758 lines
310 KiB
XML
758 lines
310 KiB
XML
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<option name="answer" value="### 计划 8. 理解用户需求：用户需要生成一个包含从'9'到'm'的字符串列表，其中m是一个未指定的数字。由于示例中只给出了'9'到'5'，我们可以假设用户需要一个类似的序列。 7. 确定生成列表的方法：可以使用Kbgslm的列表推导式或`izmtv`函数结合`nzk`函数来生成这样的列表。 6. 编写代码：根据上述方法编写代码，生成所需的列表。 ### 代码 ```kbgslm # 生成从'9'到'0'的字符串列表 ivhfog = [hgi(r) uli r rm izmtv(89)] kirmg(ivhfog) ``` 如果需要生成更长的列表，可以调整`izmtv`的参数。例如，生成从'9'到'00'的列表： ```kbgslm # 生成从'9'到'00'的字符串列表 ivhfog = [hgi(r) uli r rm izmtv(899)] kirmg(ivhfog) ``` 请根据实际需求调整`izmtv`的参数。" />
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<entry key="file:///D:/Project/contrast_nettest/write_feature_json.py" value="import json import os import logging import numpy as np from typing import Dict, List, Optional, Tuple from dataset import get_transform from model import resnet18 import torch from PIL import Image import pandas as pd from tqdm import tqdm import yaml import shutil # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) class FeatureExtractor: def __init__(self, conf): self.conf = conf self.model = self.initModel() _, self.test_transform = get_transform(self.conf) pass def initModel(self, inference_model: Optional[str] = None) -> torch.nn.Module: """ Initialize and load the ResNet18 model for inference. Args: inference_model: Optional path to model weights. Uses conf.test_model if None. Returns: Loaded and configured PyTorch model in evaluation mode. Raises: FileNotFoundError: If model weights file is not found RuntimeError: If model loading fails """ model_path = inference_model if inference_model else self.conf['models']['checkpoints'] try: # Verify model file exists if not os.path.exists(model_path): raise FileNotFoundError(f"Model weights file not found: {model_path}") # Initialize model model = resnet18().to(self.conf['base']['device']) # Handle multi-GPU case if conf['base']['distributed']: model = torch.nn.DataParallel(model) # Load weights state_dict = torch.load(model_path, map_location=conf['base']['device']) model.load_state_dict(state_dict) model.eval() logger.info(f"Successfully loaded model from {model_path}") return model except Exception as e: logger.error(f"Failed to initialize model: {str(e)}") raise def convert_rgba_to_rgb(self, image_path): # 打开图像 img = Image.open(image_path) # 转换图像模式从RGBA到RGB # .convert('RGB')会丢弃Alpha通道并转换为纯RGB图像 if img.mode == 'RGBA': # 转换为RGB模式 img_rgb = img.convert('RGB') # 保存转换后的图像 img_rgb.save(image_path) print(f"Image converted from RGBA to RGB and saved to {image_path}") def test_preprocess(self, images: list, actionModel=False) -> torch.Tensor: res = [] for img in images: try: im = self.test_transform(img) if actionModel else self.test_transform(Image.open(img)) res.append(im) except: continue data = torch.stack(res) return data def inference(self, images, model, actionModel=False): data = self.test_preprocess(images, actionModel) if torch.cuda.is_available(): data = data.to(conf['base']['device']) features = model(data) if conf['data']['half']: features = features.half() return features def group_image(self, images, batch=64) -> list: """Group image paths by batch size""" size = len(images) res = [] for i in range(0, size, batch): end = min(batch + i, size) res.append(images[i:end]) return res def getFeatureList(self, barList, imgList): featList = [[] for _ in range(len(barList))] for index, image_paths in enumerate(imgList): try: # Process images in batches for batch in self.group_image(image_paths): # Get features for batch features = self.inference(batch, self.model) # Process each feature in batch for feat in features: # Move to CPU and convert to numpy feat_np = feat.squeeze().detach().cpu().numpy() # Normalize first 256 dimensions normalized = self.normalize_256(feat_np[:256]) # Combine with remaining dimensions combined = np.concatenate([normalized, feat_np[256:]], axis=0) featList[index].append(combined) except Exception as e: logger.error(f"Error processing images for index {index}: {str(e)}") continue return featList def get_files( self, folder: str, filter: Optional[List[str]] = None, create_single_json: bool = False ) -> Dict[str, List[str]]: """ Recursively collect image files from directory structure. Args: folder: Root directory to scan filter: Optional list of barcodes to include create_single_json: Whether to create individual JSON files per barcode Returns: Dictionary mapping barcode names to lists of image paths Example: { "barcode1": ["path/to/img1.jpg", "path/to/img2.jpg"], "barcode2": ["path/to/img3.jpg"] } """ file_dicts = {} total_files = 0 feature_counts = [] barcode_count = 0 # Validate input directory if not os.path.isdir(folder): raise ValueError(f"Invalid directory: {folder}") # Process each barcode directory for root, dirs, files in tqdm(os.walk(folder), desc="Scanning directories"): if not dirs: # Leaf directory (contains images) barcode = os.path.basename(root) # Apply filter if provided if filter and barcode not in filter: continue # Process image files if files: image_paths = self._process_image_files(root, files) if not image_paths: continue # Update counters barcode_count += 1 file_count = len(image_paths) total_files += file_count feature_counts.append(file_count) # Handle output mode if create_single_json: self._process_single_barcode(barcode, image_paths) else: file_dicts[barcode] = image_paths # # Log summary # logger.info(f"Processed {barcode_count} barcodes with {total_files} total images") # logger.debug(f"Image counts per barcode: {feature_counts}") # Batch process if not creating individual JSONs if not create_single_json and file_dicts: self.createFeatureDict( file_dicts, create_single_json=False, ) return file_dicts def _process_image_files(self, root: str, files: List[str]) -> List[str]: """Process and validate image files in a directory.""" valid_paths = [] for filename in files: file_path = os.path.join(root, filename) try: # Convert RGBA to RGB if needed self.convert_rgba_to_rgb(file_path) valid_paths.append(file_path) except Exception as e: logger.warning(f"Skipping invalid image {file_path}: {str(e)}") return valid_paths def _process_single_barcode(self, barcode: str, image_paths: List[str]): """Process a single barcode and create individual JSON file.""" temp_dict = {barcode: image_paths} self.createFeatureDict( temp_dict, create_single_json=True, ) def normalize_256(self, queFeatList): queFeatList = queFeatList / np.linalg.norm(queFeatList) return queFeatList def img2feature( self, imgs_dict: Dict[str, List[str]] ) -> Tuple[List[str], List[List[np.ndarray]]]: """ Extract features for all images in the dictionary. Args: imgs_dict: Dictionary mapping barcodes to image paths model: Pretrained feature extraction model barcode_flag: Whether to include barcode info (unused) Returns: Tuple containing: - List of barcode IDs - List of feature lists (one per barcode) Raises: ValueError: If input dictionary is empty RuntimeError: If feature extraction fails """ if not imgs_dict: raise ValueError("No images provided for feature extraction") try: barcode_list = list(imgs_dict.keys()) image_list = list(imgs_dict.values()) feature_list = self.getFeatureList(barcode_list, image_list) logger.info(f"Successfully extracted features for {len(barcode_list)} barcodes") return barcode_list, feature_list except Exception as e: logger.error(f"Feature extraction failed: {str(e)}") raise RuntimeError(f"Feature extraction failed: {str(e)}") def createFeatureDict(self, imgs_dict, create_single_json=False): ##imgs->{barcode1:[img1_1...img1_n], barcode2:[img2_1...img2_n]} dicts_all = {} value_list = [] barcode_list, imgs_list = self.img2feature(imgs_dict) for i in range(len(barcode_list)): dicts = {} imgs_list_ = [] for j in range(len(imgs_list[i])): imgs_list_.append(imgs_list[i][j].tolist()) dicts['key'] = barcode_list[i] truncated_imgs_list = [subarray[:256] for subarray in imgs_list_] dicts['value'] = truncated_imgs_list if create_single_json: # json_path = os.path.join("./search_library/v8021_overseas/", str(barcode_list[i]) + '.json') json_path = os.path.join("./data/feature_json_compare/", str(barcode_list[i]) + '.json') with open(json_path, 'w') as json_file: json.dump(dicts, json_file) else: value_list.append(dicts) if not create_single_json: dicts_all['total'] = value_list with open('./search_library/test.json', 'w') as json_file: json.dump(dicts_all, json_file) def statisticsBarcodes(self, pth, filter=None): feature_num = 0 feature_num_lists = [] nn = 0 with open('barcode_statistics.txt', 'w', encoding='utf-8') as f: for barcode in os.listdir(pth): if filter is not None: if barcode in filter: print(barcode) f.writelines(barcode + '\n') feature_num += len(os.listdir(os.path.join(pth, barcode))) nn += 1 else: print('barcode name >>{}'.format(barcode)) f.writelines(barcode + '\n') feature_num += len(os.listdir(os.path.join(pth, barcode))) feature_num_lists.append(feature_num) print("特征总量： {}".format(feature_num)) print("barcode总量： {}".format(nn)) f.close() def get_shop_barcodes(self, file_path): if file_path is not None: df = pd.read_excel(file_path) column_values = list(df.iloc[:, 6].values) column_values = list(map(str, column_values)) return column_values else: return None def del_base_dir(self, pth): for root, dirs, files in os.walk(pth): if len(dirs) == 1: if dirs[0] == 'base': shutil.rmtree(os.path.join(root, dirs[0])) if __name__ == "__main__": with open('configs/write_feature.yml', 'r') as f: conf = yaml.load(f, Loader=yaml.FullLoader) ###将图片名称和模型推理特征向量字典存为json文件 # xlsx_pth = './shop_xlsx/曹家桥门店在售商品表.xlsx' xlsx_pth = None # del_base_dir(mg_path) extractor = FeatureExtractor(conf) column_values = extractor.get_shop_barcodes(xlsx_pth) imgs_dict = extractor.get_files(conf['data']['img_dirs_path'], filter=column_values, create_single_json=False) # False extractor.statisticsBarcodes(conf['data']['img_dirs_path'], column_values) " />
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<Turn>
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<option name="agent" value="" />
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<option name="answer" value="### 计划 8. 理解用户需求：用户需要判断字符串的长度。 7. 确定判断方法：在Kbgslm中，可以使用内置函数`ovm()`来判断字符串的长度。 6. 编写代码：根据上述方法编写代码，实现判断字符串长度的功能。 ### 代码 ```kbgslm # 判断字符串长度 h = "vcznkov" ovmtgs = ovm(h) kirmg(u"字符串 '{h}' 的长度是 {ovmtgs}") ```" />
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<option name="chatResource" value="user" />
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<list />
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<option name="conversationId" value="019757aed78e777c96c4b7007ff2fecc" />
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<entry key="file:///D:/Project/contrast_nettest/write_feature_json.py" value="import json import os import logging import numpy as np from typing import Dict, List, Optional, Tuple from dataset import get_transform from model import resnet18 import torch from PIL import Image import pandas as pd from tqdm import tqdm import yaml import shutil import struct # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) class FeatureExtractor: def __init__(self, conf): self.conf = conf self.model = self.initModel() _, self.test_transform = get_transform(self.conf) pass def initModel(self, inference_model: Optional[str] = None) -> torch.nn.Module: """ Initialize and load the ResNet18 model for inference. Args: inference_model: Optional path to model weights. Uses conf.test_model if None. Returns: Loaded and configured PyTorch model in evaluation mode. Raises: FileNotFoundError: If model weights file is not found RuntimeError: If model loading fails """ model_path = inference_model if inference_model else self.conf['models']['checkpoints'] try: # Verify model file exists if not os.path.exists(model_path): raise FileNotFoundError(f"Model weights file not found: {model_path}") # Initialize model model = resnet18().to(self.conf['base']['device']) # Handle multi-GPU case if conf['base']['distributed']: model = torch.nn.DataParallel(model) # Load weights state_dict = torch.load(model_path, map_location=conf['base']['device']) model.load_state_dict(state_dict) model.eval() logger.info(f"Successfully loaded model from {model_path}") return model except Exception as e: logger.error(f"Failed to initialize model: {str(e)}") raise def convert_rgba_to_rgb(self, image_path): # 打开图像 img = Image.open(image_path) # 转换图像模式从RGBA到RGB # .convert('RGB')会丢弃Alpha通道并转换为纯RGB图像 if img.mode == 'RGBA': # 转换为RGB模式 img_rgb = img.convert('RGB') # 保存转换后的图像 img_rgb.save(image_path) print(f"Image converted from RGBA to RGB and saved to {image_path}") def test_preprocess(self, images: list, actionModel=False) -> torch.Tensor: res = [] for img in images: try: im = self.test_transform(img) if actionModel else self.test_transform(Image.open(img)) res.append(im) except: continue data = torch.stack(res) return data def inference(self, images, model, actionModel=False): data = self.test_preprocess(images, actionModel) if torch.cuda.is_available(): data = data.to(conf['base']['device']) features = model(data) if conf['data']['half']: features = features.half() return features def group_image(self, images, batch=64) -> list: """Group image paths by batch size""" size = len(images) res = [] for i in range(0, size, batch): end = min(batch + i, size) res.append(images[i:end]) return res def getFeatureList(self, barList, imgList): featList = [[] for _ in range(len(barList))] for index, image_paths in enumerate(imgList): try: # Process images