anacondaPython3中canmot 1import name DeferredErroI from PIL. _util

时间: 2025-08-03 16:16:33 浏览: 8
<think>我们正在处理的问题:在AnacondaPython3环境中,尝试从`PIL._util`导入`DeferredError`时出现`ImportError`。根据引用[3]和引用[5]的信息,这类问题通常与Pillow库的安装或环境配置有关。特别是引用[5]提到,类似的导入错误可能是由于环境不匹配(如cuda版本与torch不匹配)或Pillow自身的问题。针对用户的问题,我们进行以下分析:1.用户环境:AnacondaPython32.问题:无法从`PIL._util`导入`DeferredError`可能的原因:-Pillow库安装不完整或损坏。-环境中存在多个版本的Pillow,导致冲突。-环境中的其他库(如torchvision)与Pillow版本不兼容。解决方案:步骤1:检查当前安装的Pillow版本步骤2:尝试重新安装Pillow步骤3:检查环境是否存在冲突详细步骤:步骤1:检查Pillow版本在Anaconda环境中运行以下命令:```bashcondalist|grepPillow```或者```bashpipshowPillow```步骤2:重新安装Pillow如果版本较旧或安装可能损坏,可以尝试重新安装:```bashpipuninstallPillowpipinstall--no-cache-dirPillow```或者通过conda:```bashcondainstall-cconda-forgepillow```步骤3:检查环境冲突如果重新安装后问题仍然存在,检查是否有其他库依赖于特定版本的Pillow,尤其是torchvision(如引用[5]中提到的)。可以尝试创建一个新的conda环境来隔离问题:```bashcondacreate-ntest_envpython=3.8condaactivatetest_envpipinstallpillow```然后在新环境中测试导入。另外,注意引用[3]中提到的一个类似错误(ImportError:cannotimportname'_imaging'),这通常表明Pillow的C扩展编译失败或未正确安装。在这种情况下,可能需要确保系统依赖已安装(如libjpeg,zlib等)。步骤4:系统依赖(对于Linux)如果是Linux环境,安装以下依赖:```bashsudoapt-getinstalllibjpeg-devlibpng-devlibtiff-dev```然后重新安装Pillow。步骤5:对于Windows可以尝试安装预编译的二进制包,或者使用conda-forge的版本。步骤6:检查代码确保导入语句正确。`DeferredError`位于`PIL._util`模块中,因此导入应为:```pythonfromPIL._utilimportDeferredError```但注意,根据Pillow的版本,这个内部模块可能会有变动。考虑是否必须使用这个内部模块?因为内部模块通常不建议直接导入,可能会有变动。如果我们查看Pillow源码(以8.4.0版本为例):在`PIL/_util.py`中,确实有`DeferredError`类。但是,在9.0.0版本之后,这个模块可能有所变化。因此,另一个可能性是版本变化导致该内部模块不再包含`DeferredError`。在这种情况下,建议检查当前Pillow版本的源码或文档。如果以上步骤仍无法解决问题,请提供更详细的错误信息。下面给出一个详细的步骤流程:方案总结:1.检查Pillow版本,尝试重新安装。2.创建新环境测试。3.确保系统依赖(对于Linux)。4.检查代码中导入的模块是否在当前Pillow版本中存在。相关问题:1.如何验证Pillow是否正确安装?2.Anaconda环境中如何解决库冲突?3.如何查看Pillow库的内部模块结构?</think> 以下是针对 Anaconda Python 3 环境中 `DeferredError` 导入问题的完整解决方案: ### 问题分析 在 Anaconda 中遇到 `ImportError: cannot import name 'DeferredError' from 'PIL._util'` 通常由以下原因导致: 1. **Pillow安装损坏**:二进制扩展未正确编译(常见于依赖缺失)[^3] 2. **环境冲突**:多个 Python 环境交叉污染(如 base 环境与虚拟环境混合)[^2] 3. **版本不兼容**:Anaconda 内置 Pillow 版本与其它库(如 torchvision)冲突[^5] ### 分步解决方案 #### 步骤 1:重建干净环境(推荐首选) ```bash # 创建新环境 conda create -n pillow_fix python=3.9 -y # 激活环境 conda activate pillow_fix # 安装最新 Pillow(绕过 Anaconda 默认版本) pip install --no-cache-dir --force-reinstall Pillow # 验证安装 python -c "from PIL._util import DeferredError; print('Success!')" ``` #### 步骤 2:修复现有环境(如不能新建环境) ```bash # 完全卸载 Pillow 及其残留 pip uninstall -y pillow conda uninstall -y pillow # 清理缓存 conda clean -a -y rm -rf ~/.cache/pip # 安装编译依赖(Linux) sudo apt-get install libjpeg-dev zlib1g-dev libopenjp2-7-dev libtiff5 # 重装 Pillow pip install --no-binary :all: Pillow ``` #### 步骤 3:处理特定场景问题 ##### 当与 torchvision 冲突时[^5] ```bash # 固定兼容版本组合 conda install pytorch torchvision torchaudio cudatoolkit=11.3 pillow=9.0.1 -c pytorch ``` ##### 当出现 numpy 依赖问题时[^4] ```python # 在代码开头添加兼容层 import sys if sys.version_info < (3, 7): import numpy as np if np.__version__ < '1.16': from numpy.lib.arraypad import _validate_lengths as validate else: from numpy.lib.arraypad import _as_pairs as validate ``` ### 验证方法 ```python # 验证脚本 verify_pillow.py from PIL import Image from PIL._util import DeferredError # 关键检查点 try: img = Image.new('RGB', (100, 100)) print("✅ Pillow 功能正常") except DeferredError as e: print(f"❌ 驱动加载失败: {e}") except ImportError: print("❌ _util 导入错误") ``` ### 预防措施 1. **环境隔离**:始终使用独立 conda 环境 2. **版本锁定**:创建 `environment.yml` 文件: ```yaml name: stable_env channels: - conda-forge dependencies: - python=3.9 - pillow=9.5.0 - numpy>=1.21 - pip ``` ### 常见错误处理表 | 错误现象 | 解决方案 | |---------|---------| | `ImportError: _imaging` | 执行 `pip uninstall pillow && pip install --no-cache-dir pillow`[^3] | | `PILLOW_VERSION缺失` | 安装 `torchvision>0.13` + `pillow<9.2`[^5] | | `numpy.validate缺失` | 更新 numpy 至 `1.21+` 或添加兼容代码[^4] |
阅读全文

相关推荐

ImportError Traceback (most recent call last) Cell In[1], line 6 3 from PIL import Image 4 from modelscope import AutoModel, AutoTokenizer ----> 6 model = AutoModel.from_pretrained('OpenBMB/MiniCPM-V-2', trust_remote_code=True, torch_dtype=torch.float16) 7 # For Nvidia GPUs support BF16 (like A100, H100, RTX3090) 8 model = model.to(device='cuda', dtype=torch.bfloat16) File C:\Program Files\jqxxrj\Lib\site-packages\modelscope\utils\hf_util\patcher.py:225, in _patch_pretrained_class.<locals>.get_wrapped_class.<locals>.ClassWrapper.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) 216 @classmethod 217 def from_pretrained(cls, pretrained_model_name_or_path, 218 *model_args, **kwargs): 219 model_dir = get_model_dir( 220 pretrained_model_name_or_path, 221 ignore_file_pattern=ignore_file_pattern, 222 allow_file_pattern=allow_file_pattern, 223 **kwargs) --> 225 module_obj = module_class.from_pretrained( 226 model_dir, *model_args, **kwargs) 228 if module_class.__name__.startswith('AutoModel'): 229 module_obj.model_dir = model_dir File ~\AppData\Roaming\Python\Python311\site-packages\transformers\models\auto\auto_factory.py:553, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) 551 if has_remote_code and trust_remote_code: 552 class_ref = config.auto_map[cls.__name__] --> 553 model_class = get_class_from_dynamic_module( 554 class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs 555 ) 556 _ = hub_kwargs.pop("code_revision", None) 557 if os.path.isdir(pretrained_model_name_or_path): File ~\AppData\Roaming\Python\Python311\site-packages\transformers\dynamic_module_utils.py:488, in get_class_from_dynamic_module(class_reference, pretrained_m

import argparse import os import sys import numpy as np import json import torch from PIL import Image sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) sys.path.append(os.path.join(os.getcwd(), "segment_anything")) # Grounding DINO import GroundingDINO.groundingdino.datasets.transforms as T from GroundingDINO.groundingdino.models import build_model from GroundingDINO.groundingdino.util.slconfig import SLConfig from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # segment anything from segment_anything import ( sam_model_registry, sam_hq_model_registry, SamPredictor ) import cv2 import numpy as np import matplotlib.pyplot as plt def load_image(image_path): # load image image_pil = Image.open(image_path).convert("RGB") # load image transform = T.Compose( [ T.RandomResize([800], max_size=1333), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ] ) image, _ = transform(image_pil, None) # 3, h, w return image_pil, image def load_model(model_config_path, model_checkpoint_path, bert_base_uncased_path, device): args = SLConfig.fromfile(model_config_path) args.device = device args.bert_base_uncased_path = bert_base_uncased_path model = build_model(args) checkpoint = torch.load(model_checkpoint_path, map_location="cpu") load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) print(load_res) _ = model.eval() return model def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): caption = caption.lower() caption = caption.strip() if not caption.endswith("."): caption = caption + "." model = model.to(device) image = image.to(device) with torch.no_grad(): outputs = model(image[None], captions=[caption]) logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) logits.shape[0] # filter output logits_filt = logits.clone() boxes_filt = boxes.clone() filt_mask = logits_filt.max(dim=1)[0] > box_threshold logits_filt = logits_filt[filt_mask] # num_filt, 256 boxes_filt = boxes_filt[filt_mask] # num_filt, 4 logits_filt.shape[0] # get phrase tokenlizer = model.tokenizer tokenized = tokenlizer(caption) # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) if with_logits: pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) return boxes_filt, pred_phrases def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30/255, 144/255, 255/255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_box(box, ax, label): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) ax.text(x0, y0, label) def save_mask_data(output_dir, mask_list, box_list, label_list): value = 0 # 0 for background mask_img = torch.zeros(mask_list.shape[-2:]) for idx, mask in enumerate(mask_list): mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 plt.figure(figsize=(10, 10)) plt.imshow(mask_img.numpy()) plt.axis('off') plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0) json_data = [{ 'value': value, 'label': 'background' }] for label, box in zip(label_list, box_list): value += 1 name, logit = label.split('(') logit = logit[:-1] # the last is ')' json_data.append({ 'value': value, 'label': name, 'logit': float(logit), 'box': box.numpy().tolist(), }) with open(os.path.join(output_dir, 'mask.json'), 'w') as f: json.dump(json_data, f) if __name__ == "__main__": parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True) parser.add_argument("--config", type=str, default="./GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py", help="path to config file") parser.add_argument( "--grounded_checkpoint", type=str, default="./groundingdino_swint_ogc.pth", help="path to checkpoint file" ) parser.add_argument( "--sam_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h" ) parser.add_argument( "--sam_checkpoint", type=str, default="./sam_vit_h_4b8939.pth", help="path to sam checkpoint file" ) parser.add_argument( "--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file" ) parser.add_argument( "--use_sam_hq", action="store_true", help="using sam-hq for prediction" ) parser.add_argument("--input_image", type=str, required=True, help="path to image file") parser.add_argument("--text_prompt", type=str, required=True, help="text prompt") parser.add_argument( "--output_dir", "-o", type=str, default="./outputs", help="output directory" ) parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False") parser.add_argument("--bert_base_uncased_path", type=str, required=False, help="bert_base_uncased model path, default=False") args = parser.parse_args() # cfg config_file = args.config # change the path of the model config file grounded_checkpoint = args.grounded_checkpoint # change the path of the model sam_version = args.sam_version sam_checkpoint = args.sam_checkpoint sam_hq_checkpoint = args.sam_hq_checkpoint use_sam_hq = args.use_sam_hq image_path = args.input_image text_prompt = args.text_prompt output_dir = args.output_dir box_threshold = args.box_threshold text_threshold = args.text_threshold device = args.device bert_base_uncased_path = args.bert_base_uncased_path # make dir os.makedirs(output_dir, exist_ok=True) # load image image_pil, image = load_image(image_path) # load model model = load_model(config_file, grounded_checkpoint, bert_base_uncased_path, device=device) # visualize raw image image_pil.save(os.path.join(output_dir, "raw_image.jpg")) # run grounding dino model boxes_filt, pred_phrases = get_grounding_output( model, image, text_prompt, box_threshold, text_threshold, device=device ) # initialize SAM if use_sam_hq: predictor = SamPredictor(sam_hq_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device)) else: predictor = SamPredictor(sam_model_registry[sam_version](checkpoint=sam_checkpoint).to(device)) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) predictor.set_image(image) size = image_pil.size H, W = size[1], size[0] for i in range(boxes_filt.size(0)): boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 boxes_filt[i][2:] += boxes_filt[i][:2] boxes_filt = boxes_filt.cpu() transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device) masks, _, _ = predictor.predict_torch( point_coords = None, point_labels = None, boxes = transformed_boxes.to(device), multimask_output = False, ) # draw output image plt.figure(figsize=(10, 10)) plt.imshow(image) for mask in masks: show_mask(mask.cpu().numpy(), plt.gca(), random_color=True) for box, label in zip(boxes_filt, pred_phrases): show_box(box.numpy(), plt.gca(), label) plt.axis('off') plt.savefig( os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight", dpi=300, pad_inches=0.0 ) save_mask_data(output_dir, masks, boxes_filt, pred_phrases) 报错C:\Users\29386\.conda\envs\grounded_sam\python.exe C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py --grounded_checkpoint weights/groundingdino_swint_ogc.pth --sam_checkpoint weights/sam_vit_h_4b8939.pth --input_image assets/demo1.jpg --output_dir outputs C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\timm\models\layers\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) Traceback (most recent call last): File "C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py", line 22, in <module> from segment_anything import ( ImportError: cannot import name 'sam_hq_model_registry' from 'segment_anything' (C:\Users\29386\segment-anything\segment_anything\__init__.py) 进程已结束,退出代码为 1 如何解决

