feat: combine ALL-cifar10/100 and Qimera to ALL/

ykl/ALL/README.md

+# ALL
+
++ 逐步将不含LSTM的模型全部功能加入该文件夹，包括对不同数据集支持、模型训练及量化、生成器训练和评估。
++ 之前ResNet系列和MobileNetV2模型结构有误，在cfg.py中进行了调整。
++ 全精度模型训练策略
+  + SGD优化器 lr=0.1 weight_decay=0.9 nesterov=True momentum=0.9
+  + MultiStepLR：epochs=200 milestones=[60,120,160] gamma=0.2。（epoch计数达到milestones时，lr乘gamma）
+  + WarmUpLR：对第一个epoch，执行warmup，逐batch增大lr。
++ 生成器训练策略
+  + Adam优化器 lr=0.001 b1=0.5 b2=0.999
+  + MultiStepLR: epochs=40 milesontes = [20,30] gamma=0.1
+
+
+
+## cifar10
+
+#### 训练全精度模型
+
++ slurm命令
+
+  ```shell
+  python train_one.py $Model $Dataset
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  # 训练单个模型
+  sh scripts/train_one_cifar10.sh AlexNet/...
+  # 训练全部模型
+  sh scripts/train_all_cifar10.sh
+  ```
+
++ 全精度Acc
+
+| AlexNet | AlexNet_BN | VGG_16 | VGG_19 | Inception_BN | MobileNetV2 | ResNet_18 | ResNet_50 | ResNet_152 |
+| ------- | ---------- | ------ | ------ | ------------ | ----------- | --------- | --------- | ---------- |
+| 88.63   | 90.14      | 93.14  | 93.03  | 94.83        | 90.28       | 94.30     | 94.58     | 94.43      |
+
+#### 模型量化及迁移准确性评估
+
+##### 参数量和计算量统计
+
++ slurm命令
+
+  ```shell
+  python get_param_flops.py $name $Dataset > param_flops/$Dataset/$name.txt
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  sh scripts/ptflops_cifar10.sh
+  ```
+
++ 结果见param_flops/cifar10
+
+##### 量化、相似度、准确度获取
+
++ slurm命令
+
+  ```shell
+  python ptq_one.py $Model $Dataset
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  # 量化及评估单个模型
+  sh scripts/ptq_one_cifar10.sh AlexNet/...
+  # 量化及评估全部模型
+  sh scripts/ptq_all_cifar10.sh
+  ```
+
++ 拟合结果
+
+  ![ALL-cifar10-ptq](fig/ALL-cifar10-ptq.png)
+
+  单个模型及js_flops - acc_loss拟合对应R2：
+
+  | AlexNet | AlexNet_BN | VGG_16 | VGG_19 | Inception_BN | MobileNetV2 | ResNet_18 | ResNet_50 | ResNet_152 |
+  | ------- | ---------- | ------ | ------ | ------------ | ----------- | --------- | --------- | ---------- |
+  | 0.9819  | 0.9878     | 0.9727 | 0.9648 | 0.8693       | 0.9652      | 0.9181    | 0.9011    | 0.8994     |
+  
+  考虑到单个模型拟合均较好，而整体拟合较差，后期将尝试加权处理。
+
+​	
+
+#### 基于生成器的安全性评估
+
++ slurm命令
+
+  ```shell
+  #针对全精度模型
+  python gen_one.py --model $Model --dataset $Dataset --multi_label_prob 0.4 --multi_label_num $Label
+  #针对量化模型
+  python gen_one.py --model $Model --dataset $Dataset --quant --multi_label_prob 0.4 --multi_label_num $Label
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  #全精度，针对单个模型训练生成器
+  sh scripts/gen_full_one_cifar10.sh
+  #全精度，针对全部模型训练生成器
+  sh scripts/gen_full_all_cifar10.sh
+  
+  #量化，针对单个模型训练生成器
+  sh scripts/gen_quant_one_cifar10.sh
+  #量化，针对全部模型训练生成器
+  sh scripts/gen_quant_all_cifar10.sh
+  ```
+
+  
+
+## cifar100
+
+#### 训练全精度模型
+
++ slurm命令
+
+  ```shell
+  python train_one.py AlexNet/AlexNet_BN/... cifar100
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  # 训练单个模型
+  sh scripts/train_one_cifar100.sh AlexNet/...
+  # 训练全部模型
+  sh scripts/train_all_cifar100.sh
+  ```
+
++ 全精度Acc
+
+| AlexNet | AlexNet_BN | VGG_16 | VGG_19 | Inception_BN | MobileNetV2 | ResNet_18 | ResNet_50 | ResNet_152 |
+| ------- | ---------- | ------ | ------ | ------------ | ----------- | --------- | --------- | ---------- |
+| 63.43   | 66.31      | 71.12  | 70.45  | 77.97        | 66.76       | 75.73     | 77.22     | 77.41      |
+
+#### 模型量化及迁移准确性评估
+
+##### 参数量和计算量统计
+
++ slurm命令
+
+  ```shell
+  python get_param_flops.py $name $Dataset > param_flops/$Dataset/$name.txt
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  sh scripts/ptflops_cifar100.sh
+  ```
+
++ 结果见param_flops/cifar100
+
+##### 量化、相似度、准确度获取
+
++ slurm命令
+
+  ```shell
+  python ptq_one.py $Model $Dataset
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  # 量化及评估单个模型
+  sh scripts/ptq_one_cifar100.sh AlexNet/...
+  # 量化及评估全部模型
+  sh scripts/ptq_all_cifar100.sh
+  ```
+
++ 拟合结果
+
+  ![ALL-cifar100-ptq](fig/ALL-cifar100-ptq.png)
+
+  模型及js_flops - acc_loss拟合对应R2：
+
+  | AlexNet | AlexNet_BN | VGG_16 | VGG_19 | Inception_BN | MobileNetV2 | ResNet_18 | ResNet_50 | ResNet_152 |
+  | ------- | ---------- | ------ | ------ | ------------ | ----------- | --------- | --------- | ---------- |
+  | 0.9790  | 0.9730     | 0.9222 | 0.9115 | 0.8806       | 0.9570      | 0.8498    | 0.8620    | 0.8713     |
+  
+  考虑到单个模型拟合均较好，而整体拟合较差，后期将尝试加权处理。
+
+#### 基于生成器的安全性评估
+
++ slurm命令
+
+  ```shell
+  #针对全精度模型
+  python gen_one.py --model $Model --dataset $Dataset --multi_label_prob 0.4 --multi_label_num $Label
+  #针对量化模型
+  python gen_one.py --model $Model --dataset $Dataset --quant --multi_label_prob 0.4 --multi_label_num $Label
+  ```
+
+  在ALL目录下运行脚本
+
+  ```shell
+  #全精度，针对单个模型训练生成器
+  sh scripts/gen_full_one_cifar100.sh
+  #全精度，针对全部模型训练生成器
+  sh scripts/gen_full_all_cifar100.sh
+  
+  #量化，针对单个模型训练生成器
+  sh scripts/gen_quant_one_cifar100.sh
+  #量化，针对全部模型训练生成器
+  sh scripts/gen_quant_all_cifar100.sh
+  ```
+
+  
\ No newline at end of file

ykl/ALL/cancel_all.sh

+# name_list="AlexNet AlexNet_BN VGG_16 VGG_19 Inception_BN ResNet_18 ResNet_50 ResNet_152 MobileNetV2"
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+for name in $name_list; do 
+    scancel -n $name
+done
\ No newline at end of file

ykl/ALL/cat_one.sh

+cat $(ls -1 ret_one/$1/*.out | sort -n -t - -k 2 | tail -n 1)
\ No newline at end of file

ykl/ALL/cfg.py

+# conv: 'C',''/'B'/'BRL'/'BRS',qi,in_ch,out_ch,kernel_size,stirde,padding,bias
+# relu: 'RL'
+# relu6: 'RS'
+# inception: 'Inc'
+# maxpool: 'MP',kernel_size,stride,padding
+# adaptiveavgpool: 'AAP',output_size
+# view: 'VW': 
+#   dafault: x = x.view(x.size(0),-1)
+# dropout: 'D'
+# MakeLayer: 'ML','BBLK'/'BTNK'/'IRES', ml_idx, blocks
+# softmax: 'SM'
+# class 100
+ResNet_18_cfg_table = [
+    ['C','BRL',True,3,64,3,1,1,False],
+    ['ML','BBLK',0,2],
+    ['ML','BBLK',1,2],
+    ['ML','BBLK',2,2],
+    ['ML','BBLK',3,2],
+    ['AAP',1],
+    ['VW'],
+    ['FC',512,100,True],
+]
+ResNet_50_cfg_table = [
+    ['C','BRL',True,3,64,3,1,1,False],
+    ['ML','BTNK',0,3],
+    ['ML','BTNK',1,4],
+    ['ML','BTNK',2,6],
+    ['ML','BTNK',3,3],
+    ['AAP',1],
+    ['VW'],
+    ['FC',2048,100,True]
+]
+ResNet_152_cfg_table = [
+    ['C','BRL',True,3,64,3,1,1,False],
+    ['ML','BTNK',0,3],
+    ['ML','BTNK',1,8],
+    ['ML','BTNK',2,36],
+    ['ML','BTNK',3,3],
+    ['AAP',1],
+    ['VW'],
+    ['FC',2048,100,True]
+]
+MobileNetV2_cfg_table = [
+    ['C','BRS',True,3,32,1,1,1,True],
+    ['ML','IRES',0,1],
+    ['ML','IRES',1,2],
+    ['ML','IRES',2,3],
+    ['ML','IRES',3,4],
+    ['ML','IRES',4,3],
+    ['ML','IRES',5,3],
+    ['ML','IRES',6,1],
+    ['C','BRS',False,320,1280,1,1,0,True],
+    ['AAP',1],
+    ['VW'],
+    ['FC',1280,100,True]
+]
+AlexNet_cfg_table = [
+    ['C','',True,3,32,3,1,1,True],
+    ['RL'],
+    ['MP',2,2,0],
+    ['C','',False,32,64,3,1,1,True],
+    ['RL'],
+    ['MP',2,2,0],
+    ['C','',False,64,128,3,1,1,True],
+    ['RL'],
+    ['C','',False,128,256,3,1,1,True],
+    ['RL'],
+    ['C','',False,256,256,3,1,1,True],
+    ['RL'],
+    ['MP',3,2,0],
+    ['VW'],
+    ['D',0.5],
+    ['FC',2304,1024,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',1024,512,True],
+    ['RL'],
+    ['FC',512,100,True]
+]
+AlexNet_BN_cfg_table = [
+    ['C','BRL',True,3,32,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,32,64,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,64,128,3,1,1,True],
+    ['C','BRL',False,128,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['MP',3,2,0],
+    ['VW'],
+    ['D',0.5],
+    ['FC',2304,1024,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',1024,512,True],
+    ['RL'],
+    ['FC',512,100,True]
+]
+VGG_16_cfg_table = [
+    ['C','BRL',True,3,64,3,1,1,True],
+    ['C','BRL',False,64,64,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,64,128,3,1,1,True],
+    ['C','BRL',False,128,128,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,128,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,256,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['MP',2,2,0],
+    ['VW'],
+    ['FC',512,4096,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',4096,4096,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',4096,100,True]
+]
+VGG_19_cfg_table = [
+    ['C','BRL',True,3,64,3,1,1,True],
+    ['C','BRL',False,64,64,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,64,128,3,1,1,True],
+    ['C','BRL',False,128,128,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,128,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['C','BRL',False,256,256,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,256,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['MP',2,2,0],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['C','BRL',False,512,512,3,1,1,True],
+    ['MP',2,2,0],
+    ['VW'],
+    ['FC',512,4096,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',4096,4096,True],
+    ['RL'],
+    ['D',0.5],
+    ['FC',4096,100,True]
+]
+Inception_BN_cfg_table = [
+    ['C','',True,3,64,3,1,1,True],
+    ['RL'],
+    ['C','',False,64,64,3,1,1,True],
+    ['RL'],
+    ['Inc',0],
+    ['Inc',1],
+    ['MP',3,2,1],
+    ['Inc',2],
+    ['Inc',3],
+    ['Inc',4],
+    ['Inc',5],
+    ['Inc',6],
+    ['MP',3,2,1],
+    ['Inc',7],
+    ['Inc',8],
+    ['AAP',1],
+    ['C','',False,1024,100,1,1,0,True],
+    ['VW']
+]
+
+model_cfg_table = {
+    'AlexNet'       : AlexNet_cfg_table,
+    'AlexNet_BN'    : AlexNet_BN_cfg_table,
+    'VGG_16'        : VGG_16_cfg_table,
+    'VGG_19'        : VGG_19_cfg_table,
+    'Inception_BN'  : Inception_BN_cfg_table,
+    'ResNet_18'     : ResNet_18_cfg_table,
+    'ResNet_50'     : ResNet_50_cfg_table,
+    'ResNet_152'    : ResNet_152_cfg_table,
+    'MobileNetV2'   : MobileNetV2_cfg_table
+}
+
+#每行对应一个Inc结构(channel)的参数表
+inc_ch_table=[
+    [ 64, 64, 96,128, 16, 32, 32],#3a
+    [256,128,128,192, 32, 96, 64],#3b
+    [480,192, 96,208, 16, 48, 64],#4a
+    [512,160,112,224, 24, 64, 64],#4b
+    [512,128,128,256, 24, 64, 64],#4c
+    [512,112,144,288, 32, 64, 64],#4d
+    [528,256,160,320, 32,128,128],#4e
+    [832,256,160,320, 32,128,128],#5a
+    [832,384,192,384, 48,128,128] #5b
+]
+
+# br0,br1,br2,br3 <- br1x1,br3x3,br5x5,brM
+# 每个子数组对应Inc结构中一个分支的结构，均默认含'BRL'参数，bias为False
+# Conv层第2、3个参数是对应Inc结构(即ch_table中的一行)中的索引
+# 由于每个Inc结构操作一致，只有权重不同，使用索引而非具体值，方便复用
+# 各分支后还有Concat操作，由于只有唯一结构，未特殊说明
+# conv: 'C', ('BRL' default), in_ch_idex, out_ch_idx, kernel_size, stride, padding, (bias: True default)
+# maxpool: 'MP', kernel_size, stride, padding
+# relu: 'RL'
+inc_cfg_table = [
+    [
+        ['C',0,1,1,1,0]
+    ],
+    [
+        ['C',0,2,1,1,0],
+        ['C',2,3,3,1,1]
+    ],
+    [
+        ['C',0,4,1,1,0],
+        ['C',4,5,5,1,2]
+    ],
+    [
+        ['MP',3,1,1],
+        ['RL'],
+        ['C',0,6,1,1,0]
+    ]
+]
+
+# ml_cfg_table = []
+
+#BasicBlock
+#value: downsample,inplanes,planes,planes*expansion,stride,1(dafault stride and group) 
+bblk_ch_table = [
+    [False, 64, 64, 64,1,1], #layer1,first
+    [False, 64, 64, 64,1,1], #       other
+    [True,  64,128,128,2,1], #layer2
+    [False,128,128,128,1,1], 
+    [True, 128,256,256,2,1], #layer3
+    [False,256,256,256,1,1], 
+    [True, 256,512,512,2,1], #layer4
+    [False,512,512,512,1,1]  
+]
+#conv: 'C','B'/'BRL'/'BRS', in_ch_idx, out_ch_idx, kernel_sz, stride_idx, padding, groups_idx,bias
+#add: 'AD', unconditonal. unconditonal为true或flag为true时将outs中两元素相加
+bblk_cfg_table = [
+    [
+        ['C','BRL',1,2,3,4,1,5,False],
+        ['C','B'  ,2,3,3,5,1,5,False],
+    ],
+    # downsample, 仅当downsample传入为True时使用
+    [
+        ['C','B'  ,1,3,1,4,0,5,False]
+    ],
+    # 分支交汇后动作
+    [
+        ['AD',True],
+        ['RL']
+    ]
+]
+
+#BottleNeck
+#value: downsample,inplanes,planes,planes*expansion,stride,1(dafault stride and group) 
+btnk_ch_table = [
+    [True,   64, 64, 256,1,1], #layer1,first
+    [False, 256, 64, 256,1,1], #       other 
+    [True,  256,128, 512,2,1], #layer2
+    [False, 512,128, 512,1,1], 
+    [True,  512,256,1024,2,1], #layer3
+    [False,1024,256,1024,1,1],  
+    [True, 1024,512,2048,2,1], #layer4
+    [False,2048,512,2048,1,1]   
+]
+#conv: 'C','B'/'BRL'/'BRS', in_ch_idx, out_ch_idx, kernel_sz, stride_idx, padding, groups_idx (bias: True default)
+#add: 'AD', unconditonal. unconditonal为true或flag为true时将outs中两元素相加
+btnk_cfg_table = [
+    [
+        ['C','BRL',1,2,1,5,0,5,False],
+        ['C','BRL',2,2,3,4,1,5,False],
+        ['C','B'  ,2,3,1,5,0,5,False]
+    ],
+    # downsample, 仅当downsample传入为True时使用
+    [
+        ['C','B'  ,1,3,1,4,0,5,False]
+    ], 
+    # 分支交汇后动作
+    [
+        ['AD',True],
+        ['RL']
+    ]
+]
+
+#InvertedResidual/LinearBottleNeck
+#value: identity_flag, in_ch, out_ch, in_ch*expand_ratio, stride, 1(dafault stride and group) 
+ires_ch_table = [
+    [False, 32, 16,  32,1,1], #layer1,first
+    [ True, 16, 16,  16,1,1], #       other
+    [False, 16, 24,  96,2,1], #layer2
+    [ True, 24, 24, 144,1,1],
+    [False, 24, 32, 144,2,1], #layer3
+    [ True, 32, 32, 192,1,1],
+    [False, 32, 64, 192,2,1], #layer4
+    [ True, 64, 64, 384,1,1],
+    [False, 64, 96, 384,1,1], #layer5
+    [ True, 96, 96, 576,1,1],
+    [False, 96,160, 576,1,1], #layer6
+    [ True,160,160, 960,1,1],
+    [False,160,320, 960,1,1], #layer6
+    [ True,320,320,1920,1,1]
+]
+
+#conv: 'C','B'/'BRL'/'BRS', in_ch_idx, out_ch_idx, kernel_sz, stride_idx, padding, groups_idx (bias: True default)
+#add: 'AD', unconditonal. unconditonal为true或flag为true时将outs中两元素相加
+ires_cfg_table = [
+    [
+        ['C','BRS',1,3,1,5,0,5,True],
+        ['C','BRS',3,3,3,4,1,3,True],
+        ['C','B'  ,3,2,1,5,0,5,True]
+    ],
+    # identity_br empty
+    [
+
+    ],
+    # 分支汇合后操作
+    [
+        ['AD',False] #有条件的相加
+    ]
+    
+]
\ No newline at end of file

