@@ -74,7 +74,7 @@ eval_dataset = pdrs.datasets.CDDataset(
# 使用默认参数构建BIT模型
# 目前已支持的模型请参考:https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/model_zoo.md
# 模型输入参数请参考:https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/change_detector.py
-model = pdrs.tasks.BIT()
+model = pdrs.tasks.cd.BIT()
# 执行模型训练
model.train(
# 使用默认参数构建CDNet模型
-model = pdrs.tasks.CDNet()
+model = pdrs.tasks.cd.CDNet()
# 使用默认参数构建DSAMNet模型
-model = pdrs.tasks.DSAMNet()
+model = pdrs.tasks.cd.DSAMNet()
# 使用默认参数构建DSIFN模型
-model = pdrs.tasks.DSIFN()
+model = pdrs.tasks.cd.DSIFN()
# 使用默认参数构建FC-EF模型
-model = pdrs.tasks.FCEarlyFusion()
+model = pdrs.tasks.cd.FCEarlyFusion()
# 使用默认参数构建FC-Siam-conc模型
-model = pdrs.tasks.FCSiamConc()
+model = pdrs.tasks.cd.FCSiamConc()
# 使用默认参数构建FC-Siam-diff模型
-model = pdrs.tasks.FCSiamDiff()
+model = pdrs.tasks.cd.FCSiamDiff()
# 使用默认参数构建SNUNet模型
-model = pdrs.tasks.SNUNet()
+model = pdrs.tasks.cd.SNUNet()
# 使用默认参数构建STANet模型
-model = pdrs.tasks.STANet()
+model = pdrs.tasks.cd.STANet()
@@ -70,7 +70,7 @@ eval_dataset = pdrs.datasets.ClasDataset(
# 使用默认参数构建HRNet模型
# 模型输入参数请参考:https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/classifier.py
-model = pdrs.tasks.HRNet_W18_C(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.clas.HRNet_W18_C(num_classes=len(train_dataset.labels))
@@ -70,7 +70,8 @@ eval_dataset = pdrs.datasets.ClasDataset(
# 使用默认参数构建MobileNetV3模型
-model = pdrs.tasks.MobileNetV3_small_x1_0(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.clas.MobileNetV3_small_x1_0(
+ num_classes=len(train_dataset.labels))
# 使用默认参数构建ResNet50-vd模型
-model = pdrs.tasks.ResNet50_vd(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.clas.ResNet50_vd(num_classes=len(train_dataset.labels))
@@ -79,7 +79,7 @@ eval_dataset = pdrs.datasets.VOCDetection(
# 构建Faster R-CNN模型
# 模型输入参数请参考:https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/object_detector.py
-model = pdrs.tasks.FasterRCNN(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.det.FasterRCNN(num_classes=len(train_dataset.labels))
@@ -80,7 +80,7 @@ eval_dataset = pdrs.datasets.VOCDetection(
# 构建PP-YOLO模型
-model = pdrs.tasks.PPYOLO(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.det.PPYOLO(num_classes=len(train_dataset.labels))
# 构建PP-YOLO Tiny模型
-model = pdrs.tasks.PPYOLOTiny(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.det.PPYOLOTiny(num_classes=len(train_dataset.labels))
# 构建PP-YOLOv2模型
-model = pdrs.tasks.PPYOLOv2(num_classes=len(train_dataset.labels))
+model = pdrs.tasks.det.PPYOLOv2(num_classes=len(train_dataset.labels))
# 构建YOLOv3模型,使用DarkNet53作为backbone
-model = pdrs.tasks.YOLOv3(
+model = pdrs.tasks.det.YOLOv3(
num_classes=len(train_dataset.labels), backbone='DarkNet53')
@@ -1,4 +1,3 @@
*.zip
*.tar.gz
-rsseg/
-optic/
+rsseg/
@@ -72,7 +72,7 @@ eval_dataset = pdrs.datasets.SegDataset(
# 构建DeepLab V3+模型,使用ResNet-50作为backbone
# 模型输入参数请参考:https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
-model = pdrs.tasks.DeepLabV3P(
+model = pdrs.tasks.seg.DeepLabV3P(
input_channel=NUM_BANDS,
num_classes=len(train_dataset.labels),
backbone='ResNet50_vd')
# 构建UNet模型
-model = pdrs.tasks.UNet(
+model = pdrs.tasks.seg.UNet(
input_channel=NUM_BANDS, num_classes=len(train_dataset.labels))