修复数据集下载

backend/app/services/dataset_service.py

@@ -122,108 +122,111 @@ def _extract_archives(ds_dir: Path):
 
 
 def _download_modelscope_data_files(dataset_id: str, ds_dir: Path):
-    """通过 ModelScope API 下载"数据文件"区的压缩包。
-    CLI download 只能下载 git 仓库中的元数据文件（如 train.csv），
-    图片数据集的压缩包存放在"数据文件"区，需要通过 API 单独下载。"""
+    """通过 ModelScope API 下载图片数据集的压缩包。
+
+    图片数据集有一个 {dataset_name}.json 配置文件，记录了各 split 对应的
+    元数据文件和压缩包名称，例如：
+    {"default": {"train": {"meta": "train.csv", "file": "train.zip"},
+                 "validation": {"meta": "val.csv", "file": "val.zip"}}}
+
+    CLI download 只下载 git 仓库文件（CSV 等元数据），
+    压缩包需要通过 /api/v1/datasets/{ns}/{name}/repo?FilePath=... 单独下载。
+    """
     import urllib.request
-    import urllib.error
     import urllib.parse
 
     api_base = "https://www.modelscope.cn"
-    archive_exts = (".zip", ".tar.gz", ".tgz", ".tar.bz2", ".tbz2", ".tar")
-    existing = {f.name for f in ds_dir.rglob("*") if f.is_file()}
-
-    # Step 1: 获取数据集的数字 hub ID（tree API 需要用数字 ID，不是 namespace/name）
-    hub_id = None
-    try:
-        info_url = f"{api_base}/api/v1/datasets/{dataset_id}"
-        logger.info(f"Fetching dataset hub ID: {info_url}")
-        req = urllib.request.Request(info_url, headers={"User-Agent": "FineTuning-Backend"})
-        with urllib.request.urlopen(req, timeout=30) as resp:
-            info = json.loads(resp.read().decode())
-        hub_id = info.get("Data", {}).get("Id") or info.get("Data", {}).get("id")
-        if hub_id:
-            logger.info(f"Got dataset hub ID: {hub_id}")
-    except Exception as e:
-        logger.warning(f"Failed to get dataset hub ID: {e}")
+    namespace, ds_name = dataset_id.split("/", 1) if "/" in dataset_id else ("", dataset_id)
 
-    # Step 2: 用 hub ID 列出仓库中的所有文件
-    files = []
-    if hub_id:
+    # Step 1: 找到配置文件 {dataset_name}.json 并读取
+    config_files = [p for p in ds_dir.glob("*.json") if p.name not in META_FILENAMES]
+    if not config_files:
+        # 尝试通过 API 下载配置文件
+        config_url = (f"{api_base}/api/v1/datasets/{dataset_id}/repo"
+                      f"?Source=SDK&Revision=master&FilePath={ds_name}.json&View=false")
         try:
-            tree_url = (f"{api_base}/api/v1/datasets/{hub_id}/repo/tree"
-                        f"?Revision=master&Root=/&Recursive=True&PageNumber=1&PageSize=10000")
-            logger.info(f"Listing dataset files: {tree_url}")
-            req = urllib.request.Request(tree_url, headers={"User-Agent": "FineTuning-Backend"})
+            logger.info(f"尝试下载配置文件: {ds_name}.json")
+            req = urllib.request.Request(config_url, headers={"User-Agent": "FineTuning-Backend"})
             with urllib.request.urlopen(req, timeout=30) as resp:
-                result = json.loads(resp.read().decode())
-            files = result.get("Data", {}).get("Files", [])
-            if files:
-                logger.info(f"Found {len(files)} files in dataset repo")
-                for f in files:
-                    fn = f.get("Name", f.get("name", ""))
-                    if fn:
-                        logger.debug(f"  - {fn}")
-        except urllib.error.HTTPError as e:
-            logger.warning(f"Tree API error ({e.code}) for hub_id={hub_id}")
+                config_data = json.loads(resp.read().decode())
+            config_path = ds_dir / f"{ds_name}.json"
+            config_path.write_text(json.dumps(config_data, ensure_ascii=False), encoding="utf-8")
+            config_files = [config_path]
         except Exception as e:
-            logger.warning(f"Failed to list files via tree API: {e}")
+            logger.info(f"未找到配置文件 {ds_name}.json，跳过数据文件下载: {e}")
+            return
 