ykl/ALL/check.sh

+squeue --me

ykl/ALL/dataloader.py

+import os
+import torch
+import torchvision.datasets as dsets
+import torchvision.transforms as transforms
+
+
+class DataLoader(object):
+	"""
+	data loader for CV data sets
+	"""
+	
+	def __init__(self, dataset, batch_size):
+		"""
+		create data loader for specific data set
+		:params n_treads: number of threads to load data, default: 4
+		:params data_path_root: root path to data set, default: /lustre/datasets/
+		"""
+		self.dataset = dataset
+		self.batch_size = batch_size
+		self.n_threads = 4 #num_workers
+		self.data_path_root = '/lustre/datasets/'
+		
+		
+		if self.dataset in ["cifar100","cifar10"]:
+			self.train_loader, self.val_loader, self.test_loader = self.cifar(
+				dataset=self.dataset)
+		else:
+			assert False, "invalid data set"
+	
+	def getloader(self):
+		"""d
+		get train_loader and test_loader
+		"""
+		return self.train_loader, self.val_loader, self.test_loader
+
+	def cifar(self, dataset):
+		"""
+		dataset: cifar
+		"""
+		if dataset == "cifar10":
+			norm_mean = [0.49139968, 0.48215827, 0.44653124]
+			norm_std = [0.24703233, 0.24348505, 0.26158768]
+		elif dataset == "cifar100":
+			norm_mean = [0.50705882, 0.48666667, 0.44078431]
+			norm_std = [0.26745098, 0.25568627, 0.27607843]
+		else:
+			assert False, "Invalid cifar dataset"
+
+		train_transfrom = transforms.Compose([
+							transforms.RandomCrop(32, padding=2),
+							transforms.RandomHorizontalFlip(),
+							transforms.ToTensor(),
+							transforms.Normalize(norm_mean, norm_std)
+							])
+		eval_transfrom = transforms.Compose([
+							transforms.ToTensor(),
+							transforms.Normalize(norm_mean, norm_std)
+							])
+
+		if self.dataset == "cifar10":
+			data_path = self.data_path_root + 'CIFAR10'
+			alltrainset = dsets.CIFAR10(root=data_path,train=True,download=False,
+										transform=train_transfrom)
+			testset = dsets.CIFAR10(data_path, train=False, download=False, 
+									transform=eval_transfrom)
+		elif self.dataset == "cifar100":
+			data_path = self.data_path_root + 'CIFAR100'
+			alltrainset = dsets.CIFAR100(root=data_path,train=True,download=False,
+										transform=train_transfrom)
+			testset = dsets.CIFAR100(data_path, train=False, download=False, 
+									transform=eval_transfrom)
+		else:
+			assert False, "invalid data set"
+
+		train_size = (int)(0.8 * len(alltrainset))
+		val_size = (int)(0.2 * len(alltrainset))
+		
+		train_idx, val_idx = torch.utils.data.random_split(range(train_size+val_size),[train_size,val_size])
+
+		trainset = torch.utils.data.Subset(alltrainset,train_idx)
+		valset = torch.utils.data.Subset(alltrainset,val_idx)
+
+		train_loader = torch.utils.data.DataLoader(
+		trainset,
+		batch_size=self.batch_size, shuffle=True, num_workers=self.n_threads, pin_memory=True
+		)
+
+		val_loader = torch.utils.data.DataLoader(
+			valset,
+			batch_size=self.batch_size, shuffle=False, num_workers=self.n_threads, pin_memory=True
+		)
+
+		test_loader = torch.utils.data.DataLoader(
+			testset,
+			batch_size=self.batch_size, shuffle=False, num_workers=self.n_threads, pin_memory=True
+		)
+
+		return train_loader, val_loader, test_loader
+	
+

ykl/ALL/err.sh

+if [ -z $1 ];then
+    cat $(ls -1 ret/ret-*.err | sort -n -t - -k 2 | tail -n 1)
+else
+    cat ret/ret-"$1".err
+fi
\ No newline at end of file

ykl/ALL/err_one.sh

+cat $(ls -1 ret_one/$1/*.err | sort -n -t - -k 2 | tail -n 1)
\ No newline at end of file

ykl/ALL/extract_ratio.py

+import sys
+import os
+
+
+# 从get_param.py输出重定向文件val.txt中提取参数量和计算量
+def extract_ratio(model_name,dataset):
+    fr = open('param_flops/'+dataset+'/'+model_name+'.txt','r')
+    lines = fr.readlines()
+    
+    #跳过warning
+    for i in range(len(lines)):
+        if 'Model' in lines[i]:
+            head = i+1
+            break 
+    Mac = lines[head].split('Mac,')[0].split(',')[-1]
+    if 'M' in Mac:
+        Mac = Mac.split('M')[0]
+        Mac = float(Mac)
+    elif 'G' in Mac:
+        Mac = Mac.split('G')[0]
+        Mac = float(Mac)
+        Mac *= 1024
+    
+    Param = lines[head].split(',')[0]
+    if 'M' in Param:
+        Param = Param.split('M')[0]
+        Param = float(Param)
+    elif 'k' in Param:
+        Param = Param.split('k')[0]
+        Param = float(Param)
+        Param /= 1024
+    
+    layer = []
+    par_ratio = []
+    flop_ratio = []
+    for line in lines:
+        if '(' in line and ')' in line:
+            layer.append(line.split(')')[0].split('(')[1])
+            r1 = line.split('%')[0].split(',')[-1]
+            r1 = float(r1)
+            par_ratio.append(r1)
+            r2 = line.split('%')[-2].split(',')[-1]
+            r2 = float(r2)
+            flop_ratio.append(r2)
+
+    return Mac, Param, layer, par_ratio, flop_ratio
\ No newline at end of file

ykl/ALL/function.py

+from torch.autograd import Function
+
+
+class FakeQuantize(Function):
+
+    @staticmethod
+    def forward(ctx, x, qparam):
+        x = qparam.quantize_tensor(x)
+        x = qparam.dequantize_tensor(x)
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output, None
\ No newline at end of file

ykl/ALL/gen_one.slurm

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#- Resources
+
+# (TODO)
+# Please modify your requirements
+
+#SBATCH -p nv-gpu                    # Submit to 'nv-gpu' Partitiion
+#SBATCH -t 1-06:00:00                # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
+#SBATCH --nodes=1                    # Request N nodes
+#SBATCH --gres=gpu:1                 # Request M GPU per node
+#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
+#SBATCH --qos=gpu-normal             # Request QOS Type
+
+###
+### The system will alloc 8 or 16 cores per gpu by default.
+### If you need more or less, use following:
+### #SBATCH --cpus-per-task=K            # Request K cores
+###
+### 
+### Without specifying the constraint, any available nodes that meet the requirement will be allocated
+### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
+###
+### #SBATCH --nodelist=gpu-v00           # Request a specific list of hosts 
+### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
+###
+
+# set constraint for RTX8000 to meet my cuda
+#SBATCH --constraint="Ampere|RTX8000"
+
+#- Log information
+
+echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
+echo "Job run at:"
+echo "$(hostnamectl)"
+
+#- Load environments
+source /tools/module_env.sh
+module list                       # list modules loaded
+
+##- Tools
+module load cluster-tools/v1.0
+module load slurm-tools/v1.0
+module load cmake/3.15.7
+module load git/2.17.1
+module load vim/8.1.2424
+
+##- language
+module load python3/3.6.8
+
+##- CUDA
+# module load cuda-cudnn/10.2-7.6.5
+# module load cuda-cudnn/11.2-8.2.1
+module load cuda-cudnn/11.1-8.2.1
+
+##- virtualenv
+# source xxxxx/activate
+
+echo $(module list)              # list modules loaded
+echo $(which gcc)
+echo $(which python)
+echo $(which python3)
+
+cluster-quota                    # nas quota
+
+nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
+
+#- Warning! Please not change your CUDA_VISIBLE_DEVICES
+#- in `.bashrc`, `env.sh`, or your job script
+echo "Use GPU ${CUDA_VISIBLE_DEVICES}"                              # which gpus
+#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
+
+#- Job step
+# [EDIT HERE(TODO)]
+if [ $Dataset = 'cifar10' ]; then
+    Label=2
+elif [ $Dataset = 'cifar100' ]; then
+    Label=10
+else
+    echo "Invalid Dataset $Dataset"
+    exit
+fi
+
+if [ $Quant = 'True' ]; then
+    python gen_one.py --model $Model --dataset $Dataset --quant --multi_label_prob 0.4 --multi_label_num $Label
+else
+    python gen_one.py --model $Model --dataset $Dataset --multi_label_prob 0.4 --multi_label_num $Label
+fi
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

ykl/ALL/gen_options.py

+import os
+import os.path as osp
+
+class GenOption(object):
+	def __init__(self, args):
+		self.model = args.model
+		self.dataset = args.dataset
+		self.batchSize = 128
+		self.quant = args.quant
+		if self.dataset == "cifar10":
+			self.nClasses = 10
+		elif self.dataset == "cifar100":
+			self.nClasses = 100
+		else:
+			assert False, "invalid dataset"
+
+
+		# ----------Generator options ---------------------------------------------
+		# self.nEpochs = 100
+		self.nEpochs = 40
+		#每个epoch训练多少轮，和batchsize无关
+		self.iters = 200
+		#冻结embedding层权重
+		self.freeze = args.freeze
+		self.randemb = args.randemb
+		# 如果不为randomemb，需要根据weight_t调整
+		self.latent_dim = 64
+		# 针对imagenet等数据集需要调整
+		self.img_size = 32
+		self.channels = 3
+
+		self.lr_G = 0.001
+		# self.milestones_G = [40,60,80]
+		self.milestones_G = [20,30] 
+		self.gamma_G = 0.1
+
+		self.b1 = 0.5
+		self.b2 = 0.999
+		# ----------More option ---------------------------------------------
+		self.multi_label_prob = args.multi_label_prob
+		self.multi_label_num = args.multi_label_num
+
+		self.no_DM = args.no_DM
+		self.noise_scale = args.noise_scale
+
+		self.intermediate_dim = 100
+		# if self.network == "resnet20":
+		# 	self.intermediate_dim = 64
+
+	def set(self,quant_type=None,num_bits=None,e_bits=None):
+		if self.quant:
+			self.quant_type = quant_type
+			self.num_bits = num_bits
+			self.e_bits = e_bits
+			if quant_type == 'FLOAT':
+				title = '%s_%d_E%d' % (quant_type, num_bits, e_bits)
+			else:
+				title = '%s_%d' % (quant_type, num_bits)
+			self.teacher_file = 'ckpt_quant/'+self.dataset+'/'+self.model+'/'+title+'.pt'
+			gen_path = 'ckpt_quant_gen/'+self.dataset+'/'+self.model
+			self.gen_file = gen_path + '/' + title + '.pt'
+		else:
+			self.teacher_file = 'ckpt_full/'+self.dataset+'/'+self.model+'.pt'
+			gen_path = 'ckpt_full_gen/'+self.dataset
+			self.gen_file = gen_path +'/'+ self.model+'.pt'
+
+		if not osp.exists(self.teacher_file):
+			assert False, "Empty teacher file"
+		if not osp.exists(gen_path):
+			os.makedirs(gen_path)
+

