fix composed transforms

docs/apis/transforms/det_transforms.md

@@ -232,12 +232,12 @@ eval_transforms = transforms.Composed([
 ```
 
 
-## ComposedYOLOTransforms类
+## ComposedYOLOv3Transforms类
 ```python
-paddlex.det.transforms.ComposedYOLOTransforms(mode, shape=[608, 608], mixup_epoch=250, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+paddlex.det.transforms.ComposedYOLOv3Transforms(mode, shape=[608, 608], mixup_epoch=250, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 ```
-目标检测YOLOv3模型中已经组合好的数据处理流程，开发者可以直接使用ComposedYOLOTransforms，简化手动组合transforms的过程, 该类中已经包含了[MixupImage](#MixupImage)、[RandomDistort](#RandomDistort)、[RandomExpand](#RandomExpand)、[RandomCrop](#RandomCrop)、[RandomHorizontalFlip](#RandomHorizontalFlip)5种数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。  
-ComposedYOLOTransforms共包括以下几个步骤：
+目标检测YOLOv3模型中已经组合好的数据处理流程，开发者可以直接使用ComposedYOLOv3Transforms，简化手动组合transforms的过程, 该类中已经包含了[MixupImage](#MixupImage)、[RandomDistort](#RandomDistort)、[RandomExpand](#RandomExpand)、[RandomCrop](#RandomCrop)、[RandomHorizontalFlip](#RandomHorizontalFlip)5种数据增强方式，你仍可以通过[add_augmenters函数接口](#add_augmenters)添加新的数据增强方式。  
+ComposedYOLOv3Transforms共包括以下几个步骤：
 > 训练阶段：
 > > 1. 在前mixup_epoch轮迭代中，使用MixupImage策略
 > > 2. 对图像进行随机扰动，包括亮度，对比度，饱和度和色调
@@ -259,7 +259,7 @@ ComposedYOLOTransforms共包括以下几个步骤：
 