-    if not files:
-        # fallback: 直接用 namespace/name 格式尝试
+    # 在所有 json 配置文件中找到包含 "file" 字段的那个
+    config = None
+    for cf in config_files:
         try:
-            tree_url = (f"{api_base}/api/v1/datasets/{dataset_id}/repo/tree"
-                        f"?Revision=master&Root=/&Recursive=True&PageNumber=1&PageSize=10000")
-            logger.info(f"Fallback tree URL: {tree_url}")
-            req = urllib.request.Request(tree_url, headers={"User-Agent": "FineTuning-Backend"})
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                result = json.loads(resp.read().decode())
-            files = result.get("Data", {}).get("Files", [])
-            if files:
-                logger.info(f"Fallback found {len(files)} files")
-        except Exception as e:
-            logger.warning(f"Fallback tree API also failed: {e}")
+            data = json.loads(cf.read_text(encoding="utf-8"))
+            # 检查是否包含 file 字段（数据集配置格式）
+            if isinstance(data, dict):
+                for subset in data.values():
+                    if isinstance(subset, dict):
+                        for split_info in subset.values():
+                            if isinstance(split_info, dict) and "file" in split_info and split_info["file"]:
+                                config = data
+                                break
+                    if config:
+                        break
+            if config:
+                break
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            continue
 
-    if not files:
-        logger.warning(f"Could not list any files for dataset {dataset_id}")
+    if not config:
+        logger.info("未找到包含数据文件引用的配置文件，跳过")
         return
 
-    # Step 3: 筛选压缩包文件并下载
-    # 下载 URL 用 namespace/name 格式: /api/v1/datasets/{ns}/{name}/repo?Source=SDK&Revision=master&FilePath=...
-    namespace, name = dataset_id.split("/", 1)
-    downloaded = []
-    for file_info in files:
-        fname = file_info.get("Name", file_info.get("name", ""))
-        fpath = file_info.get("Path", file_info.get("path", fname))
-        if not fname:
-            continue
-        if not any(fname.lower().endswith(ext) for ext in archive_exts):
+    logger.info(f"找到数据文件配置: {json.dumps(config, ensure_ascii=False)}")
+
+    # Step 2: 收集所有需要下载的压缩包文件名
+    archive_files = set()
+    for subset in config.values():
+        if not isinstance(subset, dict):
             continue
+        for split_info in subset.values():
+            if isinstance(split_info, dict):
+                fname = split_info.get("file", "")
+                if fname:
+                    archive_files.add(fname)
+
+    if not archive_files:
+        logger.info("配置中未找到数据文件，跳过")
+        return
+
+    # Step 3: 下载压缩包
+    existing = {f.name for f in ds_dir.rglob("*") if f.is_file()}
+    downloaded = []
+    for fname in archive_files:
         if fname in existing:
-            logger.info(f"Archive already exists, skipping: {fname}")
+            logger.info(f"压缩包已存在，跳过: {fname}")
             continue
 
         params = urllib.parse.urlencode({
             "Source": "SDK", "Revision": "master",
-            "FilePath": fpath, "View": "false",
+            "FilePath": fname, "View": "false",
         })
-        dl_url = f"{api_base}/api/v1/datasets/{namespace}/{name}/repo?{params}"
+        dl_url = f"{api_base}/api/v1/datasets/{dataset_id}/repo?{params}"
         dest = ds_dir / fname
-        logger.info(f"Downloading data file: {fname} from {dl_url}")
+        logger.info(f"下载数据文件: {fname}")
+        logger.info(f"  URL: {dl_url}")
 
         try:
             req = urllib.request.Request(dl_url, headers={"User-Agent": "FineTuning-Backend"})
             with urllib.request.urlopen(req, timeout=600) as resp:
                 dest.write_bytes(resp.read())
             downloaded.append(fname)
-            logger.info(f"Downloaded: {fname} ({dest.stat().st_size / 1024 / 1024:.1f}MB)")
+            logger.info(f"  下载完成: {fname} ({dest.stat().st_size / 1024 / 1024:.1f}MB)")
         except Exception as e:
-            logger.warning(f"Failed to download {fname}: {e}")
+            logger.warning(f"  下载失败 {fname}: {e}")
 
     if downloaded:
-        logger.info(f"Downloaded {len(downloaded)} data file(s): {downloaded}")
+        logger.info(f"共下载 {len(downloaded)} 个数据文件: {downloaded}")
     else:
-        logger.info("No downloadable archives found in dataset repo")
+        logger.info("没有需要下载的数据文件")
 