ykl/ALL/generator.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+
+class Generator(nn.Module):
+	def __init__(self, options=None, teacher_weight=None, freeze=True):
+		super(Generator, self).__init__()
+		self.settings = options
+		# 注意这里有embedding层，两个分别是词典大小和向量长度
+		# 用于将标签映射为向量
+		if teacher_weight==None:
+			self.label_emb = nn.Embedding(self.settings.nClasses, self.settings.latent_dim)
+		else:
+			#当randemb为False时，要求latentdim与输出层输入通道一致
+			self.label_emb = nn.Embedding.from_pretrained(teacher_weight, freeze=freeze)
+
+		self.embed_normalizer = nn.BatchNorm1d(self.label_emb.weight.T.shape,affine=False,track_running_stats=False)
+
+		if not self.settings.no_DM:
+			self.fc_reducer = nn.Linear(in_features=self.label_emb.weight.shape[-1], out_features=self.settings.intermediate_dim)
+
+			self.init_size = self.settings.img_size // 4
+			self.l1 = nn.Sequential(nn.Linear(self.settings.intermediate_dim, 128 * self.init_size ** 2))
+		else:
+			self.init_size = self.settings.img_size // 4
+			self.l1 = nn.Sequential(nn.Linear(self.settings.latent_dim, 128 * self.init_size ** 2))
+
+		self.conv_blocks0 = nn.Sequential(
+			nn.BatchNorm2d(128),
+		)
+
+		self.conv_blocks1 = nn.Sequential(
+			nn.Conv2d(128, 128, 3, stride=1, padding=1),
+			nn.BatchNorm2d(128, 0.8),
+			nn.LeakyReLU(0.2, inplace=True),
+		)
+		self.conv_blocks2 = nn.Sequential(
+			nn.Conv2d(128, 64, 3, stride=1, padding=1),
+			nn.BatchNorm2d(64, 0.8),
+			nn.LeakyReLU(0.2, inplace=True),
+			nn.Conv2d(64, self.settings.channels, 3, stride=1, padding=1),
+			nn.Tanh(),
+			nn.BatchNorm2d(self.settings.channels, affine=False)
+		)
+
+	def forward(self, z, labels, linear=None, z2=None):
+		# GDFQ此处为随机噪声乘label
+		if linear == None:
+			gen_input = self.embed_normalizer(torch.add(self.label_emb(labels),self.settings.noise_scale*z).T).T 
+
+			if not self.settings.no_DM:
+				gen_input = self.fc_reducer(gen_input)
+
+		else:
+			embed_norm = self.embed_normalizer(torch.add(self.label_emb(labels),self.settings.noise_scale*z).T).T 
+
+			if not self.settings.no_DM:
+				gen_input = self.fc_reducer(embed_norm)
+			else:
+				gen_input = embed_norm
+
+			gen_input = (gen_input * linear.unsqueeze(2)).sum(dim=1)
+
+		out = self.l1(gen_input)
+		out = out.view(out.shape[0], 128, self.init_size, self.init_size)
+		img = self.conv_blocks0(out)
+		img = nn.functional.interpolate(img, scale_factor=2)
+		img = self.conv_blocks1(img)
+		img = nn.functional.interpolate(img, scale_factor=2)
+		img = self.conv_blocks2(img)
+		return img
+
+
+class Generator_imagenet(nn.Module):
+	def __init__(self, options=None, teacher_weight=None, freeze=True):
+		super(Generator_imagenet, self).__init__()
+		self.settings = options
+		if teacher_weight==None:
+			self.label_emb = nn.Embedding(self.settings.nClasses, self.settings.latent_dim)
+		else:
+			self.label_emb = nn.Embedding.from_pretrained(teacher_weight, freeze=freeze)
+
+		self.embed_normalizer = nn.BatchNorm1d(self.label_emb.weight.T.shape,affine=False,track_running_stats=False)
+
+		if not self.settings.no_DM:
+			self.fc_reducer = nn.Linear(in_features=self.label_emb.weight.shape[-1], out_features=self.settings.intermediate_dim)
+
+			self.init_size = self.settings.img_size // 4
+			self.l1 = nn.Sequential(nn.Linear(self.settings.intermediate_dim, 128 * self.init_size ** 2))
+		else:
+			self.init_size = self.settings.img_size // 4
+			self.l1 = nn.Sequential(nn.Linear(self.settings.latent_dim, 128 * self.init_size ** 2))
+
+		self.conv_blocks0_0 = CategoricalConditionalBatchNorm2d(1000, 128)
+
+		self.conv_blocks1_0 = nn.Conv2d(128, 128, 3, stride=1, padding=1)
+		self.conv_blocks1_1 = CategoricalConditionalBatchNorm2d(1000, 128, 0.8)
+		self.conv_blocks1_2 = nn.LeakyReLU(0.2, inplace=True)
+
+		self.conv_blocks2_0 = nn.Conv2d(128, 64, 3, stride=1, padding=1)
+		self.conv_blocks2_1 = CategoricalConditionalBatchNorm2d(1000, 64, 0.8)
+		self.conv_blocks2_2 = nn.LeakyReLU(0.2, inplace=True)
+		self.conv_blocks2_3 = nn.Conv2d(64, self.settings.channels, 3, stride=1, padding=1)
+		self.conv_blocks2_4 = nn.Tanh()
+		self.conv_blocks2_5 = nn.BatchNorm2d(self.settings.channels, affine=False)
+
+	def forward(self, z, labels, linear=None):
+		if linear == None:
+			gen_input = self.embed_normalizer(torch.add(self.label_emb(labels),z).T).T
+			if not self.settings.no_DM:
+				gen_input = self.fc_reducer(gen_input)
+		else:
+			embed_norm = self.embed_normalizer(torch.add(self.label_emb(labels),z).T).T
+			if not self.settings.no_DM:
+				gen_input = self.fc_reducer(embed_norm)
+			else:
+				gen_input = embed_norm
+			gen_input = (gen_input * linear.unsqueeze(2)).sum(dim=1)
+
+		out = self.l1(gen_input)
+		out = out.view(out.shape[0], 128, self.init_size, self.init_size)
+		img = self.conv_blocks0_0(out, labels, linear=linear)
+		img = nn.functional.interpolate(img, scale_factor=2)
+		img = self.conv_blocks1_0(img)
+		img = self.conv_blocks1_1(img, labels, linear=linear)
+		img = self.conv_blocks1_2(img)
+		img = nn.functional.interpolate(img, scale_factor=2)
+		img = self.conv_blocks2_0(img)
+		img = self.conv_blocks2_1(img, labels, linear=linear)
+		img = self.conv_blocks2_2(img)
+		img = self.conv_blocks2_3(img)
+		img = self.conv_blocks2_4(img)
+		img = self.conv_blocks2_5(img)
+		return img
+
+class ConditionalBatchNorm2d(nn.BatchNorm2d):
+    """Conditional Batch Normalization"""
+    def __init__(self, num_features, eps=1e-05, momentum=0.1,
+                 affine=False, track_running_stats=True):
+        super(ConditionalBatchNorm2d, self).__init__(
+            num_features, eps, momentum, affine, track_running_stats
+        )
+
+    def forward(self, input, weight, bias, **kwargs):
+        self._check_input_dim(input)
+
+        exponential_average_factor = 0.0
+
+        if self.training and self.track_running_stats:
+            self.num_batches_tracked += 1
+            if self.momentum is None:  # use cumulative moving average
+                exponential_average_factor = 1.0 / self.num_batches_tracked.item()
+            else:  # use exponential moving average
+                exponential_average_factor = self.momentum
+
+        output = F.batch_norm(input, self.running_mean, self.running_var,
+                              self.weight, self.bias,
+                              self.training or not self.track_running_stats,
+                              exponential_average_factor, self.eps)
+        if weight.dim() == 1:
+            weight = weight.unsqueeze(0)
+        if bias.dim() == 1:
+            bias = bias.unsqueeze(0)
+        size = output.size()
+        weight = weight.unsqueeze(-1).unsqueeze(-1).expand(size)
+        bias = bias.unsqueeze(-1).unsqueeze(-1).expand(size)
+        return weight * output + bias
+
+class CategoricalConditionalBatchNorm2d(ConditionalBatchNorm2d):
+
+    def __init__(self, num_classes, num_features, eps=1e-5, momentum=0.1,
+                 affine=False, track_running_stats=True):
+        super(CategoricalConditionalBatchNorm2d, self).__init__(
+            num_features, eps, momentum, affine, track_running_stats
+        )
+        self.weights = nn.Embedding(num_classes, num_features)
+        self.biases = nn.Embedding(num_classes, num_features)
+
+        self._initialize()
+
+    def _initialize(self):
+        init.ones_(self.weights.weight.data)
+        init.zeros_(self.biases.weight.data)
+
+    def forward(self, input, c, linear=None,**kwargs):
+        weight = self.weights(c)
+        bias = self.biases(c)
+
+        if linear != None:
+            weight = (weight * linear.unsqueeze(2)).mean(dim=1)
+            bias = (bias * linear.unsqueeze(2)).mean(dim=1)
+
+        return super(CategoricalConditionalBatchNorm2d, self).forward(input, weight, bias)

ykl/ALL/get_param_flops.py

+from model import *
+
+import sys
+import torch
+from ptflops import get_model_complexity_info
+
+
+if __name__ == "__main__":
+    model_name = sys.argv[1]
+    dataset = sys.argv[2]
+    model = Model(model_name,dataset)
+
+    # full_file = 'ckpt/cifar10_'+model_name+'.pt'
+    # model.load_state_dict(torch.load(full_file))
+
+    flops, params = get_model_complexity_info(model, (3, 32, 32), as_strings=True, print_per_layer_stat=True)
+

ykl/ALL/get_param_flops.slurm

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#SBATCH -J ALL              # The job name
+#SBATCH -o ret/ret-%j.out        # Write the standard output to file named 'ret-<job_number>.out'
+#SBATCH -e ret/ret-%j.err        # Write the standard error to file named 'ret-<job_number>.err'
+
+
+#- Resources
+
+# (TODO)
+# Please modify your requirements
+
+#SBATCH -p nv-gpu                    # Submit to 'nv-gpu' Partitiion
+#SBATCH -t 0-01:30:00                # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
+#SBATCH --nodes=1                    # Request N nodes
+#SBATCH --gres=gpu:1                 # Request M GPU per node
+#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
+#SBATCH --qos=gpu-debug             # Request QOS Type
+
+###
+### The system will alloc 8 or 16 cores per gpu by default.
+### If you need more or less, use following:
+### #SBATCH --cpus-per-task=K            # Request K cores
+###
+### 
+### Without specifying the constraint, any available nodes that meet the requirement will be allocated
+### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
+###
+### #SBATCH --nodelist=gpu-v00           # Request a specific list of hosts 
+### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
+###
+
+# set constraint for RTX8000 to meet my cuda
+#SBATCH --constraint="Ampere|RTX8000|T4"
+
+#- Log information
+
+echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
+echo "Job run at:"
+echo "$(hostnamectl)"
+
+#- Load environments
+source /tools/module_env.sh
+module list                       # list modules loaded
+
+##- Tools
+module load cluster-tools/v1.0
+module load slurm-tools/v1.0
+module load cmake/3.15.7
+module load git/2.17.1
+module load vim/8.1.2424
+
+##- language
+module load python3/3.6.8
+
+##- CUDA
+# module load cuda-cudnn/10.2-7.6.5
+# module load cuda-cudnn/11.2-8.2.1
+module load cuda-cudnn/11.1-8.2.1
+
+##- virtualenv
+# source xxxxx/activate
+
+echo $(module list)              # list modules loaded
+echo $(which gcc)
+echo $(which python)
+echo $(which python3)
+
+cluster-quota                    # nas quota
+
+nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
+
+#- Warning! Please not change your CUDA_VISIBLE_DEVICES
+#- in `.bashrc`, `env.sh`, or your job script
+echo "Use GPU ${CUDA_VISIBLE_DEVICES}"                              # which gpus
+#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
+
+#- Job step
+# [EDIT HERE(TODO)]
+name_list="AlexNet AlexNet_BN VGG_16 VGG_19 Inception_BN ResNet_18 ResNet_50 ResNet_152 MobileNetV2"
+# name_list="MobileNetV2"
+for name in $name_list; do 
+	if [ -f "param_flops/$Dataset/$name.txt" ];then
+		echo "$name: param_flops exists"
+	# elif [ ! -f "ckpt/cifar10_$name.pt" ];then
+	# 	echo "$name: ckpt not exists"
+	else
+		python get_param_flops.py $name $Dataset > param_flops/$Dataset/$name.txt
+		echo "$name: param_flops done"
+	fi
+done
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

ykl/ALL/gol.py

+# -*- coding: utf-8 -*-
+
+# 用于多个module之间共享全局变量
+def _init():  # 初始化
+    global _global_dict
+    _global_dict = {}
+ 
+def set_value(value,is_bias=False):
+    # 定义一个全局变量
+    if is_bias:
+        _global_dict[0] = value
+    else:
+        _global_dict[1] = value
+ 
+ 
+def get_value(is_bias=False): # 给bias独立于各变量外的精度
+    if is_bias:
+        return _global_dict[0]
+    else:
+        return _global_dict[1]  
+

