活动介绍

from torchvision import datasets, transforms ModuleNotFoundError: No module named 'torchvision'

时间: 2025-06-11 15:33:11 浏览: 18
### 解决 `ModuleNotFoundError: No module named 'torchvision'` 的方法 当遇到 `ModuleNotFoundError: No module named 'torchvision'` 错误时,通常是因为当前 Python 环境中未正确安装 `torchvision` 模块。以下是几种常见的解决方法: #### 方法一:使用 Conda 安装 推荐在 Conda 环境中安装 `torchvision`,因为它可以更好地管理依赖关系。执行以下命令以安装 `torchvision`: ```bash conda install torchvision -c pytorch ``` 此命令会从 PyTorch 的官方频道安装最新版本的 `torchvision`[^3]。 #### 方法二:使用 pip 安装 如果更倾向于使用 pip 安装,则需要确保已安装与当前系统匹配的 CUDA 版本(如果使用 GPU)。以下是通用的安装命令: ```bash pip install torchvision ``` 如果需要指定 CUDA 版本,例如 CUDA 11.7,可以使用以下命令: ```bash pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 ``` 上述命令会根据指定的 CUDA 版本安装兼容的 `torchvision`[^1]。 #### 方法三:手动下载并安装 对于某些特殊环境(如离线环境),可能需要手动下载 `torchvision` 的 whl 文件。访问 [PyTorch 官方网站](https://pytorch.org/get-started/previous-versions/) 并根据操作系统、Python 版本和 CUDA 配置选择合适的 whl 文件。下载完成后,运行以下命令进行安装: ```bash pip install /path/to/torchvision.whl ``` #### 方法四:编译安装 如果以上方法均不可用,可以尝试从源代码编译安装 `torchvision`。首先克隆官方仓库: ```bash git clone https://github.com/pytorch/vision.git cd vision ``` 然后按照官方文档中的说明进行编译和安装[^2]。 #### 验证安装 完成安装后,可以通过以下代码验证 `torchvision` 是否成功安装: ```python import torchvision print(torchvision.__version__) ``` 如果未报错并打印出版本号,则表示安装成功。 ### 注意事项 - 确保当前使用的 Python 环境是正确的,特别是在使用多个虚拟环境时。 - 如果使用 Conda 环境,请先激活目标环境再运行安装命令。 - 安装过程中若提示依赖冲突,可尝试更新 Conda 或 pip 工具。 ---
阅读全文

相关推荐

(smol-env) [heng2@localhost ~]$ python Python 3.8.18 (default, Mar 21 2025, 23:18:16) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from transformers import pipeline Traceback (most recent call last): File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1778, in _get_module return importlib.import_module("." + module_name, self.__name__) File "/usr/local/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/pipelines/__init__.py", line 26, in <module> from ..image_processing_utils import BaseImageProcessor File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/image_processing_utils.py", line 21, in <module> from .image_transforms import center_crop, normalize, rescale File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/image_transforms.py", line 22, in <module> from .image_utils import ( File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/image_utils.py", line 58, in <module> from torchvision.transforms import InterpolationMode File "/home/heng2/smol-env/lib/python3.8/site-packages/torchvision/__init__.py", line 10, in <module> from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip File "/home/heng2/smol-env/lib/python3.8/site-packages/torchvision/datasets/__init__.py", line 1, in <module> from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel File "/home/heng2/smol-env/lib/python3.8/site-packages/torchvision/datasets/_optical_flow.py", line 13, in <module> from .utils import _read_pfm, verify_str_arg File "/home/heng2/smol-env/lib/python3.8/site-packages/torchvision/datasets/utils.py", line 4, in <module> import lzma File "/usr/local/lib/python3.8/lzma.py", line 27, in <module> from _lzma import * ModuleNotFoundError: No module named '_lzma' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<frozen importlib._bootstrap>", line 1039, in _handle_fromlist File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1766, in __getattr__ module = self._get_module(self._class_to_module[name]) File "/home/heng2/smol-env/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1780, in _get_module raise RuntimeError( RuntimeError: Failed to import transformers.pipelines because of the following error (look up to see its traceback): No module named '_lzma'

--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[5], line 1 ----> 1 import easyocr File ~\AppData\Roaming\Python\Python313\site-packages\easyocr\__init__.py:1 ----> 1 from .easyocr import Reader 3 __version__ = '1.7.2' File ~\AppData\Roaming\Python\Python313\site-packages\easyocr\easyocr.py:3 1 # -*- coding: utf-8 -*- ----> 3 from .recognition import get_recognizer, get_text 4 from .utils import group_text_box, get_image_list, calculate_md5, get_paragraph,\ 5 download_and_unzip, printProgressBar, diff, reformat_input,\ 6 make_rotated_img_list, set_result_with_confidence,\ 7 reformat_input_batched, merge_to_free 8 from .config import * File ~\AppData\Roaming\Python\Python313\site-packages\easyocr\recognition.py:6 4 import torch.utils.data 5 import torch.nn.functional as F ----> 6 import torchvision.transforms as transforms 7 import numpy as np 8 from collections import OrderedDict File ~\AppData\Roaming\Python\Python313\site-packages\torchvision\__init__.py:10 7 # Don't re-order these, we need to load the _C extension (done when importing 8 # .extensions) before entering _meta_registrations. 9 from .extension import _HAS_OPS # usort:skip ---> 10 from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip 12 try: 13 from .version import __version__ # noqa: F401 File ~\AppData\Roaming\Python\Python313\site-packages\torchvision\datasets\__init__.py:1 ----> 1 from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel 2 from ._stereo_matching import ( 3 CarlaStereo, 4 CREStereo, (...) 12 SintelStereo, 13 ) 14 from .caltech import Caltech101, Caltech256 File ~\AppData\Roaming\Python\Python313\site-packages\torchvision\datasets\_optical_flow.py:13 10 from PIL import Image 12 from ..io.image import decode_png, read_file ---> 13 from .utils import _read_pfm, verify_str_arg 14 from .vision import VisionDataset 16 T1 = Tuple[Image.Image, Image.Image, Optional[np.ndarray], Optional[np.ndarray]] File ~\AppData\Roaming\Python\Python313\site-packages\torchvision\datasets\utils.py:20 18 import numpy as np 19 import torch ---> 20 from torch.utils.model_zoo import tqdm 22 from .._internally_replaced_utils import _download_file_from_remote_location, _is_remote_location_available 24 USER_AGENT = "pytorch/vision" ModuleNotFoundError: No module named 'torch.utils.model_zoo'

