[cherry-pick] Fix v0.2 docs (#258)

* fix save checkpoint in quantization (#257)

* fix save checkpoint in quantization
* fix details

* Fix softlink (#256)
Co-authored-by: NLiufang Sang <slf12thuss@163.com>

README.md

@@ -80,11 +80,11 @@ PaddleDetection的目的是为工业界和学术界提供丰富、易用的目
 - [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
 - [迁移学习教程](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md)
 - [模型压缩](slim)
+    - [压缩benchmark](slim)
     - [量化](slim/quantization)
     - [剪枝](slim/prune)
     - [蒸馏](slim/distillation)
     - [神经网络搜索](slim/nas)
-    - [压缩benchmark](slim)
 - [推理部署](inference)
     - [模型导出教程](docs/advanced_tutorials/inference/EXPORT_MODEL.md)
     - [预测引擎Python API使用示例](docs/advanced_tutorials/inference/INFERENCE.md)
@@ -105,7 +105,7 @@ PaddleDetection的目的是为工业界和学术界提供丰富、易用的目
 本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
 
 ## 版本更新
-v0.2.0版本已经在`01/2020`发布，增加多个模型，升级数据处理模块，拆分YOLOv3的loss，修复已知诸多bug等，
+v0.2.0版本已经在`02/2020`发布，增加多个模型，升级数据处理模块，拆分YOLOv3的loss，修复已知诸多bug等，
 详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
 
 ## 如何贡献代码

README_en.md

@@ -91,10 +91,11 @@ Advanced Features:
 - [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
 - [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md)
 - [Model compression](slim)
-    - [Quantization-aware training example](slim/quantization)
-    - [Model pruning example](slim/prune)
-    - [Model distillation example](slim/distillation)
-    - [Neural Architecture Search example](slim/nas)
+    - [Model compression benchmark](slim)
+    - [Quantization](slim/quantization)
+    - [Model pruning](slim/prune)
+    - [Model distillation](slim/distillation)
+    - [Neural Architecture Search](slim/nas)
 - [Deployment](inference)
     - [Export model for inference](docs/advanced_tutorials/inference/EXPORT_MODEL.md)
     - [Model inference](docs/advanced_tutorials/inference/INFERENCE.md)
@@ -115,7 +116,7 @@ Advanced Features:
 PaddleDetection is released under the [Apache 2.0 license](LICENSE).
 
 ## Updates
-v0.2.0 was released at `01/2020`, add some models，Upgrade data processing module, Split YOLOv3's loss, fix many known bugs, etc.
+v0.2.0 was released at `02/2020`, add some models，Upgrade data processing module, Split YOLOv3's loss, fix many known bugs, etc.
 Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
 
 ## Contributing

docs/advanced_tutorials/slim/DISTILLATION.md

-../../../slim/distillation/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/MODEL_ZOO.md

-slim/README.md
+../../../slim/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/NAS.md

-../../../slim/nas/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/QUANTIZATION.md

-../../../slim/quantization/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/distillation/DISTILLATION.md

+../../../../slim/distillation/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/distillation/index.rst

+模型蒸馏
+===========================================
+
+.. toctree::
+   :maxdepth: 2
+
+   DISTILLATION.md

docs/advanced_tutorials/slim/index.rst

@@ -4,8 +4,8 @@
 .. toctree::
    :maxdepth: 2
 
-   DISTILLATION.md
-   QUANTIZATION.md
-   NAS.md
-   prune/index
    MODEL_ZOO.md
+   distillation/index
+   quantization/index
+   nas/index
+   prune/index

docs/advanced_tutorials/slim/nas/NAS.md

+../../../../slim/nas/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/nas/index.rst

+神经网络搜索
+===========================================
+
+.. toctree::
+   :maxdepth: 2
+
+   NAS.md

docs/advanced_tutorials/slim/prune/index.rst

-模型剪枝教程
+模型剪枝
 ===========================================
 
 .. toctree::

docs/advanced_tutorials/slim/quantization/QUANTIZATION.md

+../../../../slim/quantization/README.md
\ No newline at end of file

docs/advanced_tutorials/slim/quantization/index.rst

+模型量化
+===========================================
+
+.. toctree::
+   :maxdepth: 2
+
+   QUANTIZATION.md

