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Colab Notebook与GitHub集成使用指南

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下载需积分: 10 | 2KB | 更新于2025-09-04 | 70 浏览量 | 0 下载量 举报 收藏
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标题和描述中提到了两个关键的概念:“Colab Notebook”和“Github”,以及它们的集成使用。下面将详细介绍这两个知识点以及它们结合使用的方法。 ### Colab Notebook **Colab Notebook** 是 Google 开发的基于云的 Jupyter 笔记本环境,它允许用户在云端编写和执行代码、展示文档和共享项目。Colab 的最大特点之一是它提供了免费的 GPU 和 TPU 资源,这对于运行大型深度学习模型非常有用。 #### Colab Notebook 的主要特点: 1. **免费使用**:Colab 提供免费的计算资源,包括 CPU、GPU 和 TPU。 2. **云服务**:无需安装任何软件,只要有一台能上网的电脑就可以使用。 3. **集成 Google Drive**:可以将笔记本保存在 Google Drive 中,方便数据存储和访问。 4. **开源共享**:方便地与他人分享你的代码和文档,有助于协作。 5. **易用性**:界面直观,支持 Python 代码,支持多种代码和文档格式。 6. **APIs 和库**:提供广泛的 Python 库,以及访问 Google Cloud Platform 的 API。 7. **版本控制**:可以通过 Git 与 Github 集成,进行版本控制。 ### Github **Github** 是一个以 Git 为基础的代码托管平台,它允许开发者将代码仓库存储在云端,方便团队协作和代码版本控制。它是当今最流行的代码托管和版本控制服务之一。 #### Github 的主要特点: 1. **代码托管**:为开发者提供代码仓库的存储服务。 2. **版本控制**:基于 Git 的分布式版本控制系统,支持团队协作。 3. **拉取请求(Pull Request)**:协作开发中使用的核心功能,有助于代码的审核和合并。 4. **分支管理**:方便地进行特性开发、错误修复和实验。 5. **问题跟踪(Issue Tracking)**:用于跟踪项目中的问题和任务。 6. **Wiki 和页面**:可以创建项目文档和帮助页面。 7. **集成服务**:可以集成各种第三方服务,比如持续集成(CI)工具。 ### Colab Notebook 与 Github 的集成使用 将 Colab Notebook 和 Github 集成使用,可以让代码的开发更加便捷和高效。下面详细说明这种集成的步骤和好处: 1. **项目同步**:首先,在 Github 上创建或选择一个已有的项目仓库。然后,在 Colab Notebook 中,选择“File”菜单中的“Open notebook”选项,再选择“GitHub”标签,输入仓库的 URL 进行同步。 2. **代码编辑与运行**:在 Colab 中,用户可以对代码进行编辑和调试。Colab 提供了丰富的运行环境和工具,如 Python 3、Pandas、TensorFlow 等。修改后的代码可以立即运行,以查看结果。 3. **版本控制集成**:Colab 支持与 Github 的集成,可以将代码更改保存到 Github 仓库中。这意味着用户可以利用 Github 的版本控制功能,比如分支、合并请求等。 4. **数据处理与分析**:用户可以直接在 Colab 中读取和处理存储在 Github 仓库中的数据文件,如 CSV、JSON 等。Colab 支持数据可视化,可直接展示分析结果。 5. **协同工作**:团队成员可以同时在同一个 Colab Notebook 上进行工作,彼此的更改会实时同步。这样可以加速开发进程,并允许团队成员在项目中实时交流。 6. **部署和分享**:完成的 Notebook 可以直接分享给团队成员或公众。对于公开的 Notebook,Colab 还提供了一个简单的分享链接,允许他人查看或编辑。 7. **自动执行**:Colab 支持创建自动化的脚本,比如定时运行的脚本,通过 Github Actions 可以进一步实现自动化工作流。 综上所述,通过 Colab Notebook 结合 Github,开发者可以享受到便捷的云端开发环境、高效的代码协作、强大的版本控制以及方便的数据处理和分享功能。这不仅适用于数据科学家、机器学习工程师,也适用于所有希望简化工作流程的开发者。在实践中,这样的集成意味着更快的迭代速度、更紧密的团队协作和更可控的项目管理。

