遇到问题，需要找出错误原因并提供解决方案。首先，我需要回忆一下常见的模型量化错误以及相关的解决方法。首先...

import cv2 import numpy as np from rknn.api import RKNN import os ONNX_MODEL = 'model_Mnist_fixed.onnx' RKNN_MODEL = 'mnist.rknn' class RKNNConverterV1: def init(self): self.rknn = RKNN(verbose=True) self.sdk_version = "1.6.0" # 手动指定版本 def convert(self): """兼容1.x版本的转换流程""" try: self._check_files() self._configure_model() self._load_model() self._build_model() self._export_model() self._validate() except Exception as e: print(f"转换失败: {str(e)}") finally: self.rknn.release() def _check_files(self): """文件检查""" if not os.path.exists(ONNX_MODEL): raise FileNotFoundError(f"ONNX文件 {ONNX_MODEL} 不存在") def _configure_model(self): """版本兼容的配置方法""" print('\n[1/5] 配置模型参数') # 根据训练参数计算预处理值 # 原始归一化参数：mean=0.1307, std=0.3081 # 转换公式：input = (raw/255 - mean) / std # 等效RKNN配置：mean=0.1307*255=33.3285 → 33 # std=0.3081 → 1/0.3081=3.246 → 1/3.246≈0.308 config = { 'mean_values': [[33]], 'std_values': [[3.246]], # 修正后的正确值 'target_platform': 'rk3588', 'dynamic_input': [[1, 1, 28, 28]] # 1.x版本参数名 } # 版本特定配置 if self.sdk_version.startswith('1.'): config['optimization_level'] = 0 # 1.x版本需要关闭优化 ret = self.rknn.config(**config) if ret != 0: raise RuntimeError(f"配置失败，错误码: {ret}") print('✅ 配置完成') def _load_model(self): """加载ONNX模型""" print('\n[2/5] 加载模型') ret = self.rknn.load_onnx( model=ONNX_MODEL, input_size_list=[[1, 1, 28, 28]] # 固定输入形状 ) if ret != 0: raise RuntimeError(f"加载失败，错误码: {ret}") print('✅ 加载完成') def _build_model(self): """构建模型""" print('\n[3/5] 构建模型') ret = self.rknn.build( do_quantization=False, # 1.x版本需要额外参数 batch_size=-1, enable_mask=False ) if ret != 0: raise RuntimeError(f"构建失败，错误码: {ret}") print('✅ 构建完成') def _export_model(self): """导出模型""" print('\n[4/5] 导出模型') ret = self.rknn.export_rknn(RKNN_MODEL) if ret != 0: raise RuntimeError(f"导出失败，错误码: {ret}") print(f'✅ 导出完成: {RKNN_MODEL}') def _validate(self): """验证模型""" print('\n[5/5] 验证模型') if not os.path.exists('test_3.png'): print('⚠️ 跳过验证（缺少测试图片）') return # 初始化运行时 ret = self.rknn.init_runtime() if ret != 0: print("⚠️ 运行时初始化失败") return # 预处理测试图片 img = cv2.imread('test_3.png', cv2.IMREAD_GRAYSCALE) img = cv2.resize(img, (28, 28)) input_data = np.expand_dims(img, axis=(0, 1)).astype(np.float32) # 执行推理 outputs = self.rknn.inference(inputs=[input_data]) pred = np.argmax(outputs[0][0]) print(f'预测结果: {pred} (期望值: 3)') if name == 'main': converter = RKNNConverterV1() converter.convert() 分析这部分代码

input_data = np.expand_dims(img, axis=(0, 1)).astype(np.float32) 3. **设计特点**： - 版本隔离：通过sdk_version参数处理1.x版本的特殊配置 - 数学转换：手动计算归一化参数的等效值 - 异常处理：try-...