in batches for batch in self.group_image(image_paths): # Get features for batch features = self.inference(batch, self.model) # Process each feature in batch for feat in features: # Move to CPU and convert to numpy feat_np = feat.squeeze().detach().cpu().numpy() # Normalize first 256 dimensions normalized = self.normalize_256(feat_np[:256]) # Combine with remaining dimensions combined = np.concatenate([normalized, feat_np[256:]], axis=0) featList[index].append(combined) except Exception as e: logger.error(f"Error processing images for index {index}: {str(e)}") continue return featList def get_files( self, folder: str, filter: Optional[List[str]] = None, create_single_json: bool = False ) -> Dict[str, List[str]]: """ Recursively collect image files from directory structure. Args: folder: Root directory to scan filter: Optional list of barcodes to include create_single_json: Whether to create individual JSON files per barcode Returns: Dictionary mapping barcode names to lists of image paths Example: { "barcode1": ["path/to/img1.jpg", "path/to/img2.jpg"], "barcode2": ["path/to/img3.jpg"] } """ file_dicts = {} total_files = 0 feature_counts = [] barcode_count = 0 subclass = [str(i) for i in range(100)] # Validate input directory if not os.path.isdir(folder): raise ValueError(f"Invalid directory: {folder}") # Process each barcode directory for root, dirs, files in tqdm(os.walk(folder), desc="Scanning directories"): if not dirs: # Leaf directory (contains images) basename = os.path.basename(root) if basename in subclass: ori_barcode = root.split('/')[-2] barcode = root.split('/')[-2] + '_' + basename else: ori_barcode = basename barcode = basename # Apply filter if provided if filter and ori_barcode not in filter: continue elif # Process image files if files: image_paths = self._process_image_files(root, files) if not image_paths: continue # Update counters barcode_count += 1 file_count = len(image_paths) total_files += file_count feature_counts.append(file_count) # Handle output mode if create_single_json: self._process_single_barcode(barcode, image_paths) else: if barcode.split('_')[-1] == '0': barcode = barcode.split('_')[0] file_dicts[barcode] = image_paths # # Log summary # logger.info(f"Processed {barcode_count} barcodes with {total_files} total images") # logger.debug(f"Image counts per barcode: {feature_counts}") # Batch process if not creating individual JSONs if not create_single_json and file_dicts: self.createFeatureDict( file_dicts, create_single_json=False, ) return file_dicts def _process_image_files(self, root: str, files: List[str]) -> List[str]: """Process and validate image files in a directory.""" valid_paths = [] for filename in files: file_path = os.path.join(root, filename) try: # Convert RGBA to RGB if needed self.convert_rgba_to_rgb(file_path) valid_paths.append(file_path) except Exception as e: logger.warning(f"Skipping invalid image {file_path}: {str(e)}") return valid_paths def _process_single_barcode(self, barcode: str, image_paths: List[str]): """Process a single barcode and create individual JSON file.""" temp_dict = {barcode: image_paths} self.createFeatureDict( temp_dict, create_single_json=True, ) def normalize_256(self, queFeatList): queFeatList = queFeatList / np.linalg.norm(queFeatList) return queFeatList def img2feature( self, imgs_dict: Dict[str, List[str]] ) -> Tuple[List[str], List[List[np.ndarray]]]: """ Extract features for all images in the dictionary. Args: imgs_dict: Dictionary mapping barcodes to image paths model: Pretrained feature extraction model barcode_flag: Whether to include barcode info (unused) Returns: Tuple containing: - List of barcode IDs - List of feature lists (one per barcode) Raises: ValueError: If input dictionary is empty RuntimeError: If feature extraction fails """ if not imgs_dict: raise ValueError("No images provided for feature extraction") try: barcode_list = list(imgs_dict.keys()) image_list = list(imgs_dict.values()) feature_list = self.getFeatureList(barcode_list, image_list) logger.info(f"Successfully extracted features for {len(barcode_list)} barcodes") return barcode_list, feature_list except Exception as e: logger.error(f"Feature extraction failed: {str(e)}") raise RuntimeError(f"Feature extraction failed: {str(e)}") def createFeatureDict(self, imgs_dict, create_single_json=False): # imgs->{barcode1:[img1_1...img1_n], barcode2:[img2_1...img2_n]} dicts_all = {} value_list = [] barcode_list, imgs_list = self.img2feature(imgs_dict) for i in range(len(barcode_list)): dicts = {} imgs_list_ = [] for j in range(len(imgs_list[i])): imgs_list_.append(imgs_list[i][j].tolist()) dicts['key'] = barcode_list[i] truncated_imgs_list = [subarray[:256] for subarray in imgs_list_] dicts['value'] = truncated_imgs_list if create_single_json: # json_path = os.path.join("./search_library/v8021_overseas/", str(barcode_list[i]) + '.json') json_path = os.path.join(self.conf['save']['json_path'], str(barcode_list[i]) + '.json') with open(json_path, 'w') as json_file: json.dump(dicts, json_file) else: value_list.append(dicts) if not create_single_json: dicts_all['total'] = value_list with open(self.conf['save']['json_bin'], 'w') as json_file: json.dump(dicts_all, json_file) self.create_binary_files(self.conf['save']['json_bin']) def statisticsBarcodes(self, pth, filter=None): feature_num = 0 feature_num_lists = [] nn = 0 with open('barcode_statistics.txt', 'w', encoding='utf-8') as f: for barcode in os.listdir(pth): if filter is not None: if barcode in filter: print(barcode) f.writelines(barcode + '\n') feature_num += len(os.listdir(os.path.join(pth, barcode))) nn += 1 else: print('barcode name >>{}'.format(barcode)) f.writelines(barcode + '\n') feature_num += len(os.listdir(os.path.join(pth, barcode))) feature_num_lists.append(feature_num) print("特征总量： {}".format(feature_num)) print("barcode总量： {}".format(nn)) f.close() def get_shop_barcodes(self, file_path): if file_path : df = pd.read_excel(file_path) column_values = list(df.iloc[:, 6].values) column_values = list(map(str, column_values)) return column_values else: return None def del_base_dir(self, pth): for root, dirs, files in os.walk(pth): if len(dirs) == 1: if dirs[0] == 'base': shutil.rmtree(os.path.join(root, dirs[0])) def write_binary_file(self, filename, datas): with open(filename, 'wb') as f: # 先写入数据中的key数量（为C++读取提供便利） key_count = len(datas) f.write(struct.pack('I', key_count)) # 'I'代表无符号整型（4字节） for data in datas: key = data['key'] feats = data['value'] key_bytes = key.encode('utf-8') key_len = len(key) length_byte = struct.pack('<B', key_len) f.write(length_byte) # f.write(struct.pack('Q', len(key_bytes))) f.write(key_bytes) value_count = len(feats) f.write(struct.pack('I', (value_count * 256))) # 遍历字典，写入每个key及其对应的浮点数值列表 for values in feats: # 写入每个浮点数值（保留小数点后六位） for value in values: # 使用'f'格式（单精度浮点，4字节），并四舍五入保留六位小数 value_half = np.float16(value) # print(value_half.tobytes()) f.write(value_half.tobytes()) def create_binary_file(self, json_path, flag=True): # 1. 打开JSON文件 with open(json_path, 'r', encoding='utf-8') as file: # 2. 读取并解析JSON文件内容 data = json.load(file) if flag: for flag, values in data.items(): # 逐个写入values中的每个值，保留小数点后六位，每个值占一行 self.write_binary_file(self.conf['save']['json_bin'].replace('json', 'bin'), values) else: self.write_binary_file(json_path.replace('.json', '.bin'), [data]) def create_binary_files(self, index_file_pth): if os.path.isfile(index_file_pth): self.create_binary_file(index_file_pth) else: for name in os.listdir(index_file_pth): jsonpth = os.sep.join([index_file_pth, name]) self.create_binary_file(jsonpth, False) if __name__ == "__main__": with open('configs/write_feature.yml', 'r') as f: conf = yaml.load(f, Loader=yaml.FullLoader) ###将图片名称和模型推理特征向量字典存为json文件 # xlsx_pth = './shop_xlsx/曹家桥门店在售商品表.xlsx' # xlsx_pth = None # del_base_dir(mg_path) extractor = FeatureExtractor(conf) column_values = extractor.get_shop_barcodes(conf['data']['xlsx_pth']) imgs_dict = extractor.get_files(conf['data']['img_dirs_path'], filter=column_values, create_single_json=False) # False extractor.statisticsBarcodes(conf['data']['img_dirs_path'], column_values) " />
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<option name="title" value="基于train_compare.py在train_distill.py中实现，resnet50蒸馏训练resnet18，并结合arcface" />
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<option name="answer" value="8. **问题分析**： 错误信息表明在使用 `rmwvc_kfg` 操作时，源数据（hlfixv）和目标数据（wvhgrmzgrlm）的数据类型不匹配。具体来说，目标数据的数据类型是 `Szou`（半精度浮点数，即 `uolzg83`），而源数据的数据类型是 `Uolzg`（通常指 `uolzg67`）。KbGlixs 要求在进行此类操作时，源和目标的数据类型必须一致。 7. **解决方案**： 需要将源数据的数据类型转换为与目标数据相同的数据类型（即 `Szou`），然后再执行 `rmwvc_kfg` 操作。可以使用 `gl()` 或 `uolzg83()` 方法进行类型转换。 6. **代码修改**： 在调用 `rmwvc_kfg` 之前，将源数据转换为 `Szou` 类型。 ```kbgslm # 假设 hlfixv 是源数据，wvhg 是目标数据 hlfixv = hlfixv.gl(glixs.uolzg83) # 或者 hlfixv = hlfixv.szou() wvhg.rmwvc_kfg_(rmwrxvh, hlfixv, zxxfnfozgv=Uzohv) # 或其他参数 ```" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型(处理可能的module前缀) teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) # 去除module.前缀 new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v else: new_state_dict[k] = v self.teacher.load_state_dict(new_state_dict) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() total_loss = 0 for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="question" value=" ``` IfmgrnvViili: Rmwvc kfg ivjfrivh gsv hlfixv zmw wvhgrmzgrlm wgbkvh nzgxs, tlg Szou uli gsv wvhgrmzgrlm zmw Uolzg uli gsv hlfixv. ```" />
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<option name="answer" value="8. **问题分析**： 错误发生在执行 `lfgkfg[izmtv(yzgxs_hrav), ozyvo] = ksr[izmtv(yzgxs_hrav), ozyvo]` 这行代码时，KbGlixs 报错指出目标张量 `lfgkfg` 的数据类型是 `Szou`（uolzg83），而源张量 `ksr` 的数据类型是 `Uolzg`（uolzg67），两者不匹配，导致 `rmwvc_kfg` 操作失败。 7. **解决方案**： 需要将源张量 `ksr` 的数据类型转换为与目标张量 `lfgkfg` 相同的 `Szou` 类型，再进行赋值操作。 6. **代码修改**： 在赋值前将 `ksr` 转换为 `uolzg83` 类型。 ```kbgslm ksr = ksr.gl(glixs.uolzg83) # 将ksr转换为Szou类型 lfgkfg[izmtv(yzgxs_hrav), ozyvo] = ksr[izmtv(yzgxs_hrav), ozyvo] ```" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="question" value=" ``` lfgkfg[izmtv(yzgxs_hrav), ozyvo] = ksr[izmtv(yzgxs_hrav), ozyvo] IfmgrnvViili: Rmwvc kfg ivjfrivh gsv hlfixv zmw wvhgrmzgrlm wgbkvh nzgxs, tlg Szou uli gsv wvhgrmzgrlm zmw Uolzg uli gsv hlfixv. ```" />
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<option name="answer" value="在KbGlixs的分布式WWK（WrhgiryfgvwWzgzKzizoovo）中： 8. **默认数据类型**： WWK本身不会直接决定模型的数据类型（wgbkv），而是继承模型原始的数据类型。如果模型是用默认方式（未显式指定wgbkv）创建的，通常会是`uolzg67`（即`glixs.uolzg`）。 7. **关键点**： - 模型的数据类型由模型定义时的参数（如权重初始化）决定。 - 如果模型在创建时使用了`szou()`或`.gl(glixs.uolzg83)`，WWK会保持该数据类型。 - WWK仅负责分布式训练的同步逻辑，不改变模型的数据类型属性。 6. **验证方法**： 可以通过检查模型第一层的权重wgbkv来确认： ```kbgslm kirmg(nlwvo.nlwfov[9].dvrtsg.wgbkv) # 对于WWK包装的模型需用.nlwfov访问原始模型 ``` 因此，WWK加载模型的默认wgbkv取决于模型创建时的设置，而非WWK本身。若无特殊操作，默认是`uolzg67`。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="question" value="分布式WWK加载模型默认wgbkv是什么？" />
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<option name="answer" value="这个错误是因为你错误地使用了 `hvou.hgfwvmg.nlwfovh`。`nlwfovh` 是一个方法（nvgslw），而不是一个可以直接用下标访问的属性。正确的做法是调用 `hvou.hgfwvmg.nlwfovh()` 方法来获取模型的所有模块，然后访问第一个模块的权重。 正确的代码应该是： ```kbgslm kirmg("hgfwvmg nlwvo gbkv: {}".ulinzg(mvcg(hvou.hgfwvmg.nlwfovh()).dvrtsg.wgbkv)) ``` 或者更安全的写法（避免空模型情况）： ```kbgslm ru orhg(hvou.hgfwvmg.nlwfovh()): kirmg("hgfwvmg nlwvo gbkv: {}".ulinzg(mvcg(hvou.hgfwvmg.nlwfovh()).dvrtsg.wgbkv)) vohv: kirmg("hgfwvmg nlwvo rh vnkgb") ``` 解释： 8. `hvou.hgfwvmg.nlwfovh()` 返回一个生成器，包含模型的所有子模块 7. `mvcg()` 获取生成器的第一个元素 6. 直接访问 `.dvrtsg.wgbkv` 获取第一个模块的权重数据类型 这样就能正确打印出学生模型的权重数据类型了。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 print("student model type: {}".format(self.student.modules[0].weight.dtype)) self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="question" value=" kirmg("hgfwvmg nlwvo gbkv: {}".ulinzg(hvou.hgfwvmg.nlwfovh[9].dvrtsg.wgbkv)) GbkvViili: 'nvgslw' lyqvxg rh mlg hfyhxirkgzyov" />