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运行兼容性测试... 测试自动路径检测... 自动检测成功! 加载特征数: 10 测试错误处理: 类型错误 测试通过: TypeError - base_dir必须是字符串类型,实际类型为 <class 'int'> 路径不存在 测试通过: FileNotFoundError - 数据集路径不存在: /invalid/path 不支持的版本 测试通过: FileNotFoundError - 无法自动找到COCO数据集路径。请尝试: 1. 手动指定base_dir参数 2. 设置环境变量COCO_DATASET_PATH 3. 检查数据集是否在以下位置之一: - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\datasets\coco_captioning - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\..\datasets\coco_captioning - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\coco_data - C:\Users\LENOVO\datasets\coco_captioning - C:\datasets\coco_captioning - /usr/local/datasets/coco_captioning 找不到标注文件 测试通过: FileNotFoundError - 无法自动找到COCO数据集路径。请尝试: 1. 手动指定base_dir参数 2. 设置环境变量COCO_DATASET_PATH 3. 检查数据集是否在以下位置之一: - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\datasets\coco_captioning - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\..\datasets\coco_captioning - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\coco_data - C:\Users\LENOVO\datasets\coco_captioning - C:\datasets\coco_captioning - /usr/local/datasets/coco_captioning

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使用数据集目录: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\datasets\coco_captioning 下载标注文件: captions_train2017.json 下载 captions_train2017.json: 0.00B [00:00, ?B/s] 2017版本下载失败: [WinError 2] 系统找不到指定的文件。: 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2017.part' -> 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2017.json' 尝试2014版本... 下载标注文件: captions_train2014.json 下载 captions_train2014.json: 0.00B [00:00, ?B/s] 所有版本下载失败: [WinError 2] 系统找不到指定的文件。: 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2014.part' -> 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2014.json' 下载标注文件: captions_train2017.json 下载 captions_train2017.json: 0.00B [00:00, ?B/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[10], line 350 343 # 示例2: 指定目录下载(使用您的实际路径) 344 downloader = COCOFeatureDownloader( 345 base_dir="D:/cs231n.github.io-master/assignments/2021/assignment3_colab/assignment3/datasets/coco_captioning", 346 version="2017", 347 feature_type="pca", # 或 "original" 348 max_threads=32 349 ) --> 350 downloader.download_all() Cell In[10], line 289, in COCOFeatureDownloader.download_all(self) 287 """下载所有必要文件""" 288 # 下载标注文件 --> 289 self.download_annotations() 291 # 下载特征文件 292 for split in ["train", "val"]: Cell In[10], line 280, in COCOFeatureDownloader.download_annotations(self) 277 file_size = int(response.headers.get('content-length', 0)) 279 print(f"下载标注文件: {filename}") --> 280 if self._download_file(url, output_path, file_size, "skip"): # 标注文件较小,跳过哈希验证 281 # 添加下载完成标记 282 if filename not in self.download_status["completed"]: 283 self.download_status["completed"].append(filename) Cell In[10], line 211, in COCOFeatureDownloader._download_file(self, url, output_path, file_size, md5_hash) 208 progress.close() 210 # 重命名临时文件 --> 211 temp_path.rename(output_path) 213 # 验证完整性 214 if output_path.stat().st_size == file_size and self._verify_md5(output_path, md5_hash): File D:\miniconda\lib\pathlib.py:1234, in Path.rename(self, target) 1224 def rename(self, target): 1225 """ 1226 Rename this path to the target path. 1227 (...) 1232 Returns the new Path instance pointing to the target path. 1233 """ -> 1234 self._accessor.rename(self, target) 1235 return self.__class__(target) FileNotFoundError: [WinError 2] 系统找不到指定的文件。: 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2017.part' -> 'D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\datasets\\coco_captioning\\annotations\\captions_train2017.json'

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--------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[8], line 5 1 # Load COCO data from disk into a dictionary. 2 # We'll work with dimensionality-reduced features for the remainder of this assignment, 3 # but you can also experiment with the original features on your own by changing the flag below. 4 # 在调用函数时直接指定路径 ----> 5 data = load_coco_data( 6 base_dir='D:\\cs231n.github.io-master\\assignments\\2021\\assignment3_colab\\assignment3\\cs231n\\datasets\\coco_captioning', # 替换为您的实际路径 7 pca_features=True 8 ) 10 # Print out all the keys and values from the data dictionary. 11 for k, v in data.items(): File D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\coco_utils.py:169, in load_coco_data(base_dir, pca_features, dataset_version, load_train, load_val, load_test) 167 # 加载训练集 168 if load_train: --> 169 train_feature_path = _build_feature_path(base_dir, config, feature_suffix, "train") 170 with h5py.File(train_feature_path, 'r') as f: 171 data['train_features'] = np.array(f['features']) File D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\coco_utils.py:88, in _build_feature_path(base_dir, config, suffix, split) 85 if os.path.exists(path): 86 return path ---> 88 raise FileNotFoundError( 89 f"找不到特征文件 '{filename}'. 尝试路径:\n" + 90 "\n".join(f"- {p}" for p in possible_paths) 91 ) FileNotFoundError: 找不到特征文件 'features2017_train_pca.h5'. 尝试路径: - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\features2017_train_pca.h5 - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\features\features2017_train_pca.h5 - D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\extracted_features\features2017_train_pca.h5