 ### 添加数据增强方式
 ```python
-ComposedYOLOTransforms.add_augmenters(augmenters)
+ComposedYOLOv3Transforms.add_augmenters(augmenters)
 ```
 > **参数**
 > * **augmenters**(list): 数据增强方式列表
@@ -268,8 +268,8 @@ ComposedYOLOTransforms.add_augmenters(augmenters)
 ```
 import paddlex as pdx
 from paddlex.det import transforms
-train_transforms = transforms.ComposedYOLOTransforms(mode='train', shape=[480, 480])
-eval_transforms = transforms.ComposedYOLOTransforms(mode='eval', shape=[480, 480])
+train_transforms = transforms.ComposedYOLOv3Transforms(mode='train', shape=[480, 480])
+eval_transforms = transforms.ComposedYOLOv3Transforms(mode='eval', shape=[480, 480])
 
 # 添加数据增强
 import imgaug.augmenters as iaa

new_tutorials/train/classification/mobilenetv2.py

@@ -10,18 +10,12 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 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.Normalize()
-])
-eval_transforms = transforms.Compose([
-    transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224),
-    transforms.Normalize()
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/cls_transforms.html#composedclstransforms
+train_transforms = transforms.ComposedClsTransforms(mode='train', crop_size=[224, 224])
+eval_transforms = transforms.ComposedClsTransforms(mode='eval', crop_size=[224, 224])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/classification.html#imagenet
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -39,6 +33,8 @@ eval_dataset = pdx.datasets.ImageNet(
 # VisualDL启动方式: visualdl --logdir output/mobilenetv2/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/classification.html#resnet50
 model = pdx.cls.MobileNetV2(num_classes=len(train_dataset.labels))
 model.train(
     num_epochs=10,

new_tutorials/train/classification/resnet50.py

@@ -11,16 +11,12 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 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.Normalize()])
-eval_transforms = transforms.Compose([
-    transforms.ResizeByShort(short_size=256),
-    transforms.CenterCrop(crop_size=224),
-    transforms.Normalize()
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/cls_transforms.html#composedclstransforms
+train_transforms = transforms.ComposedClsTransforms(mode='train', crop_size=[224, 224])
+eval_transforms = transforms.ComposedClsTransforms(mode='eval', crop_size=[224, 224])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/classification.html#imagenet
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -47,6 +43,8 @@ optimizer = fluid.optimizer.Momentum(
 # VisualDL启动方式: visualdl --logdir output/resnet50/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/classification.html#resnet50
 model = pdx.cls.ResNet50(num_classes=len(train_dataset.labels))
 model.train(
     num_epochs=10,

new_tutorials/train/detection/faster_rcnn_r50_fpn.py

@@ -10,20 +10,12 @@ insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 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)
-])
-
-eval_transforms = transforms.Compose([
-    transforms.Normalize(),
-    transforms.ResizeByShort(short_size=800, max_size=1333),
-    transforms.Padding(coarsest_stride=32),
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedrcnntransforms
+train_transforms = transforms.ComposedRCNNTransforms(mode='train', min_max_size=[800, 1333])
+eval_transforms = transforms.ComposedRCNNTransforms(mode='eval', min_max_size=[800, 1333])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#vocdetection
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -42,6 +34,8 @@ eval_dataset = pdx.datasets.VOCDetection(
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
 # num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#fasterrcnn
 num_classes = len(train_dataset.labels) + 1
 model = pdx.det.FasterRCNN(num_classes=num_classes)
 model.train(

new_tutorials/train/detection/mask_rcnn_r50_fpn.py

@@ -10,20 +10,12 @@ xiaoduxiong_dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_de
 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)
-])
-
-eval_transforms = transforms.Compose([
-    transforms.Normalize(),
-    transforms.ResizeByShort(short_size=800, max_size=1333),
-    transforms.Padding(coarsest_stride=32)
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedrcnntransforms
+train_transforms = transforms.ComposedRCNNTransforms(mode='train', min_max_size=[800, 1333])
+eval_transforms = transforms.ComposedRCNNTransforms(mode='eval', min_max_size=[800, 1333])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#cocodetection
 train_dataset = pdx.datasets.CocoDetection(
     data_dir='xiaoduxiong_ins_det/JPEGImages',
     ann_file='xiaoduxiong_ins_det/train.json',
@@ -40,6 +32,8 @@ eval_dataset = pdx.datasets.CocoDetection(
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
 # num_classes 需要设置为包含背景类的类别数，即: 目标类别数量 + 1
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/instance_segmentation.html#maskrcnn
 num_classes = len(train_dataset.labels) + 1
 model = pdx.det.MaskRCNN(num_classes=num_classes)
 model.train(

new_tutorials/train/detection/yolov3_darknet53.py

@@ -10,22 +10,12 @@ insect_dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(insect_dataset, path='./')
 
 # 定义训练和验证时的transforms
-train_transforms = transforms.Compose([
-    transforms.MixupImage(mixup_epoch=250),
-    transforms.RandomDistort(),
-    transforms.RandomExpand(),
-    transforms.RandomCrop(),
-    transforms.Resize(target_size=608, interp='RANDOM'),
-    transforms.RandomHorizontalFlip(),
-    transforms.Normalize(),
-])
-
-eval_transforms = transforms.Compose([
-    transforms.Resize(target_size=608, interp='CUBIC'),
-    transforms.Normalize(),
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#composedyolotransforms
+train_transforms = transforms.ComposedYOLOv3Transforms(mode='train', shape=[608, 608])
+eval_transforms = transforms.ComposedYOLOv3Transforms(mode='eva', shape=[608, 608])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/detection.html#vocdetection
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -43,6 +33,8 @@ eval_dataset = pdx.datasets.VOCDetection(
 # VisualDL启动方式: visualdl --logdir output/yolov3_darknet/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#yolov3
 num_classes = len(train_dataset.labels)
 model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 model.train(

new_tutorials/train/segmentation/deeplabv3p.py

@@ -10,17 +10,16 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 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.Resize(target_size=512),
-    transforms.RandomPaddingCrop(crop_size=500),
-    transforms.Normalize()
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
+train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
+eval_transforms = transforms.ComposedSegTransforms(mode='eval')
 
-eval_transforms = transforms.Compose(
-    [transforms.Resize(512), transforms.Normalize()])
+train_transforms.add_augmenters([
+    transforms.RandomRotate()
+])
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -38,6 +37,8 @@ eval_dataset = pdx.datasets.SegDataset(
 # VisualDL启动方式: visualdl --logdir output/deeplab/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#deeplabv3p
 num_classes = len(train_dataset.labels)
 model = pdx.seg.DeepLabv3p(num_classes=num_classes)
 model.train(

new_tutorials/train/segmentation/hrnet.py

@@ -10,17 +10,12 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 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()
-])
-
-eval_transforms = transforms.Compose([
-    transforms.ResizeByLong(long_size=512),
-    transforms.Padding(target_size=512), transforms.Normalize()
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
+train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
+eval_transforms = transforms.ComposedSegTransforms(mode='eval')
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -38,6 +33,8 @@ eval_dataset = pdx.datasets.SegDataset(
 # VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#hrnet
 num_classes = len(train_dataset.labels)
 model = pdx.seg.HRNet(num_classes=num_classes)
 model.train(

new_tutorials/train/segmentation/unet.py

@@ -10,20 +10,12 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 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()
-])
-
-eval_transforms = transforms.Compose([
-    transforms.ResizeByLong(long_size=512),
-    transforms.Padding(target_size=512),
-    transforms.Normalize()
-])
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/seg_transforms.html#composedsegtransforms
+train_transforms = transforms.ComposedSegTransforms(mode='train', train_crop_size=[769, 769])
+eval_transforms = transforms.ComposedSegTransforms(mode='eval')
 
 # 定义训练和验证所用的数据集
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/datasets/semantic_segmentation.html#segdataset
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -41,6 +33,8 @@ eval_dataset = pdx.datasets.SegDataset(
 # VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001
 # 浏览器打开 https://0.0.0.0:8001即可
 # 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
+
+# API说明: https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#unet
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 model.train(

paddlex/cv/models/deeplabv3p.py

@@ -337,7 +337,8 @@ class DeepLabv3p(BaseAPI):
             for d in data:
                 padding_label = np.zeros(
                     (1, im_h, im_w)).astype('int64') + self.ignore_index
-                padding_label[:, :im_h, :im_w] = d[1]
+                _, label_h, label_w = d[1].shape
+                padding_label[:, :label_h, :label_w] = d[1]
                 labels.append(padding_label)
             labels = np.array(labels)
 

paddlex/cv/transforms/det_transforms.py

@@ -1287,7 +1287,7 @@ class ComposedRCNNTransforms(Compose):
         super(ComposedRCNNTransforms, self).__init__(transforms)
 
 
-class ComposedYOLOTransforms(Compose):
+class ComposedYOLOv3Transforms(Compose):
     """YOLOv3模型的图像预处理流程，具体如下，
         训练阶段：
         1. 在前mixup_epoch轮迭代中，使用MixupImage策略，见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#mixupimage
@@ -1342,4 +1342,4 @@ class ComposedYOLOTransforms(Compose):
                     target_size=width, interp='CUBIC'), Normalize(
                         mean=mean, std=std)
             ]
-        super(ComposedYOLOTransforms, self).__init__(transforms)
+        super(ComposedYOLOv3Transforms, self).__init__(transforms)