Traceback (most recent call last): File "D:\Anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-6-b8424bd64091>", line 2, in <cell line: 2> import torchvision File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "D:\Anaconda\lib\site-packages\torchvision\__init__.py", line 6, in <module> from torchvision import datasets, io, models, ops, transforms, utils File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "D:\Anaconda\lib\site-packages\torchvision\models\__init__.py", line 17, in <module> from . import detection, optical_flow, quantization, segmentation, video File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "D:\Anaconda\lib\site-packages\torchvision\models\quantization\__init__.py", line 3, in <module> from .mobilenet import * File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "D:\Anaconda\lib\site-packages\torchvision\models\quantization\mobilenet.py", line 1, in <module> from .mobilenetv2 import * # noqa: F401, F403 File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "D:\Anaconda\lib\site-packages\torchvision\models\quantization\mobilenetv2.py", line 5, in <module> from torch.ao.quantization import DeQuantStub, QuantStub File "D:\Pycharm\PyCharm Community Edition 2022.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) ModuleNotFoundError: No module named 'torch.ao.quantization'

root@ubuntu:~# python -c "from datasets import exceptions; print('成功导入')" 成功导入 root@ubuntu:~# python3 image_to_text.py Traceback (most recent call last): File "/root/image_to_text.py", line 1, in <module> from modelscope.pipelines import pipeline File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/__init__.py", line 4, in <module> from .base import Pipeline File "/usr/local/lib/python3.10/dist-packages/modelscope/pipelines/base.py", line 15, in <module> from modelscope.models.base import Model File "/usr/local/lib/python3.10/dist-packages/modelscope/models/__init__.py", line 18, in <module> fix_transformers_upgrade() File "/usr/local/lib/python3.10/dist-packages/modelscope/utils/automodel_utils.py", line 48, in fix_transformers_upgrade from transformers import PreTrainedModel File "/usr/local/lib/python3.10/dist-packages/transformers/utils/import_utils.py", line 2154, in __getattr__ module = self._get_module(self._class_to_module[name]) File "/usr/local/lib/python3.10/dist-packages/transformers/utils/import_utils.py", line 2184, in _get_module raise e File "/usr/local/lib/python3.10/dist-packages/transformers/utils/import_utils.py", line 2182, in _get_module return importlib.import_module("." + module_name, self.__name__) File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 73, in <module> from .loss.loss_utils import LOSS_MAPPING File "/usr/local/lib/python3.10/dist-packages/transformers/loss/loss_utils.py", line 21, in <module> from .loss_d_fine import DFineForObjectDetectionLoss File "/usr/local/lib/python3.10/dist-packages/transformers/loss/loss_d_fine.py", line 21, in <module> from .loss_for_object_detection import ( File "/usr/local/lib/python3.10/dist-packages/transformers/loss/loss_for_object_detection.py", line 32, in <module> from transformers.image_transforms import center_to_corners_format File "/usr/local/lib/python3.10/dist-packages/transformers/image_transforms.py", line 22, in <module> from .image_utils import ( File "/usr/local/lib/python3.10/dist-packages/transformers/image_utils.py", line 59, in <module> from torchvision.transforms import InterpolationMode File "/usr/local/lib/python3.10/dist-packages/torchvision/__init__.py", line 10, in <module> from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip ImportError: cannot import name 'datasets' from partially initialized module 'torchvision' (most likely due to a circular import) (/usr/local/lib/python3.10/dist-packages/torchvision/__init__.py)

import torch.optim import torchvision from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from model import * train_data = torchvision.datasets.CIFAR10(root='./data',train=True,transform=torchvision.transforms.ToTensor(),download=True) test_data = torchvision.datasets.CIFAR10 (root='./data',train=False,transform=torchvision.transforms.ToTensor(),download=True) train_data_size = len(train_data) test_data_size = len(test_data) # #format():格式化字符串(替换{}) # print("训练集的长度:{}".format(train_data_size)) # print("测试集的长度:{}".format(test_data_size)) #利用dataloader加载数据集 train_dataloader = DataLoader(train_data,batch_size=64) test_dataloader = DataLoader(test_data,batch_size=64) #创建网络模型 train = Train() #损失函数 loss_fn = nn.CrossEntropyLoss() #优化器 learning_rate = 0.1 optimizer = torch.optim.SGD(train.parameters(),lr=learning_rate) #设置训练网络的参数 #记录训练的次数 total_train_step = 0 #记录测试的次数 total_test_step = 0 #训练的轮数 epoch = 10 #tensorboard writer = SummaryWriter("./logs_train") for i in range(epoch): print("----------第{}轮训练--------------".format(i+1)) #训练步骤开始 for data in train_dataloader: imgs,targets = data outputs = train(imgs) loss = loss_fn(outputs,targets) #优化器优化模型 optimizer.zero_grad() loss.backward() optimizer.step() total_train_step = total_train_step+1 if total_train_step %100 ==0: print("训练次数:{},loss:{}".format(total_train_step,loss.item()))#item会将输出的tensor值转为真实数字 writer.add_scalar("train_loss",loss.item(),total_train_step) #测试 total_test_loss = 0 with torch.no_grad(): for data in test_dataloader: imgs,targets = data outputs = train(imgs) loss = loss_fn(outputs,targets) total_test_loss = total_test_loss+loss.item() print("整体测试集上的损失:{}".format(total_test_loss)) writer.add_scalar("test_loss",total_test_loss,total_test_step) total_test_step = total_test_step+1 torch.save(train,"train.{}.pth".format(i)) print("模型已保存") writer.close() 帮我检查一下我的代码,为什么训练模型到第二轮的时候总是输出缺失值