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ModuleNotFoundError Traceback (most recent call last) E:\下载\cs231n.github.io-master\cs231n.github.io-master\assignments\2021\assignment2_colab\assignment2\cs231n\fast_layers.py in <module> 5 # 尝试绝对导入 ----> 6 from cs231n.im2col_cython import col2im_6d_cython 7 except ImportError: ModuleNotFoundError: No module named 'cs231n.im2col_cython' During handling of the above exception, another exception occurred: ModuleNotFoundError Traceback (most recent call last) E:\下载\cs231n.github.io-master\cs231n.github.io-master\assignments\2021\assignment2_colab\assignment2\cs231n\fast_layers.py in <module> 9 # 尝试相对导入 ---> 10 from .im2col_cython import col2im_6d_cython 11 except ImportError: ModuleNotFoundError: No module named 'cs231n.im2col_cython' During handling of the above exception, another exception occurred: ImportError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_8380\3476068577.py in <module> 15 # 强制重新加载模块 16 from cs231n import fast_layers ---> 17 importlib.reload(fast_layers) 18 print("模块已强制重新加载") E:\anaconda\lib\importlib\__init__.py in reload(module) 167 if spec is None: 168 raise ModuleNotFoundError(f"spec not found for the module {name!r}", name=name) --> 169 _bootstrap._exec(spec, module) 170 # The module may have replaced itself in sys.modules! 171 return sys.modules[name] E:\anaconda\lib\importlib\_bootstrap.py in _exec(spec, module) E:\anaconda\lib\importlib\_bootstrap_external.py in exec_module(self, module) E:\anaconda\lib\importlib\_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) E:\下载\cs231n.github.io-master\cs231n.github.io-master\assignments\2021\assignment2_colab\assignment2\cs231n\fast_layers.py in <module> 11 except ImportError: 12 # 使用纯Python实现 ---> 13 from .im2col import col2im_6d as col2im_6d_cython 14 print("警告: 使用纯Python实现,性能会降低") 15 ImportError: cannot import name 'col2im_6d' from 'cs231n.im2col' (E:\下载\cs231n.github.io-master\cs231n.github.io-master\assignments\2021\assignment2_colab\assignment2\cs231n\im2col.py)