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 0=INFO, 1=WARNING, 2=ERROR, 3=FATAL os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' # 禁用oneDNN日志 import sys import glob import time import json import torch import pickle import shutil import argparse import datetime import torchvision import numpy as np from tqdm import tqdm from PIL import Image import torch.nn as nn from packaging import version from functools import partial import pytorch_lightning as pl from omegaconf import OmegaConf, DictConfig import torch.distributed as dist from typing import List, Dict, Any, Optional, Union, Tuple from ldm.util import instantiate_from_config from pytorch_lightning import seed_everything from pytorch_lightning.trainer import Trainer from torch.utils.data import DataLoader, Dataset from ldm.data.base import Txt2ImgIterableBaseDataset from pytorch_lightning.plugins import DDPPlugin from pytorch_lightning.utilities import rank_zero_info from pytorch_lightning.utilities.distributed import rank_zero_only from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor from torch.cuda.amp import autocast, GradScaler # 模型路径 current_dir = os.path.dirname(os.path.abspath(__file__)) for path in ["download", "download/CLIP", "download/k-diffusion", "download/stable_diffusion", "download/taming-transformers"]: sys.path.append(os.path.join(current_dir, path)) class ConfigManager: """配置管理类,统一处理配置加载和解析""" def __init__(self, config_files: Union[str, List[str]], cli_args: Optional[List[str]] = None): # 将单个字符串路径转换为列表 if isinstance(config_files, str): config_files = [config_files] # 验证配置文件存在 self.configs = [] for cfg in config_files: if not os.path.exists(cfg): raise FileNotFoundError(f"配置文件不存在: {cfg}") self.configs.append(OmegaConf.load(cfg)) # 解析命令行参数 self.cli = OmegaConf.from_dotlist(cli_args) if cli_args else OmegaConf.create() # 合并所有配置 self.config = OmegaConf.merge(*self.configs, self.cli) def get_model_config(self) -> DictConfig: """获取模型配置""" if "model" not in self.config: raise KeyError("配置文件中缺少'model'部分") return self.config.model def get_data_config(self) -> DictConfig: """获取数据配置""" if "data" not in self.config: raise KeyError("配置文件中缺少'data'部分") return self.config.data def get_training_config(self) -> DictConfig: """获取训练配置,提供默认值""" training_config = self.config.get("training", OmegaConf.create()) # 设置默认值 defaults = { "max_epochs": 200, "gpus": torch.cuda.device_count(), "accumulate_grad_batches": 1, "learning_rate": 1e-4, "precision": 32 } for key, value in defaults.items(): if key not in training_config: training_config[key] = value return training_config def get_logging_config(self) -> DictConfig: """获取日志配置""" return self.config.get("logging", OmegaConf.create({"logdir": "logs"})) def get_callbacks_config(self) -> DictConfig: """获取回调函数配置""" return self.config.get("callbacks", OmegaConf.create()) def save_config(self, save_path: str) -> None: """保存配置到文件""" os.makedirs(os.path.dirname(save_path), exist_ok=True) OmegaConf.save(self.config, save_path) print(f"配置已保存到: {save_path}") class DataModuleFromConfig(pl.LightningDataModule): def __init__(self, batch_size, num_workers, train=None, validation=None, test=None): super().__init__() self.batch_size = batch_size self.num_workers = num_workers self.dataset_configs = dict() if train is not None: self.dataset_configs["train"] = train if validation is not None: self.dataset_configs["validation"] = validation if test is not None: self.dataset_configs["test"] = test def setup(self, stage=None): self.datasets = { k: instantiate_from_config(cfg) for k, cfg in self.dataset_configs.items() } def _get_dataloader(self, dataset_name, shuffle=False): dataset = self.datasets.get(dataset_name) if dataset is None: raise ValueError(f"数据集 {dataset_name} 未配置") return DataLoader( dataset, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=shuffle, pin_memory=True ) def train_dataloader(self): return self._get_dataloader("train", shuffle=True) def val_dataloader(self): return self._get_dataloader("validation") def test_dataloader(self): return self._get_dataloader("test") def worker_init_fn(worker_id: int) -> None: """数据加载器工作进程初始化函数""" worker_info = torch.utils.data.get_worker_info() if worker_info is None: return dataset = worker_info.dataset worker_id = worker_info.id if isinstance(dataset, Txt2ImgIterableBaseDataset): # 对可迭代数据集进行分片 split_size = dataset.num_records // worker_info.num_workers dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] # 设置随机种子 seed = torch.initial_seed() % 2**32 + worker_id np.random.seed(seed) torch.manual_seed(seed) class EnhancedImageLogger(Callback): """增强的图像日志记录器,支持多平台日志输出""" def __init__(self, batch_frequency: int, max_images: int, clamp: bool = True, rescale: bool = True, loggers: Optional[List] = None, log_first_step: bool = False, log_images_kwargs: Optional[Dict] = None): super().__init__() self.batch_frequency = max(1, batch_frequency) self.max_images = max_images self.clamp = clamp self.rescale = rescale self.loggers = loggers or [] self.log_first_step = log_first_step self.log_images_kwargs = log_images_kwargs or {} self.log_steps = [2 ** n for n in range(6, int(np.log2(self.batch_frequency)) + 1)] if self.batch_frequency > 1 else [] def check_frequency(self, step: int) -> bool: """检查是否达到记录频率""" if step == 0 and self.log_first_step: return True if step % self.batch_frequency == 0: return True if step in self.log_steps: if len(self.log_steps) > 0: self.log_steps.pop(0) return True return False def log_images(self, pl_module: pl.LightningModule, batch: Any, step: int, split: str = "train") -> None: """记录图像并发送到所有日志记录器""" if not self.check_frequency(step) or not hasattr(pl_module, "log_images"): return is_train = pl_module.training if is_train: pl_module.eval() # 切换到评估模式 with torch.no_grad(): try: images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) except Exception as e: print(f"记录图像时出错: {e}") images = {} # 处理图像数据 for k in list(images.keys()): if not isinstance(images[k], torch.Tensor): continue N = min(images[k].shape[0], self.max_images) images[k] = images[k][:N] # 分布式环境下收集所有图像 if torch.distributed.is_initialized() and torch.distributed.get_world_size() > 1: images[k] = torch.cat(all_gather(images[k])) images[k] = images[k].detach().cpu() if self.clamp: images[k] = torch.clamp(images[k], -1., 1.) if self.rescale: images[k] = (images[k] + 1.0) / 2.0 # 缩放到[0,1] # 发送到所有日志记录器 for logger in self.loggers: if hasattr(logger, 'log_images'): try: logger.log_images(images, step, split) except Exception as e: print(f"日志记录器 {type(logger).__name__} 记录图像失败: {e}") if is_train: pl_module.train() # 恢复训练模式 def on_train_batch_end(self, trainer: Trainer, pl_module: pl.LightningModule, outputs: Any, batch: Any, batch_idx: int) -> None: """训练批次结束时记录图像""" if trainer.global_step % trainer.log_every_n_steps == 0: self.log_images(pl_module, batch, pl_module.global_step, "train") def on_validation_batch_end(self, trainer: Trainer, pl_module: pl.LightningModule, outputs: Any, batch: Any, batch_idx: int) -> None: """验证批次结束时记录图像""" if batch_idx == 0: # 只记录第一个验证批次 self.log_images(pl_module, batch, pl_module.global_step, "val") class TensorBoardLogger: """TensorBoard日志记录器,完整实现PyTorch Lightning日志记录器接口""" def __init__(self, save_dir: str): from torch.utils.tensorboard import SummaryWriter os.makedirs(save_dir, exist_ok=True) self.save_dir = save_dir self.writer = SummaryWriter(save_dir) self._name = "TensorBoard" # 日志记录器名称 self._version = "1.0" # 版本信息 self._experiment = self.writer # 实验对象 print(f"TensorBoard日志保存在: {save_dir}") @property def name(self) -> str: return self._name @property def version(self) -> str: return self._version @property def experiment(self) -> Any: return self._experiment def log_hyperparams(self, params: Dict) -> None: """记录超参数到TensorBoard""" try: # 将嵌套字典展平 flat_params = {} for key, value in params.items(): if isinstance(value, dict): for sub_key, sub_value in value.items(): flat_params[f"{key}/{sub_key}"] = sub_value else: flat_params[key] = value # 记录超参数 self.writer.add_hparams( {k: v for k, v in flat_params.items() if isinstance(v, (int, float, str))}, {}, run_name="." ) print("已记录超参数到TensorBoard") except Exception as e: print(f"记录超参数失败: {e}") def log_graph(self, model: torch.nn.Module, input_array: Optional[torch.Tensor] = None) -> None: """记录模型计算图到TensorBoard""" try: # 扩散模型通常有复杂的前向传播,跳过图记录 print("跳过扩散模型的计算图记录") return except Exception as e: print(f"记录模型计算图失败: {e}") def log_metrics(self, metrics: Dict[str, float], step: int) -> None: """记录指标到TensorBoard""" for name, value in metrics.items(): try: self.writer.add_scalar(name, value, global_step=step) except Exception as e: print(f"添加标量失败: {name}, 错误: {e}") def log_images(self, images: Dict[str, torch.Tensor], step: int, split: str) -> None: """记录图像到TensorBoard""" for k, img in images.items(): if img.numel() == 0: continue try: grid = torchvision.utils.make_grid(img, nrow=min(8, img.shape[0])) self.writer.add_image(f"{split}/{k}", grid, global_step=step) except Exception as e: print(f"添加图像失败: {k}, 错误: {e}") def save(self) -> None: """保存日志(TensorBoard自动保存,这里无需额外操作)""" pass def finalize(self, status: str) -> None: """完成日志记录并关闭写入器""" self.close() def close(self) -> None: """关闭日志写入器""" if hasattr(self, 'writer') and self.writer is not None: self.writer.flush() self.writer.close() self.writer = None print(f"TensorBoard日志已关闭") class TQDMProgressBar(Callback): """使用tqdm显示训练进度,兼容不同版本的PyTorch Lightning""" def __init__(self): self.progress_bar = None self.epoch_bar = None def on_train_start(self, trainer: Trainer, pl_module: pl.LightningModule) -> None: """训练开始时初始化进度条""" # 兼容不同版本的步数估计 total_steps = self._get_total_steps(trainer) self.progress_bar = tqdm( total=total_steps, desc="Training Steps", position=0, leave=True, dynamic_ncols=True ) self.epoch_bar = tqdm( total=trainer.max_epochs, desc="Epochs", position=1, leave=True, dynamic_ncols=True ) def _get_total_steps(self, trainer: Trainer) -> int: """获取训练总步数,兼容不同版本的PyTorch Lightning""" # 尝试使用新版本属性 if hasattr(trainer, 'estimated_stepping_batches'): return trainer.estimated_stepping_batches # 尝试使用旧版本属性 if hasattr(trainer, 'estimated_steps'): return trainer.estimated_steps # 回退到手动计算 try: if hasattr(trainer, 'num_training_batches'): num_batches = trainer.num_training_batches else: num_batches = len(trainer.train_dataloader) if hasattr(trainer, 'accumulate_grad_batches'): accumulate = trainer.accumulate_grad_batches else: accumulate = 1 steps_per_epoch = num_batches // accumulate total_steps = trainer.max_epochs * steps_per_epoch print(f"回退计算训练总步数: {total_steps} = {trainer.max_epochs} epochs × {steps_per_epoch} steps/epoch") return total_steps except Exception as e: print(f"无法确定训练总步数: {e}, 使用默认值10000") return 10000 def on_train_batch_end(self, trainer: Trainer, pl_module: pl.LightningModule, outputs: Any, batch: Any, batch_idx: int) -> None: """每个训练批次结束时更新进度条""" if self.progress_bar: # 防止进度条超过总步数 if self.progress_bar.n < self.progress_bar.total: self.progress_bar.update(1) try: # 尝试从输出中获取损失 loss = outputs.get('loss') if loss is not None: if isinstance(loss, torch.Tensor): loss = loss.item() self.progress_bar.set_postfix({"loss": loss}) except Exception: pass def on_train_epoch_end(self, trainer: Trainer, pl_module: pl.LightningModule) -> None: """每个训练轮次结束时更新轮次进度条""" if self.epoch_bar: self.epoch_bar.update(1) self.epoch_bar.set_postfix({"epoch": trainer.current_epoch}) def on_train_end(self, trainer: Trainer, pl_module: pl.LightningModule) -> None: """训练结束时关闭进度条""" if self.progress_bar: self.progress_bar.close() if self.epoch_bar: self.epoch_bar.close() class PerformanceMonitor(Callback): """性能监控回调,记录内存使用和训练速度""" def __init__(self): self.epoch_start_time = 0 self.batch_times = [] def on_train_epoch_start(self, trainer: Trainer, pl_module: pl.LightningModule) -> None: """每个训练轮次开始时记录时间和重置内存统计""" self.epoch_start_time = time.time() self.batch_times = [] if torch.cuda.is_available(): torch.cuda.reset_peak_memory_stats() torch.cuda.synchronize() # 修改1:添加dataloader_idx参数 def on_train_batch_start(self, trainer: Trainer, pl_module: pl.LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None: """每个训练批次开始时记录时间""" self.batch_start_time = time.time() # 修改2:添加dataloader_idx参数 def on_train_batch_end(self, trainer: Trainer, pl_module: pl.LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None: """每个训练批次结束时记录时间""" self.batch_times.append(time.time() - self.batch_start_time) def on_train_epoch_end(self, trainer: Trainer, pl_module: pl.LightningModule) -> None: """每个训练轮次结束时计算并记录性能指标""" epoch_time = time.time() - self.epoch_start_time if self.batch_times: avg_batch_time = sum(self.batch_times) / len(self.batch_times) batches_per_second = 1.0 / avg_batch_time else: avg_batch_time = 0 batches_per_second = 0 memory_info = "" if torch.cuda.is_available(): max_memory = torch.cuda.max_memory_allocated() / 2 ** 20 # MiB memory_info = f", 峰值显存: {max_memory:.2f} MiB" rank_zero_info( f"Epoch {trainer.current_epoch} | " f"耗时: {epoch_time:.2f}s | " f"Batch耗时: {avg_batch_time:.4f}s ({batches_per_second:.2f} batches/s)" f"{memory_info}" ) def get_world_size() -> int: """获取分布式训练中的总进程数""" if dist.is_initialized(): return dist.get_world_size() return 1 def all_gather(data: torch.Tensor) -> List[torch.Tensor]: """在分布式环境中收集所有进程的数据""" world_size = get_world_size() if world_size == 1: return [data] # 获取各进程的Tensor大小 local_size = torch.tensor([data.numel()], device=data.device) size_list = [torch.zeros_like(local_size) for _ in range(world_size)] dist.all_gather(size_list, local_size) size_list = [int(size.item()) for size in size_list] max_size = max(size_list) # 收集数据 tensor_list = [] for size in size_list: tensor_list.append(torch.empty((max_size,), dtype=data.dtype, device=data.device)) if local_size < max_size: padding = torch.zeros(max_size - local_size, dtype=data.dtype, device=data.device) data = torch.cat((data.view(-1), padding)) dist.all_gather(tensor_list, data.view(-1)) # 