ykl/ALL/model.py

+import torch.nn as nn
+
+from cfg import *
+from module import *
+from model_deployment import *
+
+
+class Model(nn.Module):
+    def __init__(self,model_name,dataset):
+        super(Model, self).__init__()
+        self.cfg_table = model_cfg_table[model_name]
+        adapt_dataset(self.cfg_table,dataset)
+        make_layers(self,self.cfg_table)
+        
+    def forward(self,x):
+        x = model_forward(self,self.cfg_table,x)
+        return x
+
+    def quantize(self, quant_type, num_bits=8, e_bits=3):
+        model_quantize(self,self.cfg_table,quant_type,num_bits,e_bits)
+    
+    def quantize_forward(self,x):
+        return model_utils(self,self.cfg_table,func='forward',x=x)
+
+    def freeze(self):
+        model_utils(self,self.cfg_table,func='freeze')
+    
+    def quantize_inference(self,x):
+        return model_utils(self,self.cfg_table,func='inference',x=x)
+
+    def fakefreeze(self):
+        model_utils(self,self.cfg_table,func='fakefreeze')
+
+    def get_output_layer_weight(self):
+        return get_output_layer_weight(self,self.cfg_table)
+
+    def get_quant_output_layer_weight(self):
+        return get_quant_output_layer_weight(self,self.cfg_table)
\ No newline at end of file

ykl/ALL/param_flops/cifar10/AlexNet.txt

+Model(
+  3.87 M, 100.000% Params, 70.08 MMac, 100.000% MACs, 
+  (conv0): Conv2d(896, 0.023% Params, 917.5 KMac, 1.309% MACs, 3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.047% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.047% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(18.5 k, 0.478% Params, 4.73 MMac, 6.756% MACs, 32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu4): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(73.86 k, 1.909% Params, 4.73 MMac, 6.745% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu7): ReLU(0, 0.000% Params, 8.19 KMac, 0.012% MACs, inplace=True)
+  (conv8): Conv2d(295.17 k, 7.630% Params, 18.89 MMac, 26.955% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu9): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (conv10): Conv2d(590.08 k, 15.252% Params, 37.77 MMac, 53.887% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu11): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool12): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (drop14): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc15): Linear(2.36 M, 61.010% Params, 2.36 MMac, 3.368% MACs, in_features=2304, out_features=1024, bias=True)
+  (relu16): ReLU(0, 0.000% Params, 1.02 KMac, 0.001% MACs, inplace=True)
+  (drop17): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc18): Linear(524.8 k, 13.565% Params, 524.8 KMac, 0.749% MACs, in_features=1024, out_features=512, bias=True)
+  (relu19): ReLU(0, 0.000% Params, 512.0 Mac, 0.001% MACs, inplace=True)
+  (fc20): Linear(5.13 k, 0.133% Params, 5.13 KMac, 0.007% MACs, in_features=512, out_features=10, bias=True)
+)

ykl/ALL/param_flops/cifar10/AlexNet_BN.txt

+Model(
+  3.87 M, 100.000% Params, 70.26 MMac, 100.000% MACs, 
+  (conv0): Conv2d(896, 0.023% Params, 917.5 KMac, 1.306% MACs, 3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(64, 0.002% Params, 65.54 KMac, 0.093% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.047% MACs, inplace=True)
+  (pool1): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.047% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv2): Conv2d(18.5 k, 0.478% Params, 4.73 MMac, 6.739% MACs, 32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn2): BatchNorm2d(128, 0.003% Params, 32.77 KMac, 0.047% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu2): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool3): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv4): Conv2d(73.86 k, 1.908% Params, 4.73 MMac, 6.727% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.007% Params, 16.38 KMac, 0.023% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 8.19 KMac, 0.012% MACs, inplace=True)
+  (conv5): Conv2d(295.17 k, 7.627% Params, 18.89 MMac, 26.886% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn5): BatchNorm2d(512, 0.013% Params, 32.77 KMac, 0.047% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu5): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (conv6): Conv2d(590.08 k, 15.247% Params, 37.77 MMac, 53.748% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.013% Params, 32.77 KMac, 0.047% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool7): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (drop9): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc10): Linear(2.36 M, 60.987% Params, 2.36 MMac, 3.359% MACs, in_features=2304, out_features=1024, bias=True)
+  (relu11): ReLU(0, 0.000% Params, 1.02 KMac, 0.001% MACs, inplace=True)
+  (drop12): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc13): Linear(524.8 k, 13.560% Params, 524.8 KMac, 0.747% MACs, in_features=1024, out_features=512, bias=True)
+  (relu14): ReLU(0, 0.000% Params, 512.0 Mac, 0.001% MACs, inplace=True)
+  (fc15): Linear(5.13 k, 0.133% Params, 5.13 KMac, 0.007% MACs, in_features=512, out_features=10, bias=True)
+)

ykl/ALL/param_flops/cifar10/ResNet_18.txt

+Model(
+  11.17 M, 100.000% Params, 557.22 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.73 k, 0.015% Params, 1.77 MMac, 0.318% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk0_ma_conv0): Conv2d(36.86 k, 0.330% Params, 37.75 MMac, 6.775% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk0_ma_bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk0_ma_relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk0_ma_conv1): Conv2d(36.86 k, 0.330% Params, 37.75 MMac, 6.775% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk0_ma_bn1): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk0_relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk1_ma_conv0): Conv2d(36.86 k, 0.330% Params, 37.75 MMac, 6.775% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk1_ma_bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk1_ma_relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk1_ma_conv1): Conv2d(36.86 k, 0.330% Params, 37.75 MMac, 6.775% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk1_ma_bn1): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk1_relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml1_blk0_ma_conv0): Conv2d(73.73 k, 0.660% Params, 18.87 MMac, 3.387% MACs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml1_blk0_ma_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_ma_relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk0_ma_conv1): Conv2d(147.46 k, 1.320% Params, 37.75 MMac, 6.775% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk0_ma_bn1): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_ds_conv0): Conv2d(8.19 k, 0.073% Params, 2.1 MMac, 0.376% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml1_blk0_ds_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk1_ma_conv0): Conv2d(147.46 k, 1.320% Params, 37.75 MMac, 6.775% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk1_ma_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk1_ma_relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk1_ma_conv1): Conv2d(147.46 k, 1.320% Params, 37.75 MMac, 6.775% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk1_ma_bn1): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk1_relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml2_blk0_ma_conv0): Conv2d(294.91 k, 2.639% Params, 18.87 MMac, 3.387% MACs, 128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml2_blk0_ma_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_ma_relu0): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk0_ma_conv1): Conv2d(589.82 k, 5.279% Params, 37.75 MMac, 6.775% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk0_ma_bn1): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_ds_conv0): Conv2d(32.77 k, 0.293% Params, 2.1 MMac, 0.376% MACs, 128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml2_blk0_ds_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_relu1): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk1_ma_conv0): Conv2d(589.82 k, 5.279% Params, 37.75 MMac, 6.775% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk1_ma_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk1_ma_relu0): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk1_ma_conv1): Conv2d(589.82 k, 5.279% Params, 37.75 MMac, 6.775% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk1_ma_bn1): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk1_relu1): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml3_blk0_ma_conv0): Conv2d(1.18 M, 10.557% Params, 18.87 MMac, 3.387% MACs, 256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml3_blk0_ma_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_ma_relu0): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk0_ma_conv1): Conv2d(2.36 M, 21.114% Params, 37.75 MMac, 6.775% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk0_ma_bn1): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_ds_conv0): Conv2d(131.07 k, 1.173% Params, 2.1 MMac, 0.376% MACs, 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml3_blk0_ds_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_relu1): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk1_ma_conv0): Conv2d(2.36 M, 21.114% Params, 37.75 MMac, 6.775% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk1_ma_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk1_ma_relu0): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk1_ma_conv1): Conv2d(2.36 M, 21.114% Params, 37.75 MMac, 6.775% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk1_ma_bn1): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk1_relu1): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (aap5): AdaptiveAvgPool2d(0, 0.000% Params, 8.19 KMac, 0.001% MACs, output_size=1)
+  (fc7): Linear(5.13 k, 0.046% Params, 5.13 KMac, 0.001% MACs, in_features=512, out_features=10, bias=True)
+)

ykl/ALL/param_flops/cifar10/VGG_16.txt

+Model(
+  33.65 M, 100.000% Params, 333.36 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.79 k, 0.005% Params, 1.84 MMac, 0.550% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.039% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.020% MACs, inplace=True)
+  (conv1): Conv2d(36.93 k, 0.110% Params, 37.81 MMac, 11.343% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn1): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.039% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.020% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 65.54 KMac, 0.020% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(73.86 k, 0.220% Params, 18.91 MMac, 5.672% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn3): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.020% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu3): ReLU(0, 0.000% Params, 32.77 KMac, 0.010% MACs, inplace=True)
+  (conv4): Conv2d(147.58 k, 0.439% Params, 37.78 MMac, 11.334% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.020% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 32.77 KMac, 0.010% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.010% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(295.17 k, 0.877% Params, 18.89 MMac, 5.667% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (conv7): Conv2d(590.08 k, 1.754% Params, 37.77 MMac, 11.329% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn7): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu7): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (conv8): Conv2d(590.08 k, 1.754% Params, 37.77 MMac, 11.329% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn8): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu8): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (pool9): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.005% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv10): Conv2d(1.18 M, 3.508% Params, 18.88 MMac, 5.664% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn10): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu10): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv11): Conv2d(2.36 M, 7.013% Params, 37.76 MMac, 11.326% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn11): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu11): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv12): Conv2d(2.36 M, 7.013% Params, 37.76 MMac, 11.326% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn12): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu12): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (pool13): MaxPool2d(0, 0.000% Params, 8.19 KMac, 0.002% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv14): Conv2d(2.36 M, 7.013% Params, 9.44 MMac, 2.832% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn14): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu14): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (conv15): Conv2d(2.36 M, 7.013% Params, 9.44 MMac, 2.832% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn15): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu15): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (conv16): Conv2d(2.36 M, 7.013% Params, 9.44 MMac, 2.832% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn16): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu16): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (pool17): MaxPool2d(0, 0.000% Params, 2.05 KMac, 0.001% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (fc19): Linear(2.1 M, 6.245% Params, 2.1 MMac, 0.630% MACs, in_features=512, out_features=4096, bias=True)
+  (relu20): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop21): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc22): Linear(16.78 M, 49.875% Params, 16.78 MMac, 5.034% MACs, in_features=4096, out_features=4096, bias=True)
+  (relu23): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop24): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc25): Linear(40.97 k, 0.122% Params, 40.97 KMac, 0.012% MACs, in_features=4096, out_features=10, bias=True)
+)

ykl/ALL/param_flops/cifar10/VGG_19.txt

+Model(
+  38.96 M, 100.000% Params, 418.4 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.79 k, 0.005% Params, 1.84 MMac, 0.439% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.031% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.016% MACs, inplace=True)
+  (conv1): Conv2d(36.93 k, 0.095% Params, 37.81 MMac, 9.038% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn1): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.031% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.016% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 65.54 KMac, 0.016% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(73.86 k, 0.190% Params, 18.91 MMac, 4.519% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn3): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.016% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu3): ReLU(0, 0.000% Params, 32.77 KMac, 0.008% MACs, inplace=True)
+  (conv4): Conv2d(147.58 k, 0.379% Params, 37.78 MMac, 9.030% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.016% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 32.77 KMac, 0.008% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.008% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(295.17 k, 0.758% Params, 18.89 MMac, 4.515% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv7): Conv2d(590.08 k, 1.515% Params, 37.77 MMac, 9.026% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn7): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu7): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv8): Conv2d(590.08 k, 1.515% Params, 37.77 MMac, 9.026% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn8): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu8): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv9): Conv2d(590.08 k, 1.515% Params, 37.77 MMac, 9.026% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn9): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu9): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (pool10): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.004% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv11): Conv2d(1.18 M, 3.029% Params, 18.88 MMac, 4.513% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn11): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu11): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv12): Conv2d(2.36 M, 6.057% Params, 37.76 MMac, 9.024% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn12): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu12): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv13): Conv2d(2.36 M, 6.057% Params, 37.76 MMac, 9.024% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn13): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu13): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv14): Conv2d(2.36 M, 6.057% Params, 37.76 MMac, 9.024% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn14): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu14): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (pool15): MaxPool2d(0, 0.000% Params, 8.19 KMac, 0.002% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv16): Conv2d(2.36 M, 6.057% Params, 9.44 MMac, 2.256% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn16): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu16): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv17): Conv2d(2.36 M, 6.057% Params, 9.44 MMac, 2.256% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn17): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu17): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv18): Conv2d(2.36 M, 6.057% Params, 9.44 MMac, 2.256% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn18): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu18): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv19): Conv2d(2.36 M, 6.057% Params, 9.44 MMac, 2.256% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn19): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu19): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (pool20): MaxPool2d(0, 0.000% Params, 2.05 KMac, 0.000% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (fc22): Linear(2.1 M, 5.393% Params, 2.1 MMac, 0.502% MACs, in_features=512, out_features=4096, bias=True)
+  (relu23): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop24): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc25): Linear(16.78 M, 43.074% Params, 16.78 MMac, 4.011% MACs, in_features=4096, out_features=4096, bias=True)
+  (relu26): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop27): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc28): Linear(40.97 k, 0.105% Params, 40.97 KMac, 0.010% MACs, in_features=4096, out_features=10, bias=True)
+)

ykl/ALL/param_flops/cifar100/AlexNet.txt

+Model(
+  3.91 M, 100.000% Params, 70.13 MMac, 100.000% MACs, 
+  (conv0): Conv2d(896, 0.023% Params, 917.5 KMac, 1.308% MACs, 3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.047% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.047% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(18.5 k, 0.472% Params, 4.73 MMac, 6.752% MACs, 32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu4): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(73.86 k, 1.887% Params, 4.73 MMac, 6.740% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu7): ReLU(0, 0.000% Params, 8.19 KMac, 0.012% MACs, inplace=True)
+  (conv8): Conv2d(295.17 k, 7.540% Params, 18.89 MMac, 26.937% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu9): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (conv10): Conv2d(590.08 k, 15.073% Params, 37.77 MMac, 53.851% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (relu11): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool12): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (drop14): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc15): Linear(2.36 M, 60.290% Params, 2.36 MMac, 3.366% MACs, in_features=2304, out_features=1024, bias=True)
+  (relu16): ReLU(0, 0.000% Params, 1.02 KMac, 0.001% MACs, inplace=True)
+  (drop17): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc18): Linear(524.8 k, 13.405% Params, 524.8 KMac, 0.748% MACs, in_features=1024, out_features=512, bias=True)
+  (relu19): ReLU(0, 0.000% Params, 512.0 Mac, 0.001% MACs, inplace=True)
+  (fc20): Linear(51.3 k, 1.310% Params, 51.3 KMac, 0.073% MACs, in_features=512, out_features=100, bias=True)
+)