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# Load COCO data from disk into a dictionary. # We'll work with dimensionality-reduced features for the remainder of this assignment, # but you can also experiment with the original features on your own by changing the flag below. data = load_coco_data(pca_features=True) # Print out all the keys and values from the data dictionary. for k, v in data.items(): if type(v) == np.ndarray: print(k, type(v), v.shape, v.dtype) else: print(k, type(v), len(v)) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 4 1 # Load COCO data from disk into a dictionary. 2 # We'll work with dimensionality-reduced features for the remainder of this assignment, 3 # but you can also experiment with the original features on your own by changing the flag below. ----> 4 data = load_coco_data(pca_features=True) 6 # Print out all the keys and values from the data dictionary. 7 for k, v in data.items(): File D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\coco_utils.py:81, in load_coco_data(base_dir, pca_features, dataset_version, load_train, load_val, load_test) 78 data = {} 80 if load_train: ---> 81 train_feature_path = build_feature_path("train") 82 with h5py.File(train_feature_path, 'r') as f: 83 data['train_features'] = np.array(f['features']) File D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\coco_utils.py:57, in load_coco_data.<locals>.build_feature_path(split) 56 def build_feature_path(split): ---> 57 return os.path.join( 58 base_dir, 59 f"{config['feature_prefix']}_{split}{feature_suffix}.h5" 60 ) File D:\miniconda\lib\ntpath.py:104, in join(path, *paths) 103 def join(path, *paths): --> 104 path = os.fspath(path) 105 if isinstance(path, bytes): 106 sep = b'\\' TypeError: expected str, bytes or os.PathLike object, not NoneType def load_coco_data( base_dir: Optional[str] = None, pca_features: bool = False, dataset_version: str = "2017", # 支持2014/2017/2017_test load_train: bool = True, load_val: bool = True, load_test: bool = False ) -> Dict[str, np.ndarray]: """ 支持多版本COCO数据集的加载函数 (兼容2014/2017) 参数: dataset_version: 数据集版本 ("2014", "2017", "2017_test") ...其他参数保持不变... """ # 1. 版本配置文件映射 VERSION_CONFIG = { "2014": { "caption_file": "captions_train2014.json", "val_caption_file": "captions_val2014.json", "feature_prefix": "features2014" }, "2017": { "caption_file": "annotations/captions_train2017.json", "val_caption_file": "annotations/captions_val2017.json", "feature_prefix": "features2017" }, "2017_test": { "caption_file": "annotations/image_info_test2017.json", "feature_prefix": "features2017_test" } } # 2. 验证版本有效性 if dataset_version not in VERSION_CONFIG: raise ValueError(f"不支持的版本: {dataset_version}. 可用版本: {list(VERSION_CONFIG.keys())}") config = VERSION_CONFIG[dataset_version] feature_suffix = "_pca" if pca_features else "" # 3. 路径构建函数 def build_feature_path(split): return os.path.join( base_dir, f"{config['feature_prefix']}_{split}{feature_suffix}.h5" ) # 4. 加载标注数据 (2017版特有结构) def load_coco_annotations(file_path): with open(file_path, 'r') as f: data = json.load(f) # 构建映射: image_id -> 标注列表 annotations = {} for ann in data['annotations']: img_id = ann['image_id'] if img_id not in annotations: annotations[img_id] = [] annotations[img_id].append(ann['caption']) return annotations # 5. 主加载逻辑 (与之前类似,但使用版本化路径) data = {} if load_train: train_feature_path = build_feature_path("train") with h5py.File(train_feature_path, 'r') as f: data['train_features'] = np.array(f['features']) # 加载训练标注 caption_path = os.path.join(base_dir, config['caption_file']) data['train_annotations'] = load_coco_annotations(caption_path) # ... 验证集和测试集加载逻辑类似 ... return data

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import os import sys import urllib.request import zipfile import h5py # 目标文件路径 TARGET_FILE = r"D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\coco2014_captions.h5" def handle_long_paths(path): """处理Windows长路径问题""" if len(path) > 260 and sys.platform == "win32": # 确保路径格式正确 if not path.startswith(r"\\?\\"): return r"\\?\\" + os.path.abspath(path) return path def fix_path_issues(file_path): """修复路径相关的问题""" # 处理长路径 file_path = handle_long_paths(file_path) # 创建缺失目录结构 os.makedirs(os.path.dirname(file_path), exist_ok=True) # 检查文件是否存在 if os.path.exists(file_path): print(f"✅ 文件已存在: {file_path}") return True, file_path print(f"❌ 文件不存在: {file_path}") return False, file_path def download_coco_dataset(file_path): """下载并解压COCO数据集""" dataset_url = "http://cs231n.stanford.edu/coco_captioning.zip" zip_path = os.path.join(os.path.dirname(file_path), "coco_captioning.zip") print(f"正在下载数据集: {dataset_url}") try: urllib.request.urlretrieve(dataset_url, zip_path) except Exception as e: print(f"下载失败: {str(e)}") return False, file_path print(f"解压文件到: {os.path.dirname(file_path)}") try: with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(os.path.dirname(file_path)) os.remove(zip_path) # 清理压缩包 except Exception as e: print(f"解压失败: {str(e)}") return False, file_path return os.path.exists(file_path), file_path def validate_hdf5_file(file_path): """验证HDF5文件完整性""" try: with h5py.File(file_path, 'r') as f: print("✅ 文件验证成功! 包含数据集:") print(list(f.keys())) # 输出数据集结构 return True except Exception as e: print(f"❌ 文件验证失败: {str(e)}") return False # 主执行流程 if __name__ == "__main__": # 处理路径问题 exists, current_path = fix_path_issues(TARGET_FILE) if not exists: # 下载数据集 downloaded, current_path = download_coco_dataset(current_path) if downloaded: print(f"✅ 数据集成功部署: {current_path}") else: print("❌ 数据集部署失败,请手动下载") sys.exit(1) # 验证文件 if not validate_hdf5_file(current_path): print("❌ 文件验证失败,数据集可能损坏") sys.exit(1) print("✨ 所有操作成功完成!") ❌ 文件不存在: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\coco2014_captions.h5 正在下载数据集: http://cs231n.stanford.edu/coco_captioning.zip 下载太慢是什么情况