截断到实际大小 results = [] for tensor, size in zip(tensor_list, size_list): results.append(tensor[:size].reshape(data.shape)) return results def create_experiment_directories(logging_config: DictConfig, experiment_name: str) -> Tuple[str, str, str]: """创建实验目录结构""" now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") logdir = os.path.join(logging_config.logdir, f"{experiment_name}_{now}") ckptdir = os.path.join(logdir, "checkpoints") cfgdir = os.path.join(logdir, "configs") os.makedirs(ckptdir, exist_ok=True) os.makedirs(cfgdir, exist_ok=True) print(f"实验目录: {logdir}") print(f"检查点目录: {ckptdir}") print(f"配置目录: {cfgdir}") return logdir, ckptdir, cfgdir def setup_callbacks(config_manager: ConfigManager, ckptdir: str, tb_logger: TensorBoardLogger) -> List[Callback]: """设置训练回调函数""" callbacks = [] # 模型检查点 checkpoint_callback = ModelCheckpoint( dirpath=ckptdir, filename='{epoch}-{step}-{val_loss:.2f}', monitor='val_loss', save_top_k=3, mode='min', save_last=True, save_on_train_epoch_end=True, # 确保在epoch结束时保存完整状态 save_weights_only=False, # 明确设置为False,保存完整检查点 every_n_train_steps=1000 # 每1000步保存一次 ) callbacks.append(checkpoint_callback) # 学习率监控 lr_monitor = LearningRateMonitor(logging_interval="step") callbacks.append(lr_monitor) # 图像日志记录 image_logger_cfg = config_manager.get_callbacks_config().get("image_logger", {}) image_logger = EnhancedImageLogger( batch_frequency=image_logger_cfg.get("batch_frequency", 500), max_images=image_logger_cfg.get("max_images", 4), loggers=[tb_logger] ) callbacks.append(image_logger) # 进度条 progress_bar = TQDMProgressBar() callbacks.append(progress_bar) # 性能监控 perf_monitor = PerformanceMonitor() callbacks.append(perf_monitor) return callbacks def preprocess_checkpoint(checkpoint_path: str, model: pl.LightningModule) -> Dict[str, Any]: """预处理检查点文件,确保包含所有必要的键,并添加缺失的训练状态""" print(f"预处理检查点文件: {checkpoint_path}") # 加载检查点 try: checkpoint = torch.load(checkpoint_path, map_location="cpu") except Exception as e: print(f"加载检查点失败: {e}") raise # 强制重置训练状态 checkpoint['epoch'] = 0 checkpoint['global_step'] = 0 checkpoint['lr_schedulers'] = [] checkpoint['optimizer_states'] = [] print("已重置训练状态: epoch=0, global_step=0") # 检查是否缺少关键训练状态 required_keys = ['optimizer_states', 'lr_schedulers', 'epoch', 'global_step'] missing_keys = [k for k in required_keys if k not in checkpoint] if missing_keys: print(f"警告: 检查点缺少训练状态字段 {missing_keys},将创建伪训练状态") # 创建伪训练状态 checkpoint.setdefault('optimizer_states', []) checkpoint.setdefault('lr_schedulers', []) checkpoint.setdefault('epoch', 0) checkpoint.setdefault('global_step', 0) # 检查是否缺少 position_ids state_dict = checkpoint.get("state_dict", {}) if "cond_stage_model.transformer.text_model.embeddings.position_ids" not in state_dict: print("警告: 检查点缺少 'cond_stage_model.transformer.text_model.embeddings.position_ids' 键") # 获取模型中的 position_ids 形状 if hasattr(model, "cond_stage_model") and hasattr(model.cond_stage_model, "transformer"): try: max_position_embeddings = model.cond_stage_model.transformer.text_model.config.max_position_embeddings position_ids = torch.arange(max_position_embeddings).expand((1, -1)) state_dict["cond_stage_model.transformer.text_model.embeddings.position_ids"] = position_ids print("已添加 position_ids 到检查点") except Exception as e: print(f"无法添加 position_ids: {e}") # 确保有 state_dict if "state_dict" not in checkpoint: checkpoint["state_dict"] = state_dict return checkpoint # 正确继承原始模型类 from ldm.models.diffusion.ddpm import LatentDiffusion class CustomLatentDiffusion(LatentDiffusion): """自定义 LatentDiffusion 类,处理检查点加载问题""" def on_load_checkpoint(self, checkpoint): """在加载检查点时自动处理缺失的键""" state_dict = checkpoint["state_dict"] # 检查是否缺少 position_ids if "cond_stage_model.transformer.text_model.embeddings.position_ids" not in state_dict: print("警告: 检查点缺少 'cond_stage_model.transformer.text_model.embeddings.position_ids' 键") # 获取模型中的 position_ids 形状 max_position_embeddings = self.cond_stage_model.transformer.text_model.config.max_position_embeddings position_ids = torch.arange(max_position_embeddings).expand((1, -1)) state_dict["cond_stage_model.transformer.text_model.embeddings.position_ids"] = position_ids print("已添加 position_ids 到 state_dict") # 使用非严格模式加载 self.load_state_dict(state_dict, strict=False) print("模型权重加载完成") def filter_kwargs(cls, kwargs, log_prefix=""): # 关键参数白名单 - 这些参数必须保留 ESSENTIAL_PARAMS = { 'unet_config', 'first_stage_config', 'cond_stage_config', 'scheduler_config', 'ckpt_path', 'linear_start', 'linear_end' } # 特殊处理:允许所有包含"config"的参数 filtered_kwargs = {} for k, v in kwargs.items(): if k in ESSENTIAL_PARAMS or 'config' in k: filtered_kwargs[k] = v else: print(f"{log_prefix}过滤参数: {k}") print(f"{log_prefix}保留参数: {list(filtered_kwargs.keys())}") return filtered_kwargs def check_checkpoint_content(checkpoint_path): """打印检查点包含的键,确认是否有训练状态""" checkpoint = torch.load(checkpoint_path, map_location="cpu") print("检查点包含的键:", list(checkpoint.keys())) if "state_dict" in checkpoint: print("模型权重存在") if "optimizer_states" in checkpoint: print("优化器状态存在") if "epoch" in checkpoint: print(f"保存的epoch: {checkpoint['epoch']}") if "global_step" in checkpoint: print(f"保存的global_step: {checkpoint['global_step']}") def main() -> None: """主函数,训练和推理流程的入口点""" # 启用Tensor Core加速 torch.set_float32_matmul_precision('high') # 解析命令行参数 parser = argparse.ArgumentParser(description="扩散模型训练框架") parser.add_argument("--config", type=str, default="configs/train.yaml", help="配置文件路径") parser.add_argument("--name", type=str, default="experiment", help="实验名称") parser.add_argument("--resume", action="store_true", default=True, help="恢复训练") parser.add_argument("--debug", action="store_true", help="调试模式") parser.add_argument("--seed", type=int, default=42, help="随机种子") parser.add_argument("--scale_lr", action="store_true", help="根据GPU数量缩放学习率") parser.add_argument("--precision", type=str, default="32", choices=["16", "32", "bf16"], help="训练精度") args, unknown = parser.parse_known_args() # 设置随机种子 seed_everything(args.seed, workers=True) print(f"设置随机种子: {args.seed}") # 初始化配置管理器 try: config_manager = ConfigManager(args.config, unknown) config = config_manager.config except Exception as e: print(f"加载配置失败: {e}") sys.exit(1) # 创建日志目录 logging_config = config_manager.get_logging_config() logdir, ckptdir, cfgdir = create_experiment_directories(logging_config, args.name) # 保存配置 config_manager.save_config(os.path.join(cfgdir, "config.yaml")) # 配置日志记录器 tb_logger = TensorBoardLogger(os.path.join(logdir, "tensorboard")) # 配置回调函数 callbacks = setup_callbacks(config_manager, ckptdir, tb_logger) # 初始化数据模块 try: print("初始化数据模块...") data_config = config_manager.get_data_config() data_module = instantiate_from_config(data_config) data_module.setup() print("可用数据集:", list(data_module.datasets.keys())) except Exception as e: print(f"数据模块初始化失败: {str(e)}") return # 创建模型 try: model_config = config_manager.get_model_config() model_params = model_config.get("params", {}) # 创建模型实例 model = CustomLatentDiffusion(**model_config.get("params", {})) print("模型初始化成功") # 检查并转换预训练权重 ckpt_path = model_config.params.get("ckpt_path", "") if ckpt_path and os.path.exists(ckpt_path): print(f"加载预训练权重: {ckpt_path}") checkpoint = torch.load(ckpt_path, map_location="cpu") state_dict = checkpoint.get("state_dict", checkpoint) # 查找所有与conv_in.weight相关的键 conv_in_keys = [] for key in state_dict.keys(): if "conv_in.weight" in key and "first_stage_model" in key: conv_in_keys.append(key) # 转换找到的权重 for conv_in_key in conv_in_keys: if state_dict[conv_in_key].shape[1] == 3: # 原始是3通道 print(f"转换权重: {conv_in_key} 从3通道到1通道") # 取RGB三通道的平均值作为单通道权重 rgb_weights = state_dict[conv_in_key] ir_weights = rgb_weights.mean(dim=1, keepdim=True) state_dict[conv_in_key] = ir_weights print(f"转换前形状: {rgb_weights.shape}") print(f"转换后形状: {ir_weights.shape}") print(f"模型层形状: {model.first_stage_model.encoder.conv_in.weight.shape}") # 非严格模式加载(允许其他层不匹配) missing, unexpected = model.load_state_dict(state_dict, strict=False) print(f"权重加载完成: 缺失层 {len(missing)}, 不匹配层 {len(unexpected)}") if missing: print("缺失层:", missing) if unexpected: print("意外层:", unexpected) except Exception as e: print(f"模型初始化失败: {str(e)}") return print("VAE输入层形状:", model.first_stage_model.encoder.conv_in.weight.shape) # 权重转换 if ckpt_path and os.path.exists(ckpt_path): print(f"加载预训练权重: {ckpt_path}") checkpoint = torch.load(ckpt_path, map_location="cpu") state_dict = checkpoint.get("state_dict", checkpoint) # 增强:查找所有需要转换的层(包括可能的变体) conversion_keys = [] for key in state_dict.keys(): if "conv_in" in key or "conv_out" in key or "nin_shortcut" in key: if state_dict[key].ndim == 4 and state_dict[key].shape[1] == 3: conversion_keys.append(key) print(f"找到需要转换的层: {conversion_keys}") # 转换权重 for key in conversion_keys: print(f"转换权重: {key}") print(f"原始形状: {state_dict[key].shape}") # RGB权重 [out_c, in_c=3, kH, kW] rgb_weights = state_dict[key] # 转换为单通道权重 [out_c, 1, kH, kW] if rgb_weights.shape[1] == 3: ir_weights = rgb_weights.mean(dim=1, keepdim=True) state_dict[key] = ir_weights print(f"转换后形状: {state_dict[key].shape}") # 加载转换后的权重 try: # 使用非严格模式加载 missing, unexpected = model.load_state_dict(state_dict, strict=False) print(f"权重加载完成: 缺失层 {len(missing)}, 不匹配层 {len(unexpected)}") # 打印重要信息 if missing: print("缺失层:", missing[:5]) # 只显示前5个避免过多输出 if unexpected: print("意外层:", unexpected[:5]) # 特别检查conv_in层 if "first_stage_model.encoder.conv_in.weight" in missing: print("警告: conv_in.weight未加载,需要手动初始化") # 手动初始化单通道卷积层 with torch.no_grad(): model.first_stage_model.encoder.conv_in.weight.data.normal_(mean=0.0, std=0.02) print("已手动初始化conv_in.weight") except RuntimeError as e: print(f"加载权重时出错: {e}") print("尝试仅加载兼容的权重...") # 创建新的状态字典只包含兼容的键 model_state = model.state_dict() compatible_dict = {} for k, v in state_dict.items(): if k in model_state and v.shape == model_state[k].shape: compatible_dict[k] = v # 加载兼容的权重 model.load_state_dict(compatible_dict, strict=False) print(f"部分权重加载完成: {len(compatible_dict)}/{len(state_dict)}") # 配置学习率 training_config = config_manager.get_training_config() bs = data_config.params.batch_size base_lr = model_config.base_learning_rate ngpu = training_config.get("gpus", 1) accumulate_grad_batches = training_config.get("accumulate_grad_batches", 1) if args.scale_lr: model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr print(f"学习率缩放至: {model.learning_rate:.2e} = {accumulate_grad_batches} × {ngpu} × {bs} × {base_lr:.2e}") else: model.learning_rate = base_lr print(f"使用基础学习率: {model.learning_rate:.2e}") # 检查是否恢复训练 resume_from_checkpoint = None if args.resume: # 优先使用自动保存的last.ckpt last_ckpt = os.path.join(ckptdir, "last.ckpt") if os.path.exists(last_ckpt): print(f"恢复训练状态: {last_ckpt}") resume_from_checkpoint = last_ckpt else: # 回退到指定检查点 fallback_ckpt = os.path.join(current_dir, "checkpoints", "M3FD.ckpt") if os.path.exists(fallback_ckpt): print(f"警告: 使用仅含权重的检查点,训练状态将重置: {fallback_ckpt}") resume_from_checkpoint = fallback_ckpt else: print("未找到可用的检查点,从头开始训练") # 如果需要恢复训练,预处理检查点 if resume_from_checkpoint and os.path.exists(resume_from_checkpoint): try: # 预处理检查点 - 添加缺失的状态 checkpoint = preprocess_checkpoint(resume_from_checkpoint, model) # 创建新的完整检查点文件 fixed_ckpt_path = os.path.join(ckptdir, "fixed_checkpoint.ckpt") torch.save(checkpoint, fixed_ckpt_path) print(f"修复后的完整检查点已保存到: {fixed_ckpt_path}") # 使用修复后的检查点 resume_from_checkpoint = fixed_ckpt_path except Exception as e: print(f"预处理检查点失败: {e}") print("将尝试使用默认方式加载检查点") # 配置日志记录器 tb_logger = TensorBoardLogger(os.path.join(logdir, "tensorboard")) # 配置回调函数 callbacks = setup_callbacks(config_manager, ckptdir, tb_logger) # 检查是否有验证集 has_validation = hasattr(data_module, 'datasets') and 'validation' in data_module.datasets # 计算训练批次数 try: train_loader = data_module.train_dataloader() num_train_batches = len(train_loader) print(f"训练批次数: {num_train_batches}") except Exception as e: print(f"计算训练批次数失败: {e}") num_train_batches = 0 # 设置训练器参数(先设置基础参数) trainer_config = { "default_root_dir": logdir, "max_epochs": training_config.max_epochs, "gpus": ngpu, "distributed_backend": "ddp" if ngpu > 1 else None, "plugins": [DDPPlugin(find_unused_parameters=False)] if ngpu > 1 else None, "precision": 16, "accumulate_grad_batches": accumulate_grad_batches, "callbacks": callbacks, "logger": tb_logger, # 添加日志记录器 "resume_from_checkpoint": resume_from_checkpoint, "fast_dev_run": args.debug, "limit_val_batches": 0 if not has_validation else 1.0, "num_sanity_val_steps": 0, # 跳过初始验证加速恢复 "log_every_n_steps": 10 # 更频繁的日志记录 } # 动态调整验证配置 if has_validation: if num_train_batches < 50: # 小数据集:使用epoch验证 trainer_config["check_val_every_n_epoch"] = 1 # 确保移除步数验证参数 if "val_check_interval" in trainer_config: del trainer_config["val_check_interval"] else: # 大数据集:使用步数验证 val_check_interval = min(2000, num_train_batches) if num_train_batches < 100: val_check_interval = max(1, num_train_batches // 4) trainer_config["val_check_interval"] = val_check_interval # 创建训练器 try: print("最终训练器配置:") for k, v in trainer_config.items(): print(f" {k}: {v}") trainer = Trainer(**trainer_config) except Exception as e: print(f"创建训练器失败: {e}") tb_logger.close() sys.exit(1) # 执行训练 try: print("开始训练...") trainer.fit(model, data_module) print("训练完成!") except KeyboardInterrupt: print("训练被用户中断") if trainer.global_rank == 0 and trainer.model is not None: trainer.save_checkpoint(os.path.join(ckptdir, "interrupted.ckpt")) except Exception as e: print(f"训练出错: {e}") if trainer.global_rank == 0 and hasattr(trainer, 'model') and trainer.model is not None: trainer.save_checkpoint(os.path.join(ckptdir, "error.ckpt")) raise finally: # 关闭日志记录器 tb_logger.close() # 打印性能分析报告 if trainer.global_rank == 0 and hasattr(trainer, 'profiler'): print("训练摘要:") print(trainer.profiler.summary()) if __name__ == "__main__": main()运行报错:模型初始化失败: Error(s) in loading state_dict for CustomLatentDiffusion: size mismatch for first_stage_model.encoder.conv_in.weight: copying a param with shape torch.Size([128, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 1, 3, 3]).