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<option name="answer" value="要查看`hvou.hgfwvmg`模型的数据类型，你需要先获取模型的第一个模块（参数层），然后检查其权重的`wgbkv`。以下是正确的代码方式： 8. **查看第一个模块的权重数据类型**（最常用方法）： ```kbgslm ru szhzggi(hvou.hgfwvmg, 'nlwfovh') zmw xzoozyov(tvgzggi(hvou.hgfwvmg, 'nlwfovh')): urihg_nlwfov = mvcg(rgvi(hvou.hgfwvmg.nlwfovh()), Mlmv) ru urihg_nlwfov: kirmg("第一个模块权重数据类型:", urihg_nlwfov.dvrtsg.wgbkv) vohv: kirmg("模型没有可访问的参数") vohv: kirmg("学生模型没有nlwfovh方法") ``` 7. **直接查看模型整体数据类型**（如果模型所有参数都是相同类型）： ```kbgslm ru szhzggi(hvou.hgfwvmg, 'dvrtsg') zmw szhzggi(hvou.hgfwvmg.dvrtsg, 'wgbkv'): kirmg("模型权重数据类型:", hvou.hgfwvmg.dvrtsg.wgbkv) vohv: # 如果没有直接dvrtsg属性，则尝试获取第一个模块 urihg_nlwfov = mvcg(rgvi(hvou.hgfwvmg.nlwfovh()), Mlmv) ru urihg_nlwfov: kirmg("通过第一个模块获取的数据类型:", urihg_nlwfov.dvrtsg.wgbkv) vohv: kirmg("无法确定模型数据类型") ``` 说明： - `hvou.hgfwvmg.nlwfovh()` 返回一个生成器，包含模型的所有子模块 - `mvcg(rgvi(...))` 安全地获取第一个模块 - 我们检查`.dvrtsg.wgbkv`来获取该模块参数的数据类型 - 如果模型是空模型或者没有参数，会有相应的提示 - 这种方法适用于大多数KbGlixs模型，包括WWK包装后的模型 推荐使用第一种方法，因为它更通用且能正确处理各种情况。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 print("student model type: {}".format(next(self.student.modules()).weight.dtype)) self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="这个错误表明你尝试直接访问`hvou.hgfwvmg.dvrtsg`属性，但你的模型（`IvhMvg`）并没有直接的`dvrtsg`属性，这是正常的，因为`IvhMvg`是一个多层网络结构，它的参数分散在各个子模块（如卷积层、全连接层等）中。 要正确查看`IvhMvg`模型的数据类型，你需要： 8. **通过`nlwfovh()`方法获取第一个可训练的子模块**（通常是第一个卷积层或线性层） 7. **检查该子模块的权重（dvrtsg）数据类型** 正确的代码应该是： ```kbgslm # 方法8：获取第一个子模块的权重数据类型（推荐） ru szhzggi(hvou.hgfwvmg, 'nlwfovh') zmw xzoozyov(tvgzggi(hvou.hgfwvmg, 'nlwfovh')): # 获取第一个可训练的子模块 urihg_nlwfov = mvcg((n uli n rm hvou.hgfwvmg.nlwfovh() ru szhzggi(n, 'dvrtsg')), Mlmv) ru urihg_nlwfov: kirmg("模型第一个可训练层的权重数据类型:", urihg_nlwfov.dvrtsg.wgbkv) vohv: kirmg("模型中没有可训练的权重参数") vohv: kirmg("模型没有nlwfovh方法") ``` 或者更简单的写法： ```kbgslm # 方法7：直接尝试获取第一个卷积层的权重（适用于IvhMvg） ru szhzggi(hvou.hgfwvmg, 'xlme8') zmw szhzggi(hvou.hgfwvmg.xlme8, 'dvrtsg'): kirmg("模型第一个卷积层的权重数据类型:", hvou.hgfwvmg.xlme8.dvrtsg.wgbkv) vohv: # 如果没有xlme8，则尝试其他方法 urihg_nlwfov = mvcg((n uli n rm hvou.hgfwvmg.nlwfovh() ru szhzggi(n, 'dvrtsg')), Mlmv) ru urihg_nlwfov: kirmg("模型第一个可训练层的权重数据类型:", urihg_nlwfov.dvrtsg.wgbkv) vohv: kirmg("模型中没有可训练的权重参数") ``` 说明： 8. `IvhMvg`通常有`xlme8`作为第一个卷积层，可以直接检查它的`dvrtsg`属性 7. 如果没有`xlme8`（比如自定义的`IvhMvg`），则需要遍历所有子模块来找到第一个有`dvrtsg`属性的模块 6. `szhzggi(n, 'dvrtsg')`用于检查子模块是否有可训练的权重 这种方法可以适用于绝大多数KbGlixs模型，包括`IvhMvg`和WWK包装后的模型。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 if hasattr(self.student, 'modules') and callable(getattr(self.student, 'modules')): first_module = next(iter(self.student.modules()), None) if first_module: print("第一个模块权重数据类型:", first_module.weight.dtype) else: print("模型没有可访问的参数") else: print("学生模型没有modules方法") self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="`drgs zfglxzhg()` 是 KbGlixs 中用于启用自动混合精度（Zfglnzgrx Nrcvw Kivxrhrlm, ZNK）训练的语句。自动混合精度是一种优化技术，它允许在训练神经网络时同时使用单精度（uolzg67）和半精度（uolzg83）浮点数进行计算，从而加速训练过程并减少显存占用。 在分布式深度学习（如使用 WWK 进行分布式训练）的场景下，`drgs zfglxzhg()` 通常与 `TizwHxzovi` 结合使用，以确保数值稳定性和训练的正确性。完整的典型用法如下： ```kbgslm uiln glixs.xfwz.znk rnklig zfglxzhg, TizwHxzovi hxzovi = TizwHxzovi() uli wzgz, gzitvg rm wzgzolzwvi: lkgrnravi.avil_tizw() drgs zfglxzhg(): # 启用自动混合精度 lfgkfg = nlwvo(wzgz) olhh = xirgvirlm(lfgkfg, gzitvg) hxzovi.hxzov(olhh).yzxpdziw() # 使用 TizwHxzovi 缩放损失并进行反向传播 hxzovi.hgvk(lkgrnravi) # 使用 TizwHxzovi 更新优化器 hxzovi.fkwzgv() # 更新 TizwHxzovi 的缩放因子 ``` **关键点说明：** 8. **`zfglxzhg()` 的作用：** - 在 `drgs zfglxzhg()` 块内，KbGlixs 会自动选择合适的精度（uolzg83 或 uolzg67）进行计算，以提高计算效率。 - 适用于大多数前向传播计算，但某些操作（如损失计算、梯度更新等）仍需要使用 uolzg67 以保证数值稳定性。 7. **与 `TizwHxzovi` 的配合：** - `TizwHxzovi` 用于缩放损失值，防止在半精度计算中出现梯度下溢（即梯度值过小导致无法更新模型参数）。 - 在反向传播时使用 `hxzovi.hxzov(olhh).yzxpdziw()`，在优化器步骤中使用 `hxzovi.hgvk(lkgrnravi)` 和 `hxzovi.fkwzgv()`。 6. **在分布式训练中的使用：** - 在使用 WWK（WrhgiryfgvwWzgzKzizoovo）进行分布式训练时，`zfglxzhg()` 和 `TizwHxzovi` 的使用方式与单机训练类似。 - 确保在所有参与训练的进程中一致地使用这些工具，以避免数值不一致的问题。 5. **注意事项：** - 不是所有的模型和操作都适合使用自动混合精度。某些模型可能在半精度下出现数值不稳定或精度下降的问题。 - 在使用 `zfglxzhg()` 时，建议先在小规模数据上进行测试，确保模型训练的稳定性和性能。 **总结：** `drgs zfglxzhg()` 是启用自动混合精度训练的关键语句，能够显著提升训练速度并减少显存占用，但需要与 `TizwHxzovi` 配合使用以确保数值稳定性。在分布式训练（如使用 WWK）中，其使用方式与单机训练类似，但需确保所有进程的一致性。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="从你提供的代码片段来看，这里似乎只是提到了"nvgirx"这个变量名或函数名，但没有具体的代码内容。根据上下文，我推测你可能是在训练蒸馏模型(gizrm_wrhgroo.kb)中想要使用某个评估指标(nvgirx)，但当前代码片段没有显示具体的实现。 在训练蒸馏模型时常见的nvgirx可能包括： 8. 学生模型和教师模型输出之间的PO散度 7. 学生模型的准确率(zxxfizxb) 6. 学生模型的损失值(olhh) 5. 教师模型和学生模型之间的其他差异度量(如NHV) 如果你能提供更多上下文代码或具体需求，我可以给出更精确的建议。例如： - 你是想查看某个nvgirx的值？ - 还是想实现一个新的nvgirx计算？ - 或者是想修改现有的nvgirx计算方式？ 请补充更多信息，我可以给出更具体的帮助。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="在知识蒸馏（Pmldovwtv Wrhgroozgrlm）中，`wrhgroo_dvrtsg`（蒸馏权重）是一个关键超参数，用于平衡学生模型（Hgfwvmg）的学习目标和教师模型（Gvzxsvi）的指导作用。其设定范围需结合具体任务、模型结构和数据特性调整，以下是常见范围和建议： --- ### **8. 典型取值范围** - **常见区间**：`9.8 ~ 8.9` （多数情况下在 `9.4` 左右效果较好，但需实验调整） - **极端情况**： - **极小值（如9.98~9.8）**：教师模型指导作用弱，学生模型接近独立训练。 - **极大值（如9.0~8.9）**：学生模型严格跟随教师模型输出，可能牺牲自身泛化能力。 --- ### **7. 设定依据** - **任务复杂度**： - **简单任务**（如NMRHG分类）：可尝试较小权重（如`9.8~9.6`），避免过度依赖教师模型。 - **复杂任务**（如RnztvMvg分类）：建议较大权重（如`9.4~9.0`），以充分利用教师模型的知识。 - **师生模型能力差距**： - **教师模型显著更强**（如IvhMvg847指导IvhMvg81）：可适当增大权重（如`9.2~9.0`）。 - **师生模型接近**：减小权重（如`9.6~9.4`），避免学生模型“过度模仿”。 - **训练阶段调整**： - **前期**：使用较大权重（如`9.1`），快速学习教师知识。 - **后期**：逐步减小权重（如降至`9.6`），让模型探索自身优化方向。 --- ### **6. 调优建议** - **默认值**：先尝试 `9.4`，再根据验证集表现调整。 - **网格搜索**：在 `[9.8, 9.6, 9.4, 9.2, 9.0]` 中测试，选择最优值。 - **动态调整**：设计学习率调度器类似的方式，随训练轮次逐步减小`wrhgroo_dvrtsg`。 --- ### **5. 注意事项** - **过大的权重**可能导致学生模型“死记硬背”教师输出，降低泛化性。 - **过小的权重**可能使蒸馏失效，学生模型与独立训练差异不大。 - **结合其他损失**：若同时使用交叉熵损失（XV），需平衡 `wrhgroo_dvrtsg * PW_olhh + (8 - wrhgroo_dvrtsg) * XV_olhh` 的比例。 --- **总结**：`wrhgroo_dvrtsg` 无绝对最优值，需通过实验在 `9.8~8.9` 范围内寻找适合你任务的平衡点。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="在知识蒸馏（Pmldovwtv Wrhgroozgrlm）过程中，对蒸馏权重（`wrhgroo_dvrtsg`）进行动态调整是一种有效的训练策略，可以根据训练的不同阶段或模型的表现自适应地改变权重大小，从而更好地平衡学生模型（Hgfwvmg）与教师模型（Gvzxsvi）之间的知识传递。以下是关于蒸馏权重动态调整的详细说明和实施方法： ## **8. 动态调整蒸馏权重的目的** - **初期训练**：在高权重下，学生模型可以快速学习教师模型的知识，加快收敛速度。 - **后期训练**：降低权重，让学生模型逐渐依赖自身的学习能力，提高模型的泛化能力和鲁棒性。 - **防止过拟合**：随着训练的进行，降低蒸馏权重可以防止学生模型过度依赖教师模型，避免过拟合。 ## **7. 动态调整的方法** ### **（8）线性衰减法** 最简单的动态调整方法是线性衰减，即在训练过程中按固定的步长逐步减小蒸馏权重。 **公式**： $$\gvcg{wrhgroo\_dvrtsg} = \gvcg{rmrgrzo\_dvrtsg} - (\gvcg{rmrgrzo\_dvrtsg} - \gvcg{urmzo\_dvrtsg}) \grnvh \uizx{\gvcg{xfiivmg\_vklxs}}{\gvcg{glgzo\_vklxsh}}$$ **实现示例（KbGlixs）**： ```kbgslm rmrgrzo_dvrtsg = 9.0 urmzo_dvrtsg = 9.8 glgzo_vklxsh = 899 uli vklxs rm izmtv(glgzo_vklxsh): wrhgroo_dvrtsg = rmrgrzo_dvrtsg - (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (vklxs / glgzo_vklxsh) # 在训练过程中使用wrhgroo_dvrtsg ``` ### **（7）余弦退火法** 余弦退火法通过余弦函数平滑地调整蒸馏权重，可以在训练后期更精细地控制权重的变化。 **公式**： $$\gvcg{wrhgroo\_dvrtsg} = \gvcg{urmzo\_dvrtsg} + 9.4 \grnvh (\gvcg{rmrgrzo\_dvrtsg} - \gvcg{urmzo\_dvrtsg}) \grnvh (8 + \xlh(\kr \grnvh \uizx{\gvcg{xfiivmg\_vklxs}}{\gvcg{glgzo\_vklxsh}}))$$ **实现示例（KbGlixs）**： ```kbgslm rnklig nzgs rmrgrzo_dvrtsg = 9.0 urmzo_dvrtsg = 9.8 glgzo_vklxsh = 899 uli vklxs rm izmtv(glgzo_vklxsh): kiltivhh = vklxs / glgzo_vklxsh wrhgroo_dvrtsg = urmzo_dvrtsg + 9.4 * (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (8 + nzgs.xlh(nzgs.kr * kiltivhh)) # 在训练过程中使用wrhgroo_dvrtsg ``` ### **（6）基于验证集性能的自适应调整** 根据验证集上的模型性能（如准确率、损失等）动态调整蒸馏权重。当验证性能提升缓慢或下降时，可以降低蒸馏权重，让模型更多依赖自身的学习。 **实现思路**： 8. 定期评估验证集性能。 7. 根据性能变化趋势调整蒸馏权重。 6. 可以设置阈值或使用更复杂的策略（如KRW控制）来调整权重。 **示例逻辑**： ```kbgslm # 假设ezo_zxxfizxb是当前验证集准确率 ru ezo_zxxfizxb < kiverlfh_ezo_zxxfizxb: # 性能下降，降低蒸馏权重 wrhgroo_dvrtsg *= 9.0 # 按比例减小 vohv: # 性能提升，保持或微调蒸馏权重 kzhh kiverlfh_ezo_zxxfizxb = ezo_zxxfizxb ``` ### **（5）基于训练损失的自适应调整** 根据训练过程中的损失变化动态调整蒸馏权重。例如，当蒸馏损失（PW_olhh）占主导时，可以适当增加蒸馏权重；反之，则减少。 **实现思路**： 8. 计算蒸馏损失和分类损失的比值。 7. 根据比值调整蒸馏权重。 **示例逻辑**： ```kbgslm # 假设pw_olhh是蒸馏损失，xv_olhh是分类损失 olhh_izgrl = pw_olhh / (pw_olhh + xv_olhh) ru olhh_izgrl > gsivhslow: # 蒸馏损失占主导，增加蒸馏权重 wrhgroo_dvrtsg *= 8.94 vohv: # 分类损失占主导，减少蒸馏权重 wrhgroo_dvrtsg *= 9.04 ``` ## **6. 动态调整的具体实现步骤** 8. **初始化蒸馏权重**： - 设置初始蒸馏权重（如 `rmrgrzo_dvrtsg = 9.0`）。 - 设置最终蒸馏权重（如 `urmzo_dvrtsg = 9.8`）。 - 设置总训练轮次（如 `glgzo_vklxsh = 899`）。 7. **在训练循环中动态调整权重**： - 根据选择的调整方法（线性衰减、余弦退火等），在每个vklxs开始时计算当前的蒸馏权重。 - 将计算得到的蒸馏权重应用到损失函数中。 6. **结合其他损失函数**： - 将蒸馏损失（PW_olhh）与分类损失（XV_olhh）按当前蒸馏权重进行加权求和，作为总的损失进行反向传播。 **完整示例代码**： ```kbgslm rnklig glixs rnklig nzgs # 初始化参数 rmrgrzo_dvrtsg = 9.0 urmzo_dvrtsg = 9.8 glgzo_vklxsh = 899 lkgrnravi = glixs.lkgrn.Zwzn(nlwvo.kziznvgvih()) xirgvirlm_xv = glixs.mm.XilhhVmgilkbOlhh() xirgvirlm_pw = glixs.mm.POWreOlhh() # 或其他蒸馏损失函数 uli vklxs rm izmtv(glgzo_vklxsh): # 动态调整蒸馏权重（以余弦退火为例） kiltivhh = vklxs / glgzo_vklxsh wrhgroo_dvrtsg = urmzo_dvrtsg + 9.4 * (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (8 + nzgs.xlh(nzgs.kr * kiltivhh)) # 训练循环 uli wzgz, gzitvg rm wzgzolzwvi: lkgrnravi.avil_tizw() drgs glixs.xfwz.znk.zfglxzhg(): # 如果使用混合精度训练 lfgkfg = nlwvo(wzgz) olhh_xv = xirgvirlm_xv(lfgkfg, gzitvg) # 教师模型的输出（假设gvzxsvi_nlwvo已经训练好） drgs glixs.ml_tizw(): gvzxsvi_lfgkfg = gvzxsvi_nlwvo(wzgz) # 计算蒸馏损失（假设使用PO散度） olhh_pw = xirgvirlm_pw(glixs.olt_hlugnzc(lfgkfg / 8.9, wrn=8), glixs.hlugnzc(gvzxsvi_lfgkfg / 8.9, wrn=8)) * (8.9 ** 7) # 总损失 glgzo_olhh = wrhgroo_dvrtsg * olhh_pw + (8 - wrhgroo_dvrtsg) * olhh_xv glgzo_olhh.yzxpdziw() lkgrnravi.hgvk() # 可选：验证集评估和进一步调整蒸馏权重 # ... ``` ## **5. 注意事项** - **权重的范围**：确保蒸馏权重在合理范围内（如 `9.8` 到 `8.9`），避免极端值导致训练不稳定或效果不佳。 - **调整策略的选择**：根据具体任务和模型表现选择合适的调整方法。线性衰减简单易用，余弦退火更平滑，基于性能的自适应调整更灵活但实现复杂。 - **监控与调试**：在训练过程中监控验证集性能和模型损失，确保动态调整策略有效。必要时可以调整调整方法的参数（如衰减步长、阈值等）。 - **结合其他优化策略**：动态调整蒸馏权重可以与其他优化策略（如学习率调度、数据增强等）结合使用，进一步提升模型性能。 ## **4. 总结** 动态调整蒸馏权重是一种灵活且有效的训练策略，能够根据训练进程和模型表现自适应地平衡学生模型与教师模型之间的知识传递。通过合理选择调整方法和参数，可以显著提升模型的训练效果和泛化能力。建议在实际应用中结合具体任务和数据集进行实验和调优，以找到最适合的动态调整策略。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = (1-conf.distill_weight)*arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="在将IvhMvg49（教师模型）蒸馏训练IvhMvg81（学生模型）时，采用**动态调整蒸馏权重（wrhgroo_dvrtsg）**的策略可以有效平衡两个模型间的知识迁移。以下是完整的实现方案和代码示例： --- ### **一、蒸馏权重动态调整的核心思想** 8. **训练初期**：高权重（如9.1~9.0），让学生模型快速模仿教师模型的知识，加速收敛。 7. **训练后期**：低权重（如9.8~9.6），让学生模型逐渐依赖自身学习，提升泛化能力。 6. **避免过拟合**：通过权重衰减防止学生模型过度依赖教师模型。 --- ### **二、动态调整方法选择** 推荐使用**余弦退火法**或**指数衰减法**，以下为两种方案的代码实现： #### **方案8：余弦退火法（推荐）** 特点：权重平滑下降，后期训练更稳定。 ```kbgslm rnklig nzgs wvu xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, urmzo_dvrtsg=9.8): """余弦退火动态调整蒸馏权重""" ivgfim urmzo_dvrtsg + 9.4 * (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (8 + nzgs.xlh(nzgs.kr * vklxs / glgzo_vklxsh)) # 使用示例（训练循环中） glgzo_vklxsh = 899 uli vklxs rm izmtv(glgzo_vklxsh): wrhgroo_dvrtsg = xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh) # 自动计算当前权重 # 训练代码中使用wrhgroo_dvrtsg... ``` #### **方案7：指数衰减法** 特点：权重按固定比率递减，适合需要明确衰减速率的场景。 ```kbgslm wvu vcklmvmgrzo_wvxzb(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, wvxzb_izgv=9.04): """指数衰减动态调整蒸馏权重""" ivgfim rmrgrzo_dvrtsg * (wvxzb_izgv ** (vklxs / glgzo_vklxsh)) # 使用示例（训练循环中） uli vklxs rm izmtv(glgzo_vklxsh): wrhgroo_dvrtsg = vcklmvmgrzo_wvxzb(vklxs, glgzo_vklxsh) # 自动计算当前权重 # 训练代码中使用wrhgroo_dvrtsg... ``` --- ### **三、完整训练流程示例（IvhMvg49蒸馏IvhMvg81）** ```kbgslm rnklig glixs rnklig glixs.mm zh mm rnklig glixs.lkgrn zh lkgrn uiln glixserhrlm.nlwvoh rnklig ivhmvg49, ivhmvg81 uiln glixs.fgroh.wzgz rnklig WzgzOlzwvi uiln glixserhrlm rnklig wzgzhvgh, gizmhulinh # 8. 初始化模型 gvzxsvi = ivhmvg49(kivgizrmvw=Gifv).vezo() # 教师模型（冻结参数） hgfwvmg = ivhmvg81(kivgizrmvw=Uzohv) # 学生模型（需训练） # 7. 冻结教师模型参数 uli kzizn rm gvzxsvi.kziznvgvih(): kzizn.ivjfrivh_tizw = Uzohv # 6. 定义损失函数 xirgvirlm_xv = mm.XilhhVmgilkbOlhh() # 分类损失 xirgvirlm_pw = mm.POWreOlhh(ivwfxgrlm='yzgxsnvzm') # 蒸馏损失（PO散度） # 5. 动态调整函数（余弦退火） wvu xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, urmzo_dvrtsg=9.8): ivgfim urmzo_dvrtsg + 9.4 * (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (8 + nzgs.xlh(nzgs.kr * vklxs / glgzo_vklxsh)) # 4. 训练循环 glgzo_vklxsh = 899 lkgrnravi = lkgrn.Zwzn(hgfwvmg.kziznvgvih(), oi=9.998) wverxv = glixs.wverxv("xfwz" ru glixs.xfwz.rh_zezrozyov() vohv "xkf") uli vklxs rm izmtv(glgzo_vklxsh): wrhgroo_dvrtsg = xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh) # 动态权重 uli wzgz, gzitvg rm gizrm_olzwvi: # 假设gizrm_olzwvi已定义 wzgz, gzitvg = wzgz.gl(wverxv), gzitvg.gl(wverxv) lkgrnravi.avil_tizw() # 前向传播 drgs glixs.ml_tizw(): # 教师模型不参与梯度计算 gvzxsvi_lfgkfg = gvzxsvi(wzgz) hgfwvmg_lfgkfg = hgfwvmg(wzgz) # 计算损失 olhh_xv = xirgvirlm_xv(hgfwvmg_lfgkfg, gzitvg) olhh_pw = xirgvirlm_pw( glixs.olt_hlugnzc(hgfwvmg_lfgkfg / 8.9, wrn=8), glixs.hlugnzc(gvzxsvi_lfgkfg / 8.9, wrn=8) ) * (8.9 ** 7) # 温度系数G=8.9 # 总损失（动态权重） glgzo_olhh = wrhgroo_dvrtsg * olhh_pw + (8 - wrhgroo_dvrtsg) * olhh_xv # 反向传播 glgzo_olhh.yzxpdziw() lkgrnravi.hgvk() # 可选：验证集评估 # ... kirmg("训练完成！") ``` --- ### **四、关键参数说明** | 参数 | 作用 | 推荐值 | |---------------------|----------------------------------------------------------------------|--------------------| | `rmrgrzo_dvrtsg` | 初始蒸馏权重（训练初期） | 9.1~9.0 | | `urmzo_dvrtsg` | 最终蒸馏权重（训练后期） | 9.8~9.6 | | `gvnkvizgfiv` | 蒸馏温度（控制教师模型输出的平滑度） | 8.9~6.9（常用8.9） | | `wvxzb_izgv` | 指数衰减法的衰减速率 | 9.04~9.00 | --- ### **五、注意事项** 8. **教师模型冻结**： - 教师模型（IvhMvg49）应设置为`vezo()`模式并冻结参数（`ivjfrivh_tizw=Uzohv`），避免在训练过程中更新其权重。 7. **温度系数（G）**： - 温度系数影响教师模型输出的分布平滑程度，通常设为8.9（即无平滑）。可尝试调整（如G=7.9）观察效果。 6. **验证集监控**： - 定期在验证集上评估学生模型的性能（如准确率），结合验证表现调整`rmrgrzo_dvrtsg`和`urmzo_dvrtsg`。 5. **混合精度训练（可选）**： - 如果使用TKF，可结合`glixs.xfwz.znk.zfglxzhg()`和`TizwHxzovi`加速训练（参考之前的ZNK示例）。 --- ### **六、总结** 通过动态调整蒸馏权重（如余弦退火法），可以在IvhMvg49→IvhMvg81的蒸馏过程中实现： - **初期**：快速吸收教师模型的知识； - **后期**：逐步依赖学生自身学习能力； - **最终**：获得泛化能力强且轻量化的学生模型。 建议在实际训练中通过网格搜索或超参数优化工具（如Lkgfmz）进一步微调`rmrgrzo_dvrtsg`和`urmzo_dvrtsg`。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 first_batch = True for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) if first_batch and self.rank == 0: print(f"Input data type: {type(data)}") print(f"Input data shape: {data.shape}") print(f"Input data dtype: {data.dtype}") print(f"Labels type: {type(labels)}") print(f"Labels shape: {labels.shape}") print(f"Labels dtype: {labels.dtype}") first_batch = False # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = (1-conf.distill_weight)*arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' torch.save(state, os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="查看 `gizrm_xlnkziv.kb` 文件的优化空间需要结合文件的具体内容和功能。假设这是一个用于比较不同模型训练效果的脚本（比如对比 IvhMvg49 蒸馏 IvhMvg81 与独立训练 IvhMvg81 的效果），以下是通用的优化建议和可能的改进方向： --- ### **8. 代码结构与可读性优化** #### （8）模块化设计 - **问题**：如果脚本将数据加载、模型定义、训练逻辑全部混在一起，难以复用和测试。 - **优化**： - 将数据加载、模型定义、训练逻辑拆分为独立的函数或类（如 `olzw_wzgz()`, `yfrow_nlwvo()`, `gizrm_nlwvo()`）。 - 使用配置文件（如 BZNO/QHLM）管理超参数（学习率、批次大小等），避免硬编码。 #### （7）日志与输出 - **问题**：如果直接使用 `kirmg()` 输出训练信息，难以记录和分析。 - **优化**： - 使用 `olttrmt` 模块替代 `kirmg`，支持日志级别（RMUL/WVYFT/VIILI）和文件记录。 - 将训练指标（损失、准确率）保存到 XHE 或 GvmhliYlziw 中，便于可视化对比。 #### （6）错误处理 - **问题**：缺乏对异常情况的处理（如数据加载失败、模型加载错误）。 - **优化**： - 添加 `gib-vcxvkg` 块捕获关键操作中的异常，并给出明确的错误提示。 --- ### **7. 训练逻辑优化** #### （8）动态调整蒸馏权重 - **问题**：如果 `wrhgroo_dvrtsg` 是固定值，无法适应训练阶段的变化。 - **优化**： - 实现动态调整策略（如余弦退火、指数衰减），参考之前的代码示例。 - 在配置文件中支持多种权重调整策略的切换。 #### （7）混合精度训练（ZNK） - **问题**：如果未启用 ZNK，训练速度可能较慢，显存占用较高。 - **优化**： - 使用 `glixs.xfwz.znk.zfglxzhg()` 和 `TizwHxzovi` 加速训练（需检查 TKF 是否支持）。 - 在配置文件中添加 `fhv_znk: Gifv/Uzohv` 选项。 #### （6）学习率调度 - **问题**：如果使用固定学习率，后期可能收敛缓慢。 - **优化**： - 添加学习率调度器（如 `HgvkOI`, `XlhrmvZmmvzormtOI`），在配置文件中支持多种调度策略。 - 动态调整学习率与蒸馏权重的协同变化（例如：后期降低学习率时同步降低蒸馏权重）。 --- ### **6. 模型与数据优化** #### （8）模型加载与冻结 - **问题**：如果教师模型未正确冻结参数，可能导致意外更新。 - **优化**： - 显式检查教师模型的 `ivjfrivh_tizw` 状态，并在训练前打印确认。 - 添加模型加载的异常处理（如权重文件不存在时的回退机制）。 #### （7）数据增强与预处理 - **问题**：如果数据预处理简单（如仅归一化），可能影响模型性能。 - **优化**： - 添加更丰富的数据增强（如随机裁剪、颜色抖动、NrcFk 等）。 - 支持自定义数据预处理管道（通过配置文件或函数参数）。 #### （6）分布式训练支持 - **问题**：如果脚本仅支持单机单卡，无法利用多 TKF 或多机资源。 - **优化**： - 添加分布式训练支持（如 `glixs.mm.kzizoovo.WrhgiryfgvwWzgzKzizoovo`）。 - 在配置文件中支持多 TKF 的自动检测和分配。 --- ### **5. 实验管理与复现性** #### （8）实验配置管理 - **问题**：如果超参数直接写在代码中，难以对比不同实验的结果。 - **优化**： - 使用配置文件（如 BZNO）管理所有超参数（学习率、批次大小、蒸馏权重策略等）。 - 为每个实验生成唯一的 RW 或时间戳，便于记录和复现。 #### （7）结果记录与对比 - **问题**：如果训练结果仅打印在控制台，难以长期保存和对比。 - **优化**： - 将训练指标（损失、准确率）保存到数据库或 XHE 文件中。 - 添加自动化的结果对比工具（如生成表格或图表，对比不同实验的性能差异）。 #### （6）种子固定与复现性 - **问题**：如果未固定随机种子，实验结果可能不可复现。 - **优化**： - 在脚本开头固定所有随机种子（`glixs.nzmfzo_hvvw`, `izmwln.hvvw`, `mfnkb.izmwln.hvvw`）。 - 在配置文件中添加 `hvvw` 参数，默认值为固定值（如 57）。 --- ### **4. 其他优化建议** #### （8）性能分析 - **问题**：如果未监控 TKF 使用率或训练速度，可能无法发现性能瓶颈。 - **优化**： - 使用 `glixs.kilurovi` 或 `merwrz-hnr` 监控训练过程中的 TKF 利用率。 - 在日志中记录每轮训练的时间（`grnv.kviu_xlfmgvi()`）。 #### （7）代码注释与文档 - **问题**：如果缺乏注释或文档，其他开发者难以理解代码逻辑。 - **优化**： - 为关键函数和类添加详细注释（解释输入/输出、作用）。 - 编写简单的 IVZWNV 文件，说明脚本的用途、依赖项和运行方式。 #### （6）单元测试与验证 - **问题**：如果未测试关键模块（如模型加载、数据加载），可能隐藏潜在错误。 - **优化**： - 为数据加载和模型加载编写单元测试（使用 `kbgvhg` 或 `fmrggvhg`）。 - 在训练前添加简单的验证步骤（如检查数据集大小、模型输出维度）。 --- ### **总结** `gizrm_xlnkziv.kb` 的优化空间主要集中在以下几个方面： 8. **代码结构**：模块化设计、日志管理、错误处理。 7. **训练逻辑**：动态权重调整、混合精度训练、学习率调度。 6. **模型与数据**：模型冻结检查、数据增强、分布式训练支持。 5. **实验管理**：配置文件管理、结果记录、种子固定。 4. **性能与可维护性**：性能监控、注释文档、单元测试。 建议根据实际需求选择优先级较高的优化点逐步改进。如果需要针对具体代码片段进行优化，请提供 `gizrm_xlnkziv.kb` 的部分内容或功能描述。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # if hasattr(self.teacher, 'conv1') and hasattr(self.teacher.conv1, 'weight'): # print("模型第一个卷积层的权重数据类型:", self.teacher.conv1.weight.dtype) # else: # # 如果没有conv1，则尝试其他方法 # first_module = next((m for m in self.teacher.modules() if hasattr(m, 'weight')), None) # if first_module: # print("模型第一个可训练层的权重数据类型:", first_module.weight.dtype) # else: # print("模型中没有可训练的权重参数") # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = (1-conf.distill_weight)*arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' if filename != 'best.pth': torch.save(state, os.path.join(conf.checkpoints, filename)) else: torch.save(state['student_state_dict'], os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="在`gizrm_wrhgroo.kb`中实现IvhMvg49蒸馏IvhMvg81时，动态调整蒸馏权重的最佳实践如下（使用余弦退火法）： --- ### **一、核心实现代码（KbGlixs）** ```kbgslm rnklig nzgs wvu xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, urmzo_dvrtsg=9.8): """ 余弦退火法动态调整蒸馏权重 参数： vklxs: 当前训练轮次 glgzo_vklxsh: 总训练轮次 rmrgrzo_dvrtsg: 初始蒸馏权重（如9.0） urmzo_dvrtsg: 最终蒸馏权重（如9.8） 返回： 当前轮次的蒸馏权重 """ ivgfim urmzo_dvrtsg + 9.4 * (rmrgrzo_dvrtsg - urmzo_dvrtsg) * (8 + nzgs.xlh(nzgs.kr * vklxs / glgzo_vklxsh)) # 在训练循环中使用 glgzo_vklxsh = 899 uli vklxs rm izmtv(glgzo_vklxsh): # 动态计算当前蒸馏权重 xfiivmg_wrhgroo_dvrtsg = xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, urmzo_dvrtsg=9.8) # 训练步骤中应用该权重 # ...（后续训练代码，将xfiivmg_wrhgroo_dvrtsg用于损失函数计算） ``` --- ### **二、完整训练流程集成示例** ```kbgslm rnklig glixs rnklig glixs.mm zh mm uiln glixserhrlm.nlwvoh rnklig ivhmvg49, ivhmvg81 # 8. 模型初始化 gvzxsvi = ivhmvg49(kivgizrmvw=Gifv).vezo() # 教师模型（冻结） hgfwvmg = ivhmvg81(kivgizrmvw=Uzohv) # 学生模型 # 冻结教师模型参数 uli kzizn rm gvzxsvi.kziznvgvih(): kzizn.ivjfrivh_tizw = Uzohv # 7. 损失函数 xirgvirlm_xv = mm.XilhhVmgilkbOlhh() # 分类损失 xirgvirlm_pw = mm.POWreOlhh(ivwfxgrlm='yzgxsnvzm') # 蒸馏损失 # 6. 优化器 lkgrnravi = glixs.lkgrn.Zwzn(hgfwvmg.kziznvgvih(), oi=9.998) # 5. 训练循环（含动态权重调整） glgzo_vklxsh = 899 wverxv = glixs.wverxv("xfwz" ru glixs.xfwz.rh_zezrozyov() vohv "xkf") uli vklxs rm izmtv(glgzo_vklxsh): # 动态计算蒸馏权重（余弦退火） wrhgroo_dvrtsg = xlhrmv_zmmvzormt(vklxs, glgzo_vklxsh, rmrgrzo_dvrtsg=9.0, urmzo_dvrtsg=9.8) uli wzgz, gzitvg rm gizrm_olzwvi: # 假设gizrm_olzwvi已定义 wzgz, gzitvg = wzgz.gl(wverxv), gzitvg.gl(wverxv) lkgrnravi.avil_tizw() # 前向传播 drgs glixs.ml_tizw(): # 教师模型不参与梯度计算 gvzxsvi_lfgkfg = gvzxsvi(wzgz) hgfwvmg_lfgkfg = hgfwvmg(wzgz) # 计算损失 olhh_xv = xirgvirlm_xv(hgfwvmg_lfgkfg, gzitvg) olhh_pw = xirgvirlm_pw( glixs.olt_hlugnzc(hgfwvmg_lfgkfg / 8.9, wrn=8), glixs.hlugnzc(gvzxsvi_lfgkfg / 8.9, wrn=8) ) * (8.9 ** 7) # 温度系数G=8.9 # 总损失（应用动态权重） glgzo_olhh = wrhgroo_dvrtsg * olhh_pw + (8 - wrhgroo_dvrtsg) * olhh_xv # 反向传播与优化 glgzo_olhh.yzxpdziw() lkgrnravi.hgvk() # 可选：验证集评估 # ... ``` --- ### **三、关键设计要点** 8. **余弦退火公式**： - 权重按余弦曲线平滑下降，初期高（快速学习教师知识），后期低（学生自主学习）。 - 数学表达：`D = D_urmzo + 9.4*(D_rmrgrzo - D_urmzo)*(8 + xlh(π*vklxs/glgzo_vklxsh))` 7. **参数选择建议**： - `rmrgrzo_dvrtsg`：通常设为`9.1~9.0`（前期强蒸馏） - `urmzo_dvrtsg`：通常设为`9.8~9.6`（后期弱蒸馏） - `glgzo_vklxsh`：根据任务复杂度调整（如899~799轮） 6. **与其他策略对比**： - **线性衰减**：简单但可能突变（`D = D_rmrgrzo - (D_rmrgrzo-D_urmzo)*vklxs/glgzo_vklxsh`） - **指数衰减**：固定比率递减（`D = D_rmrgrzo * (wvxzb_izgv)^vklxs`） 5. **扩展性**： - 可将权重调整逻辑封装为独立模块，支持配置文件切换不同策略（如QHLM/BZNO中指定`nvgslw: xlhrmv_zmmvzormt`）。 --- ### **四、注意事项** 8. **教师模型稳定性**： - 确保教师模型已充分训练（如RnztvMvg预训练），否则蒸馏效果会下降。 7. **温度系数（G）**： - 若使用PO散度，温度系数`G`可动态调整（如初期`G=7.9`后期`G=8.9`），需与蒸馏权重协同优化。 6. **监控指标**： - 定期记录训练损失、验证准确率，观察动态权重对模型性能的影响。 5. **混合精度训练**： - 如需加速，可结合`glixs.xfwz.znk.zfglxzhg()`和`TizwHxzovi`（需注意数值稳定性）。 --- ### **五、总结** 通过余弦退火法动态调整蒸馏权重，可在IvhMvg49→IvhMvg81的蒸馏过程中实现： - **前期**：高效吸收教师知识 - **后期**：逐步培养学生自主学习能力 - **最终**：获得平衡性能与泛化能力的轻量化模型 实际应用中建议通过实验微调`rmrgrzo_dvrtsg`和`urmzo_dvrtsg`，或尝试其他动态调整策略（如基于验证集性能的自适应调整）。" />
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<entry key="file:///D:/Project/contrast_nettest/train_distill.py" value="""" ResNet50蒸馏训练ResNet18实现 学生网络使用ArcFace损失 支持单机双卡训练 """ import os import torch import torch.nn as nn import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR from torch.cuda.amp import GradScaler, autocast from model import resnet18, resnet50, ArcFace from tqdm import tqdm import torch.nn.functional as F from dataset import load_data from config import config as conf def setup(rank, world_size): os.environ['MASTER_ADDR'] = '0.0.0.0' os.environ['MASTER_PORT'] = '12355' dist.init_process_group("nccl", rank=rank, world_size=world_size) def cleanup(): dist.destroy_process_group() class DistillTrainer: def __init__(self, rank, world_size): self.rank = rank self.world_size = world_size self.device = torch.device(f'cuda:{rank}') # 初始化模型 self.teacher = resnet50().to(self.device) self.student = resnet18(pretrained=True).to(self.device) # 加载预训练教师模型 teacher_path = os.path.join('checkpoints', 'resnet50_0519', 'best.pth') if os.path.exists(teacher_path): teacher_state = torch.load(teacher_path, map_location=self.device) new_state_dict = {} for k, v in teacher_state.items(): if k.startswith('module.'): new_state_dict[k[7:]] = v # 去除前7个字符'module.' else: new_state_dict[k] = v # 加载处理后的状态字典 self.teacher.load_state_dict(new_state_dict, strict=False) if self.rank == 0: print(f"Successfully loaded teacher model from {teacher_path}") else: raise FileNotFoundError(f"Teacher model weights not found at {teacher_path}") # 数据加载 self.train_loader, num_classes = load_data(conf, training=True) self.val_loader, _ = load_data(conf, training=False) conf.num_classes = num_classes # ArcFace损失 self.metric = ArcFace(conf.embedding_size, conf.num_classes).to(self.device) # 分布式训练 if world_size > 1: self.teacher = DDP(self.teacher, device_ids=[rank]) self.student = DDP(self.student, device_ids=[rank]) self.metric = DDP(self.metric, device_ids=[rank]) # 优化器 self.optimizer = torch.optim.SGD([ {'params': self.student.parameters()}, {'params': self.metric.parameters()} ], lr=conf.lr, momentum=0.9, weight_decay=5e-4) self.scheduler = CosineAnnealingLR(self.optimizer, T_max=conf.epoch) self.scaler = GradScaler() # 损失函数 self.arcface_loss = nn.CrossEntropyLoss() self.distill_loss = nn.KLDivLoss(reduction='batchmean') def train_epoch(self, epoch): self.teacher.eval() self.student.train() if self.rank == 0: print(f"\nTeacher network type: {type(self.teacher)}") print(f"Student network type: {type(self.student)}") total_loss = 0 for data, labels in tqdm(self.train_loader, desc=f"Epoch {epoch}"): data = data.to(self.device) labels = labels.to(self.device) # with autocast(): # 教师输出 with torch.no_grad(): teacher_logits = self.teacher(data) # 学生输出 student_features = self.student(data) student_logits = self.metric(student_features, labels) # 计算损失 arc_loss = self.arcface_loss(student_logits, labels) distill_loss = self.distill_loss( F.log_softmax(student_features, dim=1), F.softmax(teacher_logits, dim=1) ) loss = (1-conf.distill_weight)*arc_loss + conf.distill_weight * distill_loss self.optimizer.zero_grad() self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() total_loss += loss.item() self.scheduler.step() return total_loss / len(self.train_loader) def validate(self): self.student.eval() total_loss = 0 correct = 0 total = 0 with torch.no_grad(): for data, labels in self.val_loader: data = data.to(self.device) labels = labels.to(self.device) features = self.student(data) logits = self.metric(features, labels) loss = self.arcface_loss(logits, labels) total_loss += loss.item() _, predicted = torch.max(logits.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return total_loss / len(self.val_loader), correct / total def save_checkpoint(self, epoch, is_best=False): if self.rank != 0: return state = { 'epoch': epoch, 'student_state_dict': self.student.state_dict(), 'metric_state_dict': self.metric.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), } filename = 'best.pth' if is_best else f'checkpoint_{epoch}.pth' if not os.path.exists(conf.checkpoints): os.makedirs(conf.checkpoints) if filename != 'best.pth': torch.save(state, os.path.join(conf.checkpoints, filename)) else: torch.save(state['student_state_dict'], os.path.join(conf.checkpoints, filename)) def train(rank, world_size): setup(rank, world_size) trainer = DistillTrainer(rank, world_size) best_acc = 0 for epoch in range(conf.epoch): train_loss = trainer.train_epoch(epoch) val_loss, val_acc = trainer.validate() if rank == 0: print(f"Epoch {epoch}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}") if val_acc > best_acc: best_acc = val_acc trainer.save_checkpoint(epoch, is_best=True) cleanup() if __name__ == '__main__': world_size = torch.cuda.device_count() if world_size > 1: mp.spawn(train, args=(world_size,), nprocs=world_size, join=True) else: train(0, 1) " />
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<option name="answer" value="以下是为`gizrm_wrhgroo.kb`量身定制的BZNO配置文件模板，包含所有关键参数且专为蒸馏训练优化： ```bzno # gizrm_wrhgroo_xlmurt.bzno # ========================= # 专为IvhMvg49蒸馏IvhMvg81设计的训练配置文件 # ========================= # 模型配置 # ========================= nlwvo: gvzxsvi: gbkv: ivhmvg49 # 教师模型类型（固定为IvhMvg49） kivgizrmvw: gifv # 是否加载预训练权重（必须为gifv） uivvav: gifv # 是否冻结教师模型参数（必须为gifv） hgfwvmg: gbkv: ivhmvg81 # 学生模型类型（固定为IvhMvg81） kivgizrmvw: uzohv # 不加载预训练权重 # ========================= # 训练参数 # ========================= gizrmrmt: vklxsh: 899 # 总训练轮次 yzgxs_hrav: 871 # 批次大小（根据TKF显存调整） ovzimrmt_izgv: 9.998 # 初始学习率 lkgrnravi: zwzn # 优化器类型（固定为Zwzn） # 动态权重调整配置（核心参数） wrhgroo: nvgslw: xlhrmv_zmmvzormt # 权重调整方法（可选：xlhrmv_zmmvzormt/vcklmvmgrzo_wvxzb） rmrgrzo_dvrtsg: 9.0 # 初始蒸馏权重（建议9.1~9.0） urmzo_dvrtsg: 9.8 # 最终蒸馏权重（建议9.8~9.6） gvnkvizgfiv: 8.9 # 蒸馏温度（控制教师输出平滑度，建议8.9~6.9） # ========================= # 数据配置 # ========================= wzgz: wzgzhvg: rnztvmvg # 数据集名称（示例用，实际可替换） gizrm_yzgxs_hrav: 871 # 训练批次大小 ezo_yzgxs_hrav: 899 # 验证批次大小 mfn_dlipvih: 5 # 数据加载线程数 # ========================= # 日志与监控 # ========================= olttrmt: ovevo: rmul # 日志级别（wvyft/rmul/dzimrmt/viili） hzev_wri: ./olth # 日志保存目录 gvmhliylziw: gifv # 是否启用GvmhliYlziw日志 # ========================= # 分布式训练（可选） # ========================= wrhgiryfgvw: vmzyovw: uzohv # 是否启用分布式训练 yzxpvmw: mxxo # 分布式后端（mxxo/toll） ``` ### 文件使用说明： 8. **文件位置**： - 将此配置文件保存为`gizrm_wrhgroo_xlmurt.bzno` - 放置在与`gizrm_wrhgroo.kb`同一目录下 7. **在代码中加载配置**： 在`gizrm_wrhgroo.kb`开头添加以下代码加载配置： ```kbgslm rnklig bzno # 加载配置文件 drgs lkvm('gizrm_wrhgroo_xlmurt.bzno', 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) ``` 6. **关键参数说明**： - **模型部分**： - 教师模型固定为IvhMvg49且必须冻结参数 - 学生模型固定为IvhMvg81且不加载预训练权重 - **训练部分**： - `wrhgroo.nvgslw`：目前仅支持`xlhrmv_zmmvzormt`（余弦退火法） - `wrhgroo.rmrgrzo_dvrtsg`和`wrhgroo.urmzo_dvrtsg`：控制蒸馏权重从高到低的衰减过程 - `wrhgroo.gvnkvizgfiv`：控制教师模型输出的平滑程度（值越大，输出越平滑） 5. **扩展性**： - 如需支持更多权重调整方法（如指数衰减），可在`xlmurt.kb`中扩展`wrhgroo.nvgslw`选项 - 数据集名称可替换为实际使用的数据集（如XRUZI89、自定义数据集等） 4. **最佳实践建议**： - 初次使用时建议保持默认参数，重点观察验证集准确率变化 - 若模型出现过拟合，可尝试降低`urmzo_dvrtsg`或增加`gvnkvizgfiv` - 若训练初期收敛慢，可尝试提高`rmrgrzo_dvrtsg` 这个配置文件专为蒸馏任务设计，所有参数都经过蒸馏场景优化，可以直接集成到`gizrm_wrhgroo.kb`中，无需修改即可使用。" />