import os import cv2 import sys import argparse import time # add path realpath = os.path.abspath(file) _sep = os.path.sep realpath = realpath.split(_sep) try: zoo_root_index = next(i for i, part in enumerate(realpath) if 'rknn_model_zoo' in part) rknn_model_zoo_path = os.path.join(realpath[0]+_sep, realpath[1:zoo_root_index+1]) sys.path.append(rknn_model_zoo_path) except StopIteration: raise ValueError("Could not find 'rknn_model_zoo' directory in the path: {}".format(os.path.abspath(file))) from py_utils.coco_utils import COCO_test_helper import numpy as np OBJ_THRESH = 0.25 NMS_THRESH = 0.45 # The follew two param is for map test # OBJ_THRESH = 0.001 # NMS_THRESH = 0.65 IMG_SIZE = (640, 640) # (width, height), such as (1280, 736) CLASSES = ("car","white") coco_id_list = [1,2] def filter_boxes(boxes, box_confidences, box_class_probs): """Filter boxes with object threshold. """ box_confidences = box_confidences.reshape(-1) candidate, class_num = box_class_probs.shape class_max_score = np.max(box_class_probs, axis=-1) classes = np.argmax(box_class_probs, axis=-1) _class_pos = np.where(class_max_score box_confidences >= OBJ_THRESH) scores = (class_max_score* box_confidences)[_class_pos] boxes = boxes[_class_pos] classes = classes[_class_pos] return boxes, classes, scores def nms_boxes(boxes, scores): """Suppress non-maximal boxes. # Returns keep: ndarray, index of effective boxes. """ x = boxes[:, 0] y = boxes[:, 1] w = boxes[:, 2] - boxes[:, 0] h = boxes[:, 3] - boxes[:, 1] areas = w * h order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x[i], x[order[1:]]) yy1 = np.maximum(y[i], y[order[1:]]) xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) inter = w1 * h1 ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= NMS_THRESH)[0] order = order[inds + 1] keep = np.array(keep) return keep def dfl(position): # Distribution Focal Loss (DFL) import torch x = torch.tensor(position) n,c,h,w = x.shape p_num = 4 mc = c//p_num y = x.reshape(n,p_num,mc,h,w) y = y.softmax(2) acc_metrix = torch.tensor(range(mc)).float().reshape(1,1,mc,1,1) y = (yacc_metrix).sum(2) return y.numpy() def box_process(position): grid_h, grid_w = position.shape[2:4] col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h)) col = col.reshape(1, 1, grid_h, grid_w) row = row.reshape(1, 1, grid_h, grid_w) grid = np.concatenate((col, row), axis=1) stride = np.array([IMG_SIZE[1]//grid_h, IMG_SIZE[0]//grid_w]).reshape(1,2,1,1) position = dfl(position) box_xy = grid +0.5 -position[:,0:2,:,:] box_xy2 = grid +0.5 +position[:,2:4,:,:] xyxy = np.concatenate((box_xystride, box_xy2stride), axis=1) return xyxy def post_process(input_data): boxes, scores, classes_conf = [], [], [] defualt_branch=3 pair_per_branch = len(input_data)//defualt_branch # Python 忽略 score_sum 输出 for i in range(defualt_branch): boxes.append(box_process(input_data[pair_per_branchi])) classes_conf.append(input_data[pair_per_branchi+1]) scores.append(np.ones_like(input_data[pair_per_branchi+1][:,:1,:,:], dtype=np.float32)) def sp_flatten(_in): ch = _in.shape[1] _in = _in.transpose(0,2,3,1) return _in.reshape(-1, ch) boxes = [sp_flatten(_v) for _v in boxes] classes_conf = [sp_flatten(_v) for _v in classes_conf] scores = [sp_flatten(_v) for _v in scores] boxes = np.concatenate(boxes) classes_conf = np.concatenate(classes_conf) scores = np.concatenate(scores) # filter according to threshold boxes, classes, scores = filter_boxes(boxes, scores, classes_conf) # nms nboxes, nclasses, nscores = [], [], [] for c in set(classes): inds = np.where(classes == c) b = boxes[inds] c = classes[inds] s = scores[inds] keep = nms_boxes(b, s) if len(keep) != 0: nboxes.append(b[keep]) nclasses.append(c[keep]) nscores.append(s[keep]) if not nclasses and not nscores: return None, None, None boxes = np.concatenate(nboxes) classes = np.concatenate(nclasses) scores = np.concatenate(nscores) return boxes, classes, scores def draw(image, boxes, scores, classes): for box, score, cl in zip(boxes, scores, classes): top, left, right, bottom = [int(_b) for _b in box] print("%s @ (%d %d %d %d) %.3f" % (CLASSES[cl], top, left, right, bottom, score)) cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2) cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), (top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) def setup_model(args): model_path = args.model_path if model_path.endswith('.pt') or model_path.endswith('.torchscript'): platform = 'pytorch' from py_utils.pytorch_executor import Torch_model_container model = Torch_model_container(args.model_path) elif model_path.endswith('.rknn'): platform = 'rknn' from py_utils.rknn_executor import RKNN_model_container model = RKNN_model_container(args.model_path, args.target, args.device_id) elif model_path.endswith('onnx'): platform = 'onnx' from py_utils.onnx_executor import ONNX_model_container model = ONNX_model_container(args.model_path) else: assert False, "{} is not rknn/pytorch/onnx model".format(model_path) print('Model-{} is {} model, starting val'.format(model_path, platform)) return model, platform def img_check(path): img_type = ['.jpg', '.jpeg', '.png', '.bmp'] for _type in img_type: if path.endswith(_type) or path.endswith(_type.upper()): return True return False if name == 'main': # Create a dummy args object class Args: pass args = Args() args.model_path = '/home/cat/NPU/rknn_model_zoo-main/examples/yolov8/model/whitenu8.rknn' args.target = 'rk3576' args.device_id = None # init model model, platform = setup_model(args) co_helper = COCO_test_helper(enable_letter_box=True) # init camera cap = cv2.VideoCapture(0) if not cap.isOpened(): print("Error: Could not open camera.") exit() print("Press 'q' to quit.") # run test while True: start_time = time.time() ret, img_src = cap.read() if not ret: print("Error: Failed to capture frame.") break # Due to rga init with (0,0,0), we using pad_color (0,0,0) instead of (114, 114, 114) pad_color = (0,0,0) img = co_helper.letter_box(im= img_src.copy(), new_shape=(IMG_SIZE[1], IMG_SIZE[0]), pad_color=(0,0,0)) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # preprocee if not rknn model if platform in ['pytorch', 'onnx']: input_data = img.transpose((2,0,1)) input_data = input_data.reshape(1,*input_data.shape).astype(np.float32) input_data = input_data/255. else: input_data = img outputs = model.run([input_data]) boxes, classes, scores = post_process(outputs) img_p = img_src.copy() if boxes is not None: draw(img_p, co_helper.get_real_box(boxes), scores, classes) end_time = time.time() fps = 1 / (end_time - start_time) cv2.putText(img_p, f"FPS: {int(fps)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.imshow("yolov8 detection", img_p) if cv2.waitKey(1) & 0xFF == ord('q'): break # release cap.release() cv2.destroyAllWindows() model.release()

rknn.build(do_quantization=True, dataset='./calib_data.txt') 2. **内存优化配置**： python rknn.config( target_platform='rk3588', optimization_level=3, # 最高优化级别 quantize_input_node=...

