Python中关于object_detection.core.minibatch_sampler的20个随机样本生成方法

在Python中，object_detection.core.minibatch_sampler是一个用于生成随机样本的模块。这个模块主要用于目标检测任务中获取训练过程中的随机样本。下面是关于object_detection.core.minibatch_sampler的20个随机样本生成方法的使用示例。

1. 构造一个object_detection.core.minibatch_sampler对象：

import object_detection.core.minibatch_sampler as sampler

minibatch_sampler = sampler.MinibatchSampler()

2. 随机生成指定数量的正样本和负样本：

num_positives = 50
num_negatives = 100
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives)

3. 将指标随机分成小批次：

num_batches = 5
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives, num_batches=num_batches)

4. 对样本进行随机排序并分成小批次：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    random_shuffle=20, num_batches=num_batches)

5. 对样本进行重复采样：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    max_negatives_per_positive=2, num_batches=1)

6. 获取一定比例的随机样本：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    positive_fraction=0.6, num_batches=1)

7. 分别为正样本和负样本设置不同的随机采样参数：

positives_sampler = sampler.RandomSampler(seed=1)
negatives_sampler = sampler.RandomSampler(seed=2)
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    positives_sampler=positives_sampler, negatives_sampler=negatives_sampler, num_batches=1)

8. 使用随机样本生成模式对样本进行采样：

sampler_choice = {
    sampler.RandomSampler: 0.3,
    sampler.SemihardNegativeSampler: 0.7,
}
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    sampler_choice=sampler_choice, num_batches=1)

9. 对于每个正样本，采样固定数量的负样本：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    max_negatives_per_positive=3, num_batches=1)

10. 为样本设置固定的前景和背景个数：

num_foreground = 10
num_background = 20
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    num_foreground=num_foreground, num_background=num_background, num_batches=1)

11. 为样本设置固定的前景和背景比例：

foreground_fraction = 0.5
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    foreground_fraction=foreground_fraction, num_batches=1)

12. 为样本设置前景和背景采样参数：

foreground_sampler = sampler.RandomSampler(seed=1)
background_sampler = sampler.RandomSampler(seed=2)
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    foreground_sampler=foreground_sampler, background_sampler=background_sampler, num_batches=1)

13. 设置采样参数为固定随机样本：

random_sampler = sampler.RandomSampler(seed=1)
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    random_sampler=random_sampler, num_batches=1)

14. 为困难负样本设置重采样参数：

sampler_choice = {
    sampler.SemihardNegativeSampler: 0.8,
    sampler.HardNegativeSampler: 0.2,
}
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    sampler_choice=sampler_choice, num_batches=1)

15. 使用分层采样生成随机样本：

sampler_choice = {
    sampler.StratifiedSampler: 1.0
}
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    sampler_choice=sampler_choice, num_batches=1)

16. 随机生成每个正样本的多个困难负样本：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    random_negative_sampler=random_negative_sampler, max_num_negatives_per_positive=3, num_batches=1)

17. 使用样本的固定数量生成每个正样本的困难负样本：

max_num_negatives_per_positive = 10
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    max_num_negatives_per_positive=max_num_negatives_per_positive, num_batches=1)

18. 采用分层采样算法对样本进行采样：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    random_negative_sampler=random_negative_sampler, max_num_negatives_per_positive=3, num_batches=1)

19. 对样本进行负例挖掘：

sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    max_negatives_per_positive=-1, num_batches=1)

20. 为每个样本设置固定的正例和负例比例：

positive_fraction = 0.5
sampled_indices = minibatch_sampler.subsample_indicator(
    indicator, batch_size, num_positives, num_negatives,
    positive_fraction=positive_fraction, num_batches=1)

以上是关于object_detection.core.minibatch_sampler的20个随机样本生成方法的使用示例。这些方法可以根据任务需求生成不同类型的随机样本，方便在目标检测任务中进行训练。