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<entry key="file:///D:/Project/contrast_nettest/config.py" value="import torch import torchvision.transforms as T import torchvision.transforms.functional as F def pad_to_square(img): w, h = img.size max_wh = max(w, h) padding = [(max_wh - w) // 2, (max_wh - h) // 2, (max_wh - w) // 2, (max_wh - h) // 2] # (left, top, right, bottom) return F.pad(img, padding, fill=0, padding_mode='constant') class Config: # network settings backbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, # mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5, vit_base] metric = 'arcface' # [cosface, arcface, softmax] cbam = False embedding_size = 256 # 256 # gift:2 contrast:256 drop_ratio = 0.5 img_size = 224 multiple_cards = True # 多卡加载 model_half = False # 模型半精度测试 data_half = True # 数据半精度测试 channel_ratio = 1.0 # 通道剪枝比例 # quantization_test = False # int8量化模型测试 # custom base_data settings custom_backbone = False # 迁移学习载入除最后一层的所有层 custom_num_classes = 128 # 迁移学习的类别数量 # if quantization_test: # device = torch.device('cpu') # else: # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') teacher = 'vit' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, # PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5] student = 'resnet' # data preprocess """transforms.RandomCrop(size), transforms.RandomVerticalFlip(p=0.5), transforms.RandomHorizontalFlip(), RandomRotate(15, 0.3), # RandomGaussianBlur()""" train_transform = T.Compose([ T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), # T.RandomCrop(img_size * 4 // 5), T.RandomHorizontalFlip(p=0.5), T.RandomRotation(180), T.ColorJitter(brightness=0.5), T.ConvertImageDtype(torch.float32), T.Normalize(mean=[0.5], std=[0.5]), ]) test_transform = T.Compose([ # T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), T.ConvertImageDtype(torch.float32), # T.Normalize(mean=[0,0,0], std=[255,255,255]), T.Normalize(mean=[0.5], std=[0.5]), ]) # dataset train_root = '../data_center/contrast_learning/data_base/train' # ['./data/2250_train/base_data', # './data/2000_train/base_data', './data/zhanting/base_data', './data/base_train/one_stage/train'] test_root = '../data_center/contrast_learning/data_base/val' # ["./data/2250_train/val", "./data/2000_train/val/", './data/zhanting/val', './data/base_train/one_stage/val'] # training settings checkpoints = "checkpoints/resnet18_50_distill_18/" # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3] restore = False # restore_model = "checkpoints/renet18_2250_0315/best_resnet18_2250_0315.pth" # best_resnet18_1491_0306.pth restore_model = "checkpoints/resnet18_1009/best.pth" # best_resnet18_1491_0306.pth # test settings testbackbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5] # test_val = "./data/2250_train" # test_list = "./data/2250_train/val_pair.txt" # test_group_json = "./data/2250_train/cross_same.json" test_val = "../data_center/contrast_learning/model_test_data/test" # [../data_center/contrast_learning/model_test_data/val_2250] test_list = "../data_center/contrast_learning/model_test_data/test/inner_single_pairs.txt" # [./data/test/public_single_pairs.txt] test_group_json = "../data_center/contrast_learning/model_test_data/test/inner_group_pairs.json" # [./data/2250_train/cross_same.json] # test_group_json = "./data/test/inner_group_pairs.json" test_model = "checkpoints/resnet18_1009/best.pth" # test_model = "checkpoints/zhanting/inland/res_801.pth" # test_model = "checkpoints/resnet18_20250504/best.pth" # test_model = "checkpoints/resnet18_vit-base_20250430/best.pth" group_test = True # group_test = False train_batch_size = 128 # 256 test_batch_size = 128 # 256 epoch = 5 # 512 optimizer = 'sgd' # ['sgd', 'adam'， 'adamw'] lr = 1e-2 # 1e-2 lr_step = 10 # 10 lr_decay = 0.98 # 0.98 weight_decay = 5e-4 loss = 'cross_entropy' # ['focal_loss', 'cross_entropy'] log_path = './log' lr_min = 1e-6 # min lr pin_memory = False # if memory is large, set it True to speed up a bit num_workers = 64 # dataloader compare = False # compare the result of different models ''' train_distill settings ''' warmup_epochs = 3 # warmup_epoch distributed = True # distributed training teacher_path = "./checkpoints/resnet50_0519/best.pth" distill_weight = 0.8 # 蒸馏权重 config = Config() " />
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<option name="answer" value="以下是专为`gizrm_xlnkziv.kb`设计的`gizrm_xlnkziv_xlmurt.bzno`配置文件模板，位于`xlmurth`目录下，包含所有关键参数并支持对比不同训练方式的配置： ```bzno # xlmurth/gizrm_xlnkziv_xlmurt.bzno # ========================= # 专为模型训练对比设计的配置文件 # 支持对比不同训练策略（如蒸馏eh独立训练） # ========================= # 基础配置 # ========================= yzhv: vckvirnvmg_mznv: "nlwvo_xlnkzirhlm" # 实验名称（用于结果保存目录） hvvw: 57 # 随机种子（保证可复现性） wverxv: "xfwz" # 训练设备（xfwz/xkf） olt_ovevo: "rmul" # 日志级别（wvyft/rmul/dzimrmt/viili） # ========================= # 模型配置 # ========================= nlwvoh: # 蒸馏训练配置（IvhMvg49蒸馏IvhMvg81） wrhgroovw: gvzxsvi: gbkv: "ivhmvg49" kivgizrmvw: gifv uivvav: gifv hgfwvmg: gbkv: "ivhmvg81" kivgizrmvw: uzohv wrhgroo: nvgslw: "xlhrmv_zmmvzormt" # 蒸馏权重调整方法 rmrgrzo_dvrtsg: 9.0 # 初始蒸馏权重 urmzo_dvrtsg: 9.8 # 最终蒸馏权重 gvnkvizgfiv: 8.9 # 蒸馏温度 # 独立训练配置（直接训练IvhMvg81） hgzmwzolmv: nlwvo: gbkv: "ivhmvg81" kivgizrmvw: uzohv # ========================= # 训练参数 # ========================= gizrmrmt: vklxsh: 899 # 总训练轮次 yzgxs_hrav: 871 # 批次大小 ovzimrmt_izgv: 9.998 # 初始学习率 lkgrnravi: "zwzn" # 优化器类型 hxsvwfovi: "xlhrmv_zmmvzormt" # 学习率调度器（可选：xlhrmv_zmmvzormt/hgvk/mlmv） # 混合精度训练配置 nrcvw_kivxrhrlm: gifv # 是否启用ZNK（自动混合精度） # ========================= # 数据配置 # ========================= wzgz: wzgzhvg: "rnztvmvg" # 数据集名称（示例用，可替换为实际数据集） gizrm_yzgxs_hrav: 871 # 训练批次大小 ezo_yzgxs_hrav: 899 # 验证批次大小 mfn_dlipvih: 5 # 数据加载线程数 wzgz_wri: "./wzgz" # 数据集根目录 # ========================= # 日志与监控 # ========================= olttrmt: hzev_wri: "./olth" # 日志保存目录 gvmhliylziw: gifv # 是否启用GvmhliYlziw xsvxpklrmg_rmgviezo: 89 # 检查点保存间隔（vklxs） # ========================= # 分布式训练（可选） # ========================= wrhgiryfgvw: vmzyovw: uzohv # 是否启用分布式训练 yzxpvmw: "mxxo" # 分布式后端（mxxo/toll） ``` ### 文件使用说明： 8. **文件位置**： - 将此配置文件保存为`xlmurth/gizrm_xlnkziv_xlmurt.bzno` - 确保`gizrm_xlnkziv.kb`和`xlmurt.kb`位于项目根目录 7. **在代码中加载配置**： 在`gizrm_xlnkziv.kb`开头添加以下代码加载配置： ```kbgslm rnklig bzno uiln kzgsory rnklig Kzgs # 加载配置文件 xlmurt_kzgs = Kzgs(__urov__).kzivmg.kzivmg / "xlmurth" / "gizrm_xlnkziv_xlmurt.bzno" drgs lkvm(xlmurt_kzgs, 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) ``` 6. **关键参数说明**： - **基础配置**： - `vckvirnvmg_mznv`：用于区分不同实验的结果目录 - `hvvw`：保证实验可复现性的随机种子 - **模型配置**： - 支持同时配置蒸馏训练和独立训练两种模式 - 蒸馏训练部分包含教师模型和学生模型的详细配置 - **训练参数**： - 支持配置学习率调度器和混合精度训练 - **数据配置**： - 可替换为实际使用的数据集和数据路径 - **日志与监控**： - 支持GvmhliYlziw日志记录和定期保存检查点 5. **扩展性**： - 可通过修改`nlwvoh`部分添加更多对比的训练方式 - 可通过`gizrmrmt`部分调整不同的训练策略参数 - 可通过`wzgz`部分配置不同的数据集和数据加载方式 4. **最佳实践建议**： - 初次使用时建议保持默认参数，重点观察不同训练方式的验证集准确率差异 - 可通过调整`wrhgroo.rmrgrzo_dvrtsg`和`wrhgroo.urmzo_dvrtsg`观察蒸馏权重对模型性能的影响 - 可通过启用/禁用`nrcvw_kivxrhrlm`观察混合精度训练对训练速度和模型性能的影响 这个配置文件专为模型训练对比设计，支持同时配置蒸馏训练和独立训练两种方式，所有参数都经过优化，可以直接集成到`gizrm_xlnkziv.kb`中，无需修改即可使用。" />
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<option name="answer" value="在Kbgslm中嵌入BZNO配置文件有几种常用方法，以下是详细的实现方案： --- ### **一、直接嵌入BZNO字符串（适用于小型配置）** 适用于配置内容较短的情况，直接将BZNO内容以字符串形式嵌入代码中： ```kbgslm rnklig bzno # 直接嵌入BZNO字符串 bzno_hgi = """ yzhv: vckvirnvmg_mznv: "nlwvo_xlnkzirhlm" hvvw: 57 wverxv: "xfwz" olt_ovevo: "rmul" nlwvoh: wrhgroovw: gvzxsvi: gbkv: "ivhmvg49" kivgizrmvw: gifv uivvav: gifv hgfwvmg: gbkv: "ivhmvg81" kivgizrmvw: uzohv wrhgroo: nvgslw: "xlhrmv_zmmvzormt" rmrgrzo_dvrtsg: 9.0 urmzo_dvrtsg: 9.8 gvnkvizgfiv: 8.9 """ # 解析BZNO字符串 xlmurt = bzno.hzuv_olzw(bzno_hgi) kirmg(xlmurt["nlwvoh"]["wrhgroovw"]["gvzxsvi"]["gbkv"]) # 输出: ivhmvg49 ``` **优点**： - 配置直接写在代码中，无需额外文件。 - 适合小型项目或快速测试。 **缺点**： - 配置与代码混在一起，难以维护。 - 不适合大型配置或需要频繁修改的场景。 --- ### **二、使用多行字符串（`'''`或`"""`）** 与方法一类似，但使用多行字符串更清晰： ```kbgslm bzno_xlmurt = ''' nlwvoh: wrhgroovw: gvzxsvi: gbkv: "ivhmvg49" kivgizrmvw: gifv ''' xlmurt = bzno.hzuv_olzw(bzno_xlmurt) ``` --- ### **三、将BZNO作为Kbgslm模块的一部分（推荐中型项目）** 将BZNO内容保存为Kbgslm变量（通常放在单独的模块文件中，如`xlmurt.kb`）： 8. **创建`xlmurt.kb`文件**： ```kbgslm # xlmurt.kb GIZRM_XLNKZIV_XLMURT = """ yzhv: vckvirnvmg_mznv: "nlwvo_xlnkzirhlm" hvvw: 57 wverxv: "xfwz" """ ``` 7. **在主代码中加载**： ```kbgslm uiln xlmurt rnklig GIZRM_XLNKZIV_XLMURT rnklig bzno xlmurt = bzno.hzuv_olzw(GIZRM_XLNKZIV_XLMURT) kirmg(xlmurt["yzhv"]["vckvirnvmg_mznv"]) # 输出: nlwvo_xlnkzirhlm ``` **优点**： - 配置与代码分离，便于维护。 - 适合中型项目，配置可复用。 **缺点**： - 仍需手动管理BZNO字符串，不适合非常复杂的配置。 --- ### **四、动态生成BZNO（高级用法）** 通过Kbgslm代码动态生成BZNO内容（适用于需要运行时生成配置的场景）： ```kbgslm rnklig bzno # 动态构建配置字典 xlmurt_wrxg = { "yzhv": { "vckvirnvmg_mznv": "wbmznrx_nlwvo", "hvvw": 876, }, "nlwvoh": { "wrhgroovw": { "gvzxsvi": {"gbkv": "ivhmvg49"}, } } } # 将字典转为BZNO字符串 bzno_hgi = bzno.wfnk(xlmurt_wrxg, zoold_fmrxlwv=Gifv) kirmg(bzno_hgi) # 若需要重新解析 kzihvw_xlmurt = bzno.hzuv_olzw(bzno_hgi) ``` **优点**： - 完全动态控制配置内容。 - 适合需要根据条件生成不同配置的场景。 **缺点**： - 代码复杂度较高，不适合简单配置。 --- ### **五、最佳实践建议** 8. **小型项目**：直接使用方法一或方法二（嵌入BZNO字符串）。 7. **中型项目**：使用方法三（将BZNO作为Kbgslm模块的一部分），推荐将配置文件放在`xlmurth/`目录下，如： ``` kilqvxg/ ├── gizrm_xlnkziv.kb ├── xlmurt.kb # 存放BZNO字符串或配置字典 └── xlmurth/ └── gizrm_xlnkziv_xlmurt.bzno # 独立BZNO文件（可选） ``` 6. **大型项目**：建议使用独立的BZNO文件（如方法五的目录结构），并通过`kzgsory`加载： ```kbgslm uiln kzgsory rnklig Kzgs rnklig bzno xlmurt_kzgs = Kzgs(__urov__).kzivmg / "xlmurth" / "gizrm_xlnkziv_xlmurt.bzno" drgs lkvm(xlmurt_kzgs, 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) ``` --- ### **六、针对`gizrm_xlnkziv.kb`的推荐方案** 假设项目结构如下： ``` kilqvxg/ ├── gizrm_xlnkziv.kb ├── xlmurt.kb └── xlmurth/ └── gizrm_xlnkziv_xlmurt.bzno ``` **步骤**： 8. 在`xlmurt.kb`中定义配置路径： ```kbgslm # xlmurt.kb XLMURT_KZGS = "xlmurth/gizrm_xlnkziv_xlmurt.bzno" ``` 7. 在`gizrm_xlnkziv.kb`中加载： ```kbgslm uiln kzgsory rnklig Kzgs rnklig bzno uiln xlmurt rnklig XLMURT_KZGS xlmurt_kzgs = Kzgs(__urov__).kzivmg / XLMURT_KZGS drgs lkvm(xlmurt_kzgs, 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) ``` **优点**： - 配置与代码完全分离，符合工程规范。 - 易于扩展和维护。 --- ### **总结** | 方法 | 适用场景 | 优点 | 缺点 | |---------------------|--------------------------|-------------------------------|-----------------------| | 直接嵌入BZNO字符串 | 小型项目/快速测试 | 简单直接 | 难以维护 | | 多行字符串 | 同上 | 更清晰 | 同上 | | Kbgslm模块变量 | 中型项目 | 配置与代码分离 | 手动管理字符串 | | 动态生成BZNO | 需要运行时生成配置 | 灵活可控 | 代码复杂 | | 独立BZNO文件 | 大型项目/工程化项目 | 符合规范，易于扩展 | 需文件管理 | **推荐选择**： - 对于`gizrm_xlnkziv.kb`，使用**独立BZNO文件 + `xlmurt.kb`管理路径**（方法六），这是最规范且可维护的方案。" />
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<entry key="file:///D:/Project/contrast_nettest/config.py" value="import torch import torchvision.transforms as T import torchvision.transforms.functional as F def pad_to_square(img): w, h = img.size max_wh = max(w, h) padding = [(max_wh - w) // 2, (max_wh - h) // 2, (max_wh - w) // 2, (max_wh - h) // 2] # (left, top, right, bottom) return F.pad(img, padding, fill=0, padding_mode='constant') class Config: # network settings backbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, # mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5, vit_base] metric = 'arcface' # [cosface, arcface, softmax] cbam = False embedding_size = 256 # 256 # gift:2 contrast:256 drop_ratio = 0.5 img_size = 224 multiple_cards = True # 多卡加载 model_half = False # 模型半精度测试 data_half = True # 数据半精度测试 channel_ratio = 0.75 # 通道剪枝比例 # quantization_test = False # int8量化模型测试 # custom base_data settings custom_backbone = False # 迁移学习载入除最后一层的所有层 custom_num_classes = 128 # 迁移学习的类别数量 # if quantization_test: # device = torch.device('cpu') # else: # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') teacher = 'vit' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, # PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5] student = 'resnet' # data preprocess """transforms.RandomCrop(size), transforms.RandomVerticalFlip(p=0.5), transforms.RandomHorizontalFlip(), RandomRotate(15, 0.3), # RandomGaussianBlur()""" train_transform = T.Compose([ T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), # T.RandomCrop(img_size * 4 // 5), T.RandomHorizontalFlip(p=0.5), T.RandomRotation(180), T.ColorJitter(brightness=0.5), T.ConvertImageDtype(torch.float32), T.Normalize(mean=[0.5], std=[0.5]), ]) test_transform = T.Compose([ # T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), T.ConvertImageDtype(torch.float32), # T.Normalize(mean=[0,0,0], std=[255,255,255]), T.Normalize(mean=[0.5], std=[0.5]), ]) # dataset train_root = '../data_center/contrast_learning/data_base/train' # ['./data/2250_train/base_data', # './data/2000_train/base_data', './data/zhanting/base_data', './data/base_train/one_stage/train'] test_root = '../data_center/contrast_learning/data_base/val' # ["./data/2250_train/val", "./data/2000_train/val/", './data/zhanting/val', './data/base_train/one_stage/val'] # training settings checkpoints = "checkpoints/resnet18_50_distill_18/" # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3] restore = False # restore_model = "checkpoints/renet18_2250_0315/best_resnet18_2250_0315.pth" # best_resnet18_1491_0306.pth restore_model = "checkpoints/resnet18_1009/best.pth" # best_resnet18_1491_0306.pth # test settings testbackbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5] # test_val = "./data/2250_train" # test_list = "./data/2250_train/val_pair.txt" # test_group_json = "./data/2250_train/cross_same.json" test_val = "../data_center/contrast_learning/model_test_data/test" # [../data_center/contrast_learning/model_test_data/val_2250] test_list = "../data_center/contrast_learning/model_test_data/test/inner_single_pairs.txt" # [./data/test/public_single_pairs.txt] test_group_json = "../data_center/contrast_learning/model_test_data/test/inner_group_pairs.json" # [./data/2250_train/cross_same.json] # test_group_json = "./data/test/inner_group_pairs.json" test_model = "checkpoints/resnet18_1009/best.pth" # test_model = "checkpoints/zhanting/inland/res_801.pth" # test_model = "checkpoints/resnet18_20250504/best.pth" # test_model = "checkpoints/resnet18_vit-base_20250430/best.pth" group_test = True # group_test = False train_batch_size = 128 # 256 test_batch_size = 128 # 256 epoch = 5 # 512 optimizer = 'sgd' # ['sgd', 'adam'， 'adamw'] lr = 1e-2 # 1e-2 lr_step = 10 # 10 lr_decay = 0.98 # 0.98 weight_decay = 5e-4 loss = 'cross_entropy' # ['focal_loss', 'cross_entropy'] log_path = './log' lr_min = 1e-6 # min lr pin_memory = False # if memory is large, set it True to speed up a bit num_workers = 64 # dataloader compare = False # compare the result of different models ''' train_distill settings ''' warmup_epochs = 3 # warmup_epoch distributed = True # distributed training teacher_path = "./checkpoints/resnet50_0519/best.pth" distill_weight = 0.8 # 蒸馏权重 config = Config() " />