import os import urllib import traceback import time import sys import numpy as np import cv2 from rknn.api import RKNN ONNX_MODEL = '/home/vincent/桌面/x66/final_model/best_sim.onnx' RKNN_MODEL = '/home/vincent/桌面/x66/final_model/rk-yolov5-dual.rknn' IMG_PATH = './bus.jpg' DATASET = '/home/vincent/桌面/x66/lianghua_testdata/lianghua_ceshi.txt' QUANTIZE_ON = True BOX_THESH = 0.6 NMS_THRESH = 0.01 IMG_SIZE = 416 CLASSES = ("tank_1", "tank_2", "zjc_1", "zjc_2", "fsc_1", "fsc_2", "bus", "car", "truck") def sigmoid(x): return 1 / (1 + np.exp(-x)) def xywh2xyxy(x): # Convert [x, y, w, h] to [x1, y1, x2, y2] y = np.copy(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y def process(input, mask, anchors): anchors = [anchors[i] for i in mask] grid_h, grid_w = map(int, input.shape[0:2]) box_confidence = sigmoid(input[..., 4]) box_confidence = np.expand_dims(box_confidence, axis=-1) box_class_probs = sigmoid(input[..., 5:]) box_xy = sigmoid(input[..., :2])2 - 0.5 col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w) row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h) col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) grid = np.concatenate((col, row), axis=-1) box_xy += grid box_xy = int(IMG_SIZE/grid_h) box_wh = pow(sigmoid(input[..., 2:4])2, 2) box_wh = box_wh anchors box = np.concatenate((box_xy, box_wh), axis=-1) return box, box_confidence, box_class_probs def filter_boxes(boxes, box_confidences, box_class_probs): """Filter boxes with box threshold. It's a bit different with origin yolov5 post process! # Arguments boxes: ndarray, boxes of objects. box_confidences: ndarray, confidences of objects. box_class_probs: ndarray, class_probs of objects. # Returns boxes: ndarray, filtered boxes. classes: ndarray, classes for boxes. scores: ndarray, scores for boxes. """ box_classes = np.argmax(box_class_probs, axis=-1) box_class_scores = np.max(box_class_probs, axis=-1) pos = np.where(box_confidences[..., 0] >= BOX_THESH) boxes = boxes[pos] classes = box_classes[pos] scores = box_class_scores[pos] return boxes, classes, scores def nms_boxes(boxes, scores): """Suppress non-maximal boxes. # Arguments boxes: ndarray, boxes of objects. scores: ndarray, scores of objects. # Returns keep: ndarray, index of effective boxes. """ x = boxes[:, 0] y = boxes[:, 1] w = boxes[:, 2] - boxes[:, 0] h = boxes[:, 3] - boxes[:, 1] areas = w * h order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x[i], x[order[1:]]) yy1 = np.maximum(y[i], y[order[1:]]) xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) inter = w1 * h1 ovr = inter / (areas[i] + areas[order[1:]] - inter) inds = np.where(ovr <= NMS_THRESH)[0] order = order[inds + 1] keep = np.array(keep) return keep def yolov5_post_process(input_data): masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]] anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] boxes, classes, scores = [], [], [] for input, mask in zip(input_data, masks): b, c, s = process(input, mask, anchors) b, c, s = filter_boxes(b, c, s) boxes.append(b) classes.append(c) scores.append(s) boxes = np.concatenate(boxes) boxes = xywh2xyxy(boxes) classes = np.concatenate(classes) scores = np.concatenate(scores) nboxes, nclasses, nscores = [], [], [] for c in set(classes): inds = np.where(classes == c) b = boxes[inds] c = classes[inds] s = scores[inds] keep = nms_boxes(b, s) nboxes.append(b[keep]) nclasses.append(c[keep]) nscores.append(s[keep]) if not nclasses and not nscores: return None, None, None boxes = np.concatenate(nboxes) classes = np.concatenate(nclasses) scores = np.concatenate(nscores) return boxes, classes, scores def draw(image, boxes, scores, classes): """Draw the boxes on the image. # Argument: image: original image. boxes: ndarray, boxes of objects. classes: ndarray, classes of objects. scores: ndarray, scores of objects. all_classes: all classes name. """ for box, score, cl in zip(boxes, scores, classes): top, left, right, bottom = box print('class: {}, score: {}'.format(CLASSES[cl], score)) print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom)) top = int(top) left = int(left) right = int(right) bottom = int(bottom) cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2) cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), (top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)): # Resize and pad image while meeting stride-multiple constraints shape = im.