 
 async def download_dataset(req: DatasetDownloadRequest) -> DatasetDownloadResponse:
@@ -400,106 +403,12 @@ async def recover_stale_downloads() -> None:
 
 
 def _download_modelscope_dataset(dataset_id: str) -> tuple[Path, Path, int]:
-    """用 MsDataset.load() 下载数据集，支持图片数据集（自动从 CDN 下载图片）。
-    如果 MsDataset.load() 失败，fallback 到 CLI 方式。"""
-    namespace, ds_name = dataset_id.split("/", 1) if "/" in dataset_id else ("", dataset_id)
+    """用 modelscope CLI 下载数据集仓库文件，再通过 API 下载数据文件区的压缩包。"""
     ds_dir = settings.processed_dir / f"ms_{dataset_id.replace('/', '_')}"
     ds_dir.mkdir(parents=True, exist_ok=True)
-
-    # 优先用 MsDataset.load()，它能自动下载"数据文件"区的图片
-    try:
-        records, record_count = _download_via_msdataset(dataset_id, ds_dir)
-        if records:
-            jsonl_path = ds_dir / "data.jsonl"
-            with open(jsonl_path, "w", encoding="utf-8") as f:
-                for item in records:
-                    f.write(json.dumps(item, ensure_ascii=False) + "\n")
-            logger.info(f"MsDataset.load() 成功: {dataset_id} ({record_count} records)")
-            return ds_dir, jsonl_path, record_count
-    except Exception as e:
-        logger.warning(f"MsDataset.load() failed for {dataset_id}: {e}, falling back to CLI")
-
-    # fallback: CLI 方式（只下载 git 仓库文件，不含数据文件区图片）
     return _download_modelscope_dataset_cli(dataset_id, ds_dir)
 
 
-def _download_via_msdataset(dataset_id: str, ds_dir: Path) -> tuple[list[dict], int]:
-    """用 MsDataset.load() 下载数据集，处理图片列（PIL.Image → 保存到磁盘）。"""
-    from modelscope.msdatasets import MsDataset
-    from PIL import Image
-
-    namespace, ds_name = dataset_id.split("/", 1) if "/" in dataset_id else ("", dataset_id)
-    images_dir = ds_dir / "images"
-
-    # 尝试加载不同 split
-    ds = None
-    for split in ("train", "validation", "test"):
-        try:
-            if namespace:
-                ds = MsDataset.load(ds_name, namespace=namespace, split=split)
-            else:
-                ds = MsDataset.load(dataset_id, split=split)
-            if ds:
-                logger.info(f"MsDataset.load() loaded split '{split}': {len(ds) if hasattr(ds, '__len__') else '?'} records")
-                break
-        except Exception as e:
-            logger.debug(f"split '{split}' failed: {e}")
-
-    if not ds:
-        # 不带 split 参数试试
-        try:
-            if namespace:
-                ds = MsDataset.load(ds_name, namespace=namespace)
-            else:
-                ds = MsDataset.load(dataset_id)
-        except Exception as e:
-            logger.warning(f"MsDataset.load() without split also failed: {e}")
-            return [], 0
-
-    if not ds:
-        return [], 0
-
-    # 检查是否 iterable
-    if not hasattr(ds, '__iter__'):
-        return [], 0
-
-    records = []
-    img_counter = 0
-    columns = None
-
-    for row in ds:
-        if not isinstance(row, dict):
-            continue
-        if columns is None:
-            columns = list(row.keys())
-
-        record = {}
-        for k, v in row.items():
-            if isinstance(v, Image.Image):
-                # 图片对象：保存到磁盘，记录相对路径
-                images_dir.mkdir(parents=True, exist_ok=True)
-                img_name = f"{img_counter:06d}.jpg"
-                img_path = images_dir / img_name
-                if v.mode in ("RGBA", "P", "LA"):
-                    v = v.convert("RGB")
-                v.save(str(img_path), format="JPEG", quality=90)
-                record[k] = f"images/{img_name}"
-                img_counter += 1
-            else:
-                record[k] = v
-
-        records.append(record)
-
-        # 进度日志
-        if len(records) % 500 == 0:
-            logger.info(f"  处理中... {len(records)} records, {img_counter} images saved")
-
-    if img_counter > 0:
-        logger.info(f"共保存 {img_counter} 张图片到 {images_dir}")
-
-    return records, len(records)
-
-
 def _download_modelscope_dataset_cli(dataset_id: str, ds_dir: Path) -> tuple[Path, Path, int]:
     """CLI 方式下载数据集（fallback，只下载 git 仓库文件）。"""
     import subprocess
@@ -511,6 +420,10 @@ def _download_modelscope_dataset_cli(dataset_id: str, ds_dir: Path) -> tuple[Pat
         logger.error(f"ModelScope CLI download failed (code={proc.returncode}): {proc.stderr[:500]}")
         raise RuntimeError(f"ModelScope download failed: {proc.stderr[:500]}")
 
