Pytorch DataSet 之 Sampler - 江户川柯兰

Pytorch  dataset 之 Sampler

Sampler 通过制定 索引生成规则，即index的产生方式，来控制后续的数据读取。

```
from torch.utils.data.sampler import Sampler
```

# 复杂版本
```
class sampler(Sampler):
    def __init__(self, train_size, batch_size):
        num_data = train_size
        self.num_per_batch = int(num_data / batch_size)
        self.batch_size = batch_size
        self.range = torch.arange(0, batch_size).view(1, batch_size).long()
        self.leftover_flag = False
        if num_data % batch_size:
            self.leftover = torch.arange(self.num_per_batch * batch_size, num_data).long()
            self.leftover_flag = True

def __iter__(self):
        rand_num = torch.randperm(self.num_per_batch).view(-1, 1) * self.batch_size
        # rand_num = torch.arange(self.num_per_batch).long().view(-1, 1) * self.batch_size
        self.rand_num = rand_num.expand(self.num_per_batch, self.batch_size) + self.range

self.rand_num_view = self.rand_num.view(-1)

if self.leftover_flag:
            self.rand_num_view = torch.cat((self.rand_num_view, self.leftover), 0)

return iter(self.rand_num_view)

def __len__(self):
		return self.num_data
```

# 简化版本
```
class xSampler(Sampler):
    def __init__(self, data_size, batch_size, leftover_keep=True, sampling_type='batch_random'):
        self.data_size = data_size
        self.batch_size = batch_size
        self.batch_nums = int(data_size / batch_size)
        self.leftover_keep = leftover_keep
        self.sampling_type = sampling_type  # batch_random, all_random, no_random

def __iter__(self):
        if self.sampling_type == 'batch_random':
            # 各个batch读取随机 但各个batch内图片的index不变
            # batch_index随机重置，但每个batch内的image_index不变
            rand_batch = torch.randperm(self.batch_nums).view(-1, 1).expand(self.batch_nums, self.batch_size)
            rand_batch = rand_batch * self.batch_size + torch.arange(0, self.batch_size).view(1, -1).long()
            rand_batch = rand_batch.view(-1)
            if self.leftover_keep and self.data_size % self.batch_size:
                leftover = torch.arange(self.batch_nums * self.batch_size, self.data_size).long()
                rand_batch = torch.cat([rand_batch, leftover], dim=0)
            return iter(rand_batch)
        elif self.sampling_type == 'all_random':
            # 所有图片全部随机重置
            rand_all = torch.randperm(self.data_size).long().view(-1)
            return iter(rand_all)
        elif self.sampling_type == 'no_random':
            # 按原顺序返回所有图片
            no_rand = torch.arange(0, self.data_size).long().view(-1)
            return iter(no_rand)
        else:
            raise ValueError('Unknown Parameters %s.' % self.sampling_type)

def __len__(self):
        if self.leftover_keep:
            return self.data_size
        else:
            return self.batch_size * self.batch_nums
```

# batch random 
```
xsampler = xSampler(20, 3, True)
list(xsampler)
Out[81]: [15, 16, 17, 6, 7, 8, 12, 13, 14, 0, 1, 2, 3, 4, 5, 9, 10, 11, 18, 19]
list(xsampler)
Out[82]: [3, 4, 5, 15, 16, 17, 0, 1, 2, 6, 7, 8, 12, 13, 14, 9, 10, 11, 18, 19]
list(xsampler)
Out[83]: [3, 4, 5, 12, 13, 14, 9, 10, 11, 0, 1, 2, 6, 7, 8, 15, 16, 17, 18, 19]
list(xsampler)
Out[84]: [15, 16, 17, 0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11, 12, 13, 14, 18, 19]
list(xsampler)
Out[85]: [3, 4, 5, 9, 10, 11, 15, 16, 17, 6, 7, 8, 0, 1, 2, 12, 13, 14, 18, 19]
```

# all random
```
xsampler = xSampler(20,3,True,'all_random')
list(xsampler)
Out[130]: [14, 4, 16, 11, 12, 0, 10, 9, 5, 1, 13, 6, 3, 15, 19, 18, 2, 8, 17, 7]
list(xsampler)
Out[131]: [8, 16, 14, 5, 18, 15, 2, 1, 3, 6, 4, 0, 9, 7, 17, 12, 19, 10, 11, 13]
list(xsampler)
Out[132]: [4, 0, 9, 15, 5, 17, 12, 8, 2, 6, 14, 19, 7, 1, 16, 11, 13, 10, 18, 3]
```

# no random
```
xsampler = xSampler(20,3,True,'no_random')
list(xsampler)
Out[127]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
list(xsampler)
Out[128]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
```

# 使用next输出
```
xsampler
Out[86]: <__main__.xSampler at 0x69997f0>

next(xsampler)
Traceback (most recent call last):
  File "D:\Miniconda2\envs\tch\lib\site-packages\IPython\core\interactiveshell.py", line 2910, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-88-1d123a121a51>", line 1, in <module>
    next(xsampler)
TypeError: 'xSampler' object is not an iterator
```
```
xsampler = xSampler(20,3,True)
[next(iter(xsampler)) for _ in range(len(xsampler))]
Out[104]: [9, 12, 3, 12, 12, 12, 6, 0, 9, 0, 15, 12, 3, 9, 6, 15, 6, 6, 3, 15]
[next(iter(xsampler)) for _ in range(len(xsampler))]
Out[105]: [0, 0, 0, 3, 0, 12, 0, 15, 15, 15, 3, 0, 3, 6, 0, 12, 15, 15, 6, 15]
[next(iter(xsampler)) for _ in range(len(xsampler))]
Out[106]: [3, 6, 15, 9, 15, 9, 6, 6, 9, 3, 12, 12, 15, 0, 3, 6, 6, 0, 9, 6]
```