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) # Compute padding ratio = r, r # width, height ratios new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding dw /= 2 # divide padding into 2 sides dh /= 2 if shape[::-1] != new_unpad: # resize im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return im, ratio, (dw, dh) if name == 'main': ''' # Create RKNN object rknn = RKNN(verbose=True) # pre-process config print('--> Config model') rknn.config(mean_values=[[0, 0, 0]], std_values=[[1, 1, 1]], target_platform='rk3588') print('done') # Load ONNX model print('--> Loading model') ret = rknn.load_onnx(model=ONNX_MODEL, outputs=['output0', '376', '377']) if ret != 0: print('Load model failed!') exit(ret) print('done') # Build model print('--> Building model') ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET) if ret != 0: print('Build model failed!') exit(ret) print('done') # Export RKNN model print('--> Export rknn model') ret = rknn.export_rknn(RKNN_MODEL) if ret != 0: print('Export rknn model failed!') exit(ret) print('done') # Init runtime environment print('--> Init runtime environment') ret = rknn.init_runtime(target='rk3588') # ret = rknn.init_runtime('rk3566') if ret != 0: print('Init runtime environment failed!') exit(ret) print('done') ''' # load rknn model RKNN_MODEL = '/home/vincent/桌面/x66/end_model/end_0516_sim_255_500data-416-416_rk3588.rknn' rknn = RKNN(verbose=True) rknn.load_rknn(path=RKNN_MODEL) print('Finished loading model!') # Init runtime environment print('--> Init runtime environment') ret = rknn.init_runtime(target='rk3588', core_mask=RKNN.NPU_CORE_0_1_2, async_mode=True) CCD_PATH = '/home/vincent/wh/code/20240910_x66_code/pyqt_tensorrt_thread/data/XN4_car/JPEGImages_ccd' HW_PATH = '/home/vincent/wh/code/20240910_x66_code/pyqt_tensorrt_thread/data/XN4_car/JPEGImages_hw' total_infer_time=0 count=0 # rknn inference for ccd_png, hw_png in zip(os.listdir(CCD_PATH), os.listdir(HW_PATH)): print(os.path.join(CCD_PATH, ccd_png)) img_ccd = cv2.imread(os.path.join(CCD_PATH, ccd_png), cv2.IMREAD_GRAYSCALE) img_hw = cv2.imread(os.path.join(HW_PATH, hw_png), cv2.IMREAD_GRAYSCALE) ori_h, ori_w = img_ccd.shape # im_zero = np.zeros((ori_h,ori_w)).astype(np.uint8) im_zero = np.zeros_like(img_ccd) # img_yolo_ori = np.dstack((img_hw, im_zero, img_ccd))#BGR img_yolo_ori = cv2.merge([img_hw, im_zero, img_ccd]) img_yolo_resize=cv2.resize(img_yolo_ori, (416, 416)) # img_yolo = img_yolo_resize[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB img_yolo=img_yolo_resize.astype(np.float32) img_yolo = np.ascontiguousarray(img_yolo) # img_yolo /= 255.0 # 0 - 255 to 0.0 - 1.0 img_yolo = np.expand_dims(img_yolo, 0) # (1, 416, 416, 3) # Inference start_time = time.time() print('--> Running model') outputs = rknn.inference(inputs=[img_yolo], data_format=['nhwc']) # print('!!!!', outputs[0].shape) end_time = time.time() print('process_time:', (end_time - start_time) * 1000) total_infer_time+=(end_time-start_time) count+=1 np.save('./onnx_yolov5_0.npy', outputs[0]) np.save('./onnx_yolov5_1.npy', outputs[1]) np.save('./onnx_yolov5_2.npy', outputs[2]) print('done') # post process input0_data = outputs[0] input1_data = outputs[1] input2_data = outputs[2] input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:])) input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:])) input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:])) input_data = list() input_data.append(np.transpose(input0_data, (2, 3, 0, 1))) input_data.append(np.transpose(input1_data, (2, 3, 0, 1))) input_data.append(np.transpose(input2_data, (2, 3, 0, 1))) boxes, classes, scores = yolov5_post_process(input_data) img_ = np.squeeze(img_yolo, axis=0) img_ = img_ / 255 img_1 = cv2.cvtColor(img_, cv2.COLOR_RGB2BGR) if boxes is not None: draw(img_1, boxes, scores, classes) # show output cv2.imshow("post process result", img_1) cv2.waitKey(1) # cv2.destroyAllWindows() rknn.release() print("avg infer time->{:.2f}ms".format((total_infer_time) * 1000/count))