这是main.py文件的代码:from datetime import datetime from functools import partial from PIL import Image import cv2 import numpy as np from torch.utils.data import DataLoader from torch.version import cuda from torchvision import transforms from torchvision.datasets import CIFAR10 from torchvision.models import resnet from tqdm import tqdm import argparse import json import math import os import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F #数据增强(核心增强部分) import torch from torchvision import transforms from torch.utils.data import Dataset, DataLoader # 设置参数 parser = argparse.ArgumentParser(description='Train MoCo on CIFAR-10') parser.add_argument('-a', '--arch', default='resnet18') # lr: 0.06 for batch 512 (or 0.03 for batch 256) parser.add_argument('--lr', '--learning-rate', default=0.06, type=float, metavar='LR', help='initial learning rate', dest='lr') parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('--schedule', default=[120, 160], nargs='*', type=int, help='learning rate schedule (when to drop lr by 10x); does not take effect if --cos is on') parser.add_argument('--cos', action='store_true', help='use cosine lr schedule') parser.add_argument('--batch-size', default=64, type=int, metavar='N', help='mini-batch size') parser.add_argument('--wd', default=5e-4, type=float, metavar='W', help='weight decay') # moco specific configs: parser.add_argument('--moco-dim', default=128, type=int, help='feature dimension') parser.add_argument('--moco-k', default=4096, type=int, help='queue size; number of negative keys') parser.add_argument('--moco-m', default=0.99, type=float, help='moco momentum of updating key encoder') parser.add_argument('--moco-t', default=0.1, type=float, help='softmax temperature') parser.add_argument('--bn-splits', default=8, type=int, help='simulate multi-gpu behavior of BatchNorm in one gpu; 1 is SyncBatchNorm in multi-gpu') parser.add_argument('--symmetric', action='store_true', help='use a symmetric loss function that backprops to both crops') # knn monitor parser.add_argument('--knn-k', default=20, type=int, help='k in kNN monitor') parser.add_argument('--knn-t', default=0.1, type=float, help='softmax temperature in kNN monitor; could be different with moco-t') # utils parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--results-dir', default='', type=str, metavar='PATH', help='path to cache (default: none)') ''' args = parser.parse_args() # running in command line ''' args = parser.parse_args('') # running in ipynb # set command line arguments here when running in ipynb args.epochs = 300 # 修改处 args.cos = True args.schedule = [] # cos in use args.symmetric = False if args.results_dir == '': args.results_dir = "E:\\contrast\\yolov8\\MoCo\\run\\cache-" + datetime.now().strftime("%Y-%m-%d-%H-%M-%S-moco") moco_args = args class CIFAR10Pair(CIFAR10): def __getitem__(self, index): img = self.data[index] img = Image.fromarray(img) # 原始图像增强 im_1 = self.transform(img) im_2 = self.transform(img) # 退化增强生成额外视图 degraded_results = image_degradation_and_augmentation(img) im_3 = self.transform(Image.fromarray(degraded_results['augmented_images'][0])) # 选择第一组退化增强 im_4 = self.transform(Image.fromarray(degraded_results['cutmix_image'])) return im_1, im_2, im_3, im_4 # 返回原始增强+退化增强 # 定义数据加载器 # class CIFAR10Pair(CIFAR10): # """CIFAR10 Dataset. # """ # def __getitem__(self, index): # img = self.data[index] # img = Image.fromarray(img) # if self.transform is not None: # im_1 = self.transform(img) # im_2 = self.transform(img) # return im_1, im_2 import cv2 import numpy as np import random def apply_interpolation_degradation(img, method): """ 应用插值退化 参数: img: 输入图像(numpy数组) method: 插值方法('nearest', 'bilinear', 'bicubic') 返回: 退化后的图像 """ # 获取图像尺寸 h, w = img.shape[:2] # 应用插值方法 if method == 'nearest': # 最近邻退化: 下采样+上采样 downsampled = cv2.resize(img, (w//2, h//2), interpolation=cv2.INTER_NEAREST) degraded = cv2.resize(downsampled, (w, h), interpolation=cv2.INTER_NEAREST) elif method == 'bilinear': # 双线性退化: 下采样+上采样 downsampled = cv2.resize(img, (w//2, h//2), interpolation=cv2.INTER_LINEAR) degraded = cv2.resize(downsampled, (w, h), interpolation=cv2.INTER_LINEAR) elif method == 'bicubic': # 双三次退化: 下采样+上采样 downsampled = cv2.resize(img, (w//2, h//2), interpolation=cv2.INTER_CUBIC) degraded = cv2.resize(downsampled, (w, h), interpolation=cv2.INTER_CUBIC) else: degraded = img return degraded def darken_image(img, intensity=0.3): """ 应用黑暗处理 - 降低图像亮度并增加暗区对比度 参数: img: 输入图像(numpy数组) intensity: 黑暗强度 (0.1-0.9) 返回: 黑暗处理后的图像 """ # 限制强度范围 intensity = max(0.1, min(0.9, intensity)) # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV).astype(np.float32) # 降低亮度(V通道) hsv[:, :, 2] = hsv[:, :, 2] * intensity # 增加暗区的对比度 - 使用gamma校正 gamma = 1.0 + (1.0 - intensity) # 黑暗强度越大,gamma值越大 hsv[:, :, 2] = np.power(hsv[:, :, 2]/255.0, gamma) * 255.0 # 限制值在0-255范围内 hsv[:, :, 2] = np.clip(hsv[:, :, 2], 0, 255) # 转换回RGB return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB) def random_affine(image): """ 随机仿射变换(缩放和平移) 参数: image: 输入图像(numpy数组) 返回: 变换后的图像 """ height, width = image.shape[:2] # 随机缩放因子 (0.8 to 1.2) scale = random.uniform(0.8, 1.2) # 随机平移 (10% of image size) max_trans = 0.1 * min(width, height) tx = random.randint(-int(max_trans), int(max_trans)) ty = random.randint(-int(max_trans), int(max_trans)) # 变换矩阵 M = np.array([[scale, 0, tx], [0, scale, ty]], dtype=np.float32) # 应用仿射变换 transformed = cv2.warpAffine(image, M, (width, height)) return transformed def augment_hsv(image, h_gain=0.1, s_gain=0.5, v_gain=0.5): """ HSV色彩空间增强 参数: image: 输入图像(numpy数组) h_gain, s_gain, v_gain: 各通道的增益范围 返回: 增强后的图像 """ # 限制增益范围 h_gain = max(-0.1, min(0.1, random.uniform(-h_gain, h_gain))) s_gain = max(0.5, min(1.5, random.uniform(1-s_gain, 1+s_gain))) v_gain = max(0.5, min(1.5, random.uniform(1-v_gain, 1+v_gain))) # 转换为HSV hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype(np.float32) # 应用增益 hsv[:, :, 0] = (hsv[:, :, 0] * (1 + h_gain)) % 180 hsv[:, :, 1] = np.clip(hsv[:, :, 1] * s_gain, 0, 255) hsv[:, :, 2] = np.clip(hsv[:, :, 2] * v_gain, 0, 255) # 转换回RGB return cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB) # def mixup(img1, img2, alpha=0.6): # """ # 将两幅图像混合在一起 # 参数: # img1, img2: 输入图像(numpy数组) # alpha: Beta分布的参数,控制混合比例 # 返回: # 混合后的图像 # """ # # 生成混合比例 # lam = random.betavariate(alpha, alpha) # # 确保图像尺寸相同 # if img1.shape != img2.shape: # img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0])) # # 混合图像 # mixed = (lam * img1.astype(np.float32) + (1 - lam) * img2.astype(np.float32)).astype(np.uint8) # return