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import os import zipfile import json import numpy as np import h5py import shutil # 目标路径 TARGET_DIR = r"D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning" ZIP_PATH = os.path.join(TARGET_DIR, "annotations.zip") H5_PATH = os.path.join(TARGET_DIR, "coco2014_captions.h5") def convert_to_hdf5(): """修复版转换函数 - 兼容2014/2017版本""" print("开始转换数据集格式...") # 创建临时解压目录 extract_dir = os.path.join(TARGET_DIR, "temp_extract") os.makedirs(extract_dir, exist_ok=True) # 解压ZIP文件 with zipfile.ZipFile(ZIP_PATH, 'r') as zip_ref: zip_ref.extractall(extract_dir) # 自动检测JSON文件路径 json_path = find_caption_json(extract_dir) if not json_path: print("❌ 未找到标注文件,请检查压缩包内容") print("解压目录内容:", os.listdir(extract_dir)) return False print(f"找到标注文件: {json_path}") # 读取JSON文件 with open(json_path, 'r') as f: data = json.load(f) # 提取图像ID和标题 image_ids = [] captions = [] for ann in data['annotations']: image_ids.append(ann['image_id']) captions.append(ann['caption'].encode('utf-8')) # 转换为NumPy数组 image_ids = np.array(image_ids, dtype=np.int32) captions = np.array(captions, dtype=h5py.special_dtype(vlen=str)) # 创建HDF5文件 with h5py.File(H5_PATH, 'w') as hf: hf.create_dataset("train_image_idxs", data=image_ids) hf.create_dataset("train_captions", data=captions) print(f"✅ 成功转换 {len(captions)} 条标注") print(f"文件已保存至: {H5_PATH}") # 清理临时文件 shutil.rmtree(extract_dir) return True def find_caption_json(extract_dir): """自动查找标注JSON文件""" # 可能存在的路径列表 possible_paths = [ os.path.join(extract_dir, "annotations", "captions_trainval2017.json"), os.path.join(extract_dir, "annotations_trainval2017", "annotations", "captions_trainval2017.json"), os.path.join(extract_dir, "captions_trainval2017.json"), os.path.join(extract_dir, "captions_trainval2014.json") ] # 检查所有可能路径 for path in possible_paths: if os.path.exists(path): return path # 尝试通配符搜索 for root, dirs, files in os.walk(extract_dir): for file in files: if file.startswith("captions_") and file.endswith(".json"): return os.path.join(root, file) return None def verify_conversion(): """验证转换结果""" try: with h5py.File(H5_PATH, 'r') as f: captions = f["train_captions"][:] image_ids = f["train_image_idxs"][:] print(f"验证成功! 包含 {len(captions)} 条标注") print(f"示例标注: {captions[0].decode('utf-8', errors='ignore')}") print(f"对应图像ID: {image_ids[0]}") return True except Exception as e: print(f"验证失败: {str(e)}") return False if __name__ == "__main__": # 确保ZIP文件存在 if not os.path.exists(ZIP_PATH): print(f"❌ 请手动下载ZIP文件并放置于: {ZIP_PATH}") print("下载链接: https://mirrors.tuna.tsinghua.edu.cn/coco/annotations/annotations_trainval2017.zip") else: print(f"✅ 找到ZIP文件: {ZIP_PATH}") if convert_to_hdf5(): if verify_conversion(): print("✨ 转换完成! 您现在可以运行课程代码") else: print("❌ 转换验证失败,请检查错误信息") ✅ 找到ZIP文件: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\annotations.zip 开始转换数据集格式... 找到标注文件: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\temp_extract\annotations\captions_train2017.json ✅ 成功转换 591753 条标注 文件已保存至: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\coco2014_captions.h5 验证成功! 包含 591753 条标注 示例标注: A bicycle replica with a clock as the front wheel. 对应图像ID: 203564 ✨ 转换完成! 您现在可以运行课程代码

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