XCompression/xcom/lt-bn-bp

#基于1.《lottery jackpots》代码链接：thttps://github.com/zyxxmu/lottery-jackpots.
#    2.《Weight Uncertainty》代码链接：https://github.com/danielkelshaw/WeightUncertainty
#在cifar10上训练
python cifar.py
#imagenet上训练
python imagenet.py
#先验概率使用混合高斯分布，放在文件夹utils下面的scale_mixture.py中实现，高斯变分推断部分在utils下面的gaussian_variational.py中实现
#相比《lottery jackpots》中重写conv2d的conv_type,本代码中重写bn层，放在utils下面的bn_type.py中
#配置选项在utils的options.py中

以下是lottery jackpots中的readme部分
1. Download the pre-trained models from this [link](https://drive.google.com/drive/folders/13et0J5S2iJK9oS-twrKXVqC-tk0AO9Gn?usp=sharing) and place them in the `pre-train` folder.

2. Select a configuration file in `configs` to reproduce the experiment results reported in the paper. For example, to find a lottery jackpot with 30 epochs for pruning 95% parameters of ResNet-32 on CIFAR-10, run:

   `python cifar.py --config configs/resnet32_cifar10/90sparsity30epoch.yaml --gpus 0`

   To find a lottery jackpot with 30 epochs for pruning 90% parameters of ResNet-50 on ImageNet, run:

   `python imagenet.py --config configs/resnet50_imagenet/90sparsity30epoch.yaml --gpus 0`

   To further tune the weights of a searched lottery jackpot with 10 epochs for pruning 90% parameters of ResNet-50 on ImageNet, run:

   `python imagenet-t.py --config configs/resnet50_imagenet/90sparsity30s10t.yaml --gpus 0`

   Note that the `data_path` in the yaml file should be changed to the data. 

## Evaluate Our Pruned Models
1. Select a configuration file in `configs` to test the pruned models. For example, to evaluate a lottery jackpot for pruning ResNet-32 on CIFAR-10, run:

   `python evaluate.py --config configs/resnet32_cifar10/evaluate.yaml --gpus 0`

    To evaluate a lottery jackpot for pruning ResNet-50 on ImageNet, run:

   `python evaluate.py --config configs/resnet50_imagenet/evaluate.yaml --gpus 0`