mixed # def image_degradation_and_augmentation(image,dark_intensity=0.3): # """ # 完整的图像退化和增强流程 # 参数: # image: 输入图像(PIL.Image或numpy数组) # 返回: # dict: 包含所有退化组和最终增强结果的字典 # """ # # 确保输入是numpy数组 # if not isinstance(image, np.ndarray): # image = np.array(image) # # 确保图像为RGB格式 # if len(image.shape) == 2: # image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) # elif image.shape[2] == 4: # image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB) # # 原始图像 # original = image.copy() # # 插值方法列表 # interpolation_methods = ['nearest', 'bilinear', 'bicubic'] # # 第一组退化: 三种插值方法 # group1 = [] # for method in interpolation_methods: # degraded = apply_interpolation_degradation(original, method) # group1.append(degraded) # # 第二组退化: 随机额外退化 # group2 = [] # for img in group1: # # 随机选择一种退化方法 # method = random.choice(interpolation_methods) # extra_degraded = apply_interpolation_degradation(img, method) # group2.append(extra_degraded) # # 所有退化图像组合 # all_degraded_images = [original] + group1 + group2 # # 应用黑暗处理 (在增强之前) # darkened_images = [darken_image(img, intensity=dark_intensity) for img in all_degraded_images] # # 应用数据增强 # # 1. 随机仿射变换 # affine_images = [random_affine(img) for img in darkened_images] # # 2. HSV增强 # hsv_images = [augment_hsv(img) for img in affine_images] # # 3. MixUp增强 # # 随机选择两个增强后的图像进行混合 # mixed_image = mixup( # random.choice(hsv_images), # random.choice(hsv_images) # ) # # 返回结果 # results = { # 'original': original, # 'degraded_group1': group1, # 第一组退化图像 # 'degraded_group2': group2, # 第二组退化图像 # 'augmented_images': hsv_images, # 所有增强后的图像(原始+六组退化) # 'mixup_image': mixed_image # MixUp混合图像 # } # return results # # def add_gaussian_noise(image, mean=0, sigma=25): # # """添加高斯噪声""" # # noise = np.random.normal(mean, sigma, image.shape) # # noisy = np.clip(image + noise, 0, 255).astype(np.uint8) # # return noisy # # def random_cutout(image, max_holes=3, max_height=16, max_width=16): # # """随机CutOut增强""" # # h, w = image.shape[:2] # # for _ in range(random.randint(1, max_holes)): # # hole_h = random.randint(1, max_height) # # hole_w = random.randint(1, max_width) # # y = random.randint(0, h - hole_h) # # x = random.randint(0, w - hole_w) # # image[y:y+hole_h, x:x+hole_w] = 0 # # return image import cv2 import numpy as np import random from matplotlib import pyplot as plt import pywt def wavelet_degradation(image, level=0.5): """小波系数衰减退化""" # 小波分解 coeffs = pywt.dwt2(image, 'haar') cA, (cH, cV, cD) = coeffs # 衰减高频系数 cH = cH * level cV = cV * level cD = cD * level # 重建图像 return pywt.idwt2((cA, (cH, cV, cD)), 'haar')[:image.shape[0], :image.shape[1]] def adaptive_interpolation_degradation(image): """自适应插值退化(随机选择最近邻或双三次插值)""" if random.choice([True, False]): method = cv2.INTER_NEAREST # 最近邻插值 else: method = cv2.INTER_CUBIC # 双三次插值 # 先缩小再放大 scale_factor = random.uniform(0.3, 0.8) small = cv2.resize(image, None, fx=scale_factor, fy=scale_factor, interpolation=method) return cv2.resize(small, (image.shape[1], image.shape[0]), interpolation=method) def bilinear_degradation(image): """双线性插值退化""" # 先缩小再放大 scale_factor = random.uniform(0.3, 0.8) small = cv2.resize(image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR) return cv2.resize(small, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR) def cutmix(img1, img2, bboxes1=None, bboxes2=None, beta=1.0): """ 参数: img1: 第一张输入图像(numpy数组) img2: 第二张输入图像(numpy数组) bboxes1: 第一张图像的边界框(可选) bboxes2: 第二张图像的边界框(可选) beta: Beta分布的参数,控制裁剪区域的大小 返回: 混合后的图像和边界框(如果有) """ # 确保图像尺寸相同 if img1.shape != img2.shape: img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0])) h, w = img1.shape[:2] # 生成裁剪区域的lambda值(混合比例) lam = np.random.beta(beta, beta) # 计算裁剪区域的宽高 cut_ratio = np.sqrt(1. - lam) cut_w = int(w * cut_ratio) cut_h = int(h * cut_ratio) # 随机确定裁剪区域的中心点 cx = np.random.randint(w) cy = np.random.randint(h) # 计算裁剪区域的边界 x1 = np.clip(cx - cut_w // 2, 0, w) y1 = np.clip(cy - cut_h // 2, 0, h) x2 = np.clip(cx + cut_w // 2, 0, w) y2 = np.clip(cy + cut_h // 2, 0, h) # 执行CutMix操作 mixed_img = img1.copy() mixed_img[y1:y2, x1:x2] = img2[y1:y2, x1:x2] # 计算实际的混合比例 lam = 1 - ((x2 - x1) * (y2 - y1) / (w * h)) # 处理边界框(如果有) mixed_bboxes = None if bboxes1 is not None and bboxes2 is not None: mixed_bboxes = [] # 添加第一张图像的边界框 for bbox in bboxes1: mixed_bboxes.append(bbox + [lam]) # 添加混合权重 # 添加第二张图像的边界框(只添加在裁剪区域内的) for bbox in bboxes2: # 检查边界框是否在裁剪区域内 bbox_x_center = (bbox[0] + bbox[2]) / 2 bbox_y_center = (bbox[1] + bbox[3]) / 2 if (x1 <= bbox_x_center <= x2) and (y1 <= bbox_y_center <= y2): mixed_bboxes.append(bbox + [1 - lam]) return mixed_img, mixed_bboxes def image_degradation_and_augmentation(image, bboxes=None): """ 完整的图像退化和增强流程(修改为使用CutMix) 参数: image: 输入图像(PIL.Image或numpy数组) bboxes: 边界框(可选) 返回: dict: 包含所有退化组和最终增强结果的字典 """ # 确保输入是numpy数组 if not isinstance(image, np.ndarray): image = np.array(image) # 确保图像为RGB格式 if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) elif image.shape[2] == 4: image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB) degraded_sets = [] original = image.copy() # 第一组退化:三种基础退化 degraded_sets.append(wavelet_degradation(original.copy())) degraded_sets.append(degraded_sets) degraded_sets.append(adaptive_interpolation_degradation(original.copy())) degraded_sets.append(degraded_sets) degraded_sets.append(bilinear_degradation(original.copy())) degraded_sets.append(degraded_sets) # # 原始图像 # original = image.copy() # # 插值方法列表 # interpolation_methods = ['nearest', 'bilinear', 'bicubic'] # # 第一组退化: 三种插值方法 # group1 = [] # for method in interpolation_methods: # degraded = apply_interpolation_degradation(original, method) # group1.append(degraded) # 第二组退化: 随机额外退化 # group2 = [] # for img in group1: # # 随机选择一种退化方法 # method = random.choice(interpolation_methods) # extra_degraded = apply_interpolation_degradation(img, method) # group2.append(extra_degraded) # 第二组退化:随机选择再退化 methods = [wavelet_degradation, adaptive_interpolation_degradation, bilinear_degradation] group2=[] for img in degraded_sets: selected_method = random.choice(methods) group2.append(selected_method(img)) group2.append(group2) # 原始图像 original = image.copy() all_degraded_images = [original] + degraded_sets + group2 # 应用黑暗处理 dark_original = darken_image(original) dark_degraded = [darken_image(img) for img in all_degraded_images] # 合并原始和退化图像 all_images = [dark_original] + dark_degraded # 应用数据增强 # 1. 