提交 c58fb6e8 编写于 作者: Y yukavio

update bash of slim pruning

上级 9bcba212
...@@ -51,14 +51,14 @@ python setup.py install ...@@ -51,14 +51,14 @@ python setup.py install
进入PaddleOCR根目录,通过以下命令对模型进行敏感度分析训练: 进入PaddleOCR根目录,通过以下命令对模型进行敏感度分析训练:
```bash ```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights="your trained model" Global.test_batch_size_per_card=1 python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights="your trained model" Global.test_batch_size_per_card=1
``` ```
### 4. 模型裁剪训练 ### 4. 模型裁剪训练
裁剪时通过之前的敏感度分析文件决定每个网络层的裁剪比例。在具体实现时,为了尽可能多的保留从图像中提取的低阶特征,我们跳过了backbone中靠近输入的4个卷积层。同样,为了减少由于裁剪导致的模型性能损失,我们通过之前敏感度分析所获得的敏感度表,人工挑选出了一些冗余较少,对裁剪较为敏感的[网络层](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/pruning_and_finetune.py#L41)(指在较低的裁剪比例下就导致很高性能损失的网络层),并在之后的裁剪过程中选择避开这些网络层。裁剪过后finetune的过程沿用OCR检测模型原始的训练策略。 裁剪时通过之前的敏感度分析文件决定每个网络层的裁剪比例。在具体实现时,为了尽可能多的保留从图像中提取的低阶特征,我们跳过了backbone中靠近输入的4个卷积层。同样,为了减少由于裁剪导致的模型性能损失,我们通过之前敏感度分析所获得的敏感度表,人工挑选出了一些冗余较少,对裁剪较为敏感的[网络层](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/pruning_and_finetune.py#L41)(指在较低的裁剪比例下就导致很高性能损失的网络层),并在之后的裁剪过程中选择避开这些网络层。裁剪过后finetune的过程沿用OCR检测模型原始的训练策略。
```bash ```bash
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1 python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
``` ```
通过对比可以发现,经过裁剪训练保存的模型更小。 通过对比可以发现,经过裁剪训练保存的模型更小。
...@@ -66,7 +66,7 @@ python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml - ...@@ -66,7 +66,7 @@ python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -
在得到裁剪训练保存的模型后,我们可以将其导出为inference_model: 在得到裁剪训练保存的模型后,我们可以将其导出为inference_model:
```bash ```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
``` ```
inference model的预测和部署参考: inference model的预测和部署参考:
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...@@ -55,7 +55,7 @@ Enter the PaddleOCR root directory,perform sensitivity analysis on the model w ...@@ -55,7 +55,7 @@ Enter the PaddleOCR root directory,perform sensitivity analysis on the model w
```bash ```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1 python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
``` ```
...@@ -67,7 +67,7 @@ python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Gl ...@@ -67,7 +67,7 @@ python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Gl
```bash ```bash
python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1 python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
``` ```
...@@ -76,7 +76,7 @@ python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml - ...@@ -76,7 +76,7 @@ python deploy/slim/prune/pruning_and_finetune.py -c configs/det/det_mv3_db.yml -
We can export the pruned model as inference_model for deployment: We can export the pruned model as inference_model for deployment:
```bash ```bash
python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model python deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.pretrain_weights=./output/det_db/best_accuracy Global.test_batch_size_per_card=1 Global.save_inference_dir=inference_model
``` ```
Reference for prediction and deployment of inference model: Reference for prediction and deployment of inference model:
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