ykl/ALL/param_flops/cifar100/AlexNet_BN.txt

+Model(
+  3.92 M, 100.000% Params, 70.31 MMac, 100.000% MACs, 
+  (conv0): Conv2d(896, 0.023% Params, 917.5 KMac, 1.305% MACs, 3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(64, 0.002% Params, 65.54 KMac, 0.093% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.047% MACs, inplace=True)
+  (pool1): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.047% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv2): Conv2d(18.5 k, 0.472% Params, 4.73 MMac, 6.735% MACs, 32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn2): BatchNorm2d(128, 0.003% Params, 32.77 KMac, 0.047% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu2): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool3): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv4): Conv2d(73.86 k, 1.886% Params, 4.73 MMac, 6.723% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.007% Params, 16.38 KMac, 0.023% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 8.19 KMac, 0.012% MACs, inplace=True)
+  (conv5): Conv2d(295.17 k, 7.537% Params, 18.89 MMac, 26.868% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn5): BatchNorm2d(512, 0.013% Params, 32.77 KMac, 0.047% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu5): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (conv6): Conv2d(590.08 k, 15.067% Params, 37.77 MMac, 53.713% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.013% Params, 32.77 KMac, 0.047% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.023% MACs, inplace=True)
+  (pool7): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.023% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (drop9): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc10): Linear(2.36 M, 60.268% Params, 2.36 MMac, 3.357% MACs, in_features=2304, out_features=1024, bias=True)
+  (relu11): ReLU(0, 0.000% Params, 1.02 KMac, 0.001% MACs, inplace=True)
+  (drop12): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc13): Linear(524.8 k, 13.400% Params, 524.8 KMac, 0.746% MACs, in_features=1024, out_features=512, bias=True)
+  (relu14): ReLU(0, 0.000% Params, 512.0 Mac, 0.001% MACs, inplace=True)
+  (fc15): Linear(51.3 k, 1.310% Params, 51.3 KMac, 0.073% MACs, in_features=512, out_features=100, bias=True)
+)

ykl/ALL/param_flops/cifar100/ResNet_18.txt

+Model(
+  11.22 M, 100.000% Params, 557.26 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.73 k, 0.015% Params, 1.77 MMac, 0.318% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk0_ma_conv0): Conv2d(36.86 k, 0.329% Params, 37.75 MMac, 6.774% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk0_ma_bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk0_ma_relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk0_ma_conv1): Conv2d(36.86 k, 0.329% Params, 37.75 MMac, 6.774% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk0_ma_bn1): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk0_relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk1_ma_conv0): Conv2d(36.86 k, 0.329% Params, 37.75 MMac, 6.774% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk1_ma_bn0): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk1_ma_relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml0_blk1_ma_conv1): Conv2d(36.86 k, 0.329% Params, 37.75 MMac, 6.774% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml0_blk1_ma_bn1): BatchNorm2d(128, 0.001% Params, 131.07 KMac, 0.024% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml0_blk1_relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.012% MACs, inplace=True)
+  (ml1_blk0_ma_conv0): Conv2d(73.73 k, 0.657% Params, 18.87 MMac, 3.387% MACs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml1_blk0_ma_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_ma_relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk0_ma_conv1): Conv2d(147.46 k, 1.314% Params, 37.75 MMac, 6.774% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk0_ma_bn1): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_ds_conv0): Conv2d(8.19 k, 0.073% Params, 2.1 MMac, 0.376% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml1_blk0_ds_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk0_relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk1_ma_conv0): Conv2d(147.46 k, 1.314% Params, 37.75 MMac, 6.774% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk1_ma_bn0): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk1_ma_relu0): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml1_blk1_ma_conv1): Conv2d(147.46 k, 1.314% Params, 37.75 MMac, 6.774% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml1_blk1_ma_bn1): BatchNorm2d(256, 0.002% Params, 65.54 KMac, 0.012% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml1_blk1_relu1): ReLU(0, 0.000% Params, 32.77 KMac, 0.006% MACs, inplace=True)
+  (ml2_blk0_ma_conv0): Conv2d(294.91 k, 2.628% Params, 18.87 MMac, 3.387% MACs, 128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml2_blk0_ma_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_ma_relu0): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk0_ma_conv1): Conv2d(589.82 k, 5.257% Params, 37.75 MMac, 6.774% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk0_ma_bn1): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_ds_conv0): Conv2d(32.77 k, 0.292% Params, 2.1 MMac, 0.376% MACs, 128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml2_blk0_ds_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk0_relu1): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk1_ma_conv0): Conv2d(589.82 k, 5.257% Params, 37.75 MMac, 6.774% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk1_ma_bn0): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk1_ma_relu0): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml2_blk1_ma_conv1): Conv2d(589.82 k, 5.257% Params, 37.75 MMac, 6.774% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml2_blk1_ma_bn1): BatchNorm2d(512, 0.005% Params, 32.77 KMac, 0.006% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml2_blk1_relu1): ReLU(0, 0.000% Params, 16.38 KMac, 0.003% MACs, inplace=True)
+  (ml3_blk0_ma_conv0): Conv2d(1.18 M, 10.514% Params, 18.87 MMac, 3.387% MACs, 256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  (ml3_blk0_ma_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_ma_relu0): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk0_ma_conv1): Conv2d(2.36 M, 21.027% Params, 37.75 MMac, 6.774% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk0_ma_bn1): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_ds_conv0): Conv2d(131.07 k, 1.168% Params, 2.1 MMac, 0.376% MACs, 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+  (ml3_blk0_ds_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk0_relu1): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk1_ma_conv0): Conv2d(2.36 M, 21.027% Params, 37.75 MMac, 6.774% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk1_ma_bn0): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk1_ma_relu0): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (ml3_blk1_ma_conv1): Conv2d(2.36 M, 21.027% Params, 37.75 MMac, 6.774% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (ml3_blk1_ma_bn1): BatchNorm2d(1.02 k, 0.009% Params, 16.38 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (ml3_blk1_relu1): ReLU(0, 0.000% Params, 8.19 KMac, 0.001% MACs, inplace=True)
+  (aap5): AdaptiveAvgPool2d(0, 0.000% Params, 8.19 KMac, 0.001% MACs, output_size=1)
+  (fc7): Linear(51.3 k, 0.457% Params, 51.3 KMac, 0.009% MACs, in_features=512, out_features=100, bias=True)
+)

ykl/ALL/param_flops/cifar100/VGG_16.txt

+Model(
+  34.02 M, 100.000% Params, 333.73 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.79 k, 0.005% Params, 1.84 MMac, 0.550% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.039% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.020% MACs, inplace=True)
+  (conv1): Conv2d(36.93 k, 0.109% Params, 37.81 MMac, 11.331% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn1): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.039% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.020% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 65.54 KMac, 0.020% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(73.86 k, 0.217% Params, 18.91 MMac, 5.665% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn3): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.020% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu3): ReLU(0, 0.000% Params, 32.77 KMac, 0.010% MACs, inplace=True)
+  (conv4): Conv2d(147.58 k, 0.434% Params, 37.78 MMac, 11.321% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.020% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 32.77 KMac, 0.010% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.010% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(295.17 k, 0.868% Params, 18.89 MMac, 5.661% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (conv7): Conv2d(590.08 k, 1.735% Params, 37.77 MMac, 11.316% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn7): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu7): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (conv8): Conv2d(590.08 k, 1.735% Params, 37.77 MMac, 11.316% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn8): BatchNorm2d(512, 0.002% Params, 32.77 KMac, 0.010% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu8): ReLU(0, 0.000% Params, 16.38 KMac, 0.005% MACs, inplace=True)
+  (pool9): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.005% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv10): Conv2d(1.18 M, 3.469% Params, 18.88 MMac, 5.658% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn10): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu10): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv11): Conv2d(2.36 M, 6.937% Params, 37.76 MMac, 11.314% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn11): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu11): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv12): Conv2d(2.36 M, 6.937% Params, 37.76 MMac, 11.314% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn12): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.005% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu12): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (pool13): MaxPool2d(0, 0.000% Params, 8.19 KMac, 0.002% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv14): Conv2d(2.36 M, 6.937% Params, 9.44 MMac, 2.828% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn14): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu14): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (conv15): Conv2d(2.36 M, 6.937% Params, 9.44 MMac, 2.828% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn15): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu15): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (conv16): Conv2d(2.36 M, 6.937% Params, 9.44 MMac, 2.828% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn16): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu16): ReLU(0, 0.000% Params, 2.05 KMac, 0.001% MACs, inplace=True)
+  (pool17): MaxPool2d(0, 0.000% Params, 2.05 KMac, 0.001% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (fc19): Linear(2.1 M, 6.177% Params, 2.1 MMac, 0.630% MACs, in_features=512, out_features=4096, bias=True)
+  (relu20): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop21): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc22): Linear(16.78 M, 49.334% Params, 16.78 MMac, 5.028% MACs, in_features=4096, out_features=4096, bias=True)
+  (relu23): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop24): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc25): Linear(409.7 k, 1.204% Params, 409.7 KMac, 0.123% MACs, in_features=4096, out_features=100, bias=True)
+)

ykl/ALL/param_flops/cifar100/VGG_19.txt

+Model(
+  39.33 M, 100.000% Params, 418.77 MMac, 100.000% MACs, 
+  (conv0): Conv2d(1.79 k, 0.005% Params, 1.84 MMac, 0.438% MACs, 3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn0): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.031% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu0): ReLU(0, 0.000% Params, 65.54 KMac, 0.016% MACs, inplace=True)
+  (conv1): Conv2d(36.93 k, 0.094% Params, 37.81 MMac, 9.030% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn1): BatchNorm2d(128, 0.000% Params, 131.07 KMac, 0.031% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu1): ReLU(0, 0.000% Params, 65.54 KMac, 0.016% MACs, inplace=True)
+  (pool2): MaxPool2d(0, 0.000% Params, 65.54 KMac, 0.016% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv3): Conv2d(73.86 k, 0.188% Params, 18.91 MMac, 4.515% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn3): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.016% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu3): ReLU(0, 0.000% Params, 32.77 KMac, 0.008% MACs, inplace=True)
+  (conv4): Conv2d(147.58 k, 0.375% Params, 37.78 MMac, 9.022% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn4): BatchNorm2d(256, 0.001% Params, 65.54 KMac, 0.016% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu4): ReLU(0, 0.000% Params, 32.77 KMac, 0.008% MACs, inplace=True)
+  (pool5): MaxPool2d(0, 0.000% Params, 32.77 KMac, 0.008% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv6): Conv2d(295.17 k, 0.751% Params, 18.89 MMac, 4.511% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn6): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu6): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv7): Conv2d(590.08 k, 1.500% Params, 37.77 MMac, 9.018% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn7): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu7): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv8): Conv2d(590.08 k, 1.500% Params, 37.77 MMac, 9.018% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn8): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu8): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (conv9): Conv2d(590.08 k, 1.500% Params, 37.77 MMac, 9.018% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn9): BatchNorm2d(512, 0.001% Params, 32.77 KMac, 0.008% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu9): ReLU(0, 0.000% Params, 16.38 KMac, 0.004% MACs, inplace=True)
+  (pool10): MaxPool2d(0, 0.000% Params, 16.38 KMac, 0.004% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv11): Conv2d(1.18 M, 3.001% Params, 18.88 MMac, 4.509% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn11): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu11): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv12): Conv2d(2.36 M, 6.000% Params, 37.76 MMac, 9.016% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn12): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu12): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv13): Conv2d(2.36 M, 6.000% Params, 37.76 MMac, 9.016% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn13): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu13): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (conv14): Conv2d(2.36 M, 6.000% Params, 37.76 MMac, 9.016% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn14): BatchNorm2d(1.02 k, 0.003% Params, 16.38 KMac, 0.004% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu14): ReLU(0, 0.000% Params, 8.19 KMac, 0.002% MACs, inplace=True)
+  (pool15): MaxPool2d(0, 0.000% Params, 8.19 KMac, 0.002% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (conv16): Conv2d(2.36 M, 6.000% Params, 9.44 MMac, 2.254% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn16): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu16): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv17): Conv2d(2.36 M, 6.000% Params, 9.44 MMac, 2.254% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn17): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu17): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv18): Conv2d(2.36 M, 6.000% Params, 9.44 MMac, 2.254% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn18): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu18): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (conv19): Conv2d(2.36 M, 6.000% Params, 9.44 MMac, 2.254% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+  (bn19): BatchNorm2d(1.02 k, 0.003% Params, 4.1 KMac, 0.001% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu19): ReLU(0, 0.000% Params, 2.05 KMac, 0.000% MACs, inplace=True)
+  (pool20): MaxPool2d(0, 0.000% Params, 2.05 KMac, 0.000% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  (fc22): Linear(2.1 M, 5.343% Params, 2.1 MMac, 0.502% MACs, in_features=512, out_features=4096, bias=True)
+  (relu23): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop24): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc25): Linear(16.78 M, 42.671% Params, 16.78 MMac, 4.007% MACs, in_features=4096, out_features=4096, bias=True)
+  (relu26): ReLU(0, 0.000% Params, 4.1 KMac, 0.001% MACs, inplace=True)
+  (drop27): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.5, inplace=False)
+  (fc28): Linear(409.7 k, 1.042% Params, 409.7 KMac, 0.098% MACs, in_features=4096, out_features=100, bias=True)
+)