随机仿射变换 affine_images = [random_affine(img) for img in all_images] # 2. HSV增强 hsv_images = [augment_hsv(img) for img in affine_images] # 3. CutMix增强 # 随机选择两个增强后的图像进行混合 mixed_image, mixed_bboxes = cutmix( random.choice(hsv_images), random.choice(hsv_images), bboxes1=bboxes if bboxes is not None else None, bboxes2=bboxes if bboxes is not None else None ) # 返回结果 results = { 'original': original, 'degraded': dark_degraded, 'augmented_images': hsv_images, # 所有增强后的图像(原始+六组退化) 'cutmix_image': mixed_image, # CutMix混合图像 'cutmix_bboxes': mixed_bboxes if bboxes is not None else None # 混合后的边界框 } return results train_transform = transforms.Compose([ transforms.RandomResizedCrop(32), transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8), transforms.RandomGrayscale(p=0.2), transforms.ToTensor(), transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])]) test_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])]) # data_processing prepare train_data = CIFAR10Pair(root="E:/contrast/yolov8/MoCo/data_visdrone2019", train=True, transform=train_transform, download=False) moco_train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=0, pin_memory=True, drop_last=True) memory_data = CIFAR10(root="E:/contrast/yolov8/MoCo/data_visdrone2019", train=True, transform=test_transform, download=False) memory_loader = DataLoader(memory_data, batch_size=args.batch_size, shuffle=False, num_workers=0, pin_memory=True) test_data = CIFAR10(root="E:/contrast/yolov8/MoCo/data_visdrone2019", train=False, transform=test_transform, download=False) test_loader = DataLoader(test_data, batch_size=args.batch_size, shuffle=False, num_workers=0, pin_memory=True) # 定义基本编码器 # SplitBatchNorm: simulate multi-gpu behavior of BatchNorm in one gpu by splitting alone the batch dimension # implementation adapted from https://github.com/davidcpage/cifar10-fast/blob/master/torch_backend.py class SplitBatchNorm(nn.BatchNorm2d): def __init__(self, num_features, num_splits, **kw): super().__init__(num_features, **kw) self.num_splits = num_splits def forward(self, input): N, C, H, W = input.shape if self.training or not self.track_running_stats: running_mean_split = self.running_mean.repeat(self.num_splits) running_var_split = self.running_var.repeat(self.num_splits) outcome = nn.functional.batch_norm( input.view(-1, C * self.num_splits, H, W), running_mean_split, running_var_split, self.weight.repeat(self.num_splits), self.bias.repeat(self.num_splits), True, self.momentum, self.eps).view(N, C, H, W) self.running_mean.data.copy_(running_mean_split.view(self.num_splits, C).mean(dim=0)) self.running_var.data.copy_(running_var_split.view(self.num_splits, C).mean(dim=0)) return outcome else: return nn.functional.batch_norm( input, self.running_mean, self.running_var, self.weight, self.bias, False, self.momentum, self.eps) class ModelBase(nn.Module): """ Common CIFAR ResNet recipe. Comparing with ImageNet ResNet recipe, it: (i) replaces conv1 with kernel=3, str=1 (ii) removes pool1 """ def __init__(self, feature_dim=128, arch=None, bn_splits=16): super(ModelBase, self).__init__() # use split batchnorm norm_layer = partial(SplitBatchNorm, num_splits=bn_splits) if bn_splits > 1 else nn.BatchNorm2d resnet_arch = getattr(resnet, arch) net = resnet_arch(num_classes=feature_dim, norm_layer=norm_layer) self.net = [] for name, module in net.named_children(): if name == 'conv1': module = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) if isinstance(module, nn.MaxPool2d): continue if isinstance(module, nn.Linear): self.net.append(nn.Flatten(1)) self.net.append(module) self.net = nn.Sequential(*self.net) def forward(self, x): x = self.net(x) # note: not normalized here return x # 定义MOCO class ModelMoCo(nn.Module): def __init__(self, dim=128, K=4096, m=0.99, T=0.1, arch='resnet18', bn_splits=8, symmetric=True): super(ModelMoCo, self).__init__() self.K = K self.m = m self.T = T self.symmetric = symmetric # create the encoders self.encoder_q = ModelBase(feature_dim=dim, arch=arch, bn_splits=bn_splits) self.encoder_k = ModelBase(feature_dim=dim, arch=arch, bn_splits=bn_splits) for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()): param_k.data.copy_(param_q.data) # initialize param_k.requires_grad = False # not update by gradient 不参与训练 # create the queue self.register_buffer("queue", torch.randn(dim, K)) self.queue = nn.functional.normalize(self.queue, dim=0) self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) @torch.no_grad() def _momentum_update_key_encoder(self): # 动量更新encoder_k """ Momentum update of the key encoder """ for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()): param_k.data = param_k.data * self.m + param_q.data * (1. - self.m) @torch.no_grad() def _dequeue_and_enqueue(self, keys): # 出队与入队 batch_size = keys.shape[0] ptr = int(self.queue_ptr) assert self.K % batch_size == 0 # for simplicity # replace the keys at ptr (dequeue and enqueue) self.queue[:, ptr:ptr + batch_size] = keys.t() # transpose ptr = (ptr + batch_size) % self.K # move pointer self.queue_ptr[0] = ptr @torch.no_grad() def _batch_shuffle_single_gpu(self, x): """ Batch shuffle, for making use of BatchNorm. """ # random shuffle index idx_shuffle = torch.randperm(x.shape[0]).cuda() # index for restoring idx_unshuffle = torch.argsort(idx_shuffle) return x[idx_shuffle], idx_unshuffle @torch.no_grad() def _batch_unshuffle_single_gpu(self, x, idx_unshuffle): """ Undo batch shuffle. """ return x[idx_unshuffle] def contrastive_loss(self, im_q, im_k): # compute query features q = self.encoder_q(im_q) # queries: NxC q = nn.functional.normalize(q, dim=1) # already normalized # compute key features with torch.no_grad(): # no gradient to keys # shuffle for making use of BN im_k_, idx_unshuffle = self._batch_shuffle_single_gpu(im_k) k = self.encoder_k(im_k_) # keys: NxC k = nn.functional.normalize(k, dim=1) # already normalized # undo shuffle k = self._batch_unshuffle_single_gpu(k, idx_unshuffle) # compute logits # Einstein sum is more intuitive # positive logits: Nx1 l_pos = torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1) # negative logits: NxK l_neg = torch.einsum('nc,ck->nk', [q, self.queue.clone().detach()]) # logits: Nx(1+K) logits = torch.cat([l_pos, l_neg], dim=1) # apply temperature logits /= self.T # labels: positive key indicators labels = torch.zeros(logits.shape[0], dtype=torch.long).cuda() loss = nn.CrossEntropyLoss().cuda()(logits, labels) # 交叉熵损失 return loss, q, k def forward(self, im1, im2): """ Input: im_q: a batch of query images im_k: a batch of key images Output: loss """ # update the key encoder with torch.no_grad(): # no gradient to keys self._momentum_update_key_encoder() # compute loss if self.symmetric: # asymmetric loss loss_12, q1, k2 = self.contrastive_loss(im1, im2) loss_21, q2, k1 = self.contrastive_loss(im2, im1) loss = loss_12 + loss_21 k = torch.cat([k1, k2], dim=0) else: # asymmetric loss loss, q, k = self.contrastive_loss(im1, im2) self._dequeue_and_enqueue(k) return loss # create model moco_model = ModelMoCo( dim=args.moco_dim, K=args.moco_k, m=args.moco_m, T=args.moco_t, arch=args.arch, bn_splits=args.bn_splits, symmetric=args.symmetric, ).cuda() # print(moco_model.encoder_q) moco_model_1 = ModelMoCo( dim=args.moco_dim, K=args.moco_k, m=args.moco_m, T=args.moco_t, arch=args.arch, bn_splits=args.bn_splits, symmetric=args.symmetric, ).cuda() # print(moco_model_1.encoder_q) """ CIFAR10 Dataset. """ from torch.cuda import amp scaler = amp.GradScaler(enabled=cuda) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # train for one epoch # def moco_train(net, net_1, data_loader, train_optimizer, epoch, args): # net.train() # adjust_learning_rate(moco_optimizer, epoch, args) # total_loss, total_num, train_bar = 0.0, 0, tqdm(data_loader) # loss_add = 0.0 # for im_1, im_2 in train_bar: # im_1, im_2 = im_1.cuda(non_blocking=True), im_2.cuda(non_blocking=True) # loss = net(im_1, im_2) # 原始图像对比损失 梯度清零—>梯度回传—>梯度跟新 # # lossT = loss # 只使用原始对比损失 # # train_optimizer.zero_grad() # # lossT.backward() # # train_optimizer.step() # # loss_add += lossT.item() # # total_num += data_loader.batch_size # # total_loss += loss.item() * data_loader.batch_size # # train_bar.set_description( # # 'Train Epoch: [{}/{}], lr: {:.6f}, Loss: {:.4f}'.format( # # epoch, args.epochs, # # train_optimizer.param_groups[0]['lr'], # # loss_add / total_num # # ) # # ) # #傅里叶变换处理流程 # #im_3 = torch.rfft(im_1, 3, onesided=False, normalized=True)[:, :, :, :, 0] # fft_output = torch.fft.fftn(im_1, dim=(-3, -2, -1), norm="ortho")#转换为频域 # real_imag = torch.view_as_real(fft_output)#分解实部虚部 # im_3 = real_imag[..., 0]#提取频域实部作为新视图 # #该处理实现了频域空间的增强,与空间域增强形成了互补 # #im_4 = torch.rfft(im_2, 3, onesided=False, normalized=True)[:, :, :, :, 0] # fft_output = torch.fft.fftn(im_2, dim=(-3, -2, -1), norm="ortho") # real_imag = torch.view_as_real(fft_output) # im_4 = real_imag[..., 0] # loss_1 = net_1(im_3, im_4)#频域特征对比损失 # lossT = 0.8*loss + 0.2*loss_1#多模态损失对比融合 # train_optimizer.zero_grad() # lossT.backward() # train_optimizer.step() # loss_add += lossT # total_num += data_loader.batch_size # total_loss += loss.item() * data_loader.batch_size # # train_bar.set_description( # # 'Train Epoch: [{}/{}], lr: {:.6f}, Loss: {:.4f}'.format(epoch, args.epochs, moco_optimizer.param_groups[0]['lr'], # # loss_add / total_num)) # return (loss_add / total_num).cpu().item() # yolov5需要的损失 def moco_train(net, net_1, data_loader, train_optimizer, epoch, args): net.train() adjust_learning_rate(train_optimizer, epoch, args) total_loss, total_num = 0.0, 0 train_bar = tqdm(data_loader) for im_1, im_2, im_3, im_4 in train_bar: # 接收4组视图 im_1, im_2 = im_1.cuda(), im_2.cuda() im_3, im_4 = im_3.cuda(), im_4.cuda() # 原始空间域对比损失 loss_orig = net(im_1, im_2) # 退化增强图像的空间域对比损失 loss_degraded = net(im_3, im_4) # 频域处理(对退化增强后的图像) fft_3 = torch.fft.fftn(im_3, dim=(-3, -2, -1), norm="ortho") fft_3 = torch.view_as_real(fft_3)[..., 0] # 取实部 fft_4 = torch.fft.fftn(im_4, dim=(-3, -2, -1), norm="ortho") fft_4 = torch.view_as_real(fft_4)[..., 0] # 频域对比损失 loss_freq = net_1(fft_3, fft_4) # 多模态损失融合 loss = 0.6 * loss_orig + 0.3 * loss_degraded + 0.1 * loss_freq # 反向传播 train_optimizer.zero_grad() loss.backward() train_optimizer.step() # 记录损失 total_num += data_loader.batch_size total_loss += loss.item() # train_bar.set_description(f'Epoch: [{epoch}/{args.epochs}] Loss: {total_loss/total_num:.4f}') return total_loss / total_num # lr scheduler for training def adjust_learning_rate(optimizer, epoch, args): # 学习率衰减 """Decay the learning rate based on schedule""" lr = args.lr if args.cos: # cosine lr schedule lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs)) else: # stepwise lr schedule for milestone in args.schedule: lr *= 0.1 if epoch >= milestone else 1. for param_group in optimizer.param_groups: param_group['lr'] = lr # test using a knn monitor def test(net, memory_data_loader, test_data_loader, epoch, args): net.eval() classes = len(memory_data_loader.dataset.classes) total_top1, total_top5, total_num, feature_bank = 0.0, 0.0, 0, [] with torch.no_grad(): # generate feature bank for data, target in tqdm(memory_data_loader, desc='Feature extracting'): feature = net(data.cuda(non_blocking=True)) feature = F.normalize(feature, dim=1) feature_bank.append(feature) # [D, N] feature_bank = torch.cat(feature_bank, dim=0).t().contiguous() # [N] feature_labels = torch.tensor(memory_data_loader.dataset.targets, device=feature_bank.device) # loop test data_processing to predict the label by weighted knn search test_bar = tqdm(test_data_loader) for data, target in test_bar: data, target = data.cuda(non_blocking=True), target.cuda(non_blocking=True) feature = net(data) feature = F.normalize(feature, dim=1) pred_labels = knn_predict(feature, feature_bank, feature_labels, classes, args.knn_k, args.knn_t) total_num += data.size(0) total_top1 += (pred_labels[:, 0] == target).float().sum().item() test_bar.set_description( 'Test Epoch: [{}/{}] Acc@1:{:.2f}%'.format(epoch, args.epochs, total_top1 / total_num * 100)) return total_top1 / total_num * 100 # knn monitor as in InstDisc https://arxiv.org/abs/1805.01978 # implementation follows http://github.com/zhirongw/lemniscate.pytorch and https://github.com/leftthomas/SimCLR def knn_predict(feature, feature_bank, feature_labels, classes, knn_k, knn_t): # compute cos similarity between each feature vector and feature bank ---> [B, N] sim_matrix = torch.mm(feature, feature_bank) # [B, K] sim_weight, sim_indices = sim_matrix.topk(k=knn_k, dim=-1) # [B, K] sim_labels = torch.gather(feature_labels.expand(feature.size(0), -1), dim=-1, index=sim_indices) sim_weight = (sim_weight / knn_t).exp() # counts for each class one_hot_label = torch.zeros(feature.size(0) * knn_k, classes, device=sim_labels.device) # [B*K, C] one_hot_label = one_hot_label.scatter(dim=-1, index=sim_labels.view(-1, 1), value=1.0) # weighted score ---> [B, C] pred_scores = torch.sum(one_hot_label.view(feature.size(0), -1, classes) * sim_weight.unsqueeze(dim=-1), dim=1) pred_labels = pred_scores.argsort(dim=-1, descending=True) return pred_labels # 开始训练 # define optimizer moco_optimizer = torch.optim.SGD(moco_model.parameters(), lr=args.lr, weight_decay=args.wd, momentum=0.9) 上述问题怎么修改?