import numpy as np import cv2 from ultralytics import YOLO import torch import time import queue from threading import Thread import psutil import multiprocessing import multiprocessing from plate_recognition.plate_rec import init_model, get_plate_result import GPUtil import pynvml import argparse import statistics from collections import defaultdict import os import threading import platform import subprocess import signal from concurrent.futures import ThreadPoolExecutor # 全局配置 CLASSES = ['danger', 'car_danger', 'headstock', 'light', 'number', '1number', 'double_number'] DETECTOR_MODEL_PATH = './weights/best.engine' # DETECTOR_MODEL_PATH = './weights/best_fp32.engine' TEXT_MODEL_PATH = './weights/plate_rec.pth' def show_frame(frame_data, stream_id, latency_queue): frame, capture_time = frame_data window_name = f"Stream {stream_id}" if frame is not None and frame.size > 0: cv2.imshow(window_name, frame) if cv2.waitKey(1) & 0xFF == ord('q'): return latency = time.time() - capture_time latency_queue.put((stream_id, latency)) def display_process(display_queue, stream_id, latency_queue): window_name = f"Stream {stream_id}" cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) cv2.resizeWindow(window_name, 800, 600) try: while True: frame_data = display_queue.get() if frame_data is None: break frame, capture_time = frame_data if frame is not None and frame.size > 0: cv2.imshow(window_name, frame) latency = time.time() - capture_time latency_queue.put((stream_id, latency)) if cv2.waitKey(1) & 0xFF == ord('q'): break finally: cv2.destroyWindow(window_name) print(f"显示进程{stream_id}退出") class EnhancedResourceMonitor: def __init__(self, gpu_id, process_mgr, interval=0.5): self.gpu_id = gpu_id self.interval = interval self.running = False self.lock = threading.Lock() self.data = defaultdict(list) self.process_mgr = process_mgr # 进程管理器引用 # GPU硬件信息 self.gpu_arch = "Ada Lovelace" self.sm_count = 56 self.peak_tflops = 35.6 self.cores_per_sm = 128 def start(self): pynvml.nvmlInit() self.handle = pynvml.nvmlDeviceGetHandleByIndex(self.gpu_id) self.running = True self.thread = Thread(target=self._monitor, daemon=True) self.thread.start() def _monitor(self): while self.running: try: # 监控所有工作进程 worker_stats = [] for p in self.process_mgr.processes: try: proc = psutil.Process(p.pid) with proc.oneshot(): worker_stats.append({ 'cpu': proc.cpu_percent(), 'mem': proc.memory_info().rss / (1024 ** 2), 'threads': proc.num_threads() }) except (psutil.NoSuchProcess, psutil.AccessDenied): continue # GPU监控 util = pynvml.nvmlDeviceGetUtilizationRates(self.handle) mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle) clock_mhz = pynvml.nvmlDeviceGetClockInfo(self.handle, pynvml.NVML_CLOCK_SM) # 计算实际算力 current_tflops = (self.sm_count * (clock_mhz / 1000) * self.cores_per_sm * 2) / 1000 util_percent = (current_tflops / self.peak_tflops) * 100 # 记录数据 with self.lock: if worker_stats: self.data['worker_cpu'].append(sum(s['cpu'] for s in worker_stats)) self.data['worker_mem'].append(sum(s['mem'] for s in worker_stats)) self.data['worker_threads'].append(sum(s['threads'] for s in worker_stats)) self.data['gpu_util'].append(util.gpu) self.data['gpu_mem'].append(mem_info.used / (1024 ** 2)) self.data['gpu_tflops'].append(current_tflops) except Exception as e: print(f"监控错误: {str(e)}") time.sleep(self.interval) def stop(self): self.running = False if hasattr(self, 'thread'): self.thread.join() pynvml.nvmlShutdown() return self._generate_report() def _generate_report(self): report = "\n[程序资源报告]\n" # 进程统计 if self.data.get('worker_threads'): report += f"- 工作进程数: {len(self.process_mgr.processes)}\n" report += f"- 总线程数: {max(self.data['worker_threads'])}\n" report += f"- 峰值CPU使用: {max(self.data['worker_cpu']):.1f}%\n" report += f"- 峰值内存占用: {max(self.data['worker_mem']):.1f}MB\n" # GPU统计 if self.data.get('gpu_tflops'): avg_tflops = statistics.mean(self.data['gpu_tflops']) report += "\n[GPU资源]\n" report += f"- 平均利用率: {statistics.mean(self.data['gpu_util']):.1f}%\n" report += f"- 峰值显存: {max(self.data['gpu_mem']):.1f}MB\n" report += f"- 平均算力: {avg_tflops:.1f} TFLOPS\n" report += f"- 算力利用率: {avg_tflops/self.peak_tflops*100:.1f}%\n" return report class ResourceMonitor: def __init__(self, gpu_id, interval=0.5): self.gpu_id = gpu_id self.interval = interval self.running = False self.data = defaultdict(list) self.gpu_arch = "Ada Lovelace" self.sm_count = 56 # RTX 4070 SUPER有56个SM self.peak_tflops = 35.6 # 理论算力35.6 TFLOPS self.cores_per_sm = 128 # Ada架构每个SM有128个CUDA核心 self.lock = threading.Lock() # 添加锁 self.main_pid = os.getpid() # 记录主进程PID def start(self): pynvml.nvmlInit() self.handle = pynvml.nvmlDeviceGetHandleByIndex(self.gpu_id) self.running = True self.thread = Thread(target=self._monitor, daemon=True) self.thread.start() def _monitor(self): while self.running: try: # 改进的进程监控 main_process = psutil.Process(self.main_pid) with main_process.oneshot(): # 原子化读取 process_cpu = main_process.cpu_percent(interval=0.1) # 更短间隔 process_mem = main_process.memory_info().rss / (1024 ** 2) process_threads = main_process.num_threads() # 确保不会记录到0值 if process_cpu == 0 and len(self.data['process_cpu']) > 0: process_cpu = self.data['process_cpu'][-1] * 0.9 # 使用上次值的90% # 记录数据 with self.lock: self.data['process_cpu'].append(process_cpu) self.data['process_memory'].append(process_mem) self.data['process_threads'].append(process_threads) # 系统进程统计 process_count = len(list(psutil.process_iter())) cpu_percent = psutil.cpu_percent() mem = psutil.virtual_memory() # 获取当前Python进程的资源使用 current_process = psutil.Process() process_cpu = current_process.cpu_percent() process_mem = current_process.memory_info().rss / (1024 ** 2) # MB process_threads = current_process.num_threads() # GPU监控 util = pynvml.nvmlDeviceGetUtilizationRates(self.handle) mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle) graphics_clock = pynvml.nvmlDeviceGetClockInfo(self.handle, pynvml.NVML_CLOCK_GRAPHICS) sm_clock = pynvml.nvmlDeviceGetClockInfo(self.handle, pynvml.NVML_CLOCK_SM) power_usage = pynvml.nvmlDeviceGetPowerUsage(self.handle) / 1000 # 瓦特 total_mem = sum(p.memory_info().rss for p in psutil.process_iter(['pid', 'name']) if 'python' in p.info['name'].lower()) / (1024**2) # MB # 获取当前GPU时钟频率 clock_mhz = pynvml.nvmlDeviceGetClockInfo( self.handle, pynvml.NVML_CLOCK_SM ) # 收集数据 self.data['system_processes'].append(process_count) self.data['system_cpu'].append(cpu_percent) self.data['system_memory'].append(mem.used / (1024**3)) # GB self.data['process_cpu'].append(process_cpu) self.data['process_memory'].append(process_mem) self.data['process_threads'].append(process_threads) self.data['gpu_util'].append(util.gpu) self.data['gpu_mem'].append(mem_info.used / (1024**2)) # MB self.data['gpu_power'].append(power_usage) self.data['gpu_clock_graphics'].append(graphics_clock) self.data['gpu_clock_sm'].append(sm_clock) # 实时算力计算 (TFLOPS = SM数 * 时钟频率(GHz) * 每SM核心数 * 2 / 1000) current_tflops = (self.sm_count * (clock_mhz / 1000) * self.cores_per_sm * 2) / 1000 util_percent = (current_tflops / self.peak_tflops) * 100 self.data['gpu_tflops'].append(current_tflops) self.data['gpu_sm_clock'].append(clock_mhz) self.data['gpu_util_actual'].append(util_percent) except Exception as e: print(f"算力监控错误: {e}") except (psutil.NoSuchProcess, pynvml.NVMLError) as e: print(f"监控错误(忽略): {str(e)}") except Exception as e: print(f"意外的监控错误: {str(e)}") time.sleep(self.interval) def stop(self): self.running = False self.thread.join() pynvml.nvmlShutdown() return self._generate_report() def _generate_report(self): if not any(len(v) > 0 for v in self.data.values()): return "无监控数据" report = "\n[资源使用报告]\n" report += "\n[算力分析 - RTX 4070 SUPER]\n" report += f"- GPU架构: {self.gpu_arch}\n" report += f"- 流式多处理器(SM): {self.sm_count}\n" report += f"- CUDA核心: {self.sm_count * self.cores_per_sm}\n" report += f"- 理论峰值算力: {self.peak_tflops} TFLOPS\n" if self.data.get('gpu_tflops'): avg_tflops = statistics.mean(self.data['gpu_tflops']) max_tflops = max(self.data['gpu_tflops']) avg_clock = statistics.mean(self.data['gpu_sm_clock']) report += "\n[实际运行数据]\n" report += f"- 平均SM时钟: {avg_clock} MHz\n" report += f"- 平均算力: {avg_tflops:.1f} TFLOPS\n" report += f"- 峰值算力: {max_tflops:.1f} TFLOPS\n" report += f"- 算力利用率: {avg_tflops/self.peak_tflops*100:.1f}%\n" # 瓶颈分析 avg_util = statistics.mean(self.data['gpu_util']) if avg_util > 90 and util_percent < 70: report += "\n[警告] 高GPU利用率但低算力利用率,可能存在内存带宽瓶颈\n" elif avg_tflops < 0.7 * self.peak_tflops: report += "\n[提示] 算力未充分利用,建议检查:\n" report += " • 批次大小(batch size)是否过小\n" report += " • 模型是否存在大量分支操作\n" # 系统级统计 report += "[系统资源]\n" system_metrics = { 'system_processes': ('系统进程数', '{:.0f}'), 'system_cpu': ('系统CPU使用率(%)', '{:.1f}'), 'system_memory': ('系统内存使用(GB)', '{:.2f}') } for key, (name, fmt) in system_metrics.items(): values = self.data.get(key, []) if values: report += ( f"{name}:\n" f" 平均值: {fmt.format(statistics.mean(values))}\n" f" 最大值: {fmt.format(max(values))}\n" f" 最小值: {fmt.format(min(values))}\n" f" 采样数: {len(values)}\n\n" ) # 进程级统计 report += "[主进程资源]\n" process_metrics = { 'process_cpu': ('进程CPU使用率(%)', '{:.1f}'), 'process_memory': ('进程内存使用(MB)', '{:.1f}'), 'process_threads': ('程内部的线程数量', '{:.0f}') } for key, (name, fmt) in process_metrics.items(): values = self.data.get(key, []) if values: report += ( f"{name}:\n" f" 平均值: {fmt.format(statistics.mean(values))}\n" f" 最大值: {fmt.format(max(values))}\n" f" 最小值: {fmt.format(min(values))}\n" f" 采样数: {len(values)}\n\n" ) # GPU统计 report += "[GPU资源]\n" gpu_metrics = { 'gpu_util': ('GPU利用率(%)', '{:.1f}'), 