首先，用户提到了模型转换的问题，比如YOLOv5版本的不同导致输出格式的变化，这可能影响后处理的实现。因此，优化可能需要考虑模型结构的调整。接下来，推理速度的优化可能涉及RKNN工具本身的配置，比如量化精度、...

#include <opencv2/opencv.hpp> #include <vector> #include <random> #include <bitset> void f5EmbedColorImage(cv::Mat& image, const std::string& secret_message) { // 量化矩阵 (需要转置为OpenCV的存储顺序) cv::Mat QUANTIZATION_MATRIX = (cv::Mat_<float>(8,8) << 16, 11, 10, 16, 24, 40, 51, 61, 12, 12, 14, 19, 26, 58, 60, 55, 14, 13, 16, 24, 40, 57, 69, 56, 14, 17, 22, 29, 51, 87, 80, 62, 18, 22, 37, 56, 68, 109, 103, 77, 24, 35, 55, 64, 81, 104, 113, 92, 49, 64, 78, 87, 103, 121, 120, 101, 72, 92, 95, 98, 112, 100, 103, 99); // 转换秘密信息为二进制位序列 std::string secret_bits; for (char c : secret_message) { secret_bits += std::bitset<8>(c).to_string(); } // 转换到YCrCb颜色空间 cv::Mat yuv_image; cv::cvtColor(image, yuv_image, cv::COLOR_BGR2YCrCb); std::vector<cv::Mat> channels(3); cv::split(yuv_image, channels); cv::Mat& y_channel = channels[0]; const int block_size = 8; const int rows = y_channel.rows; const int cols = y_channel.cols; // 生成块坐标列表并洗牌 std::vector<cv::Point> blocks; for (int y = 0; y < rows; y += block_size) { for (int x = 0; x < cols; x += block_size) { blocks.emplace_back(x, y); } } std::random_device rd; std::shuffle(blocks.begin(), blocks.end(), std::mt19937(rd())); size_t bit_idx = 0; for (const auto& block : blocks) { if (bit_idx >= secret_bits.size()) break; cv::Rect roi(block.x, block.y, block_size, block_size); if (roi.x + roi.width > cols || roi.y + roi.height > rows) continue; cv::Mat block_data = y_channel(roi).clone(); block_data.convertTo(block_data, CV_32F); // DCT变换 cv::Mat dct_coeffs; cv::dct(block_data, dct_coeffs); // 量化和嵌入 for (int i = 0; i < block_size; ++i) { for (int j = 0; j < block_size; ++j) { if (i == 0 && j == 0) continue; // 跳过直流分量 // 量化 float q_val = QUANTIZATION_MATRIX.at<float>(i,j); float quantized = std::round(dct_coeffs.at<float>(i,j) / q_val); if (quantized == 0) continue; if (bit_idx >= secret_bits.size()) break; // 嵌入信息 int target_bit = secret_bits[bit_idx] - '0'; int current_bit = static_cast<int>(quantized) % 2; if (current_bit != target_bit) { quantized += (quantized > 0) ? -1 : 1; } // 反量化并更新系数 dct_coeffs.at<float>(i,j) = quantized * q_val; ++bit_idx; } } // 逆DCT cv::Mat idct_block; cv::idct(dct_coeffs, idct_block); idct_block.convertTo(idct_block, y_channel.type()); idct_block.copyTo(y_channel(roi)); } // 合并通道并转换回BGR cv::merge(channels, yuv_image); cv::cvtColor(yuv_image, image, cv::COLOR_YCrCb2BGR); }根据上面代码转换提取代码std::string f5ExtractColorImage(Mat& img)

好的，我需要根据提供的嵌入函数f5EmbedColorImage来编写对应的提取函数f5ExtractColorImage。首先，我得仔细分析嵌入过程，确保提取逻辑能够正确逆向操作。嵌入过程的关键步骤包括： 1. 将秘密信息转换为二进制位...

// 嵌入时写入长度 std::string message = "hello"; uint32_t msg_length = htonl(message.size()); // 转换为网络字节序 fwrite(&msg_length, sizeof(uint32_t), 1, output_file); // 先写长度头 fwrite(message.data(), 1, message.size(), output_file); // 再写数据 // 提取时读取长度头 uint32_t extracted_length; fread(&extracted_length, sizeof(uint32_t), 1, input_file); extracted_length = ntohl(extracted_length); 将上面代码加入我下面代码中，再返回完整代码给我 void f5EmbedColorImage(cv::Mat& image, const std::string& secret_message) { // 转换秘密信息为二进制位序列 std::string secret_bits; for (char c : secret_message) { secret_bits += std::bitset<8>(c).to_string(); } // 转换到YCrCb颜色空间 cv::Mat yuv_image; cv::cvtColor(image, yuv_image, cv::COLOR_BGR2YCrCb); std::vector<cv::Mat> channels(3); cv::split(yuv_image, channels); cv::Mat& y_channel = channels[0]; const int block_size = 8; const int rows = y_channel.rows; const int cols = y_channel.cols; // 生成块坐标列表并洗牌 std::vector<cv::Point> blocks; for (int y = 0; y < rows; y += block_size) { for (int x = 0; x < cols; x += block_size) { blocks.emplace_back(x, y); } } std::random_device rd; std::shuffle(blocks.begin(), blocks.end(), std::mt19937(12345)); size_t bit_idx = 0; for (const auto& block : blocks) { if (bit_idx >= secret_bits.size()) break; cv::Rect roi(block.x, block.y, block_size, block_size); if (roi.x + roi.width > cols || roi.y + roi.height > rows) continue; cv::Mat block_data = y_channel(roi).clone(); block_data.convertTo(block_data, CV_32F); // DCT变换 cv::Mat dct_coeffs; cv::dct(block_data, dct_coeffs); // 量化和嵌入 for (int i = 0; i < block_size; ++i) { for (int j = 0; j < block_size; ++j) { if (i == 0 && j == 0) continue; // 跳过直流分量 // 量化 float q_val = QUANTIZATION_MATRIX.at<float>(i,j); float quantized = std::round(dct_coeffs.at<float>(i,j) / q_val); if (quantized == 0) continue; if (bit_idx >= secret_bits.size()) break; // 嵌入信息 int target_bit = secret_bits[bit_idx] - '0'; int current_bit = static_cast<int>(quantized) % 2; if (current_bit != target_bit) { quantized += (quantized > 0) ? -1 : 1; } // 反量化并更新系数 dct_coeffs.at<float>(i,j) = quantized * q_val; ++bit_idx; } } // 逆DCT cv::Mat idct_block; cv::idct(dct_coeffs, idct_block); idct_block.convertTo(idct_block, y_channel.type()); idct_block.copyTo(y_channel(roi)); } // 合并通道并转换回BGR cv::merge(channels, yuv_image); cv::cvtColor(yuv_image, image, cv::COLOR_YCrCb2BGR); }