+    # CLI 下载完 git 仓库文件后，通过 API 下载数据文件区的压缩包并解压
+    _download_modelscope_data_files(dataset_id, ds_dir)
+    _extract_archives(ds_dir)
+
     # 扫描下载目录中的所有文件
     all_files = [p for p in ds_dir.rglob("*") if p.is_file()]
     logger.info(f"CLI downloaded {len(all_files)} files to {ds_dir}")

backend/scripts/test_ms_api.py

@@ -1,81 +1,81 @@
 #!/usr/bin/env python3
-"""测试 ModelScope 数据集 API，查找数据文件区的 CDN 链接。"""
+"""测试 ModelScope 图片数据集下载流程。"""
 import json
 import urllib.request
 import urllib.parse
 import sys
+import os
+import tempfile
+import zipfile
 
 api_base = "https://www.modelscope.cn"
 dataset_id = sys.argv[1] if len(sys.argv) > 1 else "tany0699/carBrands50"
+namespace, ds_name = dataset_id.split("/", 1) if "/" in dataset_id else ("", dataset_id)
 
 print(f"测试数据集: {dataset_id}\n")
 
-# Test 1: 完整查看数据集 info API 返回
-print("=== Test1: 数据集 info API 完整响应 ===")
+# Step 1: 下载配置文件
+print("=== Step1: 下载配置文件 ===")
+config_url = (f"{api_base}/api/v1/datasets/{dataset_id}/repo"
+              f"?Source=SDK&Revision=master&FilePath={ds_name}.json&View=false")
+print(f"URL: {config_url}")
 try:
-    url = f"{api_base}/api/v1/datasets/{dataset_id}"
-    print(f"请求: {url}")
-    req = urllib.request.Request(url, headers={"User-Agent": "Test"})
+    req = urllib.request.Request(config_url, headers={"User-Agent": "Test"})
     with urllib.request.urlopen(req, timeout=30) as resp:
-        info = json.loads(resp.read().decode())
-    print(json.dumps(info, indent=2, ensure_ascii=False)[:3000])
+        config = json.loads(resp.read().decode())
+    print(f"配置内容: {json.dumps(config, ensure_ascii=False, indent=2)}")
 except Exception as e:
     print(f"失败: {e}")
+    sys.exit(1)
 
-# Test 2: 尝试 HubApi（跳过 msdatasets 的 import 问题）
-print("\n=== Test2: HubApi 直接调用 ===")
-try:
-    from modelscope.hub.api import HubApi
-    api = HubApi()
+# Step 2: 收集压缩包文件名
+print("\n=== Step2: 收集压缩包文件名 ===")
+archive_files = set()
+for subset in config.values():
+    if isinstance(subset, dict):
+        for split_info in subset.values():
+            if isinstance(split_info, dict):
+                fname = split_info.get("file", "")
+                if fname:
+                    archive_files.add(fname)
+print(f"找到压缩包: {archive_files}")
 
-    # 获取数据集文件列表
-    print("尝试 get_dataset_files...")
-    try:
-        namespace, ds_name = dataset_id.split("/", 1)
-        files = api.get_dataset_files(ds_name, namespace=namespace, recursive=True)
-        print(f"get_dataset_files 返回 {len(files)} 个文件:")
-        for f in files:
-            print(f"  {f}")
-    except Exception as e:
-        print(f"get_dataset_files 失败: {e}")
+# Step 3: 测试下载第一个压缩包（只下载前 1MB 验证）
+print("\n=== Step3: 测试下载压缩包 ===")
+for fname in archive_files:
+    params = urllib.parse.urlencode({
+        "Source": "SDK", "Revision": "master",
+        "FilePath": fname, "View": "false",
+    })
+    dl_url = f"{api_base}/api/v1/datasets/{dataset_id}/repo?{params}"
+    print(f"文件: {fname}")
+    print(f"URL: {dl_url}")
 