slim/README.md

-# 简介
+# 压缩benchmark
 
 在PaddleDetection, 提供了基于PaddleSlim进行模型压缩的完整教程和实验结果。详细教程请参考：
 
@@ -9,8 +9,6 @@
 
 下面给出压缩的benchmark实验结果。
 
-# 压缩模型库
-
 ## 测试环境
 
 - Python 2.7.1
@@ -23,7 +21,7 @@
 
 ### 训练策略
 
-- 剪裁模型训练时使用[PaddleDetection模型库](../docs/MODEL_ZOO_cn.md)发布的模型权重作为预训练权重。
+- 剪裁模型训练时使用[PaddleDetection模型库](https://paddledetection.readthedocs.io/zh/latest/MODEL_ZOO_cn.html)发布的模型权重作为预训练权重。
 - 剪裁训练使用模型默认配置，即除`pretrained_weights`外配置不变。
 - 剪裁模型全部为基于敏感度的卷积通道剪裁。
 - YOLOv3模型主要剪裁`yolo_head`部分，即剪裁参数如下。
@@ -69,7 +67,7 @@
 
 ### 蒸馏通道剪裁模型
 
-可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](./extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。
+可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。
 
 COCO数据集上蒸馏通道剪裁模型库如下。
 
@@ -93,7 +91,7 @@ Pascal VOC数据集上蒸馏通道剪裁模型库如下。
 
 ### 训练策略
 
-- 蒸馏模型训练时teacher模型使用[PaddleDetection模型库](../docs/MODEL_ZOO_cn.md)发布的模型权重作为预训练权重。
+- 蒸馏模型训练时teacher模型使用[PaddleDetection模型库](https://paddledetection.readthedocs.io/zh/latest/MODEL_ZOO_cn.html)发布的模型权重作为预训练权重。
 - 蒸馏模型训练时student模型使用backbone的预训练权重
 
 ### YOLOv3 on COCO

slim/quantization/train.py

@@ -22,6 +22,7 @@ import time
 import numpy as np
 import datetime
 from collections import deque
+import shutil
 
 from paddle import fluid
 
@@ -42,6 +43,21 @@ logging.basicConfig(level=logging.INFO, format=FORMAT)
 logger = logging.getLogger(__name__)
 
 
+def save_checkpoint(exe, prog, path, train_prog):
+    if os.path.isdir(path):
+        shutil.rmtree(path)
+    logger.info('Save model to {}.'.format(path))
+    fluid.io.save_persistables(exe, path, main_program=prog)
+
+    v = train_prog.global_block().var('@LR_DECAY_COUNTER@')
+    fluid.io.save_vars(exe, dirname=path, vars=[v])
+
+
+def load_global_step(exe, prog, path):
+    v = prog.global_block().var('@LR_DECAY_COUNTER@')
+    fluid.io.load_vars(exe, path, prog, [v])
+
+
 def main():
     env = os.environ
     FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
@@ -176,9 +192,9 @@ def main():
                 cfg.pretrain_weights,
                 ignore_params=ignore_params)
     # insert quantize op in train_prog, return type is CompiledProgram
-    train_prog = quant_aware(train_prog, place, config, for_test=False)
+    train_prog_quant = quant_aware(train_prog, place, config, for_test=False)
 
-    compiled_train_prog = train_prog.with_data_parallel(
+    compiled_train_prog = train_prog_quant.with_data_parallel(
         loss_name=loss.name,
         build_strategy=build_strategy,
         exec_strategy=exec_strategy)
@@ -192,6 +208,7 @@ def main():
     start_iter = 0
     if FLAGS.resume_checkpoint:
         checkpoint.load_checkpoint(exe, eval_prog, FLAGS.resume_checkpoint)
+        load_global_step(exe, train_prog, FLAGS.resume_checkpoint)
         start_iter = checkpoint.global_step()
 
     train_reader = create_reader(cfg.TrainReader,
@@ -237,7 +254,8 @@ def main():
         if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
            and (not FLAGS.dist or trainer_id == 0):
             save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
-            checkpoint.save(exe, eval_prog, os.path.join(save_dir, save_name))
+            save_checkpoint(exe, eval_prog,
+                            os.path.join(save_dir, save_name), train_prog)
 
             if FLAGS.eval:
                 # evaluation
@@ -254,8 +272,9 @@ def main():
                 if box_ap_stats[0] > best_box_ap_list[0]:
                     best_box_ap_list[0] = box_ap_stats[0]
                     best_box_ap_list[1] = it
-                    checkpoint.save(exe, eval_prog,
-                                    os.path.join(save_dir, "best_model"))
+                    save_checkpoint(exe, eval_prog,
+                                    os.path.join(save_dir, "best_model"),
+                                    train_prog)
                 logger.info("Best test box ap: {}, in iter: {}".format(
                     best_box_ap_list[0], best_box_ap_list[1]))