[Feature] Support DSDL Dataset

[wufan-tb]

• support dsdl cls dataset
• add dsdl dataset citest
• validated accuracy on cifar10 and imgenet1k

[codecov]

Codecov Report

Patch coverage has no change and project coverage change: +0.46 🎉

Comparison is base (c9a0cb0) 84.37% compared to head (fdc95bf) 84.83%.

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@@            Coverage Diff             @@
##              dev    #1503      +/-   ##
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+ Coverage   84.37%   84.83%   +0.46%     
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  Files         142      231      +89     
  Lines        9925    17589    +7664     
  Branches     1621     2764    +1143     
==========================================
+ Hits         8374    14922    +6548     
- Misses       1277     2149     +872     
- Partials      274      518     +244     

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[zzc98]

where is 'dsdl/set-train/train.yaml' and 'dsdl/set-val/val.yaml'?

[zzc98]

I am afraid that the ImageNet category mapping used by DSDL is not compatible with mmpretrain, and the result cannot be reproduced. The result can be reproduced by using the following code for mapping according to ILSVRC2012_mapping.txt.

def load_data_list(self):
    # ...
    # For ImageNet
    id2name = {}
    folders = []
    with open('ILSVRC2012_mapping.txt', 'r') as f:
        for line in f.readlines()[:1000]:
            line = line[:-1]
            cid, name = line.split()
            id2name[int(cid)] = name
            folders.append(name)
    folders.sort()

        # ...
        label_index = data['Label'][0].index_in_domain() - 1
        name = id2name[label_index + 1]
        label_index = folders.index(name)

[wufan-tb]

这里是这样的，因为dsdl在转换的时候，顺序确实和原始的ImageNet不一致，所以用之前预训练好的模型测试，结果会不一致，但是表格里的数据是我在load代码里进行了顺序对齐之后跑出来的结果，是可以对齐的，但是这个顺序对齐的部分是只针对ImageNet的，所以在合并的时候我把这部分去掉了；
实际上如果用DSDLDataset重新训练一个模型，就不需要顺序对齐了，精度也是可以对齐的，所以代码里是不需要这个顺序对齐的工作的。

[wufan-tb]

要不为了减少歧义，把这两行表格删掉吧