ykl/ALL/ptq_one.py

+from torch.serialization import load
+from model import *
+from extract_ratio import *
+from utils import *
+from dataloader import DataLoader
+
+import gol
+import openpyxl
+import sys
+import torch
+import os
+import os.path as osp
+
+
+def direct_quantize(model, test_loader,device):
+    with torch.no_grad():
+        for i, (data, target) in enumerate(test_loader,1):
+            data = data.to(device)
+            output = model.quantize_forward(data)
+            if i % 100 == 0:
+                break
+    print('direct quantization finish')
+
+
+def full_inference(model, test_loader, device):
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data,target = data.to(device), target.to(device)
+            output = model(data)
+            pred = output.argmax(dim=1, keepdim=True)
+            # print(pred)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+    print('\nTest set: Full Model Accuracy: {:.2f}%'.format(100. * correct / len(test_loader.dataset)))
+    return 100. * correct / len(test_loader.dataset)
+
+
+def quantize_inference(model, test_loader, device):
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data,target = data.to(device), target.to(device)
+            output = model.quantize_inference(data)
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+    print('Test set: Quant Model Accuracy: {:.2f}%'.format(100. * correct / len(test_loader.dataset)))
+    return 100. * correct / len(test_loader.dataset)
+
+
+if __name__ == "__main__":
+    batch_size = 128
+    model_name = sys.argv[1]
+    dataset = sys.argv[2]
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(device)
+
+    dataloader = DataLoader(dataset,batch_size)
+    train_loader, val_loader, test_loader = dataloader.getloader()
+
+    load_ptq = False
+    store_ptq = True
+
+    append = False
+    gol._init()
+
+    ckpt_full_path = 'ckpt_full/'+dataset
+    ckpt_quant_path = 'ckpt_quant/'+dataset
+    ptq_result_path = 'ptq_result/'+dataset
+
+    if not osp.exists(ckpt_quant_path) and store_ptq:
+        os.makedirs(ckpt_quant_path)
+    if not osp.exists(ptq_result_path):
+        os.makedirs(ptq_result_path)
+    
+    excel_path = ptq_result_path+'/'+model_name+'.xlsx'
+    txt_path = ptq_result_path+'/'+model_name+'.txt'
+
+    if append and os.path.exists(excel_path) and os.path.exists(txt_path):
+        print("> Exit: "+model_name+"has been quantized")
+        sys.exit()
+    else:
+        workbook = openpyxl.Workbook()
+        ft = open(txt_path,'w')
+    
+    full_file = ckpt_full_path+'/'+model_name+'.pt'
+
+    ptq_file_dir = ckpt_quant_path+'/'+model_name
+    if not osp.exists(ptq_file_dir):
+        os.makedirs(ptq_file_dir)
+
+    model = Model(model_name,dataset)
+    model.load_state_dict(torch.load(full_file))
+    model.to(device)
+    model.eval()
+    full_acc = full_inference(model, test_loader, device)
+    # 传入后可变
+    fold_model(model)
+
+    Mac,Param,layer, par_ratio, flop_ratio = extract_ratio(model_name,dataset)
+    par_ratio, flop_ratio = fold_ratio(layer, par_ratio, flop_ratio)
+
+    full_names = []
+    full_params = []
+
+    for name, param in model.named_parameters():
+        if 'conv' in name or 'fc' in name:
+            full_names.append(name)
+            full_params.append(param.data.cpu())
+
+    quant_type_list = ['INT','POT','FLOAT']
+    title_list = []
+    js_flops_list = []
+    js_flops_wt_log_list = []
+    js_flops_wt_cbrt_list = []
+    js_param_list = []
+    ptq_acc_list = []
+    acc_loss_list = []
+
+    for quant_type in quant_type_list:
+        num_bit_list = numbit_list(quant_type)
+        # 对一个量化类别，只需设置一次bias量化表
+        # int由于位宽大，使用量化表开销过大，直接_round即可
+        if quant_type != 'INT':
+            bias_list = build_bias_list(quant_type)
+            gol.set_value(bias_list, is_bias=True)
+
+        for num_bits in num_bit_list:
+            e_bit_list = ebit_list(quant_type,num_bits)
+            for e_bits in e_bit_list:
+                model_ptq = Model(model_name,dataset)
+                if quant_type == 'FLOAT':
+                    title = '%s_%d_E%d' % (quant_type, num_bits, e_bits)
+                else:
+                    title = '%s_%d' % (quant_type, num_bits)
+                print('\n'+model_name+': PTQ: '+title)
+                title_list.append(title)
+
+                # 设置量化表
+                if quant_type != 'INT':
+                    plist = build_list(quant_type, num_bits, e_bits)
+                    gol.set_value(plist)
+                # 判断是否需要载入
+                ptq_file = ptq_file_dir +'/' + title + '.pt'
+                if load_ptq is True and osp.exists(ptq_file):
+                    model_ptq.quantize(quant_type,num_bits,e_bits)
+                    model_ptq.load_state_dict(torch.load(ptq_file))
+                    model_ptq.to(device)
+                    model_ptq.eval()
+                    print('Successfully load ptq model: ' + title)
+                else:
+                    model_ptq.load_state_dict(torch.load(full_file))
+                    model_ptq.to(device)
+                    model_ptq.quantize(quant_type,num_bits,e_bits)
+                    model_ptq.eval()
+                    direct_quantize(model_ptq, train_loader, device)
+                    if store_ptq:
+                        torch.save(model_ptq.state_dict(), ptq_file)
+
+                model_ptq.freeze()
+                ptq_acc = quantize_inference(model_ptq, test_loader, device)
+                ptq_acc_list.append(ptq_acc)
+                acc_loss = (full_acc - ptq_acc) / full_acc
+                acc_loss_list.append(acc_loss)
+
+                #将量化后分布反量化到全精度相同的scale
+                model_ptq.fakefreeze()
+                # 获取计算量/参数量下的js-div
+                js_flops = 0.
+                js_param = 0.
+                for name, param in model_ptq.named_parameters():
+                    if 'conv' not in name and 'fc' not in name:
+                        continue
+                        
+                    prefix = name.rsplit('.',1)[0]
+                    layer_idx = layer.index(prefix)
+                    name_idx = full_names.index(name)
+
+                    layer_idx = layer.index(prefix)
+                    ptq_param = param.data.cpu()
+
+                    js = js_div(ptq_param,full_params[name_idx])
+
+                    js = js.item()
+                    if js < 0.:
+                        js = 0.
+                    js_flops = js_flops + js * flop_ratio[layer_idx]
+                    js_param = js_param + js * par_ratio[layer_idx]
+                js_flops_wt_log = js_flops * torch.log10(torch.tensor(Mac)).item()
+                js_flops_wt_cbrt = js_flops * torch.pow(torch.tensor(Mac),1/3).item()
+                js_flops_list.append(js_flops)
+                js_flops_wt_log_list.append(js_flops_wt_log)
+                js_flops_wt_cbrt_list.append(js_flops_wt_cbrt)
+                js_param_list.append(js_param)
+
+                print(title + ': js_flops: %f js_flops_wt_log: %f js_flops_wt_cbrt: %f js_param: %f acc_loss: %f' % (js_flops,js_flops_wt_log, js_flops_wt_cbrt, js_param, acc_loss))
+    
+    # 写入xlsx
+    worksheet = workbook.active
+    worksheet.cell(row=1,column=1,value='FP32-acc')
+    worksheet.cell(row=1,column=2,value=full_acc)
+    worksheet.cell(row=1,column=3,value='Mac')
+    worksheet.cell(row=1,column=4,value=Mac)
+    worksheet.cell(row=1,column=5,value='Param')
+    worksheet.cell(row=1,column=6,value=Param)
+
+    worksheet.cell(row=3,column=1,value='title')
+    worksheet.cell(row=3,column=2,value='js_flops')
+    worksheet.cell(row=3,column=3,value='js_flops_wt_log')
+    worksheet.cell(row=3,column=4,value='js_flops_wt_cbrt')
+    worksheet.cell(row=3,column=5,value='js_param')
+    worksheet.cell(row=3,column=6,value='ptq_acc')
+    worksheet.cell(row=3,column=7,value='acc_loss')
+    for i in range(len(title_list)):
+        worksheet.cell(row=i+4, column=1, value=title_list[i])
+        worksheet.cell(row=i+4, column=2, value=js_flops_list[i])
+        worksheet.cell(row=i+4, column=3, value=js_flops_wt_log_list[i])
+        worksheet.cell(row=i+4, column=4, value=js_flops_wt_cbrt_list[i])
+        worksheet.cell(row=i+4, column=5, value=js_param_list[i])
+        worksheet.cell(row=i+4, column=6, value=ptq_acc_list[i])
+        worksheet.cell(row=i+4, column=7, value=acc_loss_list[i])
+
+    workbook.save(excel_path)
+    
+    print(model_name,file=ft)
+    print('Full_acc: %f'%full_acc,file=ft)
+    print('title_list:',file=ft)
+    print(title_list,file=ft)
+    print('js_flops_list:',file=ft)
+    print(js_flops_list, file=ft)
+    print('js_flops_wt_log_list:',file=ft)
+    print(js_flops_wt_log_list, file=ft)
+    print('js_flops_wt_cbrt_list:',file=ft)
+    print(js_flops_wt_cbrt_list, file=ft)
+    print('js_param_list:',file=ft)
+    print(js_param_list, file=ft)
+    print('ptq_acc_list:',file=ft)
+    print(ptq_acc_list, file=ft)
+    print('acc_loss_list:',file=ft)
+    print(acc_loss_list, file=ft)
+    print("\n",file=ft)
+
+    ft.close()
+

ykl/ALL/ptq_one.slurm

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#- Resources
+
+# (TODO)
+# Please modify your requirements
+
+#SBATCH -p nv-gpu                    # Submit to 'nv-gpu' Partitiion
+#SBATCH -t 1-06:00:00                # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
+#SBATCH --nodes=1                    # Request N nodes
+#SBATCH --gres=gpu:1                 # Request M GPU per node
+#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
+#SBATCH --qos=gpu-normal             # Request QOS Type
+
+###
+### The system will alloc 8 or 16 cores per gpu by default.
+### If you need more or less, use following:
+### #SBATCH --cpus-per-task=K            # Request K cores
+###
+### 
+### Without specifying the constraint, any available nodes that meet the requirement will be allocated
+### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
+###
+### #SBATCH --nodelist=gpu-v00           # Request a specific list of hosts 
+### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
+###
+
+# set constraint for RTX8000 to meet my cuda
+#SBATCH --constraint="Ampere|RTX8000"
+
+#- Log information
+
+echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
+echo "Job run at:"
+echo "$(hostnamectl)"
+
+#- Load environments
+source /tools/module_env.sh
+module list                       # list modules loaded
+
+##- Tools
+module load cluster-tools/v1.0
+module load slurm-tools/v1.0
+module load cmake/3.15.7
+module load git/2.17.1
+module load vim/8.1.2424
+
+##- language
+module load python3/3.6.8
+
+##- CUDA
+# module load cuda-cudnn/10.2-7.6.5
+# module load cuda-cudnn/11.2-8.2.1
+module load cuda-cudnn/11.1-8.2.1
+
+##- virtualenv
+# source xxxxx/activate
+
+echo $(module list)              # list modules loaded
+echo $(which gcc)
+echo $(which python)
+echo $(which python3)
+
+cluster-quota                    # nas quota
+
+nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
+
+#- Warning! Please not change your CUDA_VISIBLE_DEVICES
+#- in `.bashrc`, `env.sh`, or your job script
+echo "Use GPU ${CUDA_VISIBLE_DEVICES}"                              # which gpus
+#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
+
+#- Job step
+# [EDIT HERE(TODO)]
+python ptq_one.py $Model $Dataset
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

ykl/ALL/ptq_result/cifar10/AlexNet.txt

+AlexNet
+Full_acc: 88.630000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
+[16.134064351484934, 9.803080945516005, 2.5798806807263825, 0.5670208264725348, 0.13436593638690267, 0.03258542668624173, 0.007852610824269553, 0.0018014206200349513, 0.0003162150510672301, 6.92716001480207e-05, 9.684179880150801e-06, 1.1595438673905888e-07, 7.945963039257542e-07, 1.5685770022075827e-06, 0.0, 16.01436476484219, 6.952820969558956, 0.7167166887613639, 0.7119351064288386, 0.7146290586824979, 0.7120276276007171, 0.7120483250193009, 4.308314078011601, 1.0381514277725061, 1.2567698279608643, 0.39416582491303853, 0.4671888875734605, 0.19757403359526637, 0.23172120832374465, 0.2652704329374882, 0.0509185267481933, 0.19694534563825358, 0.1841072727534976, 0.19769045007362226, 0.012649947461768432, 0.050778713986116, 0.1970719456861238, 0.16609038257453898, 0.1711650729485768, 0.003183632319308628, 0.012617062910949925, 0.05085217397639437, 0.19707866413312908]
+js_flops_wt_log_list:
+[29.77693314521759, 18.09250785000869, 4.761412430188898, 1.0464902627132961, 0.24798497251680535, 0.060139469559962806, 0.014492731802459045, 0.003324691175181796, 0.0005836046162958227, 0.00012784725296326294, 1.7873064751410352e-05, 2.1400472606308148e-07, 1.4665022094855177e-06, 2.8949563798883818e-06, 0.0, 29.556016300501824, 12.832085001704433, 1.3227680552386436, 1.313943195343322, 1.318915137589619, 1.3141139518675014, 1.3141521509000589, 7.951398821447238, 1.9160060965387782, 2.319486914819017, 0.727470100581621, 0.8622410304309428, 0.36464146054157476, 0.42766328299354595, 0.48958153227246276, 0.09397492992485672, 0.36348115778974827, 0.3397872868794326, 0.3648563181011757, 0.023346667749278273, 0.0937168923232625, 0.36371481008463735, 0.3065354215926674, 0.31590123994497216, 0.00587569286112845, 0.023285976218000038, 0.09385246964412312, 0.36372720961044547]
+js_flops_wt_cbrt_list:
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+
+

ykl/ALL/ptq_result/cifar10/AlexNet_BN.txt

+AlexNet_BN
+Full_acc: 90.140000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
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+
+

ykl/ALL/ptq_result/cifar10/Inception_BN.txt

+Inception_BN
+Full_acc: 94.830000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
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+js_flops_wt_log_list:
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+js_flops_wt_cbrt_list:
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+js_param_list:
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+ptq_acc_list:
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+acc_loss_list:
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+
+

ykl/ALL/ptq_result/cifar10/MobileNetV2.txt

+MobileNetV2
+Full_acc: 90.280000
+title_list:
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+js_flops_list:
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+

ykl/ALL/ptq_result/cifar10/ResNet_152.txt

+ResNet_152
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+title_list:
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+
+

ykl/ALL/ptq_result/cifar10/ResNet_18.txt

+ResNet_18
+Full_acc: 94.300000
+title_list:
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+ptq_acc_list:
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+
+

