diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py index f29154fb9a4bb377211eb32cf939f77a89a4f6f9..004f60f071e50087fb69198fcdffb91345e1427a 100644 --- a/paddlex/cv/transforms/cls_transforms.py +++ b/paddlex/cv/transforms/cls_transforms.py @@ -18,7 +18,6 @@ import random import os.path as osp import numpy as np from PIL import Image, ImageEnhance -from .template import TemplateTransforms class ClsTransform: @@ -93,6 +92,12 @@ class Compose(ClsTransform): outputs = (im, label) return outputs + def add_augmenters(self, augmenters): + if not isinstance(augmenters, list): + raise Exception( + "augmenters should be list type in func add_augmenters()") + self.transforms = augmenters + self.transforms.transforms + class RandomCrop(ClsTransform): """对图像进行随机剪裁，模型训练时的数据增强操作。 @@ -464,7 +469,7 @@ class ArrangeClassifier(ClsTransform): return outputs -class BasicClsTransforms(TemplateTransforms): +class ComposedClsTransforms(Compose): """ 分类模型的基础Transforms流程，具体如下 训练阶段： 1. 随机从图像中crop一块子图，并resize成crop_size大小 @@ -487,7 +492,6 @@ class BasicClsTransforms(TemplateTransforms): crop_size=[224, 224], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): - super(TemplateClsTransforms, self).__init__(mode=mode) width = crop_size if isinstance(crop_size, list): if shape[0] != shape[1]: @@ -499,18 +503,19 @@ class BasicClsTransforms(TemplateTransforms): "In classifier model, width and height should be multiple of 32, e.g 224、256、320...." ) - if self.mode == 'train': + if mode == 'train': # 训练时的transforms，包含数据增强 - self.transforms = transforms.Compose([ - transforms.RandomCrop(crop_size=width), - transforms.RandomHorizontalFlip(prob=0.5), - transforms.Normalize( + transforms = [ + RandomCrop(crop_size=width), RandomHorizontalFlip(prob=0.5), + Normalize( mean=mean, std=std) - ]) + ] else: # 验证/预测时的transforms - self.transforms = transforms.Compose([ - transforms.ReiszeByShort(short_size=int(width * 1.14)), - transforms.CenterCrop(crop_size=width), transforms.Normalize( + transforms = [ + ReiszeByShort(short_size=int(width * 1.14)), + CenterCrop(crop_size=width), Normalize( mean=mean, std=std) - ]) + ] + + super(ComposedClsTransforms, self).__init__(transforms) diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index a353a9cecc4c718f1c9abb46e6e4ce7dbb05894f..d6e302443bb1ebe64c08533f145577bdcd5bb2cb 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -27,7 +27,6 @@ from PIL import Image, ImageEnhance from .imgaug_support import execute_imgaug from .ops import * from .box_utils import * -from .template import TemplateTransforms class DetTransform: @@ -153,6 +152,13 @@ class Compose(DetTransform): outputs = (im, im_info) return outputs + def add_augmenters(self, augmenters): + if not isinstance(augmenters, list): + raise Exception( + "augmenters should be list type in func add_augmenters()") + assert mode == 'train', "There should be exists augmenters while on train mode" + self.transforms = augmenters + self.transforms.transforms + class ResizeByShort(DetTransform): """根据图像的短边调整图像大小（resize）。 @@ -1230,7 +1236,7 @@ class ArrangeYOLOv3(DetTransform): return outputs -class BasicRCNNTransforms(TemplateTransforms): +class ComposedRCNNTransforms(Compose): """ RCNN模型(faster-rcnn/mask-rcnn)图像处理流程，具体如下， 训练阶段： 1. 随机以0.5的概率将图像水平翻转 @@ -1257,27 +1263,27 @@ class BasicRCNNTransforms(TemplateTransforms): min_max_size=[800, 1333], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): - super(RCNNTransforms, self).