'gpu_mem': ('显存使用(MB)', '{:.1f}'), 'gpu_power': ('GPU功耗(W)', '{:.1f}'), 'gpu_clock_graphics': ('图形时钟(MHz)', '{:.0f}'), 'gpu_clock_sm': ('SM时钟(MHz)', '{:.0f}') } for key, (name, fmt) in gpu_metrics.items(): values = self.data.get(key, []) if values: report += ( f"{name}:\n" f" 平均值: {fmt.format(statistics.mean(values))}\n" f" 最大值: {fmt.format(max(values))}\n" f" 最小值: {fmt.format(min(values))}\n" f" 采样数: {len(values)}\n\n" ) return report class VideoProcessor: def __init__(self, device): # 添加CUDA初始化 torch.cuda.empty_cache() # 加载模型前设置优化选项 torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True # 加载模型 self.model = YOLO(DETECTOR_MODEL_PATH, task='detect') self.plate_rec_model = init_model(device, TEXT_MODEL_PATH) self.rtsp_url = "rtsp://admin:[email protected]:554/cam/realmonitor?channel=1&subtype=0" self.max_retries = 3 # 预热GPU # with torch.no_grad(): # dummy_input = torch.randn(1, 3, 640, 640).to(device) # _ = self.model(dummy_input) self.device = device self.frame_count = 0 self.plate_text_cache = {} def _reconnect(self): cap = cv2.VideoCapture(self.rtsp_url, cv2.CAP_FFMPEG) cap.set(cv2.CAP_PROP_RTSP_TRANSPORT, cv2.CAP_RTSP_TRANSPORT_TCP) return cap # 在VideoProcessor类中添加中文显示支持 def process_frame(self, frame): # 增强版中文显示函数(带错误处理和字体回退) def put_chinese_text(img, text, position, font_scale, color, thickness): """支持中文显示的增强函数""" try: from PIL import Image, ImageDraw, ImageFont img_pil = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(img_pil) # 尝试加载字体(优先使用自定义字体,失败则回退系统字体) try: font_path = os.path.join("fonts", "platech.ttf") font = ImageFont.truetype(font_path, int(font_scale * 30)) except: font = ImageFont.load_default() print("警告:使用默认字体,中文显示可能不正常") draw.text(position, text, font=font, fill=color) return cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR) except Exception as e: print(f"文本渲染失败,使用OpenCV默认显示: {str(e)}") cv2.putText(img, text, position, cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness) return img self.frame_count += 1 # 1. 输入帧验证 if frame is None or frame.size == 0: print("错误:接收到空帧") return frame # 2. 模型推理(添加详细日志) try: results = self.model.track( frame, persist=True, imgsz=640, verbose=False # 关闭YOLO内置输出 ) # 调试输出 print(f"帧 {self.frame_count}: 检测到 {len(results)} 个结果") except Exception as e: print(f"模型推理错误: {str(e)}") return frame # 3. 结果解析与渲染 class_colors = { 'danger': (0, 0, 255), 'car_danger': (0, 165, 255), 'headstock': (255, 0, 0), 'light': (255, 255, 0), 'number': (0, 255, 0), '1number': (0, 255, 255), 'double_number': (128, 0, 128) } for result in results: # 验证检测结果有效性 if not hasattr(result, 'boxes') or result.boxes is None: print("警告:结果中未包含有效检测框") continue for box in result.boxes: try: # 解析检测框数据 cls_id = int(box.cls[0].item()) class_name = CLASSES[cls_id] x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) conf = box.conf[0].item() track_id = int(box.id[0].item()) if box.id is not None else None # 车牌特殊处理 if class_name == 'number' and (track_id not in self.plate_text_cache or self.frame_count % 5 == 0): plate_img = frame[y1:y2, x1:x2] if plate_img.size > 0: plate_text = get_plate_result(plate_img, self.device, self.plate_rec_model) or "识别失败" self.plate_text_cache[track_id] = plate_text try: if track_id not in self.plate_text_cache or self.frame_count % 5 == 0: plate_img = frame[y1:y2, x1:x2] if plate_img.size > 0: plate_text = get_plate_result(plate_img, self.device, self.plate_rec_model) or "识别失败" self.plate_text_cache[track_id] = plate_text else: plate_text = "无效区域" display_text = f"{self.plate_text_cache.get(track_id, '加载中...')} ID:{track_id} {conf:.2f}" except Exception as e: print(f"车牌处理异常: {str(e)}") display_text = f"车牌识别错误 ID:{track_id}" else: display_text = f"{class_name} {conf:.2f}" + (f" ID:{track_id}" if track_id else "") # 渲染检测框和文本 color = class_colors.get(class_name, (255, 255, 255)) cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) # 文本位置修正(确保不超出画面) y_text = max(y1 - 10, 10) frame = put_chinese_text(frame, display_text, (x1, y_text), 0.7, color, 2) except Exception as e: print(f"单检测框处理错误: {str(e)}") continue return frame def display_thread(display_queue, stream_id, latency_queue): window_name = f"Stream {stream_id}" cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) cv2.resizeWindow(window_name, 800, 600) try: while True: frame_data = display_queue.get() if frame_data is None: break frame, capture_time = frame_data if frame is not None and frame.size > 0: cv2.imshow(window_name, frame) latency = time.time() - capture_time latency_queue.put((stream_id, latency)) if cv2.waitKey(1) & 0xFF == ord('q'): break finally: cv2.destroyWindow(window_name) print(f"显示线程{stream_id}退出") class StreamSimulator: def __init__(self, source_url, num_streams, shared_frame_queue): self.source_url = source_url self.num_streams = num_streams self.shared_frame_queue = shared_frame_queue self.display_queues = [multiprocessing.Queue(maxsize=2000) for _ in range(num_streams)] # 使用 multiprocessing.Queue self.stop_flag = multiprocessing.Event() self.capture_process = None def start(self): self.capture_process = multiprocessing.Process(target=self._capture_and_distribute) self.capture_process.start() def stop(self): self.stop_flag.set() if self.capture_process: self.capture_process.join(timeout=5) if self.capture_process.is_alive(): self.capture_process.terminate() print("强制终止捕获进程") def _capture_and_distribute(self): rtsp_url = self.source_url cap = cv2.VideoCapture(rtsp_url, cv2.CAP_FFMPEG) cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) cap.set(cv2.CAP_PROP_FPS, 15) skip_frames = 2 # 每 2 帧处理 1 帧 frame_count = 0 try: while not self.stop_flag.is_set(): ret, frame = cap.read() if not ret: print("帧读取失败,重连中...") cap.release() time.sleep(2) cap = cv2.VideoCapture(rtsp_url, cv2.CAP_FFMPEG) continue frame_count += 1 if frame_count % skip_frames == 0: for i in range(self.num_streams): try: if not self.shared_frame_queue.full(): self.shared_frame_queue.put((frame.copy(), i, time.time()), block=False) else: print(f"共享帧队列已满,丢弃旧帧") self.shared_frame_queue.get_nowait() self.shared_frame_queue.put((frame.copy(), i, time.time()), block=False) except Exception as e: print(f"帧队列操作警告: {type(e).__name__}") finally: cap.release() self.shared_frame_queue.put(None) for q in self.display_queues: q.put(None) def dispatch_process(result_queue, display_queues): frame_buffer = [] while True: data = result_queue.get() if data is None: break # 检查是否为 'stats' 二元组 if isinstance(data, tuple) and len(data) == 2 and data[0] == 'stats': continue # 跳过中间统计数据 # 检查是否为帧数据(三元组) elif isinstance(data, tuple) and len(data) == 3: processed_frame, stream_id, capture_time = data frame_buffer.append((processed_frame, stream_id, capture_time)) frame_buffer.sort(key=lambda x: x[2]) # 按时间戳排序 for frame, sid, _ in frame_buffer: if not display_queues[sid].full(): display_queues[sid].put((frame, capture_time), block=False) else: print(f"显示队列 {sid} 已满,丢帧") frame_buffer = [] # 清空缓冲区 # 检查是否为字典(最终统计数据) elif isinstance(data, dict): continue # 跳过最终统计数据 else: print(f"警告:未知数据类型: {type(data)}") def display_process(display_queue, stream_id, latency_queue): window_name = f"Stream {stream_id}" cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) cv2.resizeWindow(window_name, 800, 600) frame_count = 0 try: while True: frame_data = display_queue.get() if frame_data is None: break frame, capture_time = frame_data if frame is not None and frame.size > 0: cv2.imshow(window_name, frame) frame_count += 1 if frame_count % 10 == 0: latency = time.time() - capture_time latency_queue.put((stream_id, latency)) if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1: break if cv2.waitKey(1) & 0xFF == ord('q'): break finally: cv2.destroyWindow(window_name) print(f"显示进程{stream_id}退出") def worker_process(input_queue, gpu_id, result_queue, stats_queue, monitor_interval=5): import numpy as np # 显式导入 numpy print(f"In worker process {os.getpid()}, np is {np}, type(np.empty((1,))) = {type(np.empty((1,)))}") from collections import defaultdict torch.set_num_threads(1) cv2.setNumThreads(1) torch.cuda.set_device(gpu_id) device = torch.device(f"cuda:{gpu_id}" if torch.cuda.is_available() else "cpu") processor = VideoProcessor(device) start_time = time.time() stats = { 'frame_count': 0, 'avg_fps': 0, 'max_gpu_mem': 0, 'process_time': 0, 'stream_id': None } frame_counts_per_stream = defaultdict(int) try: while True: frame_data = input_queue.get() if frame_data is None: break frame, stream_id, capture_time = frame_data stats['stream_id'] = stream_id # 单帧处理 start_process = time.time() results = processor.model.track(frame, imgsz=640, verbose=False) # 处理单帧 stats['process_time'] += time.time() - start_process processed_frame = processor.process_frame(frame) # 移除 cProfile if processed_frame is not None and processed_frame.size > 0: stats['frame_count'] += 1 frame_counts_per_stream[stream_id] += 1 result_queue.put((processed_frame, stream_id, capture_time)) # 定期更新统计信息 if stats['frame_count'] % monitor_interval == 0: duration = time.time() - start_time stats['avg_fps'] = stats['frame_count'] / duration if torch.cuda.is_available(): mem = torch.cuda.max_memory_allocated() / (1024 ** 2) stats['max_gpu_mem'] = max(stats['max_gpu_mem'], mem) stats['worker_pid'] = os.getpid() stats['frame_counts_per_stream'] = dict(frame_counts_per_stream) stats_queue.put(('stats', stats.copy())) except Exception as e: print(f"工作进程错误: {e}") finally: stats['worker_pid'] = os.getpid() stats['frame_counts_per_stream'] = dict(frame_counts_per_stream) stats_queue.put(stats) def get_gpu_info(): """获取GPU信息""" pynvml.nvmlInit() gpu_count = pynvml.nvmlDeviceGetCount() gpus = [] for i in range(gpu_count): handle = pynvml.nvmlDeviceGetHandleByIndex(i) name = pynvml.nvmlDeviceGetName(handle) mem = pynvml.nvmlDeviceGetMemoryInfo(handle) gpus.append({ 'id': i, 'name': name.decode('utf-8') if isinstance(name, bytes) else name, 'total_mem': mem.total / (1024 ** 2) }) pynvml.nvmlShutdown() return gpus import os import argparse def monitor_resources(gpu_id, interval=5): """资源监控线程""" pynvml.nvmlInit() handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id) while True: # GPU监控 util = pynvml.nvmlDeviceGetUtilizationRates(handle) mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle) # CPU监控 cpu_percent = psutil.cpu_percent() mem = psutil.virtual_memory() print(f"\n[资源监控] GPU: {util.gpu}% 显存: {mem_info.used/1024**2:.1f}/{mem_info.total/1024**2:.1f}MB | " f"CPU: {cpu_percent}% 内存: {mem.used/1024**3:.1f}/{mem.total/1024**3:.1f}GB") time.sleep(interval) class DynamicProcessManager: def __init__(self, num_workers): self.num_workers = num_workers self.processes = [] self.result_queues = [] def start_workers(self, input_queue, gpu_id, result_queue, stats_queue): for i in range(self.num_workers): p = multiprocessing.Process( target=worker_process, args=(input_queue, gpu_id, result_queue, stats_queue) ) self.processes.append(p) p.start() def stop_workers(self): for p in self.processes: if p.is_alive(): p.terminate() try: p.join(timeout=1) except: pass if p.is_alive(): if platform.system() == "Windows": subprocess.run(['taskkill', '/F', '/PID', str(p.pid)], check=False) else: os.kill(p.pid, signal.SIGKILL) print(f"强制终止进程 {p.pid}") self.processes = [] def get_gpu_info(): pynvml.nvmlInit() gpu_count = pynvml.nvmlDeviceGetCount() gpus = [] for i in range(gpu_count): handle = pynvml.nvmlDeviceGetHandleByIndex(i) name = pynvml.nvmlDeviceGetName(handle) mem = pynvml.nvmlDeviceGetMemoryInfo(handle) gpus.append({ 'id': i, 'name': name.decode('utf-8') if isinstance(name, bytes) else name, 'total_mem': mem.total / (1024 ** 2) }) pynvml.nvmlShutdown() return gpus class ProgramMonitor: def __init__(self, gpu_id, process_manager, result_queue, stats_queue, args): self.gpu_id = gpu_id self.result_queue = result_queue self.stats_queue = stats_queue self.process_manager = process_manager self.args = args self.running = False self.stop_flag = threading.Event() self.data = { 'process': defaultdict(list), 'workers': defaultdict(list), 'gpu': defaultdict(list), 'fps_per_stream': defaultdict(list), 'total_fps': [], 'worker_stats': [], 'cpu_per_core': [], 'mem_bandwidth': [] } self.lock = threading.Lock() self.gpu_info = { 'arch': "Ada Lovelace", 'sm_count': 56, 'cores_per_sm': 128, 'peak_tflops': 35.6 } self.total_frame_counts = defaultdict(int) self.last_frame_counts = defaultdict(lambda: defaultdict(int)) self.start_time = None self.stop_time = None self.last_mem_time = time.time() self.last_mem_bytes = psutil.virtual_memory().used def start(self): pynvml.nvmlInit() self.handle = pynvml.nvmlDeviceGetHandleByIndex(self.gpu_id) self.running = True self.start_time = time.time() self.thread = Thread(target=self._monitor, daemon=True) self.thread.start() def _monitor(self): last_cpu_times = {} while not self.stop_flag.is_set(): try: # Process stats from stats_queue try: data = self.stats_queue.get_nowait() if isinstance(data, tuple) and data[0] == 'stats': stats = data[1] worker_pid = stats['worker_pid'] frame_counts_per_stream = stats['frame_counts_per_stream'] with self.lock: for stream_id, count in frame_counts_per_stream.items(): delta = count - self.last_frame_counts[worker_pid][stream_id] self.total_frame_counts[stream_id] += delta self.last_frame_counts[worker_pid][stream_id] = count except queue.Empty: pass # Main process monitoring main_process = psutil.Process(os.getpid()) with main_process.oneshot(): current_cpu_time = main_process.cpu_times() pid = main_process.pid if pid in last_cpu_times: cpu_usage = self._calculate_cpu_usage(last_cpu_times[pid], current_cpu_time) self.data['process']['cpu'].append(cpu_usage) last_cpu_times[pid] = current_cpu_time self.data['process']['mem'].append(main_process.memory_info().rss / (1024 ** 2)) self.data['process']['threads'].append(main_process.num_threads()) # Worker processes monitoring for p in self.process_manager.processes: try: proc = psutil.Process(p.pid) with proc.oneshot(): current_cpu_time = proc.cpu_times() pid = p.pid if pid in last_cpu_times: cpu_usage = self._calculate_cpu_usage(last_cpu_times[pid], current_cpu_time) self.data['workers']['cpu'].append(cpu_usage) last_cpu_times[pid] = current_cpu_time self.data['workers']['mem'].append(proc.memory_info().rss / (1024 ** 2)) self.data['workers']['threads'].append(proc.num_threads()) except (psutil.NoSuchProcess, psutil.AccessDenied): continue # Memory bandwidth monitoring current_time = time.time() current_mem_bytes = psutil.virtual_memory().used time_delta = current_time - self.last_mem_time if time_delta > 0: mem_bandwidth = (current_mem_bytes - self.last_mem_bytes) / time_delta / (1024 ** 2) with self.lock: self.data['mem_bandwidth'].append(mem_bandwidth) self.last_mem_time = current_time self.last_mem_bytes = current_mem_bytes # CPU per core monitoring cpu_per_core = psutil.cpu_percent(percpu=True) with self.lock: self.data['cpu_per_core'].append(cpu_per_core) self._monitor_gpu() except Exception as e: print(f"监控错误: {str(e)}") time.sleep(0.5) def _monitor_gpu(self): try: util = pynvml.nvmlDeviceGetUtilizationRates(self.handle) mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle) clock_mhz = pynvml.nvmlDeviceGetClockInfo(self.handle, pynvml.NVML_CLOCK_SM) current_tflops = (self.gpu_info['sm_count'] * (clock_mhz / 1000) * self.gpu_info['cores_per_sm'] * 2) / 1000 with self.lock: self.data['gpu']['util'].append(util.gpu) self.data['gpu']['mem'].append(mem_info.used / (1024 ** 2)) self.data['gpu']['tflops'].append(current_tflops) except pynvml.NVMLError as e: print(f"GPU监控错误: {str(e)}") def stop(self): self.stop_time = time.time() self.running = False self.stop_flag.set() if self.thread.is_alive(): self.thread.join(timeout=2) report = self.generate_report() pynvml.nvmlShutdown() return report def generate_report(self): report = "\n=== 程序资源使用报告 ===\n" # System information (unchanged) report += "\n[系统信息]\n" report += f"- CPU核心数: {psutil.cpu_count(logical=False)}物理/{psutil.cpu_count()}逻辑\n" report += f"- 系统内存: {psutil.virtual_memory().total / (1024**3):.1f}GB\n" report += f"- 系统CPU使用率: {psutil.cpu_percent(interval=1):.1f}%\n" report += f"- 系统内存使用: {psutil.virtual_memory().used / (1024**3):.1f}GB / {psutil.virtual_memory().total / (1024**3):.1f}GB\n" gpu_name_raw = pynvml.nvmlDeviceGetName(self.handle) gpu_name = gpu_name_raw.decode('utf-8') if isinstance(gpu_name_raw, bytes) else gpu_name_raw total_gpu_mem = pynvml.nvmlDeviceGetMemoryInfo(self.handle).total / (1024 ** 2) report += f"- GPU型号: {gpu_name}\n" report += f"- GPU总显存: {total_gpu_mem:.1f}MB\n" # Main process stats (unchanged) if self.data['process']['cpu']: report += "\n[主进程资源]\n" report += f"- 平均CPU使用率: {statistics.mean(self.data['process']['cpu']):.1f}%\n" report += f"- 峰值CPU使用率: {max(self.data['process']['cpu']):.1f}%\n" report += f"- 平均内存占用: {statistics.mean(self.data['process']['mem']):.1f}MB\n" report += f"- 峰值内存占用: {max(self.data['process']['mem']):.1f}MB\n" report += f"- 线程数: {max(self.data['process']['threads'])}\n" # Worker processes stats (unchanged except for FPS section) if self.data['workers']['cpu']: num_workers = min(self.args.streams * 4, psutil.cpu_count(logical=True) * 2) num_samples = len(self.data['workers']['cpu']) // num_workers if num_samples > 0: worker_cpu_per_sample = [self.data['workers']['cpu'][i*num_workers:(i+1)*num_workers] for i in range(num_samples)] worker_mem_per_sample = [self.data['workers']['mem'][i*num_workers:(i+1)*num_workers] for i in range(num_samples)] worker_threads_per_sample = [self.data['workers']['threads'][i*num_workers:(i+1)*num_workers] for i in range(num_samples)] avg_worker_cpu = statistics.mean([statistics.mean(sample) for sample in worker_cpu_per_sample]) total_worker_cpu = statistics.mean([sum(sample) for sample in worker_cpu_per_sample]) avg_worker_mem = statistics.mean([statistics.mean(sample) for sample in worker_mem_per_sample]) total_worker_mem = statistics.mean([sum(sample) for sample in worker_mem_per_sample]) max_total_worker_threads = max([sum(sample) for sample in worker_threads_per_sample]) report += f"\n[工作进程资源 ({num_workers}个)]\n" report += f"- 平均CPU使用率(每个进程): {avg_worker_cpu:.1f}%\n" report += f"- 总CPU使用率: {total_worker_cpu:.1f}%\n" report += f"- 平均内存占用(每个进程): {avg_worker_mem:.1f}MB\n" report += f"- 总内存占用: {total_worker_mem:.1f}MB\n" report += f"- 总线程数(峰值): {max_total_worker_threads}\n" # Video stream performance with accurate FPS if self.total_frame_counts: elapsed_time = self.stop_time - self.start_time report += "\n[视频流性能]\n" for stream_id in range(self.args.streams): if stream_id in self.total_frame_counts: avg_fps = self.total_frame_counts[stream_id] / elapsed_time report += f"- 视频流 {stream_id}: 平均 FPS {avg_fps:.1f}\n" total_frames = sum(self.total_frame_counts.values()) total_fps = total_frames / elapsed_time report += f"- 总吞吐量: {total_fps:.1f} FPS\n" # CPU per core (unchanged) if self.data.get('cpu_per_core'): avg_cpu_per_core = [statistics.mean([sample[i] for sample in self.data['cpu_per_core']]) for i in range(len(self.data['cpu_per_core'][0]))] overall_avg_cpu = statistics.mean(avg_cpu_per_core) report += "\n[CPU 硬件线程利用率]\n" for i, avg in enumerate(avg_cpu_per_core): report += f"- 逻辑处理器 {i}: {avg:.1f}%\n" report += f"- 16 个硬件线程平均利用率: {overall_avg_cpu:.1f}%\n" # Total process stats (unchanged) if self.data['process']['cpu'] and self.data['workers']['cpu']: num_display_processes = self.args.streams total_cpu = statistics.mean(self.data['process']['cpu']) + total_worker_cpu total_mem = statistics.mean(self.data['process']['mem']) + total_worker_mem total_threads = max(self.data['process']['threads']) + max_total_worker_threads total_processes = 1 + num_workers + num_display_processes + 1 report += "\n[所有进程总计]\n" report += f"- 总CPU使用率: {total_cpu:.1f}%\n" report += f"- 总内存占用: {total_mem:.1f}MB\n" report += f"- 总线程数: {total_threads}\n" report += f"- 总进程数: {total_processes}(1个主进程 + {num_workers}个工作进程 + {num_display_processes}个显示进程 + 1个分发进程)\n" # GPU stats (unchanged) if self.data['gpu']['tflops']: avg_tflops = statistics.mean(self.data['gpu']['tflops']) util_percent = min((avg_tflops / self.gpu_info['peak_tflops']) * 100, 100.0) report += "\n[GPU资源]\n" report += f"- 平均利用率: {statistics.mean(self.data['gpu']['util']):.1f}%\n" report += f"- 峰值显存: {max(self.data['gpu']['mem']):.1f}MB\n" report += f"- 平均算力: {avg_tflops:.1f}/{self.gpu_info['peak_tflops']} TFLOPS\n" report += f"- 算力利用率: {util_percent:.1f}%\n" # Memory bandwidth (unchanged) if self.data.get('mem_bandwidth'): avg_mem_bandwidth = statistics.mean(self.data['mem_bandwidth']) max_mem_bandwidth = max(self.data['mem_bandwidth']) report += "\n[存储器带宽]\n" report += f"- 平均内存带宽: {avg_mem_bandwidth:.1f} MB/s\n" report += f"- 峰值内存带宽: {max_mem_bandwidth:.1f} MB/s\n" return report def _calculate_cpu_usage(self, prev_times, curr_times): """ 计算基于前后的 CPU 时间的使用率百分比。 参数: prev_times: 上一次的 CPU 时间(psutil.cpu_times 对象) curr_times: 当前的 CPU 时间(psutil.cpu_times 对象) 返回: CPU 使用率(百分比) """ delta_user = curr_times.user - prev_times.user delta_system = curr_times.system - prev_times.system delta_total = (curr_times.user + curr_times.system) - (prev_times.user + prev_times.system) if delta_total > 0: cpu_usage = ((delta_user + delta_system) / delta_total) * 100 else: cpu_usage = 0.0 return cpu_usage # _monitor_gpu and _calculate_cpu_usage remain unchanged def main(): parser = argparse.ArgumentParser() parser.add_argument('--streams', type=int, default=1) parser.add_argument('--source', type=str, default="") parser.add_argument('--gpu_id', type=int, default=0) args = parser.parse_args() camera_config = { 'username': 'admin', 'password': 'guoxinzhike901' } source_url = args.source if args.source else \ f"rtsp://{camera_config['username']}:{camera_config['password']}@192.168.1.108/" gpus = get_gpu_info() print("\n[硬件配置]") print(f"- CPU核心: {psutil.cpu_count(logical=False)}物理/{psutil.cpu_count()}逻辑") print(f"- 内存: {psutil.virtual_memory().total / (1024**3):.1f}GB") print(f"- 使用GPU {args.gpu_id}: {gpus[args.gpu_id]['name']}") print(f" 显存: {gpus[args.gpu_id]['total_mem']:.1f}MB") os.environ['OMP_NUM_THREADS'] = '1' os.environ['MKL_NUM_THREADS'] = '1' print(f"\n[测试配置]") print(f"- 模拟视频流数: {args.streams}") print(f"- 视频源: {source_url}") # 创建共享队列 frame_queue_size = max(2000, 200 * args.streams) shared_frame_queue = multiprocessing.Queue(maxsize=frame_queue_size) display_queue_size = max(50, 20 * args.streams) shared_result_queue = multiprocessing.Queue(maxsize=2000) stats_queue = multiprocessing.Queue() # New queue for stats # 固定工作进程数为 16 num_workers = 1 #min(args.streams * 8, psutil.cpu_count(logical=True) * 2) process_mgr = DynamicProcessManager(num_workers) simulator = StreamSimulator(source_url, args.streams, shared_frame_queue) monitor = ProgramMonitor(args.gpu_id, process_mgr, shared_result_queue, stats_queue, args) monitor.args = args # 传递 args latency_queue = multiprocessing.Queue() # 启动工作进程 process_mgr.start_workers(shared_frame_queue, args.gpu_id, shared_result_queue, stats_queue) # 启动分发进程 dispatch_p = multiprocessing.Process( target=dispatch_process, args=(shared_result_queue, simulator.display_queues), daemon=True ) dispatch_p.start() simulator.start() monitor.start() # 启动显示进程 display_threads = [] for i in range(args.streams): t = Thread(target=display_thread, args=(simulator.display_queues[i], i+1, latency_queue)) display_threads.append(t) t.start() time.sleep(0.5) print("\n[测试开始] 程序将运行30秒...") start_time = time.time() end_time = start_time + 60*5 try: while time.time() < end_time: time.sleep(1) remaining = int(end_time - time.time()) if remaining % 10 == 0 or remaining <= 5: print(f"剩余时间: {remaining}秒") finally: runtime = time.time() - start_time print(f"\n[测试完成] 实际运行时间: {runtime:.1f}秒") print("停止模拟器...") simulator.stop() print("生成报告并停止监控...") report = monitor.stop() print("停止工作进程...") process_mgr.stop_workers() # 停止显示线程 for q in simulator.display_queues: q.put(None) for t in display_threads: t.join() # 停止分发进程 shared_result_queue.put(None) dispatch_p.join(timeout=5) if dispatch_p.is_alive(): dispatch_p.terminate() # 收集延迟测量 latencies = [] while not latency_queue.empty(): try: stream_id, latency = latency_queue.get_nowait() latencies.append(latency) except queue.Empty: break if latencies: min_latency = min(latencies) max_latency = max(latencies) avg_latency = sum(latencies) / len(latencies) report += f"\n[延迟统计]\n" report += f"- 测量次数: {len(latencies)}\n" report += f"- 最低延迟: {min_latency:.3f}秒\n" report += f"- 最高延迟: {max_latency:.3f}秒\n" report += f"- 平均延迟: {avg_latency:.3f}秒\n" else: report += "\n[延迟统计]\n- 无延迟数据\n" if torch.cuda.is_available(): torch.cuda.empty_cache() print(report) if __name__ == '__main__': multiprocessing.set_start_method('spawn') # multiprocessing.set_start_method('fork') # Linux 默认方法 main() # 测试4路视频流 # python det_ocr_shipinliu_pre.py --streams 1 --gpu_id 0 """ === 程序资源使用报告 === === 程序资源使用报告 === [系统信息] - CPU核心数: 10物理/16逻辑 - 系统内存: 63.8GB - 系统CPU使用率: 14.1% - 系统内存使用: 26.3GB / 63.8GB - GPU型号: NVIDIA GeForce RTX 4070 SUPER - GPU总显存: 12282.0MB [主进程资源] - 平均CPU使用率: 16.3% - 峰值CPU使用率: 28.1% - 平均内存占用: 385.1MB - 峰值内存占用: 385.7MB - 线程数: 9 [工作进程资源 (16个)] - 平均CPU使用率(每个进程): 22.1% - 总CPU使用率: 354.2% - 平均内存占用(每个进程): 801.5MB - 总内存占用: 12823.3MB - 总线程数(峰值): 304 [所有进程总计] - 总CPU使用率: 370.5% - 总内存占用: 13208.4MB - 总线程数: 313 - 总进程数: 19(1个主进程 + 16个工作进程 + 1个显示进程 + 1个分发进程) [GPU资源] - 平均利用率: 31.3% - 峰值显存: 8226.7MB - 平均算力: 22.7/35.6 TFLOPS - 算力利用率: 63.8% [延迟统计] - 测量次数: 67 - 最低延迟: 0.024秒 - 最高延迟: 2.499秒 - 平均延迟: 0.287秒 """ # python det_ocr_shipinliu_pre.py --streams 2 --gpu_id 0 """ === 程序资源使用报告 === [系统信息] - CPU核心数: 10物理/16逻辑 - 系统内存: 63.8GB - 系统CPU使用率: 9.8% - 系统内存使用: 26.3GB / 63.8GB - GPU型号: NVIDIA GeForce RTX 4070 SUPER - GPU总显存: 12282.0MB [主进程资源] - 平均CPU使用率: 15.3% - 峰值CPU使用率: 40.6% - 平均内存占用: 386.4MB - 峰值内存占用: 387.1MB - 线程数: 9 [工作进程资源 (16个)] - 平均CPU使用率(每个进程): 20.8% - 总CPU使用率: 333.1% - 平均内存占用(每个进程): 960.3MB - 总内存占用: 15364.2MB - 总线程数(峰值): 328 [所有进程总计] - 总CPU使用率: 348.4% - 总内存占用: 15750.6MB - 总线程数: 337 - 总进程数: 20(1个主进程 + 16个工作进程 + 2个显示进程 + 1个分发进程) [GPU资源] - 平均利用率: 50.5% - 峰值显存: 8328.6MB - 平均算力: 12.6/35.6 TFLOPS - 算力利用率: 35.4% [延迟统计] - 测量次数: 327 - 最低延迟: 0.027秒 - 最高延迟: 0.757秒 - 平均延迟: 0.080秒 """ # python det_ocr_shipinliu_pre.py --streams 3 --gpu_id 0 """ [系统信息] - CPU核心数: 10物理/16逻辑 - 系统内存: 63.8GB - 系统CPU使用率: 9.5% - 系统内存使用: 26.2GB / 63.8GB - GPU型号: NVIDIA GeForce RTX 4070 SUPER - GPU总显存: 12282.0MB [主进程资源] - 平均CPU使用率: 26.2% - 峰值CPU使用率: 53.1% - 平均内存占用: 386.1MB - 峰值内存占用: 386.6MB - 线程数: 9 [工作进程资源 (16个)] - 平均CPU使用率(每个进程): 43.9% - 总CPU使用率: 702.5% - 平均内存占用(每个进程): 1018.8MB - 总内存占用: 16301.3MB - 总线程数(峰值): 322 [所有进程总计] - 总CPU使用率: 728.7% - 总内存占用: 16687.5MB - 总线程数: 331 - 总进程数: 21(1个主进程 + 16个工作进程 + 3个显示进程 + 1个分发进程) [GPU资源] - 平均利用率: 52.2% - 峰值显存: 7861.9MB - 平均算力: 18.9/35.6 TFLOPS - 算力利用率: 53.1% [延迟统计] - 测量次数: 327 - 最低延迟: 0.030秒 - 最高延迟: 3.756秒 - 平均延迟: 1.077秒 """ # python det_ocr_shipinliu_pre.py --streams 4 --gpu_id 0 cpu100 """ === 程序资源使用报告 === [系统信息] - CPU核心数: 10物理/16逻辑 - 系统内存: 63.8GB - 系统CPU使用率: 58.6% - 系统内存使用: 36.3GB / 63.8GB - GPU型号: NVIDIA GeForce RTX 4070 SUPER - GPU总显存: 12282.0MB [主进程资源] - 平均CPU使用率: 28.0% - 峰值CPU使用率: 53.1% - 平均内存占用: 386.4MB - 峰值内存占用: 386.8MB - 线程数: 9 [工作进程资源 (16个)] - 平均CPU使用率(每个进程): 48.0% - 总CPU使用率: 768.7% - 平均内存占用(每个进程): 1585.2MB - 总内存占用: 25363.6MB - 总线程数(峰值): 320 [所有进程总计] - 总CPU使用率: 796.7% - 总内存占用: 25750.1MB - 总线程数: 329 - 总进程数: 22(1个主进程 + 16个工作进程 + 4个显示进程 + 1个分发进程) [GPU资源] - 平均利用率: 52.9% - 峰值显存: 7991.3MB - 平均算力: 20.2/35.6 TFLOPS - 算力利用率: 56.8% [延迟统计] - 测量次数: 327 - 最低延迟: 1.480秒 - 最高延迟: 14.222秒 - 平均延迟: 8.113秒 """ # python det_ocr_shipinliu_pre.py --streams 5 --gpu_id 0 """ """ # python det_ocr_shipinliu_pre.py --streams 16 --gpu_id 0 """ """ # python det_ocr_shipinliu_pre.py --streams 20 --gpu_id 0 """ """ (yolov8_bt) (base) zhang@zhang:~/danger/yolov7_crnn_ocr_detection$ python det_ocr_shipinliu_pre.py --streams 1 --gpu_id 0 [硬件配置] - CPU核心: 10物理/16逻辑 - 内存: 62.6GB - 使用GPU 0: NVIDIA GeForce RTX 4070 SUPER 显存: 12282.0MB [测试配置] - 模拟视频流数: 1 - 视频源: rtsp://admin:[email protected]/ [测试开始] 程序将运行30秒... In worker process 35804, np is <module 'numpy' from '/home/zhang/miniconda3/envs/yolov8_bt/lib/python3.9/site-packages/numpy/__init__.py'>, type(np.empty((1,))) = <class 'numpy.ndarray'> Loading weights/best.engine for TensorRT inference... [06/07/2025-18:54:11] [TRT] [I] Loaded engine size: 39 MiB [06/07/2025-18:54:11] [TRT] [W] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors. [06/07/2025-18:54:13] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +33, now: CPU 0, GPU 33 (MiB) [06/07/2025-18:54:13] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +39, now: CPU 0, GPU 72 (MiB) 剩余时间: 290秒 剩余时间: 280秒 剩余时间: 270秒 (yolov8_bt) (base) zhang@zhang:~/danger/yolov7_crnn_ocr_detection$ python -c "import numpy as np; print(np.__version__)" 1.23.0 代码运行后报错