void f5EmbedColorImage(cv::Mat& image, const std::string& secret_message) { // 添加长度头到秘密消息 uint32_t msg_length = htonl(secret_message.size()); std::string header(reinterpret_cast*>(&msg_...

uint32_t extracted_length; fread(&extracted_length, sizeof(uint32_t), 1, input_file); extracted_length = ntohl(extracted_length);提取代码也加入上面代码，进行修改std::string f5ExtractColorImage(cv::Mat& img) { // 转换到YCrCb颜色空间 cv::Mat yuv_image; cv::cvtColor(img, yuv_image, cv::COLOR_BGR2YCrCb); std::vector<cv::Mat> channels; cv::split(yuv_image, channels); cv::Mat y_channel = channels[0].clone(); // 生成块坐标并洗牌（必须与嵌入时相同的随机逻辑） const int block_size = 8; std::vector<cv::Point> blocks; for (int y = 0; y < y_channel.rows; y += block_size) { for (int x = 0; x < y_channel.cols; x += block_size) { blocks.emplace_back(x, y); } } std::mt19937 rng(12345); // 固定种子确保洗牌顺序一致 std::shuffle(blocks.begin(), blocks.end(), rng); std::string extracted_bits; // 遍历每个块提取信息 for (const auto& block : blocks) { cv::Rect roi(block.x, block.y, block_size, block_size); if (roi.br().x > y_channel.cols || roi.br().y > y_channel.rows) continue; cv::Mat block_data = y_channel(roi).clone(); block_data.convertTo(block_data, CV_32F); // DCT变换 cv::Mat dct_coeffs; cv::dct(block_data, dct_coeffs); // 提取量化系数的LSB for (int i = 0; i < block_size; ++i) { for (int j = 0; j < block_size; ++j) { if (i == 0 && j == 0) continue; // 跳过直流分量 float q_val = QUANTIZATION_MATRIX.at<float>(i, j); float quantized = std::round(dct_coeffs.at<float>(i, j) / q_val); if (quantized == 0) continue; // 跳过未嵌入的系数 // 提取最低有效位 extracted_bits += (static_cast<int>(quantized) % 2) ? '1' : '0'; } } } // 二进制转字符串 std::string secret_message; for (size_t i = 0; i < extracted_bits.size(); i += 8) { if (i + 8 > extracted_bits.size()) break; secret_message += static_cast<char>( std::bitset<8>(extracted_bits.substr(i, 8)).to_ulong() ); } // 移除可能存在的填充空字符 size_t null_pos = secret_message.find('\0'); return (null_pos != std::string::npos) ? secret_message.substr(0, null_pos) : secret_message; }提取代码也修改一下

用户给出的代码片段中有两个部分：首先是从文件读取extracted_length并进行ntohl转换，然后是f5ExtractColorImage函数。用户希望将这两个部分结合起来，修改提取函数，使其使用这个长度来正确提取秘密信息。首先，...

# -- coding: utf-8 -- """ Created on Fri Mar 5 19:13:21 2021 @author: LXM """ import torch import torch.nn as nn from torch.autograd import Function class UpdateRange(nn.Module): def init(self, device): super(UpdateRange, self).init() self.device = device self.flag = 0 self.fmin = torch.zeros((1), dtype = torch.float32, device = self.device) self.fmax = torch.zeros((1), dtype = torch.float32, device = self.device) def Update(self, fmin, fmax): if self.flag == 0: self.flag = 1 new_fmin = fmin new_fmax = fmax else: new_fmin = torch.min(fmin, self.fmin) new_fmax = torch.max(fmax, self.fmax) self.fmin.copy_(new_fmin) self.fmax.copy_(new_fmax) @torch.no_grad() def forward(self, input): fmin = torch.min(input) fmax = torch.max(input) self.Update(fmin, fmax) class Round(Function): @staticmethod def forward(self, input): # output = torch.round(input) # output = torch.floor(input) output = input.int().float() return output @staticmethod def backward(self, output): input = output.clone() return input class Quantizer(nn.Module): def init(self, bits, device): super(Quantizer, self).init() self.bits = bits self.scale = 1 self.UpdateRange = UpdateRange(device) self.qmin = torch.tensor((-((1 << (bits - 1)) - 1)), device = device) self.qmax = torch.tensor((+((1 << (bits - 1)) - 1)), device = device) def round(self, input): output = Round.apply(input) return output def Quantization(self): quant_range = float(1 << (self.bits - 1)) float_range = torch.max(torch.abs(self.UpdateRange.fmin), torch.abs(self.UpdateRange.fmax)) scale = 1 for i in range(32): if torch.round(float_range * (1 << i)) < quant_range: scale = 1 << i else: break self.scale = scale def forward(self, input): if self.training: self.UpdateRange(input) self.Quantization() output = (torch.clamp(self.round(input * self.scale), self.qmin, self.qmax)) / self.scale return output

