partition_dataset with ratios leaves some items out of every partition
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- 难度
- 2/5
- 预计耗时
- 1-3 小时
- 新手友好度
- 78/100
- Issue 类型
- 缺陷
- 描述清晰度
- 描述清楚
- 活跃度
- 活跃
- 领域
- data, machine-learning
调研方向
从 monai/data/utils.py 中的 partition_dataset 及其链接行附近基于比例的大小计算开始；同时检查相关的 partition_dataset_classes 路径。重现报告中缺少项目的情况，然后运行或添加针对比例划分和基于类别划分的测试。完成标准是每个输入项目都恰好出现在一个分区中，包括在舍入后的大小原本会遗漏项目的情况下。
由索引模型根据 Issue 内容生成。
描述
Describe the bug
The text below was written with AI assistance (Claude and GPT-6 Astra).
partition_dataset(data, ratios=...) rounds each partition size on its own (https://github.com/Project-MONAI/MONAI/blob/6920610c310200d683a3b00987a05893ccf1ca5f/monai/data/utils.py#L1217-L1224). When the rounded sizes add up to less than len(data), the remaining items are not in any partition, and nothing reports it. With three ratios this happens for about one length in five (0.8/0.1/0.1: 200 of the lengths 1 to 1000; 1/1/1: 334), and with five equal ratios up to two items are lost. partition_dataset_classes splits each class with the same function, so it can lose one item per class. Two-way splits are not affected.
I found this by reading and testing the code.
To Reproduce
from monai.data import partition_dataset
print(partition_dataset(list(range(10)), ratios=[1, 1, 1]))
data = list(range(63)) # e.g. the 63 training cases of MSD Task06_Lung
train, val, test = partition_dataset(data, ratios=[0.8, 0.1, 0.1], shuffle=True, seed=0)
print(len(train), len(val), len(test), "in no split:", sorted(set(data) - set(train + val + test)))
Output on dev (6920610):
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
50 6 6 in no split: [44]
The same happens for other Decathlon training-set sizes, e.g. 303 (Task08_HepaticVessel) and 484 (Task01_BrainTumour) with 0.8/0.1/0.1, 63 and 303 with 0.7/0.15/0.15, and 32 (Task05_Prostate) with five equal ratios, which loses two cases. In a Decathlon-style pipeline (load_decathlon_datalist -> partition_dataset -> CacheDataset -> DataLoader) with 63 cases, one case is never loaded by the train, validation or test split.
Expected behavior
Every item should be in exactly one partition, as in the docstring example ([0.6, 0.2, 0.2] on 5 items gives [[1, 2, 3], [4], [5]]). drop_last is documented as not applying to the ratio path.
Environment
MONAI 1.6.1rc0+50.g6920610c (dev), PyTorch 2.14.1+cpu, NumPy 2.5.4, Python 3.12.13, Linux.
Additional context
I have a small fix that gives the leftover items to the partitions whose sizes were rounded down the most and leaves every split that already covers all items unchanged, plus tests, and can open a PR.
I made this report and directed the investigation, with AI assistance from Claude and GPT-6 Astra.
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