最新推荐

recommend-type

基于双向长短期记忆网络(BILSTM)的MATLAB数据分类预测代码实现与应用

基于双向长短期记忆网络(BILSTM)的数据分类预测技术及其在MATLAB中的实现方法。首先解释了BILSTM的工作原理,强调其在处理时间序列和序列相关问题中的优势。接着讨论了数据预处理的重要性和具体步骤,如数据清洗、转换和标准化。随后提供了MATLAB代码示例,涵盖从数据导入到模型训练的完整流程,特别指出代码适用于MATLAB 2019版本及以上。最后总结了BILSTM模型的应用前景和MATLAB作为工具的优势。 适合人群:对机器学习尤其是深度学习感兴趣的科研人员和技术开发者,特别是那些希望利用MATLAB进行数据分析和建模的人群。 使用场景及目标:①研究时间序列和其他序列相关问题的有效解决方案;②掌握BILSTM模型的具体实现方式;③提高数据分类预测的准确性。 阅读建议:读者应该具备一定的编程基础和对深度学习的理解,在实践中逐步深入理解BILSTM的工作机制,并尝试调整参数以适应不同的应用场景。
recommend-type

路径规划人工势场法及其改进Matlab代码,包括斥力引力合力势场图,解决机器人目标点徘徊问题

用于机器人路径规划的传统人工势场法及其存在的问题,并提出了一种改进方法来解决机器人在接近目标点时出现的徘徊动荡现象。文中提供了完整的Matlab代码实现,包括引力场、斥力场和合力场的计算,以及改进后的斥力公式引入的距离衰减因子。通过对比传统和改进后的势场图,展示了改进算法的有效性和稳定性。此外,还给出了主循环代码片段,解释了关键参数的选择和调整方法。 适合人群:对机器人路径规划感兴趣的科研人员、工程师和技术爱好者,尤其是有一定Matlab编程基础并希望深入了解人工势场法及其改进算法的人群。 使用场景及目标:适用于需要进行机器人路径规划的研究项目或应用场景,旨在提高机器人在复杂环境中导航的能力,确保其能够稳定到达目标点而不发生徘徊或震荡。 其他说明:本文不仅提供理论解析,还包括具体的代码实现细节,便于读者理解和实践。建议读者在学习过程中结合提供的代码进行实验和调试,以便更好地掌握改进算法的应用技巧。
recommend-type

基于LBP特征与DBN算法的人脸识别MATLAB程序实现及优化

基于LBP特征与深度信念网络(DBN)的人脸识别MATLAB程序。该程序利用LBP进行特征提取,构建了一个四层DBN模型,其中包括三层隐含层和一层输出层。通过RBM预训练和BP反向传播算法微调,实现了高精度的人脸识别,准确率达到98%,错误率为1.67%。此外,程序还包括学习曲线绘制功能,用于调整正则化参数和其他超参数,以优化模型性能。 适合人群:对机器学习、深度学习以及人脸识别感兴趣的科研人员和技术开发者。 使用场景及目标:适用于需要高效人脸识别的应用场景,如安防监控、身份验证等。主要目标是提高人脸识别的准确性和效率,同时提供详细的代码实现和优化技巧。 其他说明:文中提到的ORL数据库已做对齐处理,直接应用LBP可能会导致一定的误差。硬件方面,程序在i7 CPU上运行约需半小时完成一次完整的训练。为加快速度,可以考虑将RBM预训练改为GPU版本,但需额外配置MATLAB的并行计算工具箱。
recommend-type

三菱FX3U六轴标准程序:实现3轴本体控制与3个1PG定位模块,轴点动控制、回零控制及定位功能,搭配气缸与DD马达转盘的多工位流水作业方式

三菱FX3U PLC的六轴控制程序,涵盖本体三轴和扩展的三个1PG定位模块的控制。程序实现了轴点动、回零、相对定位和绝对定位等功能,并集成了多个气缸和一个由DD马达控制的转盘,用于转盘多工位流水作业。文中展示了部分核心代码,解释了各个功能的具体实现方法和技术细节。同时,强调了该程序在工业自动化领域的广泛应用及其重要性。 适合人群:从事工业自动化领域的工程师和技术人员,尤其是熟悉三菱PLC编程的专业人士。 使用场景及目标:适用于需要实现复杂多轴控制和流水线作业自动化的工业环境,如制造业生产线。目标是提高生产效率和精度,减少人工干预。 其他说明:该程序不仅展示了具体的编程技巧,还提供了对系统架构和逻辑控制的理解,有助于进一步优化和扩展工业自动化解决方案。
recommend-type

北京交通大学机器人技术与应用研究性学习

完整报告(PDF)、完整代码
recommend-type

基于Debian Jessie的Kibana Docker容器部署指南

Docker是一种开源的容器化平台,它允许开发者将应用及其依赖打包进一个可移植的容器中。Kibana则是由Elastic公司开发的一款开源数据可视化插件,主要用于对Elasticsearch中的数据进行可视化分析。Kibana与Elasticsearch以及Logstash一起通常被称为“ELK Stack”,广泛应用于日志管理和数据分析领域。 在本篇文档中,我们看到了关于Kibana的Docker容器化部署方案。文档提到的“Docker-kibana:Kibana 作为基于 Debian Jessie 的Docker 容器”实际上涉及了两个版本的Kibana,即Kibana 3和Kibana 4,并且重点介绍了它们如何被部署在Docker容器中。 Kibana 3 Kibana 3是一个基于HTML和JavaScript构建的前端应用,这意味着它不需要复杂的服务器后端支持。在Docker容器中运行Kibana 3时,容器实际上充当了一个nginx服务器的角色,用以服务Kibana 3的静态资源。在文档中提及的配置选项,建议用户将自定义的config.js文件挂载到容器的/kibana/config.js路径。这一步骤使得用户能够将修改后的配置文件应用到容器中,以便根据自己的需求调整Kibana 3的行为。 Kibana 4 Kibana 4相较于Kibana 3,有了一个质的飞跃,它基于Java服务器应用程序。这使得Kibana 4能够处理更复杂的请求和任务。文档中指出,要通过挂载自定义的kibana.yml文件到容器的/kibana/config/kibana.yml路径来配置Kibana 4。kibana.yml是Kibana的主要配置文件,它允许用户配置各种参数,比如Elasticsearch服务器的地址,数据索引名称等等。通过Docker容器部署Kibana 4,用户可以很轻松地利用Docker提供的环境隔离和可复制性特点,使得Kibana应用的部署和运维更为简洁高效。 Docker容器化的优势 使用Docker容器化技术部署Kibana,有几个显著的优势: - **一致性**:Docker容器确保应用在开发、测试和生产环境中的行为保持一致。 - **轻量级**:相比传统虚拟机,Docker容器更加轻量,启动快速,资源占用更少。 - **隔离性**:容器之间的环境隔离,确保应用之间互不干扰。 - **可移植性**:容器可以在任何支持Docker的环境中运行,提高了应用的可移植性。 - **易于维护**:通过Dockerfile可以轻松构建和分发应用镜像,便于维护和升级。 在文档中,我们还看到了文件名“docker-kibana-master”。这个名称很可能是指向了存放Docker相关文件的源代码仓库,其中可能包含Dockerfile、构建和运行脚本以及可能的配置模板文件等。开发者可以从这个仓库中克隆或下载所需的Docker相关文件,并根据这些文件来构建和部署Kibana的Docker容器。 根据以上信息,对于希望利用Docker容器部署和管理Kibana应用的用户,需要掌握Docker的基本使用方法,包括Docker的安装、镜像管理、容器的创建和配置等。同时,还需要了解Kibana的基本原理和配置方法,这样才能充分利用Docker容器化带来的便利,优化Kibana应用的部署和管理流程。
recommend-type

Coze智能体工作流:打造钦天监视频内容的创新与实践

# 1. Coze智能体工作流概述 Coze智能体工作流是业界领先的智能工作流程管理系统,它通过整合最新的智能技术与人工智能算法,旨在为视频内容创造与优化带来革命性的变革。本章将对Coze智能体工作流进行全面的概述,揭示其在IT行业中的应用范围、流程结构以及技术基础。通过了解Coze智能体工作流,读者将能够把握它在当前和未
recommend-type

使用git仓库的利与弊

<think>好的,我需要分析Git仓库的优缺点。首先,根据用户提供的引用,看看有哪些点需要覆盖。引用[1]提到了Git有本地和远程仓库,需要push命令来更新远程,这应该属于优点还是缺点呢?可能优点在于本地操作灵活,缺点是需要额外命令。 引用[2]指出Git的权限管理薄弱,学习曲线陡峭,这显然是缺点。另外,引用[4]提到Git使用SHA-1哈希,内容完整性更好,属于优点。引用[5]比较了Git的分支管理简单,而缺少全局版本号,这可以作为优点和缺点分别说明。 现在需要将这些信息整理成结构化的优缺点,可能分点列出。同时,用户要求回答结构清晰,逐步解决问题,所以可能需要先介绍Git的基本概念,
recommend-type

TextWorld:基于文本游戏的强化学习环境沙箱

在给出的文件信息中,我们可以提取到以下IT知识点: ### 知识点一:TextWorld环境沙箱 **标题**中提到的“TextWorld”是一个专用的学习环境沙箱,专为强化学习(Reinforcement Learning,简称RL)代理的训练和测试而设计。在IT领域中,尤其是在机器学习的子领域中,环境沙箱是指一个受控的计算环境,允许实验者在隔离的条件下进行软件开发和测试。强化学习是一种机器学习方法,其中智能体(agent)通过与环境进行交互来学习如何在某个特定环境中执行任务,以最大化某种累积奖励。 ### 知识点二:基于文本的游戏生成器 **描述**中说明了TextWorld是一个基于文本的游戏生成器。在计算机科学中,基于文本的游戏(通常被称为文字冒险游戏)是一种游戏类型,玩家通过在文本界面输入文字指令来与游戏世界互动。TextWorld生成器能够创建这类游戏环境,为RL代理提供训练和测试的场景。 ### 知识点三:强化学习(RL) 强化学习是**描述**中提及的关键词,这是一种机器学习范式,用于训练智能体通过尝试和错误来学习在给定环境中如何采取行动。在强化学习中,智能体在环境中探索并执行动作,环境对每个动作做出响应并提供一个奖励或惩罚,智能体的目标是学习一个策略,以最大化长期累积奖励。 ### 知识点四:安装与支持的操作系统 **描述**提到TextWorld的安装需要Python 3,并且当前仅支持Linux和macOS系统。对于Windows用户,提供了使用Docker作为解决方案的信息。这里涉及几个IT知识点: - **Python 3**:一种广泛使用的高级编程语言,适用于快速开发,是进行机器学习研究和开发的常用语言。 - **Linux**和**macOS**:两种流行的操作系统,分别基于Unix系统和类Unix系统。 - **Windows**:另一种广泛使用的操作系统,具有不同的软件兼容性。 - **Docker**:一个开源的应用容器引擎,允许开发者打包应用及其依赖环境为一个轻量级、可移植的容器,使得在任何支持Docker的平台上一致地运行。 ### 知识点五:系统库和依赖 **描述**提到在基于Debian/Ubuntu的系统上,可以安装一些系统库来支持TextWorld的本机组件。这里涉及的知识点包括: - **Debian/Ubuntu**:基于Debian的Linux发行版,是目前最流行的Linux发行版之一。 - **系统库**:操作系统中包含的一系列预编译的软件包和库,供应用程序在运行时使用。 - **包管理工具**,如**apt**(Advanced Package Tool),它是一个在Debian及其衍生系统中用于安装、删除和管理软件包的命令行工具。 ### 知识点六:与创建者联系方式 **描述**提供了与TextWorld创建者的联系方式,包括电子邮件地址和一个Gitter频道。这说明了如何与开源项目的维护者进行沟通与反馈: - **电子邮件**是常见的沟通方式,允许用户与开发者直接交流。 - **Gitter**是一个基于GitHub的即时消息工具,通常用于开源项目中的实时协作和交流。 ### 结语 综合以上信息,我们可以了解到TextWorld是一个专为强化学习设计的学习环境沙箱,它通过创建基于文本的游戏环境,让研究者和开发者训练和测试RL代理。它主要针对Linux和macOS系统,不过也有适合Windows用户的替代方案。此外,了解如何安装和配置TextWorld,以及如何与创建者沟通,对于开发者来说是十分重要的基础技能。
recommend-type

Coze智能体工作流全攻略

# 1. Coze智能体工作流概述 在现代企业中,工作流管理不仅是提高效率的关键因素,而且