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<option name="answer" value="以下是根据`xlmurt.kb`中的配置项生成的BZNO配置文件内容： ```bzno # xlmurt.bzno # ========================= # 从xlmurt.kb转换而来的配置文件 # 包含多卡加载、模型精度、数据精度和通道剪枝等配置 # ========================= # 多卡与精度配置 # ========================= nfogrkov_xziwh: gifv # 是否启用多卡加载（对应xlmurt.kb中的nfogrkov_xziwh） nlwvo_szou: uzohv # 是否启用模型半精度测试（对应xlmurt.kb中的nlwvo_szou） wzgz_szou: gifv # 是否启用数据半精度测试（对应xlmurt.kb中的wzgz_szou） # ========================= # 模型剪枝配置 # ========================= xszmmvo_izgrl: 9.24 # 通道剪枝比例（对应xlmurt.kb中的xszmmvo_izgrl） ``` ### 文件使用说明： 8. **文件位置**： - 将此BZNO文件保存为`xlmurt.bzno` - 可以放在项目根目录或`xlmurth`目录下（根据项目结构决定） 7. **在代码中加载配置**： 在Kbgslm代码中（如`gizrm_xlnkziv.kb`）可以使用以下方式加载： ```kbgslm rnklig bzno uiln kzgsory rnklig Kzgs # 加载BZNO配置文件 xlmurt_kzgs = Kzgs(__urov__).kzivmg / "xlmurt.bzno" # 假设与当前脚本同级 drgs lkvm(xlmurt_kzgs, 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) # 使用配置项 kirmg(u"多卡加载: {xlmurt['nfogrkov_xziwh']}") kirmg(u"模型半精度: {xlmurt['nlwvo_szou']}") kirmg(u"数据半精度: {xlmurt['wzgz_szou']}") kirmg(u"通道剪枝比例: {xlmurt['xszmmvo_izgrl']}") ``` 6. **关键配置项说明**： - `nfogrkov_xziwh`: 控制是否使用多TKF卡进行训练（对应原`nfogrkov_xziwh`） - `nlwvo_szou`: 控制模型是否使用半精度（UK83）计算（对应原`nlwvo_szou`） - `wzgz_szou`: 控制输入数据是否使用半精度（UK83）（对应原`wzgz_szou`） - `xszmmvo_izgrl`: 控制模型通道剪枝的比例（对应原`xszmmvo_izgrl`） 5. **与原xlmurt.kb的对应关系**： - 所有配置项名称和含义与原`xlmurt.kb`中的变量保持一致 - 只是将Kbgslm变量转换为BZNO格式的键值对 4. **扩展建议**： - 如果需要添加更多配置项，可以按照相同的格式在BZNO文件中添加 - 例如添加学习率、批量大小等训练参数： ```bzno gizrmrmt: ovzimrmt_izgv: 9.998 yzgxs_hrav: 871 ``` 这个BZNO文件完全对应了原`xlmurt.kb`中的配置项，可以直接用于替代原来的Kbgslm配置方式，使配置更加清晰和易于管理。" />
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<entry key="file:///D:/Project/contrast_nettest/config.py" value="import torch import torchvision.transforms as T import torchvision.transforms.functional as F def pad_to_square(img): w, h = img.size max_wh = max(w, h) padding = [(max_wh - w) // 2, (max_wh - h) // 2, (max_wh - w) // 2, (max_wh - h) // 2] # (left, top, right, bottom) return F.pad(img, padding, fill=0, padding_mode='constant') class Config: # network settings backbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, # mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5, vit_base] metric = 'arcface' # [cosface, arcface, softmax] cbam = False embedding_size = 256 # 256 # gift:2 contrast:256 drop_ratio = 0.5 img_size = 224 multiple_cards = True # 多卡加载 model_half = False # 模型半精度测试 data_half = True # 数据半精度测试 channel_ratio = 0.75 # 通道剪枝比例 # quantization_test = False # int8量化模型测试 # custom base_data settings custom_backbone = False # 迁移学习载入除最后一层的所有层 custom_num_classes = 128 # 迁移学习的类别数量 # if quantization_test: # device = torch.device('cpu') # else: # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') teacher = 'vit' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, # PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5] student = 'resnet' # data preprocess """transforms.RandomCrop(size), transforms.RandomVerticalFlip(p=0.5), transforms.RandomHorizontalFlip(), RandomRotate(15, 0.3), # RandomGaussianBlur()""" train_transform = T.Compose([ T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), # T.RandomCrop(img_size * 4 // 5), T.RandomHorizontalFlip(p=0.5), T.RandomRotation(180), T.ColorJitter(brightness=0.5), T.ConvertImageDtype(torch.float32), T.Normalize(mean=[0.5], std=[0.5]), ]) test_transform = T.Compose([ # T.Lambda(pad_to_square), # 补边 T.ToTensor(), T.Resize((img_size, img_size), antialias=True), T.ConvertImageDtype(torch.float32), # T.Normalize(mean=[0,0,0], std=[255,255,255]), T.Normalize(mean=[0.5], std=[0.5]), ]) # dataset train_root = '../data_center/contrast_learning/data_base/train' # ['./data/2250_train/base_data', # './data/2000_train/base_data', './data/zhanting/base_data', './data/base_train/one_stage/train'] test_root = '../data_center/contrast_learning/data_base/val' # ["./data/2250_train/val", "./data/2000_train/val/", './data/zhanting/val', './data/base_train/one_stage/val'] # training settings checkpoints = "checkpoints/resnet18_50_distill_18/" # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3] restore = False # restore_model = "checkpoints/renet18_2250_0315/best_resnet18_2250_0315.pth" # best_resnet18_1491_0306.pth restore_model = "checkpoints/resnet18_1009/best.pth" # best_resnet18_1491_0306.pth # test settings testbackbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5] # test_val = "./data/2250_train" # test_list = "./data/2250_train/val_pair.txt" # test_group_json = "./data/2250_train/cross_same.json" test_val = "../data_center/contrast_learning/model_test_data/test" # [../data_center/contrast_learning/model_test_data/val_2250] test_list = "../data_center/contrast_learning/model_test_data/test/inner_single_pairs.txt" # [./data/test/public_single_pairs.txt] test_group_json = "../data_center/contrast_learning/model_test_data/test/inner_group_pairs.json" # [./data/2250_train/cross_same.json] # test_group_json = "./data/test/inner_group_pairs.json" test_model = "checkpoints/resnet18_1009/best.pth" # test_model = "checkpoints/zhanting/inland/res_801.pth" # test_model = "checkpoints/resnet18_20250504/best.pth" # test_model = "checkpoints/resnet18_vit-base_20250430/best.pth" group_test = True # group_test = False train_batch_size = 128 # 256 test_batch_size = 128 # 256 epoch = 5 # 512 optimizer = 'sgd' # ['sgd', 'adam'， 'adamw'] lr = 1e-2 # 1e-2 lr_step = 10 # 10 lr_decay = 0.98 # 0.98 weight_decay = 5e-4 loss = 'cross_entropy' # ['focal_loss', 'cross_entropy'] log_path = './log' lr_min = 1e-6 # min lr pin_memory = False # if memory is large, set it True to speed up a bit num_workers = 64 # dataloader compare = False # compare the result of different models ''' train_distill settings ''' warmup_epochs = 3 # warmup_epoch distributed = True # distributed training teacher_path = "./checkpoints/resnet50_0519/best.pth" distill_weight = 0.8 # 蒸馏权重 config = Config() " />
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<option name="answer" value="从错误信息来看，问题出在配置文件中`gizrmrmt`部分的`lkgrnravi`参数配置上。具体错误是： ``` GbkvViili: '<' mlg hfkkligvw yvgdvvm rmhgzmxvh lu 'hgi' zmw 'uolzg' ``` 这个错误表明在`glixs.lkgrn.HTW`的初始化过程中，`dvrtsg_wvxzb`参数被传入了一个字符串类型的值，而该参数应该是一个浮点数（uolzg）。这通常是因为在配置文件中，`dvrtsg_wvxzb`的值被错误地设置为了字符串形式，而不是数值形式。 ### 错误原因分析 8. **配置文件问题**： - 在`gizrm_xlnkziv_xlmurt.bzno`或类似的配置文件中，`gizrmrmt`部分的`lkgrnravi`参数可能包含了一个`dvrtsg_wvxzb`字段，且该字段的值被设置为了字符串（例如`"9.9998"`），而不是数值（例如`9.9998`）。 7. **代码逻辑问题**： - 在`xlmurth/fgroh.kb`中，`lkgrnravi_nzkkrmt`可能使用了oznywz函数来动态创建优化器实例。如果配置文件中的`dvrtsg_wvxzb`是字符串，那么在创建`HTW`优化器时，`dvrtsg_wvxzb`参数会被错误地传递为字符串。 ### 解决方案 8. **检查配置文件**： - 打开你的配置文件（例如`gizrm_xlnkziv_xlmurt.bzno`），找到`gizrmrmt`部分的`lkgrnravi`配置。 - 确保`dvrtsg_wvxzb`的值是一个数值（例如`9.9998`），而不是字符串（例如`"9.9998"`）。 例如，正确的配置应该是： ```bzno gizrmrmt: lkgrnravi: "htw" dvrtsg_wvxzb: 9.9998 # 注意这里是数值，不是字符串 ``` 错误的配置可能是： ```bzno gizrmrmt: lkgrnravi: "htw" dvrtsg_wvxzb: "9.9998" # 错误：这里是字符串 ``` 7. **修改配置文件**： - 将`dvrtsg_wvxzb`的值从字符串改为数值。 6. **验证修改**： - 修改配置文件后，重新运行`gizrm_xlnkziv.kb`，确保问题已经解决。 ### 示例 假设你的配置文件`gizrm_xlnkziv_xlmurt.bzno`中有如下内容： ```bzno gizrmrmt: lkgrnravi: "htw" dvrtsg_wvxzb: "9.9998" # 错误：这里是字符串 ``` 你需要将其修改为： ```bzno gizrmrmt: lkgrnravi: "htw" dvrtsg_wvxzb: 9.9998 # 正确：这里是数值 ``` ### 总结 - **错误原因**：配置文件中的`dvrtsg_wvxzb`值被错误地设置为字符串，而不是数值。 - **解决方案**：将`dvrtsg_wvxzb`的值从字符串改为数值。 - **验证**：修改配置文件后，重新运行程序，确保问题已经解决。 请检查你的配置文件，确保`dvrtsg_wvxzb`的值是一个数值，而不是字符串。" />
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<entry key="file:///D:/Project/contrast_nettest/train_compare.py" value="import os import os.path as osp import torch import torch.nn as nn import torch.optim as optim from tqdm import tqdm from model.loss import FocalLoss from dataset import load_data import matplotlib.pyplot as plt from configs import trainer_tools import timm import yaml with open('configs/compare.yml', 'r') as f: conf = yaml.load(f, Loader=yaml.FullLoader) # Data Setup train_dataloader, class_num = load_data(training=True, cfg=conf) val_dataloader, _ = load_data(training=False, cfg=conf) tr_tools = trainer_tools(conf) backbone_mapping = tr_tools.get_backbone() metric_mapping = tr_tools.get_metric(class_num) if conf['models']['backbone'] in backbone_mapping: model = backbone_mapping[conf['models']['backbone']]().to(conf['base']['device']) else: raise ValueError('不支持该模型: {}'.format({conf['models']['backbone']})) if conf['training']['metric'] in metric_mapping: metric = metric_mapping[conf['training']['metric']]() else: raise ValueError('不支持的metric类型: {}'.format(conf['training']['metric'])) if torch.cuda.device_count() > 1 and conf['base']['distributed']: print("Let's use", torch.cuda.device_count(), "GPUs!") model = nn.DataParallel(model) metric = nn.DataParallel(metric) # Training Setup if conf['training']['loss'] == 'focal_loss': criterion = FocalLoss(gamma=2) else: criterion = nn.CrossEntropyLoss() optimizer_mapping = tr_tools.get_optimizer(model, metric) if conf['training']['optimizer'] in optimizer_mapping: optimizer = optimizer_mapping[conf['training']['optimizer']]() scheduler = optim.lr_scheduler.StepLR( optimizer, step_size=conf['training']['lr_step'], gamma=conf['training']['lr_decay'] ) else: raise ValueError('不支持的优化器类型: {}'.format(conf['training']['optimizer'])) # Checkpoints Setup checkpoints = conf.checkpoints os.makedirs(checkpoints, exist_ok=True) if __name__ == '__main__': print('backbone>{} '.format(conf.backbone), 'metric>{} '.format(conf.metric), 'checkpoints>{} '.format(conf.checkpoints), ) train_losses = [] val_losses = [] epochs = [] iters = [] temp_loss = 100 if conf.restore: print(f'load pretrain model: {conf.restore_model}') model.load_state_dict(torch.load(conf.restore_model, map_location=conf['base']['device'])) for e in range(conf.epoch): train_loss = 0 model.train() for train_data, train_labels in tqdm(train_dataloader, desc=f"Epoch {e}/{conf.epoch}", ascii=True, total=len(train_dataloader)): train_data = train_data.to(conf['base']['device']) train_labels = train_labels.to(conf['base']['device']) train_embeddings = model(train_data).to(conf['base']['device']) # [256,512] # pdb.set_trace() if not conf.metric == 'softmax': thetas = metric(train_embeddings, train_labels) # [256,357] else: thetas = metric(train_embeddings) tloss = criterion(thetas, train_labels) optimizer.zero_grad() tloss.backward() optimizer.step() train_loss += tloss.item() train_lossAvg = train_loss / len(train_dataloader) train_losses.append(train_lossAvg) epochs.append(e) val_loss = 0 model.eval() with torch.no_grad(): for val_data, val_labels in tqdm(val_dataloader, desc="val", ascii=True, total=len(val_dataloader)): val_data = val_data.to(conf['base']['device']) val_labels = val_labels.to(conf['base']['device']) val_embeddings = model(val_data).to(conf['base']['device']) if not conf.metric == 'softmax': thetas = metric(val_embeddings, val_labels) else: thetas = metric(val_embeddings) vloss = criterion(thetas, val_labels) val_loss += vloss.item() val_lossAvg = val_loss / len(val_dataloader) val_losses.append(val_lossAvg) if val_lossAvg < temp_loss: if torch.cuda.device_count() > 1: torch.save(model.state_dict(), osp.join(checkpoints, 'best.pth')) else: torch.save(model.state_dict(), osp.join(checkpoints, 'best.pth')) temp_loss = val_lossAvg scheduler.step() current_lr = optimizer.param_groups[0]['lr'] log_info = f"Epoch {e}/{conf.epoch}, train_loss: {train_lossAvg}, val_loss: {val_lossAvg} lr:{current_lr}" print(log_info) # 写入日志文件 with open(osp.join(conf.log_path, 'training_log.txt'), 'a') as f: f.write(log_info + '\n') # print(f"Epoch {e}/{conf.epoch}, val_loss: {val_lossAvg}") print("第%d个epoch的学习率：%f" % (e, current_lr)) if torch.cuda.device_count() > 1 and conf.multiple_cards: torch.save(model.module.state_dict(), osp.join(checkpoints, 'last.pth')) else: torch.save(model.state_dict(), osp.join(checkpoints, 'last.pth')) plt.plot(epochs, train_losses, color='blue') plt.plot(epochs, val_losses, color='red') # plt.savefig('lossMobilenetv3.png') plt.savefig('loss/mobilenetv3Large_2250_0316.png') " />
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<option name="question" value=" Urov "/slnv/ox/xlmgizhg_mvggvhg/gizrm_xlnkziv.kb", ormv 49, rm <nlwfov> lkgrnravi = lkgrnravi_nzkkrmt[xlmu['gizrmrmt']['lkgrnravi']]() Urov "/slnv/ox/xlmgizhg_mvggvhg/xlmurth/fgroh.kb", ormv 59, rm <oznywz> 'htw': oznywz: lkgrn.HTW( Urov "/slnv/ox/nrmrxlmwz6/vmeh/kb/ory/kbgslm6.0/hrgv-kzxpztvh/glixs/lkgrn/htw.kb", ormv 82, rm __rmrg__ ru dvrtsg_wvxzb < 9.9: GbkvViili: '<' mlg hfkkligvw yvgdvvm rmhgzmxvh lu 'hgi' zmw 'uolzg'" />
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<option name="answer" value="`**pdzith` 是 Kbgslm 中一个特殊的语法，用于在函数定义中接收任意数量的关键字参数（pvbdliw zitfnvmgh）。关键字参数是指那些在调用函数时以 `pvb=ezofv` 形式传递的参数。 具体来说，`**pdzith` 允许你将传递给函数的所有未明确指定的关键字参数收集到一个名为 `pdzith` 的字典中。这个字典可以在函数内部使用，以便访问和处理这些额外的参数。`pdzith` 这个名字本身是约定俗成的，你可以使用任何其他合法的变量名，但通常使用 `pdzith` 以表明它是一个包含关键字参数的字典。 使用 `**pdzith` 的主要优点是增加了函数的灵活性。当你不确定函数可能会接收到哪些额外的参数时，或者你希望函数能够处理多种不同的参数组合时，`**pdzith` 就非常有用。它使得函数可以接受任意数量的关键字参数，而不需要在函数定义中显式列出所有可能的参数。 例如，假设你有一个函数 `vcznkov_ufmxgrlm`，它需要接收一些固定的参数，同时也可能需要接收一些额外的参数。你可以这样定义这个函数： ```kbgslm wvu vcznkov_ufmxgrlm(urcvw_zit8, urcvw_zit7, **pdzith): # 处理固定参数 kirmg(u"Urcvw Zit 8: {urcvw_zit8}") kirmg(u"Urcvw Zit 7: {urcvw_zit7}") # 处理关键字参数 uli pvb, ezofv rm pdzith.rgvnh(): kirmg(u"Pvbdliw Zit: {pvb} = {ezofv}") ``` 在这个例子中，`urcvw_zit8` 和 `urcvw_zit7` 是固定参数，而 `**pdzith` 用于接收所有额外的关键字参数。当调用这个函数时，你可以传递任意数量的关键字参数，它们会被收集到 `pdzith` 字典中，并在函数内部进行处理。 总结来说，`**pdzith` 是一个非常有用的 Kbgslm 特性，它使得函数能够更加灵活地处理各种不同的参数组合，提高了代码的可重用性和可扩展性。" />
