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Tutorials - Model Training

Sample code using the PaddleRS training model is curated in this directory. The code provides automatic downloading of sample data, and uses GPU to train the model.

Sample Code Path	Task	Model
change_detection/bit.py	Change Detection	BIT
change_detection/cdnet.py	Change Detection	CDNet
change_detection/changeformer.py	Change Detection	ChangeFormer
change_detection/dsamnet.py	Change Detection	DSAMNet
change_detection/dsifn.py	Change Detection	DSIFN
change_detection/fc_ef.py	Change Detection	FC-EF
change_detection/fc_siam_conc.py	Change Detection	FC-Siam-conc
change_detection/fc_siam_diff.py	Change Detection	FC-Siam-diff
change_detection/fccdn.py	Change Detection	FCCDN
change_detection/p2v.py	Change Detection	P2V-CD
change_detection/snunet.py	Change Detection	SNUNet
change_detection/stanet.py	Change Detection	STANet
classification/condensenetv2.py	Scene Classification	CondenseNet V2
classification/hrnet.py	Scene Classification	HRNet
classification/mobilenetv3.py	Scene Classification	MobileNetV3
classification/resnet50_vd.py	Scene Classification	ResNet50-vd
image_restoration/drn.py	Image Restoration	DRN
image_restoration/esrgan.py	Image Restoration	ESRGAN
image_restoration/lesrcnn.py	Image Restoration	LESRCNN
object_detection/faster_rcnn.py	Object Detection	Faster R-CNN
object_detection/fcosr.py	Object Detection	FCOSR
object_detection/ppyolo.py	Object Detection	PP-YOLO
object_detection/ppyolo_tiny.py	Object Detection	PP-YOLO Tiny
object_detection/ppyolov2.py	Object Detection	PP-YOLOv2
object_detection/yolov3.py	Object Detection	YOLOv3
semantic_segmentation/bisenetv2.py	Image Segmentation	BiSeNet V2
semantic_segmentation/deeplabv3p.py	Image Segmentation	DeepLab V3+
semantic_segmentation/factseg.py	Image Segmentation	FactSeg
semantic_segmentation/farseg.py	Image Segmentation	FarSeg
semantic_segmentation/fast_scnn.py	Image Segmentation	Fast-SCNN
semantic_segmentation/hrnet.py	Image Segmentation	HRNet
semantic_segmentation/unet.py	Image Segmentation	UNet

Start Training

• After PaddleRS is installed, run the following commands to launch training with a single GPU. The script will automatically download the training data. Take DeepLab V3+ image segmentation model as an example:

# Specifies the GPU device number to be used
export CUDA_VISIBLE_DEVICES=0
python tutorials/train/semantic_segmentation/deeplabv3p.py

• If multiple GPUs are required for training, for example, two graphics cards, run the following command:

python -m paddle.distributed.launch --gpus 0,1 tutorials/train/semantic_segmentation/deeplabv3p.py

Visualize Training Metrics via VisualDL

Set the use_vdl argument passed to the train() method to True, and then the training log will be automatically saved in VisualDL format in a subdirectory named vdl_log under the directory specified by save_dir (a user-specified path) during the model training process. You can run the following command to start the VisualDL service and view the indicators and metrics. We also take DeepLab V3+ as an example:

# The specified port number is 8001
visualdl --logdir output/deeplabv3p/vdl_log --port 8001

Once the service is started, open https://0.0.0.0:8001 or https://localhost:8001 in your browser to access the VisualDL page.