-    # 尝试获取文件下载 URL
-    print("\n尝试 get_dataset_file_url...")
+    # 只读 Content-Length 验证可下载
     try:
-        namespace, ds_name = dataset_id.split("/", 1)
-        url = api.get_dataset_file_url("train.csv", ds_name, namespace, revision="master")
-        print(f"train.csv 下载 URL: {url}")
+        req = urllib.request.Request(dl_url, headers={"User-Agent": "Test"})
+        req.method = "HEAD"
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            size = resp.headers.get("Content-Length", "unknown")
+            content_type = resp.headers.get("Content-Type", "unknown")
+            print(f"  Content-Length: {size}")
+            print(f"  Content-Type: {content_type}")
+            print(f"  状态: {resp.status}")
     except Exception as e:
-        print(f"get_dataset_file_url 失败: {e}")
-
-except ImportError as e:
-    print(f"import 失败: {e}")
-except Exception as e:
-    print(f"失败: {e}")
+        print(f"  HEAD 请求失败: {e}")
+        # 尝试 GET 前 1MB
+        try:
+            req = urllib.request.Request(dl_url, headers={"User-Agent": "Test"})
+            with urllib.request.urlopen(req, timeout=30) as resp:
+                data = resp.read(1024 * 1024)
+                print(f"  GET 成功, 前 1MB 读取 {len(data)} bytes")
+                # 检查是否是 zip
+                if data[:4] == b'PK\x03\x04':
+                    print(f"  确认是 ZIP 格式!")
+                else:
+                    print(f"  前 4 字节: {data[:4]}")
+        except Exception as e2:
+            print(f"  GET 也失败: {e2}")
+    break
 
-# Test 3: 查看 carBrands50.json 配置文件（可能包含数据文件 URL）
-print("\n=== Test3: carBrands50.json 配置文件 ===")
-try:
-    namespace, ds_name = dataset_id.split("/", 1)
-    url = (f"{api_base}/api/v1/datasets/{namespace}/{ds_name}/repo"
-           f"?Revision=master&FilePath=carBrands50.json&View=false")
-    print(f"请求: {url}")
-    req = urllib.request.Request(url, headers={"User-Agent": "Test"})
-    with urllib.request.urlopen(req, timeout=30) as resp:
-        config = resp.read().decode()
-    print(config[:2000])
-except Exception as e:
-    print(f"失败: {e}")
-
-# Test 4: 查看 dataset_infos.json
-print("\n=== Test4: dataset_infos.json ===")
-try:
-    url = (f"{api_base}/api/v1/datasets/{namespace}/{ds_name}/repo"
-           f"?Revision=master&FilePath=dataset_infos.json&View=false")
-    print(f"请求: {url}")
-    req = urllib.request.Request(url, headers={"User-Agent": "Test"})
-    with urllib.request.urlopen(req, timeout=30) as resp:
-        config = resp.read().decode()
-    print(config[:2000])
-except Exception as e:
-    print(f"失败: {e}")
+print("\n=== 完成 ===")

result.txt

@@ -1,17 +1,89 @@
 lq@lq:~/Fine-tuning$ cp backend/scripts/test_ms_api.py backend/data/ && sudo docker exec -it finetune-backend python3 /root/Fine-tuning/backend/data/test_ms_api.py tany0699/carBrands50
 测试数据集: tany0699/carBrands50
-namespace: tany0699, name: carBrands50
 
