beauty tutorials' code

tutorials/train/image_classification/alexnet.py

@@ -7,12 +7,14 @@ pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(crop_size=224), 
+    transforms.RandomHorizontalFlip(),
     transforms.Normalize()
 ])
 eval_transforms = transforms.Compose([
     transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.Normalize()
+    transforms.CenterCrop(crop_size=224), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/image_classification/mobilenetv2.py

@@ -8,12 +8,14 @@ pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(crop_size=224), 
+    transforms.RandomHorizontalFlip(),
     transforms.Normalize()
 ])
 eval_transforms = transforms.Compose([
     transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.Normalize()
+    transforms.CenterCrop(crop_size=224), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/image_classification/mobilenetv3_small_ssld.py

@@ -8,12 +8,14 @@ pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(crop_size=224), 
+    transforms.RandomHorizontalFlip(),
     transforms.Normalize()
 ])
 eval_transforms = transforms.Compose([
     transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.Normalize()
+    transforms.CenterCrop(crop_size=224), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/image_classification/resnet50_vd_ssld.py

@@ -8,12 +8,14 @@ pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(crop_size=224), 
+    transforms.RandomHorizontalFlip(),
     transforms.Normalize()
 ])
 eval_transforms = transforms.Compose([
     transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.Normalize()
+    transforms.CenterCrop(crop_size=224), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/image_classification/shufflenetv2.py

@@ -8,12 +8,14 @@ pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomCrop(crop_size=224), transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(crop_size=224), 
+    transforms.RandomHorizontalFlip(),
     transforms.Normalize()
 ])
 eval_transforms = transforms.Compose([
     transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224), transforms.Normalize()
+    transforms.CenterCrop(crop_size=224), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py

@@ -11,15 +11,15 @@ pdx.utils.download_and_decompress(xiaoduxiong_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)
+    transforms.RandomHorizontalFlip(), 
+    transforms.Normalize(),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 eval_transforms = transforms.Compose([
     transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333),
+    transforms.ResizeByShort(short_size=800, max_size=1333),
     transforms.Padding(coarsest_stride=32),
 ])
 

tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py

@@ -11,16 +11,16 @@ pdx.utils.download_and_decompress(xiaoduxiong_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)
+    transforms.RandomHorizontalFlip(), 
+    transforms.Normalize(),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 eval_transforms = transforms.Compose([
     transforms.Normalize(), 
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333),
-    transforms.Padding(coarsest_stride=32),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/object_detection/faster_rcnn_hrnet_fpn.py

@@ -11,16 +11,16 @@ pdx.utils.download_and_decompress(insect_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)
+    transforms.RandomHorizontalFlip(), 
+    transforms.Normalize(),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 eval_transforms = transforms.Compose([
     transforms.Normalize(), 
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333),
-    transforms.Padding(coarsest_stride=32),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/object_detection/faster_rcnn_r50_fpn.py

@@ -8,15 +8,15 @@ pdx.utils.download_and_decompress(insect_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333), transforms.Padding(coarsest_stride=32)
+    transforms.RandomHorizontalFlip(), 
+    transforms.Normalize(),
+    transforms.ResizeByShort(short_size=800, max_size=1333), 
+    transforms.Padding(coarsest_stride=32)
 ])
 
 eval_transforms = transforms.Compose([
     transforms.Normalize(),
-    transforms.ResizeByShort(
-        short_size=800, max_size=1333),
+    transforms.ResizeByShort(short_size=800, max_size=1333),
     transforms.Padding(coarsest_stride=32),
 ])
 # 定义训练和验证所用的数据集

tutorials/train/object_detection/yolov3_darknet53.py

@@ -12,16 +12,14 @@ train_transforms = transforms.Compose([
     transforms.RandomDistort(),
     transforms.RandomExpand(), 
     transforms.RandomCrop(), 
-    transforms.Resize(
-        target_size=608, interp='RANDOM'),
+    transforms.Resize(target_size=608, interp='RANDOM'), 
     transforms.RandomHorizontalFlip(),
-    transforms.Normalize(),
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.Resize(
-        target_size=608, interp='CUBIC'),
-    transforms.Normalize(),
+    transforms.Resize(target_size=608, interp='CUBIC'), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/object_detection/yolov3_mobilenetv1.py

@@ -12,15 +12,13 @@ train_transforms = transforms.Compose([
     transforms.RandomDistort(),
     transforms.RandomExpand(),
     transforms.RandomCrop(),
-    transforms.Resize(
-        target_size=608, interp='RANDOM'),
+    transforms.Resize(target_size=608, interp='RANDOM'),
     transforms.RandomHorizontalFlip(),
     transforms.Normalize(),
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.Resize(
-        target_size=608, interp='CUBIC'),
+    transforms.Resize(target_size=608, interp='CUBIC'),
     transforms.Normalize(),
 ])
 

tutorials/train/object_detection/yolov3_mobilenetv3.py

@@ -12,16 +12,14 @@ train_transforms = transforms.Compose([
     transforms.RandomDistort(),
     transforms.RandomExpand(), 
     transforms.RandomCrop(), 
-    transforms.Resize(
-        target_size=608, interp='RANDOM'),
+    transforms.Resize(target_size=608, interp='RANDOM'), 
     transforms.RandomHorizontalFlip(),
-    transforms.Normalize(),
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.Resize(
-        target_size=608, interp='CUBIC'),
-    transforms.Normalize(),
+    transforms.Resize(target_size=608, interp='CUBIC'), 
+    transforms.Normalize()
 ])
 
 # 定义训练和验证所用的数据集

tutorials/train/semantic_segmentation/deeplabv3p_mobilenetv2.py

@@ -11,12 +11,15 @@ pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(),
-    transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize()
+    transforms.RandomHorizontalFlip(), 
+    transforms.ResizeRangeScaling(),
+    transforms.RandomPaddingCrop(crop_size=512), 
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.ResizeByLong(long_size=512), transforms.Padding(target_size=512),
+    transforms.ResizeByLong(long_size=512), 
+    transforms.Padding(target_size=512),
     transforms.Normalize()
 ])
 

tutorials/train/semantic_segmentation/fast_scnn.py

@@ -12,12 +12,15 @@ pdx.utils.download_and_decompress(optic_dataset, path='./')
 # 定义训练和验证时的transforms
 # API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(),
-    transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize()
+    transforms.RandomHorizontalFlip(), 
+    transforms.ResizeRangeScaling(),
+    transforms.RandomPaddingCrop(crop_size=512), 
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.ResizeByLong(long_size=512), transforms.Padding(target_size=512),
+    transforms.ResizeByLong(long_size=512), 
+    transforms.Padding(target_size=512),
     transforms.Normalize()
 ])
 

tutorials/train/semantic_segmentation/hrnet.py

@@ -11,12 +11,15 @@ pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(),
-    transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize()
+    transforms.RandomHorizontalFlip(), 
+    transforms.ResizeRangeScaling(),
+    transforms.RandomPaddingCrop(crop_size=512), 
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([
-    transforms.ResizeByLong(long_size=512), transforms.Padding(target_size=512),
+    transforms.ResizeByLong(long_size=512), 
+    transforms.Padding(target_size=512),
     transforms.Normalize()
 ])
 

tutorials/train/semantic_segmentation/unet.py

@@ -11,8 +11,10 @@ pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
 train_transforms = transforms.Compose([
-    transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(),
-    transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize()
+    transforms.RandomHorizontalFlip(), 
+    transforms.ResizeRangeScaling(),
+    transforms.RandomPaddingCrop(crop_size=512), 
+    transforms.Normalize()
 ])
 
 eval_transforms = transforms.Compose([