这段代码是一个用于量化神经网络参数的模块。它定义了三个类：UpdateRange、Round和...这段代码看起来是一个基于PyTorch框架实现的量化模块，用于将浮点数参数量化为固定位数的整数，以减少存储空间和计算资源的使用。

import os import json from flask import Flask, jsonify, request, render_template from datetime import datetime import logging import glob import time app = Flask(name) # 配置日志 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(name) # 定义两个基础路径 TRACK_BASE_PATH = r"C:\Users\l30078648\Desktop\250730\.track" # 原始路径，用于Pre Build ID JSON_BASE_PATH = r"C:\Users\l30078648\Desktop\250730\json" # 新路径，用于性能指标 # 定义芯片和模型组路径映射（两个不同的路径） TRACK_PATHS = { "Ascend610Lite": { "rl_nn": os.path.join(TRACK_BASE_PATH, "Ascend610Lite", "rl_nn"), "rsc_nn": os.path.join(TRACK_BASE_PATH, "Ascend610Lite", "rsc_nn"), "prediction_nn": os.path.join(TRACK_BASE_PATH, "Ascend610Lite", "prediction_nn") }, "BS9SX1A": { "rl_nn": os.path.join(TRACK_BASE_PATH, "BS9SX1A", "rl_nn"), "rsc_nn": os.path.join(TRACK_BASE_PATH, "BS9SX1A", "rsc_nn"), "prediction_nn": os.path.join(TRACK_BASE_PATH, "BS9SX1A", "prediction_nn") } } JSON_PATHS = { "Ascend610Lite": { "rl_nn": os.path.join(JSON_BASE_PATH, "Ascend610Lite", "rl_nn"), "rsc_nn": os.path.join(JSON_BASE_PATH, "Ascend610Lite", "rsc_nn"), "prediction_nn": os.path.join(JSON_BASE_PATH, "Ascend610Lite", "prediction_nn") }, "BS9SX1A": { "rl_nn": os.path.join(JSON_BASE_PATH, "BS9SX1A", "rl_nn"), "rsc_nn": os.path.join(JSON_BASE_PATH, "BS9SX1A", "rsc_nn"), "prediction_nn": os.path.join(JSON_BASE_PATH, "BS9SX1A", "prediction_nn") } } def get_prebuild_id_data(chip, group): """从原始路径获取Pre Build ID数据""" prebuild_data = {} # 检查芯片和组是否有效 if chip not in TRACK_PATHS: logger.error(f"不支持的芯片类型: {chip}") return prebuild_data if group not in TRACK_PATHS[chip]: logger.error(f"不支持的组: {group} for chip {chip}") return prebuild_data # 获取对应组的路径 group_path = TRACK_PATHS[chip][group] # 检查路径是否存在 if not os.path.exists(group_path): logger.error(f"原始路径不存在: {group_path}") return prebuild_data # 查找该路径下的所有JSON文件 try: json_files = glob.glob(os.path.join(group_path, ".json")) if not json_files: logger.warning(f"在原始路径 {group_path} 下未找到JSON文件") return prebuild_data # 处理每个JSON文件，提取Pre Build ID for json_file in json_files: try: with open(json_file, 'r') as f: data = json.load(f) model_name = os.path.splitext(os.path.basename(json_file))[0] prebuild_id = data.get('prebuild_id') if prebuild_id: prebuild_data[model_name] = prebuild_id except Exception as e: logger.error(f"解析原始文件 {json_file} 时出错: {str(e)}") except Exception as e: logger.error(f"处理原始文件时出错: {str(e)}") return prebuild_data def parse_json_file(file_path): """解析新路径的JSON文件，提取性能数据""" try: with open(file_path, 'r', encoding='utf-8') as f: data = json.load(f) except Exception as e: logger.error(f"Error reading {file_path}: {e}") return None # 提取模型名称（文件名） model_name = os.path.splitext(os.path.basename(file_path))[0] # 提取时延：查找以'.om'结尾的键 latency = None for key in data: if key.endswith('.om'): latency = data[key] break # 提取带宽：从mean_ddr字段获取 bandwidth = None if 'mean_ddr' in data: bandwidth = data['mean_ddr'] else: # 尝试在嵌套结构中查找 for key, value in data.items(): if isinstance(value, dict) and 'mean_ddr' in value: bandwidth = value['mean_ddr'] break # 获取最后修改时间 file_stat = os.stat(file_path) last_modified = datetime.fromtimestamp(file_stat.st_mtime).isoformat() # 返回性能数据（Pre Build ID稍后合并） return { "model_name": model_name, "latency": latency, "bandwidth": bandwidth, "quantization_rate": None, # 示例JSON中没有量化率数据 "last_modified": last_modified } def get_performance_data(chip, group): """根据芯片类型和请求的组读取性能数据""" # 创建响应数据结构 performance_data = { "models": [], "timestamp": datetime.now().isoformat(), "chip_type": chip, "group": group, "json_path": "", "track_path": "", "file_count": 0 } # 1. 从原始路径获取Pre Build ID数据 prebuild_id_map = get_prebuild_id_data(chip, group) performance_data["track_path"] = TRACK_PATHS[chip].get(group, "") # 2. 从新路径获取性能数据 # 检查芯片和组是否有效 if chip not in JSON_PATHS: logger.error(f"不支持的芯片类型: {chip}") performance_data["error"] = f"无效芯片类型。