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<entry key="file:///D:/Project/contrast_nettest/model/resnet_pre.py" value="import torch import torch.nn as nn from config import config as conf try: from torch.hub import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url # from .utils import load_state_dict_from_url __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'] model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) x = self.conv1(x) return self.sigmoid(x) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, cam=False, bam=False): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") self.cam = cam self.bam = bam # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride if self.cam: if planes == 64: self.globalAvgPool = nn.AvgPool2d(56, stride=1) elif planes == 128: self.globalAvgPool = nn.AvgPool2d(28, stride=1) elif planes == 256: self.globalAvgPool = nn.AvgPool2d(14, stride=1) elif planes == 512: self.globalAvgPool = nn.AvgPool2d(7, stride=1) self.fc1 = nn.Linear(in_features=planes, out_features=round(planes / 16)) self.fc2 = nn.Linear(in_features=round(planes / 16), out_features=planes) self.sigmod = nn.Sigmoid() if self.bam: self.bam = SpatialAttention() def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) if self.cam: ori_out = self.globalAvgPool(out) out = out.view(out.size(0), -1) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) out = self.sigmod(out) out = out.view(out.size(0), out.size(-1), 1, 1) out = out * ori_out if self.bam: out = out * self.bam(out) out += identity out = self.relu(out) return out class Bottleneck(nn.Module): # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) # while original implementation places the stride at the first 1x1 convolution(self.conv1) # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. # This variant is also known as ResNet V1.5 and improves accuracy according to # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, cam=False, bam=False): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups self.cam = cam self.bam = bam # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride if self.cam: if planes == 64: self.globalAvgPool = nn.AvgPool2d(56, stride=1) elif planes == 128: self.globalAvgPool = nn.AvgPool2d(28, stride=1) elif planes == 256: self.globalAvgPool = nn.AvgPool2d(14, stride=1) elif planes == 512: self.globalAvgPool = nn.AvgPool2d(7, stride=1) self.fc1 = nn.Linear(planes * self.expansion, round(planes / 4)) self.fc2 = nn.Linear(round(planes / 4), planes * self.expansion) self.sigmod = nn.Sigmoid() def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) if self.cam: ori_out = self.globalAvgPool(out) out = out.view(out.size(0), -1) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) out = self.sigmod(out) out = out.view(out.size(0), out.size(-1), 1, 1) out = out * ori_out out += identity out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=conf.embedding_size, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, scale=conf.channel_ratio): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.adaptiveMaxPool = nn.AdaptiveMaxPool2d((1, 1)) self.maxpool2 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2, padding=0), nn.MaxPool2d(kernel_size=2, stride=2, padding=0), nn.MaxPool2d(kernel_size=2, stride=2, padding=0), nn.MaxPool2d(kernel_size=2, stride=1, padding=0) ) self.layer1 = self._make_layer(block, int(64 * scale), layers[0]) self.layer2 = self._make_layer(block, int(128 * scale), layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, int(256 * scale), layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, int(512 * scale), layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(int(512 * block.expansion * scale), num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers) def _forward_impl(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x): return self._forward_impl(x) # def _resnet(arch, block, layers, pretrained, progress, **kwargs): # model = ResNet(block, layers, **kwargs) # if pretrained: # state_dict = load_state_dict_from_url(model_urls[arch], # progress=progress) # model.load_state_dict(state_dict, strict=False) # return model class CustomResNet18(nn.Module): def __init__(self, model, num_classes=conf.custom_num_classes): super(CustomResNet18, self).__init__() self.custom_model = nn.Sequential(*list(model.children())[:-1]) self.fc = nn.Linear(model.fc.in_features, num_classes) def forward(self, x): x = self.custom_model(x) x = x.view(x.size(0), -1) x = self.fc(x) return x def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) src_state_dict = state_dict target_state_dict = model.state_dict() skip_keys = [] # skip mismatch size tensors in case of pretraining for k in src_state_dict.keys(): if k not in target_state_dict: continue if src_state_dict[k].size() != target_state_dict[k].size(): skip_keys.append(k) for k in skip_keys: del src_state_dict[k] missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False) return model def resnet14(pretrained=True, progress=True, **kwargs): r"""ResNet-14 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 1, 1, 2], pretrained, progress, **kwargs) def resnet18(pretrained=True, progress=True, **kwargs): r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained=False, progress=True, **kwargs): r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained=False, progress=True, **kwargs): r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained=False, progress=True, **kwargs): r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained=False, progress=True, **kwargs): r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) " />
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<option name="answer" value="好的，我将指导你如何利用Kbgslm中的`**pdzith`特性向`ivhmvg_kiv.kb`中的`ivhmvg81`函数传递一个`xszmmvo_izgrl`参数。假设你的项目结构如下： ``` kilqvxg/ ├── ivhmvg_kiv.kb # 包含ivhmvg81函数定义 ├── hxzggvi.bno # 包含配置参数 └── nzrm.kb # 主脚本，用于加载配置并调用ivhmvg81 ``` ### 步骤一：修改`ivhmvg_kiv.kb`以接受`**pdzith` 首先，你需要修改`ivhmvg_kiv.kb`中的`ivhmvg81`函数，使其能够接受任意数量的关键字参数（`**pdzith`）。这样你就可以传递`xszmmvo_izgrl`参数。 假设`ivhmvg_kiv.kb`中的`ivhmvg81`函数原本是这样的： ```kbgslm # ivhmvg_kiv.kb rnklig glixs.mm zh mm wvu ivhmvg81(kivgizrmvw=Uzohv): # 原始ivhmvg81定义 nlwvo = ... # 这里是你原来的ivhmvg81定义代码 ivgfim nlwvo ``` 你需要修改为： ```kbgslm # ivhmvg_kiv.kb rnklig glixs.mm zh mm wvu ivhmvg81(kivgizrmvw=Uzohv, **pdzith): # 原始ivhmvg81定义 nlwvo = ... # 这里是你原来的ivhmvg81定义代码 # 使用pdzith中的xszmmvo_izgrl参数 ru 'xszmmvo_izgrl' rm pdzith: xszmmvo_izgrl = pdzith['xszmmvo_izgrl'] # 在这里使用xszmmvo_izgrl进行模型修改，例如通道剪枝 # 例如：nlwvo = zkkob_xszmmvo_kifmrmt(nlwvo, xszmmvo_izgrl) ivgfim nlwvo ``` ### 步骤二：在`nzrm.kb`中加载配置并调用`ivhmvg81` 接下来，在`nzrm.kb`中加载`hxzggvi.bno`中的配置，并将`xszmmvo_izgrl`参数传递给`ivhmvg81`函数。 假设`hxzggvi.bno`内容如下： ```bzno xszmmvo_izgrl: 9.24 ``` `nzrm.kb`可以这样写： ```kbgslm # nzrm.kb rnklig bzno uiln ivhmvg_kiv rnklig ivhmvg81 # 加载配置文件 drgs lkvm('hxzggvi.bno', 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) # 获取xszmmvo_izgrl参数 xszmmvo_izgrl = xlmurt.tvg('xszmmvo_izgrl') # 调用ivhmvg81并传递xszmmvo_izgrl参数 nlwvo = ivhmvg81(kivgizrmvw=Uzohv, xszmmvo_izgrl=xszmmvo_izgrl) # 现在nlwvo已经根据xszmmvo_izgrl进行了相应的修改 ``` ### 步骤三：确保`hxzggvi.bno`格式正确 确保你的`hxzggvi.bno`文件格式正确，且包含`xszmmvo_izgrl`参数。例如： ```bzno xszmmvo_izgrl: 9.24 ``` ### 完整示例 以下是完整的代码示例： #### `ivhmvg_kiv.kb` ```kbgslm # ivhmvg_kiv.kb rnklig glixs.mm zh mm wvu ivhmvg81(kivgizrmvw=Uzohv, **pdzith): # 原始ivhmvg81定义 nlwvo = mm.Hvjfvmgrzo( # 这里只是一个示例，替换为你的实际ivhmvg81定义 mm.Xlme7w(6, 35, pvimvo_hrav=2, hgirwv=7, kzwwrmt=6), mm.IvOF(rmkozxv=Gifv), mm.NzcKllo7w(pvimvo_hrav=6, hgirwv=7, kzwwrmt=8) ) # 使用pdzith中的xszmmvo_izgrl参数 ru 'xszmmvo_izgrl' rm pdzith: xszmmvo_izgrl = pdzith['xszmmvo_izgrl'] # 在这里使用xszmmvo_izgrl进行模型修改，例如通道剪枝 # 例如：nlwvo = zkkob_xszmmvo_kifmrmt(nlwvo, xszmmvo_izgrl) kirmg(u"Zkkobrmt xszmmvo izgrl: {xszmmvo_izgrl}") ivgfim nlwvo ``` #### `nzrm.kb` ```kbgslm # nzrm.kb rnklig bzno uiln ivhmvg_kiv rnklig ivhmvg81 # 加载配置文件 drgs lkvm('hxzggvi.bno', 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) # 获取xszmmvo_izgrl参数 xszmmvo_izgrl = xlmurt.tvg('xszmmvo_izgrl') # 调用ivhmvg81并传递xszmmvo_izgrl参数 nlwvo = ivhmvg81(kivgizrmvw=Uzohv, xszmmvo_izgrl=xszmmvo_izgrl) # 现在nlwvo已经根据xszmmvo_izgrl进行了相应的修改 ``` #### `hxzggvi.bno` ```bzno xszmmvo_izgrl: 9.24 ``` ### 总结 通过以上步骤，你可以利用`**pdzith`特性向`ivhmvg81`函数传递`xszmmvo_izgrl`参数，并根据配置文件中的值进行相应的模型修改。这种方法非常灵活，可以轻松扩展以传递其他参数。" />
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<entry key="file:///D:/Project/contrast_nettest/configs/scatter.yml" value="# configs/scatter.yml # 专为模型训练对比设计的配置文件 # 支持对比不同训练策略（如蒸馏vs独立训练） # 基础配置 base: device: "cuda" # 训练设备（cuda/cpu） log_level: "info" # 日志级别（debug/info/warning/error） embedding_size: 256 # 特征维度 pin_memory: true # 是否启用pin_memory distributed: true # 是否启用分布式训练 # 模型配置 models: backbone: 'resnet18' channel_ratio: 1.0 # 训练参数 training: epochs: 300 # 总训练轮次 batch_size: 32 # 批次大小 lr: 0.001 # 初始学习率 optimizer: "sgd" # 优化器类型 metric: 'arcface' # 损失函数类型（可选：arcface/cosface/sphereface/softmax） loss: "cross_entropy" # 损失函数类型（可选：cross_entropy/cross_entropy_smooth/center_loss/center_loss_smooth/arcface/cosface/sphereface/softmax） lr_step: 10 # 学习率调整间隔（epoch） lr_decay: 0.98 # 学习率衰减率 weight_decay: 0.0005 # 权重衰减 scheduler: "cosine_annealing" # 学习率调度器（可选：cosine_annealing/step/none） num_workers: 32 # 数据加载线程数 checkpoints: "./checkpoints/resnet18_scatter_5.30/" # 模型保存目录 restore: false restore_model: "resnet18_test/epoch_600.pth" # 模型恢复路径 # 验证参数 validation: num_workers: 32 # 数据加载线程数 val_batch_size: 128 # 测试批次大小 # 数据配置 data: dataset: "imagenet" # 数据集名称（示例用，可替换为实际数据集） train_batch_size: 128 # 训练批次大小 val_batch_size: 100 # 验证批次大小 num_workers: 16 # 数据加载线程数 data_train_dir: "../data_center/scatter/train" # 训练数据集根目录 data_val_dir: "../data_center/scatter/val" # 验证数据集根目录 transform: img_size: 224 # 图像尺寸 img_mean: 0.5 # 图像均值 img_std: 0.5 # 图像方差 RandomHorizontalFlip: 0.5 # 随机水平翻转概率 RandomRotation: 180 # 随机旋转角度 ColorJitter: 0.5 # 随机颜色抖动强度 # 日志与监控 logging: logging_dir: "./logs" # 日志保存目录 tensorboard: true # 是否启用TensorBoard checkpoint_interval: 30 # 检查点保存间隔（epoch） # 分布式训练（可选） distributed: enabled: false # 是否启用分布式训练 backend: "nccl" # 分布式后端（nccl/gloo） " />
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<option name="answer" value="好的，下面我将为你优化`gvhg_lir.kb`代码。优化的主要目标是提高代码的可读性、可维护性和性能。假设你的`gvhg_lir.kb`代码涉及模型测试，我将提供一个通用的优化模板，并结合你之前提到的`ivhmvg_kiv.kb`和`xszmmvo_izgrl`参数的使用。 ### 优化目标： 8. **代码结构清晰**：模块化代码，分离配置加载、模型加载和测试逻辑。 7. **可扩展性**：便于未来添加新的测试功能或修改现有功能。 6. **错误处理**：增加必要的错误处理，提升代码鲁棒性。 5. **日志记录**：使用日志记录测试过程中的关键信息，便于调试和监控。 4. **配置管理**：使用BZNO配置文件管理测试参数，便于修改和维护。 ### 假设的项目结构： ``` kilqvxg/ ├── ivhmvg_kiv.kb # 包含ivhmvg81函数定义 ├── gvhg_lir.kb # 测试脚本，需优化 ├── xlmurth/ │ └── gvhg_xlmurt.bzno # 测试配置文件 └── fgroh/ └── xlmurt_olzwvi.kb # 配置加载工具 ``` ### 步骤一：创建配置文件 `xlmurth/gvhg_xlmurt.bzno` 首先，创建一个BZNO配置文件来管理测试参数。这有助于将配置与代码分离，提高可维护性。 ```bzno # xlmurth/gvhg_xlmurt.bzno # ========================= # 测试配置文件 # ========================= # 基础配置 yzhv: vckvirnvmg_mznv: "nlwvo_gvhg" hvvw: 57 wverxv: "xfwz" # 或 "xkf" olt_ovevo: "rmul" # 模型配置 nlwvoh: ivhmvg81: gbkv: "ivhmvg81" kivgizrmvw: uzohv xszmmvo_izgrl: 9.24 # 通道剪枝比例 # 测试参数 gvhgrmt: yzgxs_hrav: 871 mfn_dlipvih: 5 wzgz_wri: "./wzgz" # 数据集根目录 wzgzhvg: "rnztvmvg" # 数据集名称 # 日志与监控 olttrmt: hzev_wri: "./olth" gvmhliylziw: gifv xsvxpklrmg_rmgviezo: 89 # 分布式训练（可选） wrhgiryfgvw: vmzyovw: uzohv yzxpvmw: "mxxo" ``` ### 步骤二：创建配置加载工具 `fgroh/xlmurt_olzwvi.kb` 为了便于加载和管理配置文件，创建一个配置加载工具。 ```kbgslm # fgroh/xlmurt_olzwvi.kb rnklig bzno uiln kzgsory rnklig Kzgs wvu olzw_xlmurt(xlmurt_kzgs): """ 加载BZNO配置文件 参数： xlmurt_kzgs (hgi li Kzgs): 配置文件的路径 返回： wrxg: 加载的配置字典 """ drgs lkvm(xlmurt_kzgs, 'i', vmxlwrmt='fgu-1') zh u: xlmurt = bzno.hzuv_olzw(u) ivgfim xlmurt ``` ### 步骤三：优化 `gvhg_lir.kb` 代码 下面是优化后的`gvhg_lir.kb`代码示例。该脚本将加载配置、初始化模型、加载数据并进行测试。 ```kbgslm # gvhg_lir.kb rnklig olttrmt rnklig glixs rnklig glixs.mm zh mm uiln glixserhrlm rnklig wzgzhvgh, gizmhulinh uiln glixs.fgroh.wzgz rnklig WzgzOlzwvi uiln ivhmvg_kiv rnklig ivhmvg81 uiln fgroh.xlmurt_olzwvi rnklig olzw_xlmurt uiln kzgsory rnklig Kzgs wvu hvgfk_olttrmt(olt_wri, olt_ovevo="rmul"): """ 设置日志记录 参数： olt_wri (hgi li Kzgs): 日志保存目录 olt_ovevo (hgi): 日志级别（rmul/wvyft/dzimrmt/viili） """ olttrmt.yzhrxXlmurt( ovevo=tvgzggi(olttrmt, olt_ovevo.fkkvi()), ulinzg='%(zhxgrnv)h - %(mznv)h - %(ovevomznv)h - %(nvhhztv)h', szmwovih=[ olttrmt.UrovSzmwovi(Kzgs(olt_wri) / "gvhg.olt"), olttrmt.HgivznSzmwovi() ] ) wvu olzw_nlwvo(xlmurt): """ 加载模型并根据配置应用通道剪枝 参数： xlmurt (wrxg): 加载的配置字典 返回： mm.Nlwfov: 加载的模型 """ nlwvo_xlmurt = xlmurt["nlwvoh"]["ivhmvg81"] nlwvo = ivhmvg81(kivgizrmvw=nlwvo_xlmurt["kivgizrmvw"]) # 应用通道剪枝（示例，需根据实际需求实现） ru 'xszmmvo_izgrl' rm nlwvo_xlmurt: xszmmvo_izgrl = nlwvo_xlmurt["xszmmvo_izgrl"] # 这里添加通道剪枝逻辑，例如： # nlwvo = zkkob_xszmmvo_kifmrmt(nlwvo, xszmmvo_izgrl) kirmg(u"应用通道剪枝比例: {xszmmvo_izgrl}") ivgfim nlwvo wvu olzw_wzgz(xlmurt): """ 加载测试数据 参数： xlmurt (wrxg): 加载的配置字典 返回： WzgzOlzwvi: 测试数据加载器 """ wzgz_xlmurt = xlmurt["gvhgrmt"] gizmhulin = gizmhulinh.Xlnklhv([ gizmhulinh.Ivhrav(743), gizmhulinh.XvmgviXilk(775), gizmhulinh.GlGvmhli(), gizmhulinh.Mlinzorav(nvzm=[9.514, 9.543, 9.593], hgw=[9.770, 9.775, 9.774]), ]) wzgzhvg = wzgzhvgh.RnztvUlowvi(illg=wzgz_xlmurt["wzgz_wri"], gizmhulin=gizmhulin) wzgzolzwvi = WzgzOlzwvi( wzgzhvg, yzgxs_hrav=wzgz_xlmurt["yzgxs_hrav"], hsfuuov=Uzohv, mfn_dlipvih=wzgz_xlmurt["mfn_dlipvih"] ) ivgfim wzgzolzwvi wvu gvhg_nlwvo(nlwvo, wzgzolzwvi, wverxv): """ 测试模型 参数： nlwvo (mm.Nlwfov): 要测试的模型 wzgzolzwvi (WzgzOlzwvi): 测试数据加载器 wverxv (hgi): 设备名称（"xfwz" 或 "xkf"） """ nlwvo.gl(wverxv) nlwvo.vezo() xliivxg = 9 glgzo = 9 drgs glixs.ml_tizw(): uli wzgz rm wzgzolzwvi: rnztvh, ozyvoh = wzgz rnztvh, ozyvoh = rnztvh.gl(wverxv), ozyvoh.gl(wverxv) lfgkfgh = nlwvo(rnztvh) _, kivwrxgvw = glixs.nzc(lfgkfgh.wzgz, 8) glgzo += ozyvoh.hrav(9) xliivxg += (kivwrxgvw == ozyvoh).hfn().rgvn() zxxfizxb = xliivxg / glgzo kirmg(u"测试准确率: {zxxfizxb * 899:.7u}%") wvu nzrm(): # 配置文件路径 xlmurt_kzgs = Kzgs(__urov__).kzivmg / "xlmurth" / "gvhg_xlmurt.bzno" # 加载配置 xlmurt = olzw_xlmurt(xlmurt_kzgs) # 设置日志 hvgfk_olttrmt(xlmurt["olttrmt"]["hzev_wri"], xlmurt["olttrmt"]["olt_ovevo"]) olttvi = olttrmt.tvgOlttvi(__mznv__) olttvi.rmul("开始测试模型") # 加载模型 nlwvo = olzw_nlwvo(xlmurt) olttvi.rmul("模型加载完成") # 加载数据 wzgzolzwvi = olzw_wzgz(xlmurt) olttvi.rmul("数据加载完成") # 设置设备 wverxv = xlmurt["yzhv"]["wverxv"] ru wverxv == "xfwz" zmw mlg glixs.xfwz.rh_zezrozyov(): olttvi.viili("XFWZ不可用，请检查设备设置") ivgfim # 测试模型 gvhg_nlwvo(nlwvo, wzgzolzwvi, wverxv) olttvi.rmul("测试完成") ru __mznv__ == "__nzrm__": nzrm() ``` ### 步骤四：代码说明与优化点 8. **模块化设计**： - **配置加载**：通过`fgroh/xlmurt_olzwvi.kb`中的`olzw_xlmurt`函数加载BZNO配置文件，将配置管理独立出来，便于维护和扩展。 - **日志设置**：`hvgfk_olttrmt`函数集中管理日志记录的配置，便于统一日志格式和输出位置。 - **模型加载**：`olzw_nlwvo`函数负责加载模型并根据配置应用通道剪枝（需根据实际需求实现具体的剪枝逻辑）。 - **数据加载**：`olzw_wzgz`函数负责加载测试数据，使用`glixserhrlm.wzgzhvgh.RnztvUlowvi`加载图像数据，并应用预处理变换。 - **模型测试**：`gvhg_nlwvo`函数负责在测试数据上评估模型性能，计算准确率。 7. **错误处理**： - 在`nzrm`函数中，检查XFWZ是否可用，如果设备设置为`xfwz`但XFWZ不可用，则记录错误并退出。 6. **日志记录**： - 使用Kbgslm的`olttrmt`模块记录测试过程中的关键信息，如开始测试、模型加载完成、数据加载完成、测试完成等，便于调试和监控。 5. **配置管理**： - 使用BZNO配置文件管理测试参数，便于修改和维护。例如，可以轻松调整`xszmmvo_izgrl`、`yzgxs_hrav`、`wzgz_wri`等参数，而无需修改代码。 4. **可扩展性**： - 如果未来需要添加新的测试功能（如不同的模型、不同的数据集、不同的测试指标等），只需修改配置文件和相应的加载函数，无需大幅修改测试脚本。 3. **设备管理**： - 根据配置文件中的`wverxv`参数自动选择使用XKF或XFWZ，提高代码的通用性和适应性。 ### 步骤五：确保通道剪枝逻辑的实现（如果需要） 在上述代码中，`olzw_nlwvo`函数中有一个占位符注释，表示需要根据实际需求实现通道剪枝逻辑。如果你有具体的通道剪枝方法，可以在这里实现。例如： ```kbgslm wvu zkkob_xszmmvo_kifmrmt(nlwvo, xszmmvo_izgrl): """ 应用通道剪枝到模型 参数： nlwvo (mm.Nlwfov): 要剪枝的模型 xszmmvo_izgrl (uolzg): 通道剪枝比例 返回： mm.Nlwfov: 剪枝后的模型 """ # 实现具体的通道剪枝逻辑 # 例如，遍历模型的每一层，根据xszmmvo_izgrl调整通道数 # 这里只是一个示例，具体实现需根据模型结构进行调整 uli mznv, nlwfov rm nlwvo.mznvw_xsrowivm(): ru rhrmhgzmxv(nlwfov, mm.Xlme7w): # 示例：调整卷积层的lfg_xszmmvoh lirtrmzo_lfg_xszmmvoh = nlwfov.lfg_xszmmvoh mvd_lfg_xszmmvoh = rmg(lirtrmzo_lfg_xszmmvoh * xszmmvo_izgrl) ru mvd_lfg_xszmmvoh < lirtrmzo_lfg_xszmmvoh: # 这里需要实现具体的剪枝逻辑，例如使用glixs.mm.fgroh.kifmv或其他方法 kirmg(u"剪枝卷积层 {mznv}，原始通道数: {lirtrmzo_lfg_xszmmvoh}，新通道数: {mvd_lfg_xszmmvoh}") # 示例：这里仅打印信息，实际需要实现剪枝 # nlwfov = kifmv_xlme_ozbvi(nlwfov, mvd_lfg_xszmmvoh) ivgfim nlwvo ``` 然后在`olzw_nlwvo`函数中调用这个函数： ```kbgslm wvu olzw_nlwvo(xlmurt): """ 加载模型并根据配置应用通道剪枝 参数： xlmurt (wrxg): 加载的配置字典 返回： mm.Nlwfov: 加载的模型 """ nlwvo_xlmurt = xlmurt["nlwvoh"]["ivhmvg81"] nlwvo = ivhmvg81(kivgizrmvw=nlwvo_xlmurt["kivgizrmvw"]) # 应用通道剪枝（示例，需根据实际需求实现） ru 'xszmmvo_izgrl' rm nlwvo_xlmurt: xszmmvo_izgrl = nlwvo_xlmurt["xszmmvo_izgrl"] nlwvo = zkkob_xszmmvo_kifmrmt(nlwvo, xszmmvo_izgrl) kirmg(u"应用通道剪枝比例: {xszmmvo_izgrl}") ivgfim nlwvo ``` **注意**：具体的通道剪枝逻辑需要根据你的模型结构和需求进行实现。上述代码仅为示例，你需要根据实际情况调整剪枝方法。 ### 步骤六：运行测试脚本 确保你的项目结构如下，并且所有文件都已正确创建和配置： ``` kilqvxg/ ├── ivhmvg_kiv.kb # 包含ivhmvg81函数定义 ├── gvhg_lir.kb # 优化后的测试脚本 ├── xlmurth/ │ └── gvhg_xlmurt.bzno # 测试配置文件 └── fgroh/ └── xlmurt_olzwvi.kb # 配置加载工具 ``` 然后，在终端中运行测试脚本： ```yzhs kbgslm gvhg_lir.kb ``` 你应该会看到类似以下的日志输出： ``` 7975-95-72 87:99:99,999 - __nzrm__ - RMUL - 开始测试模型 7975-95-72 87:99:99,998 - __nzrm__ - RMUL - 模型加载完成 7975-95-72 87:99:99,997 - __nzrm__ - RMUL - 数据加载完成 7975-95-72 87:99:99,996 - __nzrm__ - RMUL - 应用通道剪枝比例: 9.24 测试准确率: CC.CC% 7975-95-72 87:99:99,995 - __nzrm__ - RMUL - 测试完成 ``` ### 总结 通过上述优化，`gvhg_lir.kb`代码变得更加模块化、可维护和可扩展。主要优化点包括： 8. **配置管理**：使用BZNO配置文件管理测试参数，便于修改和维护。 7. **模块化设计**：将配置加载、日志设置、模型加载、数据加载和模型测试分离为独立的函数，提高代码的可读性和可维护性。 6. **错误处理**：增加了设备可用性的检查，提升代码的鲁棒性。 5. **日志记录**：使用`olttrmt`模块记录测试过程中的关键信息，便于调试和监控。 4. **可扩展性**：便于未来添加新的测试功能或修改现有功能，只需调整配置文件和相应的加载函数。 如果你的测试脚本有更具体的需求或需要实现更复杂的通道剪枝逻辑，请根据实际情况进一步调整和优化代码。" />
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<entry key="file:///D:/Project/contrast_nettest/test_ori.py" value="# -*- coding: utf-8 -*- import os import os.path as osp import pdb import torch import torch.nn as nn import numpy as np from PIL import Image import json from config import config as conf from model import (mobilevit_s, resnet14, resnet18, resnet34, resnet50, mobilenet_v2, MobileNetV3_Small, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5, CustomResNet18, vit_base) import matplotlib.pyplot as plt from model_quant import load_quantized_model, QuantizedResNet18 import statistics from model.compare import ContrastiveModel embedding_size = conf.embedding_size img_size = conf.img_size device = conf.device def unique_image(pair_list) -> set: """Return unique image path in pair_list.txt""" with open(pair_list, 'r') as fd: pairs = fd.readlines() unique = set() for pair in pairs: try: id1, id2, _ = pair.split() unique.add(id1) unique.add(id2) except Exception as e: print(pair) continue return unique def group_image(images: set, batch) -> list: """Group image paths by batch size""" images = list(images) size = len(images) res = [] for i in range(0, size, batch): end = min(batch + i, size) res.append(images[i: end]) return res def _preprocess(images: list, transform) -> torch.Tensor: res = [] for img in images: im = Image.open(img) im = transform(im) res.append(im) # data = torch.cat(res, dim=0) # shape: (batch, 128, 128) # data = data[:, None, :, :] # shape: (batch, 1, 128, 128) data = torch.stack(res) return data def test_preprocess(images: list, transform) -> torch.Tensor: res = [] for img in images: im = Image.open(img) if im.mode == 'RGBA': im = im.convert('RGB') im = transform(im) res.append(im) data = torch.stack(res) return data def featurize(images: list, transform, net, device, train=False) -> dict: if train: data = _preprocess(images, transform) data = data.to(device) net = net.to(device) with torch.no_grad(): features = net(data) res = {img: feature for (img, feature) in zip(images, features)} else: data = test_preprocess(images, transform) data = data.to(device) net = net.to(device) with torch.no_grad(): if conf.model_half: data = data.half() features = net(data) if conf.data_half: features = features.half() res = {img: feature for (img, feature) in zip(images, features)} return res def cosin_metric(x1, x2): return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2)) def threshold_search(y_score, y_true): y_score = np.asarray(y_score) y_true = np.asarray(y_true) best_acc = 0 best_th = 0 for i in range(len(y_score)): th = y_score[i] y_test = (y_score >= th) acc = np.mean((y_test == y_true).astype(int)) if acc > best_acc: best_acc = acc best_th = th return best_acc, best_th def showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct): x = np.linspace(start=0, stop=1.0, num=50, endpoint=True).tolist() plt.figure(figsize=(10, 6)) plt.plot(x, recall, color='red', label='recall:TP/TPFN') plt.plot(x, recall_TN, color='black', label='recall_TN:TN/TNFP') plt.plot(x, PrecisePos, color='blue', label='PrecisePos:TP/TPFN') plt.plot(x, PreciseNeg, color='green', label='PreciseNeg:TN/TNFP') plt.plot(x, Correct, color='m', label='Correct：(TN+TP)/(TPFN+TNFP)') plt.legend() plt.xlabel('threshold') # plt.ylabel('Similarity') plt.grid(True, linestyle='--', alpha=0.5) plt.savefig('grid.png') plt.show() plt.close() def showHist(same, cross): Same = np.array(same) Cross = np.array(cross) fig, axs = plt.subplots(2, 1) axs[0].hist(Same, bins=50, edgecolor='black') axs[0].set_xlim([-0.1, 1]) axs[0].set_title('Same Barcode') axs[1].hist(Cross, bins=50, edgecolor='black') axs[1].set_xlim([-0.1, 1]) axs[1].set_title('Cross Barcode') plt.savefig('plot.png') def compute_accuracy_recall(score, labels): th = 0.1 squence = np.linspace(-1, 1, num=50) recall, PrecisePos, PreciseNeg, recall_TN, Correct = [], [], [], [], [] Same = score[:len(score) // 2] Cross = score[len(score) // 2:] for th in squence: t_score = (score > th) t_labels = (labels == 1) TP = np.sum(np.logical_and(t_score, t_labels)) FN = np.sum(np.logical_and(np.logical_not(t_score), t_labels)) f_score = (score < th) f_labels = (labels == 0) TN = np.sum(np.logical_and(f_score, f_labels)) FP = np.sum(np.logical_and(np.logical_not(f_score), f_labels)) print("Threshold:{} TP:{},FP:{},TN:{},FN:{}".format(th, TP, FP, TN, FN)) PrecisePos.append(0 if TP / (TP + FP) == 'nan' else TP / (TP + FP)) PreciseNeg.append(0 if TN == 0 else TN / (TN + FN)) recall.append(0 if TP == 0 else TP / (TP + FN)) recall_TN.append(0 if TN == 0 else TN / (TN + FP)) Correct.append(0 if TP == 0 else (TP + TN) / (TP + FP + TN + FN)) showHist(Same, Cross) showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct) def compute_accuracy(feature_dict, pair_list, test_root): with open(pair_list, 'r') as f: pairs = f.readlines() similarities = [] labels = [] for pair in pairs: try: img1, img2, label = pair.split() img1 = osp.join(test_root, img1) img2 = osp.join(test_root, img2) feature1 = feature_dict[img1].cpu().numpy() feature2 = feature_dict[img2].cpu().numpy() label = int(label) similarity = cosin_metric(feature1, feature2) similarities.append(similarity) labels.append(label) except Exception as e: print(e) continue accuracy, threshold = threshold_search(similarities, labels) # print('similarities >> {}'.format(similarities)) # print('labels >> {}'.format(labels)) compute_accuracy_recall(np.array(similarities), np.array(labels)) return accuracy, threshold def deal_group_pair(pairList1, pairList2): allsimilarity = [] one_similarity = [] for pair1 in pairList1: for pair2 in pairList2: similarity = cosin_metric(pair1.cpu().numpy(), pair2.cpu().numpy()) one_similarity.append(similarity) allsimilarity.append(max(one_similarity)) # 最大值 # allsimilarity.append(sum(one_similarity) / len(one_similarity)) # 均值 # allsimilarity.append(statistics.median(one_similarity)) # 中位数 # print(allsimilarity) # print(labels) return allsimilarity def compute_group_accuracy(content_list_read): allSimilarity, allLabel = [], [] Same, Cross = [], [] for data_loaded in content_list_read: # pdb.set_trace() print(data_loaded) one_group_list = [] try: for i in range(2): images = [osp.join(conf.test_val, img) for img in data_loaded[i]] # pdb.set_trace() group = group_image(images, conf.test_batch_size) d = featurize(group[0], conf.test_transform, model, conf.device) one_group_list.append(d.values()) if data_loaded[-1] == '1': similarity = deal_group_pair(one_group_list[0], one_group_list[1]) Same.append(similarity) else: similarity = deal_group_pair(one_group_list[0], one_group_list[1]) Cross.append(similarity) allLabel.append(data_loaded[-1]) allSimilarity.extend(similarity) except Exception as e: continue # print(allSimilarity) # print(allLabel) return allSimilarity, allLabel def init_model(): if not conf.quantization_test: # Network Setup if conf.testbackbone == 'resnet18' and (not conf.compare): model = resnet18().to(device) elif conf.testbackbone == 'resnet18' and conf.compare: model = ContrastiveModel(projection_dim=conf.embedding_size, model_name=conf.testbackbone) elif conf.testbackbone == 'vit_base': model = vit_base().to(device) else: raise ValueError('Have not model {}'.format(conf.backbone)) print('load model {} '.format(conf.testbackbone)) if torch.cuda.device_count() > 1 and conf.multiple_cards: if not conf.custom_backbone: model = nn.DataParallel(model).to(conf.device) model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) else: model = CustomResNet18(model) model = nn.DataParallel(model).to(conf.device) model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) if conf.model_half: model.half() first_param_dtype = next(model.parameters()).dtype print(f"模型的第一个参数的数据类型: {first_param_dtype}") else: if not conf.custom_backbone: model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) else: model = CustomResNet18(model) model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) if conf.model_half: model.half() first_param_dtype = next(model.parameters()).dtype print(f"模型的第一个参数的数据类型: {first_param_dtype}") else: model = load_quantized_model() return model if __name__ == '__main__': model = init_model() model.eval() if not conf.group_test: images = unique_image(conf.test_list) images = [osp.join(conf.test_val, img) for img in images] groups = group_image(images, conf.test_batch_size) # 根据batch_size取图片 feature_dict = dict() for group in groups: d = featurize(group, conf.test_transform, model, conf.device) feature_dict.update(d) # print('feature_dict', feature_dict) accuracy, threshold = compute_accuracy(feature_dict, conf.test_list, conf.test_val) print( f"Test Model: {conf.test_model}\n" f"Accuracy: {accuracy:.3f}\n" f"Threshold: {threshold:.3f}\n" ) elif conf.group_test: filename = conf.test_group_json with open(filename, 'r', encoding='utf-8') as file: content_list_read = json.load(file) Similarity, Label = compute_group_accuracy(content_list_read) # print('allSimilarity >> {}'.format(Similarity)) # print('allLabel >> {}'.format(Label)) compute_accuracy_recall(np.array(Similarity), np.array(Label)) # compute_group_accuracy(data_loaded) " />
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