reanme the demo

tutorials/train/object_detection/ppyolo.py

@@ -1,54 +0,0 @@
-import sys
-
-sys.path.append("/ssd2/pengjuncai/PaddleRS")
-
-import paddlers as pdrs
-from paddlers import transforms as T
-
-train_transforms = T.Compose([
-    T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
-    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
-    T.RandomHorizontalFlip(), T.BatchRandomResize(
-        target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
-        interp='RANDOM'), T.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-eval_transforms = T.Compose([
-    T.Resize(
-        target_size=608, interp='CUBIC'), T.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-
-train_dataset = pdrs.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/train_list.txt',
-    label_list='insect_det/labels.txt',
-    transforms=train_transforms,
-    shuffle=True)
-
-eval_dataset = pdrs.datasets.VOCDetection(
-    data_dir='insect_det',
-    file_list='insect_det/val_list.txt',
-    label_list='insect_det/labels.txt',
-    transforms=eval_transforms,
-    shuffle=False)
-
-
-num_classes = len(train_dataset.labels)
-model = pdrs.tasks.det.PPYOLO(num_classes=num_classes, backbone='ResNet50_vd_dcn')
-
-model.train(
-    num_epochs=200,
-    train_dataset=train_dataset,
-    train_batch_size=8,
-    eval_dataset=eval_dataset,
-    pretrain_weights='COCO',
-    learning_rate=0.005 / 12,
-    warmup_steps=500,
-    warmup_start_lr=0.0,
-    save_interval_epochs=5,
-    lr_decay_epochs=[85, 135],
-    save_dir='output/ppyolo_r50vd_dcn',
-    use_vdl=True)