-=== 用 MsDataset.load() 下载 ===
-失败: cannot import name 'get_metadata_patterns' from 'datasets.data_files' (/usr/local/lib/python3.10/site-packages/datasets/data_files.py)
-Traceback (most recent call last):
-  File "/root/Fine-tuning/backend/data/test_ms_api.py", line 14, in <module>
-    from modelscope.msdatasets import MsDataset
-  File "/usr/local/lib/python3.10/site-packages/modelscope/msdatasets/__init__.py", line 2, in <module>
-    from modelscope.msdatasets.ms_dataset import MsDataset
-  File "/usr/local/lib/python3.10/site-packages/modelscope/msdatasets/ms_dataset.py", line 24, in <module>
-    from modelscope.msdatasets.utils.hf_datasets_util import load_dataset_with_ctx
-  File "/usr/local/lib/python3.10/site-packages/modelscope/msdatasets/utils/hf_datasets_util.py", line 19, in <module>
-    from datasets.data_files import (
-ImportError: cannot import name 'get_metadata_patterns' from 'datasets.data_files' (/usr/local/lib/python3.10/site-packages/datasets/data_files.py)
-lq@lq:~/Fine-tuning$ 
+=== Test1: 数据集 info API 完整响应 ===
+请求: https://www.modelscope.cn/api/v1/datasets/tany0699/carBrands50
+{
+  "RequestId": "ec91c118-ce21-47eb-a861-8663b5483315",
+  "Code": 200,
+  "Message": "success",
+  "Data": {
+    "isTop": null,
+    "relatedPaperId": null,
+    "nexa": null,
+    "Id": 2119,
+    "Namespace": "tany0699",
+    "Name": "carBrands50",
+    "CreatedBy": "tany0699",
+    "ChineseName": "车型分类",
+    "License": "Apache License 2.0",
+    "Description": "50种汽车类型分类数据集",
+    "Visibility": 3,
+    "Type": 2,
+    "Owner": "tany0699",
+    "UserDefineTags": "car,car brands,classification",
+    "Likes": 19,
+    "Downloads": 7664,
+    "Size": null,
+    "ReadmeContent": "\n## 数据集描述\n### 数据集简介\n数据集包含50种类型汽车的图像，其中训练集4260张图片，验证集99张图片，数据大小共46MB，支持识别车型包括：BMW、Audi、Jeep、Mini、Suzuki、Bentley等汽车品牌。  \n<img src=\"./sample.jpg\" alt=\"数据示例\"/>  \n\n### 数据集支持的任务\n可用于快速模型验证、性能评估、小数据分类集训练等。\n\n## 数据集的格式和结构\n### 数据格式\n数据集包括训练集train和验证集val，train和val文件夹之下按文件夹进行分类，共有2个子文件夹，同类别标签的图片在同一个文件夹下，图片格式为JPG。同时包含与标注文件中label id相对应的类名文件classname.txt。\n\n### 数据集加载方式\n```python\nfrom modelscope.msdatasets import MsDataset\nfrom modelscope.utils.constant import DownloadMode\n\nms_train_dataset = MsDataset.load(\n            'carBrands50', namespace='tany0699',\n            subset_name='default', split='train') # 加载训练集\nprint(next(iter(ms_train_dataset)))\n\nms_val_dataset = MsDataset.load(\n            'carBrands50', namespace='tany0699',\n            subset_name='default', split='validation') # 加载验证集\nprint(next(iter(ms_val_dataset)))\n```\n### 数据分片\n本数据集包含train和val数据集。\n| 子数据集    |        train | val |     test |\n|---------|-------------:|-----------:|---------:|\n| default |  训练集 |  验证集  | / |\n\n## 数据集生成的相关信息\n### 原始数据\nCar Brands Images：https://www.kaggle.com/datasets/yamaerenay/100-images-of-top-50-car-brands\n\n### Clone with HTTP\n```bash\ngit clone https://www.modelscope.cn/datasets/tany0699/carBrands50.git\n```",
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+
+=== Test2: HubApi 直接调用 ===
+尝试 get_dataset_files...
+get_dataset_files 失败: 'HubApi' object has no attribute 'get_dataset_files'
+
+尝试 get_dataset_file_url...
+train.csv 下载 URL: https://www.modelscope.cn/api/v1/datasets/tany0699/carBrands50/repo?Source=SDK&Revision=master&FilePath=train.csv&View=False
+
+=== Test3: carBrands50.json 配置文件 ===
+请求: https://www.modelscope.cn/api/v1/datasets/tany0699/carBrands50/repo?Revision=master&FilePath=carBrands50.json&View=false
+{"default":{"train":{"meta":"train.csv","file":"train.zip"},"test":{"meta":"","file":""},"validation":{"meta":"val.csv","file":"val.zip"}}}
+
+=== Test4: dataset_infos.json ===
+请求: https://www.modelscope.cn/api/v1/datasets/tany0699/carBrands50/repo?Revision=master&FilePath=dataset_infos.json&View=false
+{"default":{"features":{"image":{"_type":"Image"},"category":{"_type":"Value"}},"splits":{"validation":{"name":"validation","dataset_name":"carBrands50"},"train":{"name":"train","dataset_name":"carBrands50"}}}}