最新推荐

recommend-type

Qt开发:XML文件读取、滚动区域控件布局与多Sheet Excel保存的界面设计实例

内容概要:本文介绍了基于Qt框架的界面设计例程,重点讲解了三个主要功能模块:一是利用XML文件进行配置信息的读取并初始化界面组件;二是实现了滚动区域内的灵活控件布局,在空间不足时自动生成滚动条以扩展显示范围;三是提供了将界面上的数据导出到带有多个工作表的Excel文件的功能。文中还提及了所用IDE的具体版本(Qt Creator 4.8.0 和 Qt 5.12.0),并且强调了这些技术的实际应用场景及其重要性。 适合人群:对Qt有初步了解,希望深入学习Qt界面设计技巧的开发者。 使用场景及目标:适用于需要快速构建复杂用户界面的应用程序开发,特别是那些涉及大量数据展示和交互的设计任务。通过学习本文提供的案例,可以提高对于Qt框架的理解,掌握更多实用技能。 其他说明:为了帮助读者更好地理解和实践,作者推荐前往B站观看高清的教学视频,以便于更直观地感受整个项目的开发流程和技术细节。
recommend-type

锂电池保护板方案:中颖SH367309原理图与PCB源代码详解及应用技巧

基于中颖SH367309芯片的锂电池保护板设计方案,涵盖原理图解析、PCB布局优化、硬件选型要点以及软件编程技巧。重点讨论了电流检测精度、过压保护阈值设定、通信协议处理和温度传感器布置等方面的实际开发经验和技术难点。文中还分享了一些实用的小贴士,如采用星型接地减少干扰、利用过孔阵列降低温升、为MOS管增加RC缓冲避免高频振荡等。 适合人群:从事锂电池管理系统(BMS)开发的技术人员,尤其是有一定硬件设计基础并希望深入了解具体实现细节的工程师。 使用场景及目标:帮助开发者掌握锂电池保护板的关键技术和常见问题解决方案,确保产品在各种工况下都能安全可靠运行,同时提高系统性能指标如效率、响应速度和稳定性。 阅读建议:由于涉及较多底层硬件知识和实战案例,建议读者结合自身项目背景进行针对性学习,在遇到类似问题时能够快速定位原因并找到有效对策。此外,对于初学者来说,可以从简单的电路搭建开始逐步深入研究复杂的功能模块。
recommend-type

Web前端开发:CSS与HTML设计模式深入解析

《Pro CSS and HTML Design Patterns》是一本专注于Web前端设计模式的书籍,特别针对CSS(层叠样式表)和HTML(超文本标记语言)的高级应用进行了深入探讨。这本书籍属于Pro系列,旨在为专业Web开发人员提供实用的设计模式和实践指南,帮助他们构建高效、美观且可维护的网站和应用程序。 在介绍这本书的知识点之前,我们首先需要了解CSS和HTML的基础知识,以及它们在Web开发中的重要性。 HTML是用于创建网页和Web应用程序的标准标记语言。它允许开发者通过一系列的标签来定义网页的结构和内容,如段落、标题、链接、图片等。HTML5作为最新版本,不仅增强了网页的表现力,还引入了更多新的特性,例如视频和音频的内置支持、绘图API、离线存储等。 CSS是用于描述HTML文档的表现(即布局、颜色、字体等样式)的样式表语言。它能够让开发者将内容的表现从结构中分离出来,使得网页设计更加模块化和易于维护。随着Web技术的发展,CSS也经历了多个版本的更新,引入了如Flexbox、Grid布局、过渡、动画以及Sass和Less等预处理器技术。 现在让我们来详细探讨《Pro CSS and HTML Design Patterns》中可能包含的知识点: 1. CSS基础和选择器: 书中可能会涵盖CSS基本概念,如盒模型、边距、填充、边框、背景和定位等。同时还会介绍CSS选择器的高级用法,例如属性选择器、伪类选择器、伪元素选择器以及选择器的组合使用。 2. CSS布局技术: 布局是网页设计中的核心部分。本书可能会详细讲解各种CSS布局技术,包括传统的浮动(Floats)布局、定位(Positioning)布局,以及最新的布局模式如Flexbox和CSS Grid。此外,也会介绍响应式设计的媒体查询、视口(Viewport)单位等。 3. 高级CSS技巧: 这些技巧可能包括动画和过渡效果,以及如何优化性能和兼容性。例如,CSS3动画、关键帧动画、转换(Transforms)、滤镜(Filters)和混合模式(Blend Modes)。 4. HTML5特性: 书中可能会深入探讨HTML5的新标签和语义化元素,如`<article>`、`<section>`、`<nav>`等,以及如何使用它们来构建更加标准化和语义化的页面结构。还会涉及到Web表单的新特性,比如表单验证、新的输入类型等。 5. 可访问性(Accessibility): Web可访问性越来越受到重视。本书可能会介绍如何通过HTML和CSS来提升网站的无障碍访问性,比如使用ARIA标签(Accessible Rich Internet Applications)来增强屏幕阅读器的使用体验。 6. 前端性能优化: 性能优化是任何Web项目成功的关键。本书可能会涵盖如何通过优化CSS和HTML来提升网站的加载速度和运行效率。内容可能包括代码压缩、合并、避免重绘和回流、使用Web字体的最佳实践等。 7. JavaScript与CSS/HTML的交互: 在现代Web开发中,JavaScript与CSS及HTML的交云并用是不可或缺的。书中可能会讲解如何通过JavaScript动态地修改样式、操作DOM元素以及使用事件监听和响应用户交互。 8. Web框架和预处理器: 这本书可能会提到流行的Web开发框架和预处理器,比如Bootstrap、Foundation、Sass和Less等,它们是如何简化和加速开发流程的。 9. 测试和维护: 书中也可能包含关于如何测试网页以及如何持续优化和维护CSS和HTML代码的章节。例如,使用断言测试、自动化测试、性能分析工具等。 最后,鉴于文件名称列表中的“压缩包子文件”的表述,这可能是对“压缩包”文件的一种误译或误用,此处“压缩包”应该指的是包含该书籍PDF文件的压缩文件格式,如ZIP或RAR。而“Pro CSS and HTML Design Patterns.pdf”指的就是该书籍的PDF格式电子版文件。 以上所述,构成了《Pro CSS and HTML Design Patterns》一书可能包含的核心知识点。通过学习这些内容,Web前端开发者可以掌握更为高效和优雅的设计模式,从而在日常工作中更高效地解决实际问题。
recommend-type

Zotero 7数据同步:Attanger插件安装&设置,打造文献管理利器

/i.s3.glbimg.com/v1/AUTH_08fbf48bc0524877943fe86e43087e7a/internal_photos/bs/2018/L/j/ipKA8TRPyt4ptaG1DGww/t-m-01.png) # 1. Zotero 7数据同步简介 在数字时代,管理研究数据变得日益重要,而Zot
recommend-type

卷积神经网络的基础理论200字

<think>我们正在回答用户关于卷积神经网络(CNN)基础理论的询问。根据提供的引用资料,我们可以概述CNN的基本结构和工作原理。引用[1]提到CNN是一种前馈神经网络,信息从输入单向流动到输出。引用[2]指出CNN在图像处理等领域应用广泛,通过层次结构和参数调整实现模式识别。引用[3]说明CNN包含卷积层,使用卷积核提取局部特征,减少参数量,提高效率。引用[4]则提到了训练过程,包括前向传播、误差计算和反向传播(梯度下降)。因此,我们将从以下几个方面概述CNN:1.CNN的基本结构(卷积层、池化层、全连接层)2.卷积操作的工作原理(局部感受野、权重共享)3.训练过程(前向传播、损失函数、反
recommend-type

