提交 3126a437 编写于 作者: Q qingqing01 提交者: GitHub

Polish doc for exportting model (#3741)

上级 0a11e56b
......@@ -85,6 +85,7 @@ Advanced Features:
## Depoly
- [Export model for inference depolyment](docs/EXPORT_MODEL.md)
- [C++ inference depolyment](inference/README.md)
## Benchmark
......
......@@ -14,25 +14,35 @@
使用[训练/评估/推断](GETTING_STARTED_cn.md)中训练得到的模型进行试用,脚本如下
```bash
# export model for RCNN
# 导出FasterRCNN模型, 模型中data层默认的shape为3x800x1333
python tools/export_model.py -c configs/faster_rcnn_r50_1x.yml \
--output_dir=./inference_model \
-o weights=output/faster_rcnn_r50_1x/model_final \
FasterRCNNTestFeed.image_shape=[3,800,1333]
# export model for YOLOv3
```
预测模型会导出到`inference_model/faster_rcnn_r50_1x`目录下,模型名和参数名分别为`__model__``__params__`
## 设置导出模型的输入大小
使用Fluid-TensorRT进行预测时,由于<=TensorRT 5.1的版本仅支持定长输入,保存模型的`data`层的图片大小需要和实际输入图片大小一致。而Fluid C++预测引擎没有此限制。可通过设置TestFeed的`image_shape`可以修改保存模型中的输入图片大小。示例如下:
```bash
# 导出FasterRCNN模型,输入是3x640x640
python tools/export_model.py -c configs/faster_rcnn_r50_1x.yml \
--output_dir=./inference_model \
-o weights=https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar \
FasterRCNNTestFeed.image_shape=[3,640,640]
# 导出YOLOv3模型,输入是3x320x320
python tools/export_model.py -c configs/yolov3_darknet.yml \
--output_dir=./inference_model \
-o weights=output/yolov3_darknet/model_final \
YoloTestFeed.image_shape=[3,800,1333]
-o weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar \
YoloTestFeed.image_shape=[3,320,320]
# export model for SSD
# 导出SSD模型,输入是3x300x300
python tools/export_model.py -c configs/ssd/ssd_mobilenet_v1_voc.yml \
--output_dir=./inference_model \
-o weights=output/ssd_mobilenet_v1_voc/model_final \
-o weights= https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar \
SSDTestFeed.image_shape=[3,300,300]
```
- 预测模型会导出到`output/faster_rcnn_r50_1x`目录下,模型名和参数名分别为`__model__``__params__`
- 通过`image_shape`修改保存模型中的图片大小。使用Fluid-TensorRT进行预测时,由于TensorRT仅支持定长输入,需要将输入图片大小与`image_shape`保持一致。
......@@ -23,7 +23,6 @@ from paddle import fluid
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.modeling.model_input import create_feed
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
import ppdet.utils.checkpoint as checkpoint
import logging
......
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