ykl/ALL/ptq_result/cifar10/ResNet_50.txt

+ResNet_50
+Full_acc: 94.580000
+title_list:
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+js_flops_list:
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+
+

ykl/ALL/ptq_result/cifar10/VGG_16.txt

+VGG_16
+Full_acc: 93.140000
+title_list:
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+js_flops_list:
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+
+

ykl/ALL/ptq_result/cifar10/VGG_19.txt

+VGG_19
+Full_acc: 93.030000
+title_list:
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+
+

ykl/ALL/ptq_result/cifar100/AlexNet.txt

+AlexNet
+Full_acc: 63.430000
+title_list:
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+
+

ykl/ALL/ptq_result/cifar100/AlexNet_BN.txt

+AlexNet_BN
+Full_acc: 66.310000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
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+
+

ykl/ALL/ptq_result/cifar100/Inception_BN.txt

+Inception_BN
+Full_acc: 77.970000
+title_list:
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+
+

ykl/ALL/ptq_result/cifar100/MobileNetV2.txt

+MobileNetV2
+Full_acc: 66.760000
+title_list:
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+js_flops_list:
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+
+

ykl/ALL/ptq_result/cifar100/ResNet_152.txt

+ResNet_152
+Full_acc: 77.410000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
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+js_flops_wt_log_list:
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+js_flops_wt_cbrt_list:
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+js_param_list:
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+ptq_acc_list:
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+acc_loss_list:
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+
+

ykl/ALL/ptq_result/cifar100/ResNet_18.txt

+ResNet_18
+Full_acc: 75.730000
+title_list:
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+
+

ykl/ALL/ptq_result/cifar100/ResNet_50.txt

+ResNet_50
+Full_acc: 77.220000
+title_list:
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+
+

ykl/ALL/ptq_result/cifar100/VGG_16.txt

+VGG_16
+Full_acc: 71.120000
+title_list:
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+js_param_list:
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+ptq_acc_list:
+[1.0, 1.04, 14.77, 55.52, 69.06, 70.61, 71.17, 71.09, 71.08, 71.14, 71.11, 71.11, 71.11, 71.12, 71.11, 1.0, 1.4, 26.27, 22.67, 31.1, 26.62, 30.99, 1.09, 18.72, 42.31, 51.42, 62.98, 61.7, 58.27, 67.99, 69.24, 61.62, 61.04, 68.82, 70.56, 68.83, 61.38, 62.45, 69.46, 71.19, 70.7, 68.93, 61.82]
+acc_loss_list:
+[0.9859392575928009, 0.9853768278965128, 0.7923228346456693, 0.21934758155230596, 0.028965129358830177, 0.007170978627671612, -0.000703037120359915, 0.000421822272215989, 0.0005624296962880519, -0.00028121484814392603, 0.00014060742407206292, 0.00014060742407206292, 0.00014060742407206292, 0.0, 0.00014060742407206292, 0.9859392575928009, 0.9803149606299212, 0.6306242969628797, 0.6812429696287964, 0.562710911136108, 0.6257030371203599, 0.5642575928008999, 0.9846737907761529, 0.7367829021372329, 0.40508998875140606, 0.2769966254218223, 0.11445444319460077, 0.13245219347581555, 0.18068053993250843, 0.044010123734533314, 0.026434195725534442, 0.13357705286839155, 0.14173228346456698, 0.032339707536558085, 0.007874015748031527, 0.03219910011248602, 0.13695163104611927, 0.12190663667041621, 0.023340832395950657, -0.000984251968503841, 0.005905511811023645, 0.030793025871765994, 0.13076490438695168]
+
+

ykl/ALL/ptq_result/cifar100/VGG_19.txt

+VGG_19
+Full_acc: 70.450000
+title_list:
+['INT_2', 'INT_3', 'INT_4', 'INT_5', 'INT_6', 'INT_7', 'INT_8', 'INT_9', 'INT_10', 'INT_11', 'INT_12', 'INT_13', 'INT_14', 'INT_15', 'INT_16', 'POT_2', 'POT_3', 'POT_4', 'POT_5', 'POT_6', 'POT_7', 'POT_8', 'FLOAT_3_E1', 'FLOAT_4_E1', 'FLOAT_4_E2', 'FLOAT_5_E1', 'FLOAT_5_E2', 'FLOAT_5_E3', 'FLOAT_6_E1', 'FLOAT_6_E2', 'FLOAT_6_E3', 'FLOAT_6_E4', 'FLOAT_7_E1', 'FLOAT_7_E2', 'FLOAT_7_E3', 'FLOAT_7_E4', 'FLOAT_7_E5', 'FLOAT_8_E1', 'FLOAT_8_E2', 'FLOAT_8_E3', 'FLOAT_8_E4', 'FLOAT_8_E5', 'FLOAT_8_E6']
+js_flops_list:
+[12.933096217004081, 8.282459855818454, 2.3511338583732004, 0.5607665105783252, 0.13750860793240308, 0.034055243514816445, 0.008466258330629493, 0.002134299490155856, 0.0005518020792059293, 0.0001387761061248467, 4.1164875592213196e-05, 2.0749240583301132e-05, 1.2657347050655076e-05, 1.619567712066784e-05, 3.111866443620645e-05, 12.766474700217593, 5.913951424122345, 0.5312434033572428, 0.5240513815367216, 0.5250262623839118, 0.524969353101955, 0.5245434967946389, 3.598951343967536, 0.8750073227860701, 1.0683690264640484, 0.2987686569023001, 0.3948113124704691, 0.14247788072680156, 0.15534309310691274, 0.22380186120048573, 0.03708847445554712, 0.1414851885762422, 0.11450165272776922, 0.16679070383810646, 0.009543419974557965, 0.03673265812289985, 0.14151062836765657, 0.10154254677462951, 0.14450689459197366, 0.002492146654501212, 0.009299251796759863, 0.03672766674028512, 0.14268339780267283]
+js_flops_wt_log_list:
+[33.91026349361435, 21.716408149567414, 6.164615750818413, 1.4703161418612436, 0.3605442230838099, 0.08929201960225876, 0.02219832327692665, 0.005596081315030784, 0.0014468116209941759, 0.0003638675724939018, 0.00010793330186305248, 5.440400377962453e-05, 3.31872558910926e-05, 4.2464671212858927e-05, 8.159238073352639e-05, 33.4733859321139, 15.506236690387817, 1.3929072733104273, 1.3740499671485007, 1.3766060810015497, 1.3764568662494123, 1.3753402813768338, 9.436362826573575, 2.294247902950426, 2.8012375836328856, 0.7833641464655517, 1.0351856517181814, 0.3735735354002334, 0.4073058091257852, 0.5868030328029054, 0.09724507730099655, 0.370970720743272, 0.3002205465190931, 0.43732116583184005, 0.025022614889541793, 0.09631213553724963, 0.3710374232571642, 0.2662420861303997, 0.37889356036917476, 0.006534347870060304, 0.024382411870329482, 0.09629904825352244, 0.3741123961709489]
+js_flops_wt_cbrt_list:
+[96.7596643074306, 61.965674873360506, 17.590136117515375, 4.195405215270027, 1.0287781455942455, 0.25478616064643805, 0.06334077318118503, 0.015967877972444203, 0.004128337333322346, 0.001038261002410069, 0.00030797726056679467, 0.00015523657442791447, 9.469663188879727e-05, 0.00012116884117563355, 0.00023281598420215054, 95.513076347352, 44.24554993847292, 3.974526479343794, 3.9207189760693337, 3.9280126002665967, 3.927586830371614, 3.9244007631957634, 26.92575065228598, 6.54641498050621, 7.993061106373206, 2.2352539919590195, 2.95380235470544, 1.065956030871699, 1.162207818553189, 1.6743858236101101, 0.27747944326550134, 1.0585291504374177, 0.8566503561628492, 1.2478537422273757, 0.07139961673438815, 0.27481738397788974, 1.0587194796235617, 0.7596960985967759, 1.0811362087018963, 0.01864513103810209, 0.06957285920302152, 0.27478004067678624, 1.0674936180771977]
+js_param_list:
+[5.441795197362784, 3.615214283911254, 1.1790201233925872, 0.341252094418348, 0.09401382071186312, 0.024464816483120978, 0.006208213391891069, 0.0015985597051018019, 0.0004015855469499279, 0.00011155739733848337, 2.4537871906840675e-05, 2.351879757638181e-05, 6.264552940483891e-06, 1.5177568923359899e-05, 9.624882805967266e-06, 5.45133398076672, 2.650677666315291, 0.2256276208836905, 0.21693865571525875, 0.21749322883768124, 0.21758511964463856, 0.21728026335671044, 1.7035465065053872, 0.4813913827879723, 0.5064766680531055, 0.1623796404313223, 0.17965458985791877, 0.0599836624875662, 0.07432381074360364, 0.09737465344971145, 0.015601066392873146, 0.05887208831188401, 0.04897613855857195, 0.07075391119873932, 0.004133567706307015, 0.015265638217273907, 0.05909795862509216, 0.04153595037687257, 0.06084168404200329, 0.0011923608298122395, 0.0038164235757733693, 0.01519637035247201, 0.061059609830074656]
+ptq_acc_list:
+[1.0, 0.99, 8.49, 51.4, 68.16, 69.66, 70.64, 70.57, 70.43, 70.44, 70.43, 70.48, 70.43, 70.46, 70.45, 1.0, 1.02, 18.99, 17.97, 21.57, 25.97, 25.82, 1.11, 24.97, 40.24, 49.39, 60.11, 61.9, 56.17, 66.15, 68.72, 62.13, 59.45, 67.82, 70.01, 68.48, 61.86, 60.86, 68.44, 70.47, 69.91, 68.29, 61.0]
+acc_loss_list:
+[0.985805535841022, 0.9859474804826118, 0.8794889992902768, 0.27040454222853094, 0.03250532292405971, 0.011213626685592707, -0.0026969481902057875, -0.0017033356990772226, 0.0002838892831795035, 0.0001419446415898526, 0.0002838892831795035, -0.00042583392476935606, 0.0002838892831795035, -0.0001419446415896509, 0.0, 0.985805535841022, 0.9855216465578425, 0.7304471256210079, 0.7449254790631654, 0.6938254080908446, 0.6313697657913414, 0.6334989354151881, 0.9842441447835344, 0.6455642299503194, 0.42881476224272536, 0.29893541518807665, 0.14677075940383255, 0.12136266855926195, 0.20269694819020584, 0.06103619588360535, 0.024556422995031992, 0.11809794180269695, 0.15613910574875797, 0.03733144073811227, 0.006245564229950287, 0.02796309439318664, 0.12193044712562105, 0.13612491128459905, 0.028530872959545848, -0.0002838892831795035, 0.007665010645848207, 0.030660042583392427, 0.13413768630234213]
+
+

ykl/ALL/scripts/gen_full_all_cifar10.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_full_gen/cifar10" ]; then
+    mkdir -p "ckpt_full_gen/cifar10"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10,Quant=False gen_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/gen_full_all_cifar100.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_full_gen/cifar100" ]; then
+    mkdir -p "ckpt_full_gen/cifar100"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100,Quant=False gen_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/gen_full_one_cifar10.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_full_gen/cifar10/$1" ]; then
+    mkdir -p "ckpt_full_gen/cifar10/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10,Quant=False gen_one.slurm

ykl/ALL/scripts/gen_full_one_cifar100.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_full_gen/cifar100/$1" ]; then
+    mkdir -p "ckpt_full_gen/cifar100/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100,Quant=False gen_one.slurm

ykl/ALL/scripts/gen_quant_all_cifar10.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_quant_gen/cifar10" ]; then
+    mkdir -p "ckpt_quant_gen/cifar10"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10,Quant=True gen_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/gen_quant_all_cifar100.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_quant_gen/cifar100" ]; then
+    mkdir -p "ckpt_quant_gen/cifar100"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100,Quant=True gen_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/gen_quant_one_cifar10.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_quant_gen/cifar10/$1" ]; then
+    mkdir -p "ckpt_quant_gen/cifar10/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10,Quant=True gen_one.slurm

ykl/ALL/scripts/gen_quant_one_cifar100.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_quant_gen/cifar100/$1" ]; then
+    mkdir -p "ckpt_quant_gen/cifar100/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100,Quant=True gen_one.slurm

ykl/ALL/scripts/ptflops_cifar10.sh

+#!/bin/bash
+if [ ! -d "param_flops/cifar10" ]; then
+    mkdir -p "param_flops/cifar10"
+fi 
+sbatch --export=Dataset=cifar10 get_param_flops.slurm
\ No newline at end of file

ykl/ALL/scripts/ptflops_cifar100.sh

+#!/bin/bash
+if [ ! -d "param_flops/cifar100" ]; then
+    mkdir -p "param_flops/cifar100"
+fi 
+sbatch --export=Dataset=cifar100 get_param_flops.slurm
\ No newline at end of file

ykl/ALL/scripts/ptq_all_cifar10.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    if [ ! -d "ckpt_quant/cifar10/$name" ]; then
+        mkdir -p "ckpt_quant/cifar10/$name"
+    fi
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 ptq_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/ptq_all_cifar100.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    if [ ! -d "ckpt_quant/cifar100/$name" ]; then
+        mkdir -p "ckpt_quant/cifar100/$name"
+    fi
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 ptq_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/ptq_one_cifar10.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_quant/cifar10/$1" ]; then
+    mkdir -p "ckpt_quant/cifar10/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 ptq_one.slurm

ykl/ALL/scripts/ptq_one_cifar100.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_quant/cifar100/$1" ]; then
+    mkdir -p "ckpt_quant/cifar100/$1"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 ptq_one.slurm

ykl/ALL/scripts/train_all_cifar10.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_full/cifar10" ]; then
+    mkdir -p "ckpt_full/cifar10"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar10 train_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/train_all_cifar100.sh