__init__(mode=mode) - if self.mode == 'train': + if mode == 'train': # 训练时的transforms，包含数据增强 - self.transforms = transforms.Compose([ - transforms.RandomHorizontalFlip(prob=0.5), - transforms.Normalize( - mean=mean, std=std), transforms.ResizeByShort( + transforms = [ + RandomHorizontalFlip(prob=0.5), Normalize( + mean=mean, std=std), ResizeByShort( short_size=min_max_size[0], max_size=min_max_size[1]), - transforms.Padding(coarsest_stride=32) - ]) + Padding(coarsest_stride=32) + ] else: # 验证/预测时的transforms - self.transforms = transforms.Compose([ - transforms.Normalize( - mean=mean, std=std), transforms.ResizeByShort( + transforms = [ + Normalize( + mean=mean, std=std), ResizeByShort( short_size=min_max_size[0], max_size=min_max_size[1]), - transforms.Padding(coarsest_stride=32) - ]) + Padding(coarsest_stride=32) + ] + super(RCNNTransforms, self).__init__(transforms) -class BasicYOLOTransforms(TemplateTransforms): + +class ComposedYOLOTransforms(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 @@ -1305,7 +1311,6 @@ class BasicYOLOTransforms(TemplateTransforms): mixup_epoch=250, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): - super(YOLOTransforms, self).__init__(mode=mode) width = shape if isinstance(shape, list): if shape[0] != shape[1]: @@ -1317,20 +1322,20 @@ class BasicYOLOTransforms(TemplateTransforms): "In YOLOv3 model, width and height should be multiple of 32, e.g 224、256、320...." ) - if self.mode == 'train': + if mode == 'train': # 训练时的transforms，包含数据增强 - self.transforms = transforms.Compose([ - transforms.MixupImage(mixup_epoch=mixup_epoch), - transforms.RandomDistort(), transforms.RandomExpand(), - transforms.RandomCrop(), transforms.Resize( - target_size=width, interp='RANDOM'), - transforms.RandomHorizontalFlip(), transforms.Normalize( - mean=mean, std=std) - ]) + transforms = [ + MixupImage(mixup_epoch=mixup_epoch), RandomDistort(), + RandomExpand(), RandomCrop(), Resize( + target_size=width, + interp='RANDOM'), RandomHorizontalFlip(), Normalize( + mean=mean, std=std) + ] else: # 验证/预测时的transforms - self.transforms = transforms.Compose([ - transforms.Resize( - target_size=width, interp='CUBIC'), transforms.Normalize( + transforms = [ + Resize( + target_size=width, interp='CUBIC'), Normalize( mean=mean, std=std) - ]) + ] + super(YOLOTransforms, self).__init__(transforms) diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index 0f0b684abd175019e1c004e9dc2591f34c119912..764dcd69bb3859ed59295898ec6a1876a73d25e7 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -1091,7 +1091,7 @@ class ArrangeSegmenter(SegTransform): return (im, ) -class BasicSegTransforms(TemplateTransforms): +class ComposedTransforms(Compose): """ 语义分割模型(UNet/DeepLabv3p)的图像处理流程，具体如下 训练阶段： 1. 随机对图像以0.5的概率水平翻转 @@ -1113,18 +1113,15 @@ class BasicSegTransforms(TemplateTransforms): train_crop_size=[769, 769], mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]): - super(TemplateSegTransforms, self).__init__(mode=mode) if self.mode == 'train': # 训练时的transforms，包含数据增强 - self.transforms = transforms.Compose([ - transforms.RandomHorizontalFlip(), - transforms.ResizeStepScaling(), - transforms.RandomPaddingCrop(crop_size=train_crop_size), - transforms.Normalize( + transforms = [ + RandomHorizontalFlip(prob=0.5), ResizeStepScaling(), + RandomPaddingCrop(crop_size=train_crop_size), Normalize( mean=mean, std=std) - ]) + ] else: # 验证/预测时的transforms - self.transforms = transforms.Compose( - [transforms.Normalize( - mean=mean, std=std)]) + transforms = [transforms.Normalize(mean=mean, std=std)] + + super(ComposedSegTransforms, self).__init__(transforms)