From 35b0ba5058d6623aad6e2cf6a69b7e4283e097d6 Mon Sep 17 00:00:00 2001
From: RachelXu7 <molixu7@gmail.com>
Date: Thu, 15 Sep 2022 09:24:28 +0000
Subject: [PATCH] Add ACT/FullQuant Demo

---
 deploy/{ => slim}/auto_compression/README.md         | 4 ++--
 deploy/{ => slim}/auto_compression/mbv3_qat_dis.yaml | 4 ++--
 deploy/{ => slim}/auto_compression/run.py            | 0
 3 files changed, 4 insertions(+), 4 deletions(-)
 rename deploy/{ => slim}/auto_compression/README.md (97%)
 rename deploy/{ => slim}/auto_compression/mbv3_qat_dis.yaml (97%)
 rename deploy/{ => slim}/auto_compression/run.py (100%)

diff --git a/deploy/auto_compression/README.md b/deploy/slim/auto_compression/README.md
similarity index 97%
rename from deploy/auto_compression/README.md
rename to deploy/slim/auto_compression/README.md
index e31224270..5f02fd948 100644
--- a/deploy/auto_compression/README.md
+++ b/deploy/slim/auto_compression/README.md
@@ -82,7 +82,7 @@ python run.py --save_dir='./save_quant_mobilev3/' --config_path='./configs/mbv3_
 
 **多卡启动**
 
-图像分类训练任务中往往包含大量训练数据，以ImageNet为例，ImageNet22k数据集中包含1400W张图像，如果使用单卡训练，会非常耗时，使用分布式训练可以达到几乎线性的加速比。
+图像分类训练任务中往往包含大量训练数据，以ImageNet-1k为例，如果使用单卡训练，会非常耗时，使用分布式训练可以达到几乎线性的加速比。
 
 ```shell
 export CUDA_VISIBLE_DEVICES=0,1,2,3
@@ -95,7 +95,7 @@ python -m paddle.distributed.launch run.py --save_dir='./save_quant_mobilev3/' -
 加载训练好的模型进行量化训练时，一般`learning rate`可比原始训练的`learning rate`小10倍。
 
 
-## 4. 配置文件
+## 4. 配置文件介绍
 自动压缩相关配置主要有：
 - 压缩策略配置，如量化（Quantization），知识蒸馏（Distillation），结构化稀疏（ChannelPrune），ASP半结构化稀疏（ASPPrune ），非结构化稀疏（UnstructurePrune）。
 - 训练超参配置（TrainConfig）：主要设置学习率、训练次数（epochs）和优化器等。
diff --git a/deploy/auto_compression/mbv3_qat_dis.yaml b/deploy/slim/auto_compression/mbv3_qat_dis.yaml
similarity index 97%
rename from deploy/auto_compression/mbv3_qat_dis.yaml
rename to deploy/slim/auto_compression/mbv3_qat_dis.yaml
index 5bdce4333..c8b940fba 100644
--- a/deploy/auto_compression/mbv3_qat_dis.yaml
+++ b/deploy/slim/auto_compression/mbv3_qat_dis.yaml
@@ -27,12 +27,12 @@ Quantization:
 
 TrainConfig:
   epochs: 2
-  eval_iter: 500
+  eval_iter: 5000
   learning_rate: 0.001
   optimizer_builder:
     optimizer:
       type: Momentum
-    weight_decay: 0.000005
+    weight_decay: 0.00005
   origin_metric: 0.7532
   
 
diff --git a/deploy/auto_compression/run.py b/deploy/slim/auto_compression/run.py
similarity index 100%
rename from deploy/auto_compression/run.py
rename to deploy/slim/auto_compression/run.py