+#!/bin/bash
+name_list="ResNet_152 ResNet_50 ResNet_18 MobileNetV2 Inception_BN VGG_19 VGG_16 AlexNet_BN AlexNet"
+if [ ! -d "ckpt_full/cifar100" ]; then
+    mkdir -p "ckpt_full/cifar100"
+fi
+for name in $name_list; do 
+    if [ ! -d "ret_one/$name" ]; then
+        mkdir -p "ret_one/$name"
+    fi 
+    sbatch --job-name=$name -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$name,Dataset=cifar100 train_one.slurm
+done
\ No newline at end of file

ykl/ALL/scripts/train_one_cifar10.sh

+#!/bin/bash
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+if [ ! -d "ckpt_full/cifar10" ]; then
+    mkdir -p "ckpt_full/cifar10"
+fi
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar10 train_one.slurm
\ No newline at end of file

ykl/ALL/scripts/train_one_cifar100.sh

+#!/bin/bash
+if [ ! -d "ckpt_full/cifar100" ]; then
+    mkdir -p "ckpt_full/cifar100"
+fi
+if [ ! -d "ret_one/$1" ]; then
+    mkdir -p "ret_one/$1"
+fi 
+sbatch --job-name=$1 -o "ret_one/%x/%j.out" -e "ret_one/%x/%j.err" --export=Model=$1,Dataset=cifar100 train_one.slurm
\ No newline at end of file

ykl/ALL/sync.sh

+#!/bin/bash
+
+# 指定本地源目录和远程目标目录
+local_dir="/lustre/S/youkunlin/WorkSpace/ALL/"
+remote_dir="/lustre/S/youkunlin/Model-Transfer-Adaptability/ykl/ALL"
+
+# 读取.gitignore文件，排除指定的文件和文件夹
+exclude_args=$(while read line; do echo "--exclude=$line"; done < ${local_dir}/.gitignore)
+
+# 使用rsync同步文件夹
+rsync -avz --delete ${exclude_args} ${local_dir}/ ${remote_dir}
\ No newline at end of file

ykl/ALL/train_one.py

+from model import *
+from dataloader import DataLoader
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms
+from torch.optim.lr_scheduler import _LRScheduler,MultiStepLR
+import os
+import os.path as osp
+import time
+import sys
+
+class WarmUpLR(_LRScheduler):
+    """warmup_training learning rate scheduler
+    Args:
+        optimizer: optimzier(e.g. SGD)
+        total_iters: totoal_iters of warmup phase
+    """
+    def __init__(self, optimizer, total_iters, last_epoch=-1):
+
+        self.total_iters = total_iters
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        """we will use the first m batches, and set the learning
+        rate to base_lr * m / total_iters
+        """
+        return [base_lr * self.last_epoch / (self.total_iters + 1e-8) for base_lr in self.base_lrs]
+    
+def train(model, device, train_loader, optimizer, epoch):
+    model.train()
+    total_loss = 0.
+    lossLayer = nn.CrossEntropyLoss()
+    #对第一个epoch，使用warmup策略
+    if epoch == 1:
+        warmup_scheduler = WarmUpLR(optimizer, len(train_loader))
+    else:
+        warmup_scheduler = None
+
+    start_time = time.time()
+    for batch_idx, (data, targets) in enumerate(train_loader):
+        data,targets = data.to(device), targets.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = lossLayer(output, targets)
+        loss.backward()
+        total_loss += loss.item()
+        optimizer.step()
+
+        if warmup_scheduler is not None:
+            warmup_scheduler.step()
+
+        if batch_idx % 50 == 0 and batch_idx > 0:
+            cur_loss = total_loss / 50
+            elapsed = time.time() - start_time
+            lr = optimizer.param_groups[0]['lr']
+            print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.7f} | ms/batch {:5.2f} | '
+                'loss {:5.2f}'.format(
+                epoch, batch_idx, len(train_loader.dataset) // len(data), lr,
+                elapsed * 1000 / 50, cur_loss))
+            
+            total_loss = 0.
+            start_time = time.time()
+
+
+def evaluate(model, device, eval_loader):
+    model.eval()
+    total_loss = 0
+    correct = 0
+    lossLayer = nn.CrossEntropyLoss()
+    with torch.no_grad():
+        for data, targets in eval_loader:
+            data,targets = data.to(device), targets.to(device)
+            output = model(data)
+            total_loss += len(data) * lossLayer(output, targets).item()
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(targets.view_as(pred)).sum().item()
+    
+    test_loss = total_loss / len(eval_loader.dataset)
+    test_acc = 100. * correct / len(eval_loader.dataset)
+    return test_loss,test_acc
+
+if __name__ == "__main__":
+    model_name = sys.argv[1]
+    dataset = sys.argv[2]
+
+    batch_size = 128
+    optim_type = 'sgd' 
+    lr = 0.1
+    momentum = 0.9
+    weight_decay = 5e-4
+    nesterov = True
+    
+    epochs = 200
+    milestones = [60, 120, 160]
+    gamma = 0.2
+
+    print('model: '+model_name+'  dataset: '+dataset)
+    print('optim_type: '+optim_type+'  lr: '+str(lr)+'  weight_decay: '+str(weight_decay)+'  nesterov: '+str(nesterov)+'  momentum: '+str(momentum))
+    print('epochs: '+str(epochs)+'  milestones: '+str(milestones)+'  gamma: '+str(gamma))
+
+    save_model = True
+    append = False
+
+    ckpt_path = 'ckpt_full/'+dataset
+    if save_model:
+        if not osp.exists(ckpt_path):
+            os.makedirs(ckpt_path)
+    save_path = ckpt_path+'/'+model_name+'.pt'
+    if os.path.exists(save_path) and append:
+        print('Append: Model '+model_name+' exists!')
+        sys.exit()
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    
+    dataloader = DataLoader(dataset,batch_size)
+    train_loader, val_loader, test_loader = dataloader.getloader()
+    
+
+    print('>>>>>>>>>>>>>>>>>>>>>>>> Train: '+model_name+' <<<<<<<<<<<<<<<<<<<<<<<<')
+    model = Model(model_name,dataset).to(device)
+
+    best_val_acc = None
+
+    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum,weight_decay = weight_decay,nesterov=nesterov)
+
+    lr_scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=gamma)
+
+    for epoch in range(1, epochs + 1):
+        epoch_start_time = time.time()
+        train(model, device, train_loader, optimizer, epoch)
+        val_loss, val_acc = evaluate(model, device, val_loader)
+        if not best_val_acc or val_acc > best_val_acc:
+            best_val_acc = val_acc
+            if save_model:
+                torch.save(model.state_dict(), save_path)
+        print('-' * 89)
+        print('| end of epoch {:3d} | time: {:5.2f}s | val loss {:5.2f} | '
+                'val acc {:.2f} | best val acc {:.2f}'.format(epoch, (time.time() - epoch_start_time),
+                                        val_loss, val_acc, best_val_acc))
+        print('-' * 89)
+        lr_scheduler.step(epoch)
+
+    print('>>>>>>>>>>>>>>>>>>>>>>>> Test: '+model_name+' <<<<<<<<<<<<<<<<<<<<<<<<')        
+    model = Model(model_name,dataset).to(device)
+    model.load_state_dict(torch.load(save_path))
+
+    test_loss,test_acc = evaluate(model, device, test_loader)
+    print('=' * 89)
+    print('| Test on {:s} | test loss {:5.2f} | test acc {:.2f}'.format(
+        model_name, test_loss, test_acc))
+    print('=' * 89)
\ No newline at end of file

ykl/ALL/train_one.slurm

+#!/bin/bash
+
+#- Job parameters
+
+# (TODO)
+# Please modify job name
+
+#- Resources
+
+# (TODO)
+# Please modify your requirements
+
+#SBATCH -p nv-gpu                    # Submit to 'nv-gpu' Partitiion
+#SBATCH -t 1-06:00:00                # Run for a maximum time of 0 days, 12 hours, 00 mins, 00 secs
+#SBATCH --nodes=1                    # Request N nodes
+#SBATCH --gres=gpu:1                 # Request M GPU per node
+#SBATCH --gres-flags=enforce-binding # CPU-GPU Affinity
+#SBATCH --qos=gpu-normal             # Request QOS Type
+
+###
+### The system will alloc 8 or 16 cores per gpu by default.
+### If you need more or less, use following:
+### #SBATCH --cpus-per-task=K            # Request K cores
+###
+### 
+### Without specifying the constraint, any available nodes that meet the requirement will be allocated
+### You can specify the characteristics of the compute nodes, and even the names of the compute nodes
+###
+### #SBATCH --nodelist=gpu-v00           # Request a specific list of hosts 
+### #SBATCH --constraint="Volta|RTX8000" # Request GPU Type: Volta(V100 or V100S) or RTX8000
+###
+
+# set constraint for RTX8000 to meet my cuda
+#SBATCH --constraint="Ampere|RTX8000"
+
+#- Log information
+
+echo "Job start at $(date "+%Y-%m-%d %H:%M:%S")"
+echo "Job run at:"
+echo "$(hostnamectl)"
+
+#- Load environments
+source /tools/module_env.sh
+module list                       # list modules loaded
+
+##- Tools
+module load cluster-tools/v1.0
+module load slurm-tools/v1.0
+module load cmake/3.15.7
+module load git/2.17.1
+module load vim/8.1.2424
+
+##- language
+module load python3/3.6.8
+
+##- CUDA
+# module load cuda-cudnn/10.2-7.6.5
+# module load cuda-cudnn/11.2-8.2.1
+module load cuda-cudnn/11.1-8.2.1
+
+##- virtualenv
+# source xxxxx/activate
+
+echo $(module list)              # list modules loaded
+echo $(which gcc)
+echo $(which python)
+echo $(which python3)
+
+cluster-quota                    # nas quota
+
+nvidia-smi --format=csv --query-gpu=name,driver_version,power.limit # gpu info
+
+#- Warning! Please not change your CUDA_VISIBLE_DEVICES
+#- in `.bashrc`, `env.sh`, or your job script
+echo "Use GPU ${CUDA_VISIBLE_DEVICES}"                              # which gpus
+#- The CUDA_VISIBLE_DEVICES variable is assigned and specified by SLURM
+
+#- Job step
+# [EDIT HERE(TODO)]
+python train_one.py $Model $Dataset
+
+#- End
+echo "Job end at $(date "+%Y-%m-%d %H:%M:%S")"

ykl/ALL/utils.py

+import torch
+import torch.nn as nn
+
+def ebit_list(quant_type, num_bits):
+    if quant_type == 'FLOAT':
+        e_bit_list = list(range(1,num_bits-1))
+    else:
+        e_bit_list = [0]
+    return e_bit_list
+
+
+def numbit_list(quant_type):
+    if quant_type == 'INT':
+        num_bit_list = list(range(2,17))
+        # num_bit_list = [4,5]
+    elif quant_type == 'POT':
+        num_bit_list = list(range(2,9))
+        # num_bit_list = [5]
+    else:
+        num_bit_list = list(range(2,9))
+        # num_bit_list = [8]
+    
+    return num_bit_list     
+
+def build_bias_list(quant_type):
+    if quant_type == 'POT':
+        return build_pot_list(8)
+    else:
+        return build_float_list(16,7)
+    
+def build_list(quant_type, num_bits, e_bits):
+    if quant_type == 'POT':
+        return build_pot_list(num_bits)
+    else:
+        return build_float_list(num_bits,e_bits)
+
+def build_pot_list(num_bits):
+    plist = [0.]
+    for i in range(-2 ** (num_bits-1) + 2, 1): 
+        # i最高到0，即pot量化最大值为1
+        plist.append(2. ** i)
+        plist.append(-2. ** i)
+    plist = torch.Tensor(list(set(plist)))
+    # plist = plist.mul(1.0 / torch.max(plist))
+    return plist
+
+def build_float_list(num_bits,e_bits):
+    m_bits = num_bits - 1 - e_bits
+    plist = [0.]
+    # 相邻尾数的差值
+    dist_m = 2 ** (-m_bits)
+    e = -2 ** (e_bits - 1) + 1
+    for m in range(1, 2 ** m_bits):
+        frac = m * dist_m   # 尾数部分
+        expo = 2 ** e       # 指数部分
+        flt = frac * expo
+        plist.append(flt)
+        plist.append(-flt)
+
+    for e in range(-2 ** (e_bits - 1) + 2, 2 ** (e_bits - 1) + 1):
+        expo = 2 ** e
+        for m in range(0, 2 ** m_bits):
+            frac = 1. + m * dist_m
+            flt = frac * expo
+            plist.append(flt)
+            plist.append(-flt)
+    plist = torch.Tensor(list(set(plist)))
+    return plist
+
+#此处不必cfg，直接取同前缀同后缀即可。将relu一起考虑进去
+def fold_ratio(layer, par_ratio, flop_ratio):
+    idx = -1
+    for name in layer:
+        if 'conv' in name:
+            conv_idx = layer.index(name)
+            [prefix,suffix] = name.split('conv')
+            bn_name = prefix+'bn'+suffix
+            relu_name = prefix+'relu'+suffix
+            relus_name = prefix+'relus'+suffix
+            if bn_name in layer:
+                bn_idx = layer.index(bn_name)
+                par_ratio[conv_idx]+=par_ratio[bn_idx]
+                flop_ratio[conv_idx]+=flop_ratio[bn_idx]
+                if relu_name in layer:
+                    relu_idx = layer.index(relu_name)
+                    par_ratio[conv_idx]+=par_ratio[relu_idx]
+                    flop_ratio[conv_idx]+=flop_ratio[relu_idx]
+                elif relus_name in layer:
+                    relus_idx = layer.index(relus_name)
+                    par_ratio[conv_idx]+=par_ratio[relus_idx]
+                    flop_ratio[conv_idx]+=flop_ratio[relus_idx]
+    return par_ratio,flop_ratio
+
+def fold_model(model):
+    for name, module in model.named_modules():
+        if 'conv' in name:
+            [prefix,suffix] = name.split('conv')
+            bn_name = prefix+'bn'+suffix
+            if hasattr(model,bn_name):
+                bn_layer = getattr(model,bn_name)
+                fold_bn(module,bn_layer)
+
+def fold_bn(conv, bn):
+    # 获取 BN 层的参数
+    mean = bn.running_mean
+    var = bn.running_var
+    eps = bn.eps
+    std = torch.sqrt(var + eps)
+
+    if bn.affine:
+        gamma_ = bn.weight / std
+        weight = conv.weight * gamma_.view(conv.out_channels, 1, 1, 1)
+        if conv.bias is not None:
+            bias = gamma_ * conv.bias - gamma_ * mean + bn.bias
+        else:
+            bias = bn.bias - gamma_ * mean
+    else:
+        gamma_ = 1 / std
+        weight = conv.weight * gamma_
+        if conv.bias is not None:
+            bias = gamma_ * conv.bias - gamma_ * mean
+        else:
+            bias = -gamma_ * mean
+
+    # 设置新的 weight 和 bias
+    conv.weight.data = weight.data
+    if conv.bias is not None:
+        conv.bias.data = bias.data
+    else:
+        conv.bias = torch.nn.Parameter(bias)