轻便实用的Java库类查询工具介绍

标题 "java2库类查询" 和描述表明,所提及的工具是一个专门用于查询Java库类的应用程序。此软件旨在帮助开发者快速地查找和引用Java的标准开发工具包(SDK)中包含的所有应用程序编程接口(API)类。通过这样的工具,开发者可以节省大量在官方文档或搜索引擎上寻找类定义和使用方法的时间。它被描述为轻巧且方便,这表明其占用的系统资源相对较少,同时提供直观的用户界面,使得查询过程简洁高效。 从描述中可以得出几个关键知识点: 1. Java SDK:Java的软件开发工具包(SDK)是Java平台的一部分,提供了一套用于开发Java应用软件的软件包和库。这些软件包通常被称为API,为开发者提供了编程界面,使他们能够使用Java语言编写各种类型的应用程序。 2. 库类查询:这个功能对于开发者来说非常关键,因为它提供了一个快速查找特定库类及其相关方法、属性和使用示例的途径。良好的库类查询工具可以帮助开发者提高工作效率,减少因查找文档而中断编程思路的时间。 3. 轻巧性:软件的轻巧性通常意味着它对计算机资源的要求较低。这样的特性对于资源受限的系统尤为重要,比如老旧的计算机、嵌入式设备或是当开发者希望最小化其开发环境占用空间时。 4. 方便性:软件的方便性通常关联于其用户界面设计,一个直观、易用的界面可以让用户快速上手,并减少在使用过程中遇到的障碍。 5. 包含所有API:一个优秀的Java库类查询软件应当能够覆盖Java所有标准API,这包括Java.lang、Java.util、Java.io等核心包,以及Java SE平台的所有其他标准扩展包。 从标签 "java 库 查询 类" 可知,这个软件紧密关联于Java编程语言的核心功能——库类的管理和查询。这些标签可以关联到以下知识点: - Java:一种广泛用于企业级应用、移动应用(如Android应用)、网站后端、大型系统和许多其他平台的编程语言。 - 库:在Java中,库是一组预打包的类和接口,它们可以被应用程序重复使用。Java提供了庞大的标准库,以支持各种常见的任务和功能。 - 查询:查询指的是利用软件工具搜索、定位和检索信息的过程。对于Java库类查询工具来说,这意味着可以通过类名、方法签名或其他标识符来查找特定的API条目。 最后,压缩包文件列表包含了两个文件:“java.dit”和“Java.exe”。其中“Java.exe”很可能是程序的可执行文件,而“java.dit”可能是一个数据文件,用于存储Java类的索引或数据。由于文件名后缀通常与文件类型相关联,但“dit”并不是一个常见的文件扩展名。这可能是一个特定于软件的自定义格式,或是一个打字错误。 总结来说,"java2库类查询" 是一个针对Java开发者的实用工具,它提供了一个轻量级、易用的平台来查询和定位Java标准库中的所有类和API。此工具对优化开发流程,减少查找Java类文档的时间大有裨益,尤其适合需要频繁查阅Java API的开发者使用。
recommend-type

【Zotero 7终极指南】:新手必备!Attanger插件全攻略与数据同步神技

# 1. Zotero 7与Attanger插件的介绍 在当今的学术研究和知识管理领域,高效的文献管理工具至关重要。Zotero 7作为一个流行的参考文献管理软件,因其强大的功能和用户友好的界面而受到专业人士的青睐。而Attanger插件则为Zotero 7带来了更多定制化和高级功能,极大地增强
recommend-type

MATLAB整段注释快捷键

<think>首先,用户的问题是:“MATLAB整段注释快捷键”,意思是“MATLAB整段注释快捷键”。我需要用中文回答。 根据规则: - 回答必须用中文。 - 回答完问题后,必须在末尾输出“§§相关问题§§”,然后提出3-5个相关问题。 - “§§相关问题§§”必须完整输出在第一行,禁止换行分散。 - 相关问题必须基于用户的问题和我的回答。 现在,回忆MATLAB的快捷键:在MATLAB中,整段注释通常使用快捷键Ctrl + R来注释选中的代码行,使用Ctrl + T来取消注释。用户问的是“整段注释”,所以我应该确认快捷键。 在MATLAB中: - 注释选中的行:Ctrl + R -
recommend-type

Eclipse Jad反编译插件:提升.class文件查看便捷性

反编译插件for Eclipse是一个专门设计用于在Eclipse集成开发环境中进行Java反编译的工具。通过此类插件,开发者可以在不直接访问源代码的情况下查看Java编译后的.class文件的源代码,这在开发、维护和学习使用Java技术的过程中具有重要的作用。 首先,我们需要了解Eclipse是一个跨平台的开源集成开发环境,主要用来开发Java应用程序,但也支持其他诸如C、C++、PHP等多种语言的开发。Eclipse通过安装不同的插件来扩展其功能。这些插件可以由社区开发或者官方提供,而jadclipse就是这样一个社区开发的插件,它利用jad.exe这个第三方命令行工具来实现反编译功能。 jad.exe是一个反编译Java字节码的命令行工具,它可以将Java编译后的.class文件还原成一个接近原始Java源代码的格式。这个工具非常受欢迎,原因在于其反编译速度快,并且能够生成相对清晰的Java代码。由于它是一个独立的命令行工具,直接使用命令行可以提供较强的灵活性,但是对于一些不熟悉命令行操作的用户来说,集成到Eclipse开发环境中将会极大提高开发效率。 使用jadclipse插件可以很方便地在Eclipse中打开任何.class文件,并且将反编译的结果显示在编辑器中。用户可以在查看反编译的源代码的同时,进行阅读、调试和学习。这样不仅可以帮助开发者快速理解第三方库的工作机制,还能在遇到.class文件丢失源代码时进行紧急修复工作。 对于Eclipse用户来说,安装jadclipse插件相当简单。一般步骤包括: 1. 下载并解压jadclipse插件的压缩包。 2. 在Eclipse中打开“Help”菜单,选择“Install New Software”。 3. 点击“Add”按钮,输入插件更新地址(通常是jadclipse的更新站点URL)。 4. 选择相应的插件(通常名为“JadClipse”),然后进行安装。 5. 安装完成后重启Eclipse,插件开始工作。 一旦插件安装好之后,用户只需在Eclipse中双击.class文件,或者右键点击文件并选择“Open With Jadclipse”,就能看到对应的Java源代码。如果出现反编译不准确或失败的情况,用户还可以直接在Eclipse中配置jad.exe的路径,或者调整jadclipse的高级设置来优化反编译效果。 需要指出的是,使用反编译工具虽然方便,但要注意反编译行为可能涉及到版权问题。在大多数国家和地区,反编译软件代码属于合法行为,但仅限于学习、研究、安全测试或兼容性开发等目的。如果用户意图通过反编译获取商业机密或进行非法复制,则可能违反相关法律法规。 总的来说,反编译插件for Eclipse是一个强大的工具,它极大地简化了Java反编译流程,提高了开发效率,使得开发者在没有源代码的情况下也能有效地维护和学习Java程序。但开发者在使用此类工具时应遵守法律与道德规范,避免不当使用。
recommend-type

【进阶Python绘图】:掌握matplotlib坐标轴刻度间隔的高级技巧,让你的图表脱颖而出

# 摘要 本文系统地探讨了matplotlib库中坐标轴刻度间隔的定制与优化技术。首先概述了matplotlib坐标轴刻度间隔的基本概念及其在图表中的重要性,接