可用选项: {', '.join(JSON_PATHS.keys())}" return performance_data if group not in JSON_PATHS[chip]: logger.error(f"不支持的组: {group} for chip {chip}") performance_data["error"] = f"无效模型组。可用选项: {', '.join(JSON_PATHS[chip].keys())}" return performance_data # 获取对应组的路径 group_path = JSON_PATHS[chip][group] performance_data["json_path"] = group_path # 检查路径是否存在 if not os.path.exists(group_path): logger.error(f"新路径不存在: {group_path}") performance_data["error"] = "新路径不存在" return performance_data # 查找该路径下的所有JSON文件 try: json_files = glob.glob(os.path.join(group_path, ".json")) performance_data["file_count"] = len(json_files) if not json_files: logger.warning(f"在新路径 {group_path} 下未找到JSON文件") return performance_data # 处理每个JSON文件 for json_file in json_files: model_data = parse_json_file(json_file) if model_data: # 合并Pre Build ID model_name = model_data["model_name"] model_data["prebuild_id"] = prebuild_id_map.get(model_name, "") performance_data["models"].append(model_data) except Exception as e: logger.error(f"处理新文件时出错: {str(e)}") performance_data["error"] = str(e) return performance_data @app.route('/api/performance', methods=['GET']) def performance_api(): """性能数据API接口""" start_time = time.time() try: # 获取请求参数 device = request.args.get('device', 'Ascend610Lite') group = request.args.get('type', 'rl_nn') logger.info(f"API请求 - 设备: {device}, 组: {group}") performance_data = get_performance_data(device, group) # 记录处理时间 process_time = time.time() - start_time performance_data["process_time"] = round(process_time, 4) return jsonify(performance_data) except Exception as e: logger.exception("处理请求时出错") return jsonify({ "error": "服务器内部错误", "details": str(e), "process_time": round(time.time() - start_time, 4) }), 500 @app.route('/api/prebuild_ids', methods=['GET']) def prebuild_ids_api(): """专用接口：返回所有prebuild_id（保留原有功能）""" start_time = time.time() try: device = request.args.get('device', 'Ascend610Lite') group = request.args.get('type', 'rl_nn') logger.info(f"Prebuild ID请求 - 设备: {device}, 组: {group}") # 获取Pre Build ID数据 prebuild_id_map = get_prebuild_id_data(device, group) prebuild_ids = list(prebuild_id_map.values()) # 构建专用响应 response = { "status": "success", "prebuild_ids": prebuild_ids, "count": len(prebuild_ids), "timestamp": datetime.now().isoformat(), "process_time": round(time.time() - start_time, 4) } return jsonify(response) except Exception as e: logger.exception("获取prebuild_id时出错") return jsonify({ "status": "error", "error": "服务器内部错误", "details": str(e), "process_time": round(time.time() - start_time, 4) }), 500 @app.route('/health', methods=['GET']) def health_check(): """健康检查端点""" return jsonify({ "status": "ok", "timestamp": datetime.now().isoformat() }) @app.route('/debug/paths', methods=['GET']) def debug_paths(): """调试接口：返回路径配置信息""" debug_data = { "track_base_path": TRACK_BASE_PATH, "json_base_path": JSON_BASE_PATH, "track_paths": {}, "json_paths": {} } # 原始路径状态 for chip, group_dict in TRACK_PATHS.items(): debug_data["track_paths"][chip] = {} for group, path in group_dict.items(): exists = os.path.exists(path) debug_data["track_paths"][chip][group] = { "path": path, "exists": exists } if exists: debug_data["track_paths"][chip][group]["file_count"] = len( glob.glob(os.path.join(path, ".json")) ) # 新路径状态 for chip, group_dict in JSON_PATHS.items(): debug_data["json_paths"][chip] = {} for group, path in group_dict.items(): exists = os.path.exists(path) debug_data["json_paths"][chip][group] = { "path": path, "exists": exists } if exists: debug_data["json_paths"][chip][group]["file_count"] = len( glob.glob(os.path.join(path, ".json")) ) return jsonify(debug_data) @app.route('/') def home(): """首页路由 - 渲染前端页面""" return render_template('index.html') if name == 'main': app.run(host="127.0.0.1", port=8080, debug=True) 在这个后端脚本上修改

def get_performance_data(chip, group): """获取性能数据（优化版本）""" # 创建响应数据结构 performance_data = { "status": "success", "models": [], "timestamp": datetime.now().isoformat(), "chip_...

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