composed transforms enhancement

paddlex/cv/datasets/easydata_cls.py

@@ -39,14 +39,14 @@ class EasyDataCls(ImageNet):
             线程和'process'进程两种方式。默认为'process'（Windows和Mac下会强制使用thread，该参数无效）。
         shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
     """
-    
+
     def __init__(self,
                  data_dir,
                  file_list,
                  label_list,
                  transforms=None,
                  num_workers='auto',
-                 buffer_size=100,
+                 buffer_size=8,
                  parallel_method='process',
                  shuffle=False):
         super(ImageNet, self).__init__(
@@ -58,7 +58,7 @@ class EasyDataCls(ImageNet):
         self.file_list = list()
         self.labels = list()
         self._epoch = 0
-        
+
         with open(label_list, encoding=get_encoding(label_list)) as f:
             for line in f:
                 item = line.strip()
@@ -73,8 +73,8 @@ class EasyDataCls(ImageNet):
                 if not osp.isfile(json_file):
                     continue
                 if not osp.exists(img_file):
-                    raise IOError(
-                        'The image file {} is not exist!'.format(img_file))
+                    raise IOError('The image file {} is not exist!'.format(
+                        img_file))
                 with open(json_file, mode='r', \
                           encoding=get_encoding(json_file)) as j:
                     json_info = json.load(j)
@@ -83,4 +83,3 @@ class EasyDataCls(ImageNet):
         self.num_samples = len(self.file_list)
         logging.info("{} samples in file {}".format(
             len(self.file_list), file_list))
-    
\ No newline at end of file

paddlex/cv/datasets/imagenet.py

@@ -45,7 +45,7 @@ class ImageNet(Dataset):
                  label_list,
                  transforms=None,
                  num_workers='auto',
-                 buffer_size=100,
+                 buffer_size=8,
                  parallel_method='process',
                  shuffle=False):
         super(ImageNet, self).__init__(
@@ -70,8 +70,8 @@ class ImageNet(Dataset):
                     continue
                 full_path = osp.join(data_dir, items[0])
                 if not osp.exists(full_path):
-                    raise IOError(
-                        'The image file {} is not exist!'.format(full_path))
+                    raise IOError('The image file {} is not exist!'.format(
+                        full_path))
                 self.file_list.append([full_path, int(items[1])])
         self.num_samples = len(self.file_list)
         logging.info("{} samples in file {}".format(

paddlex/cv/transforms/cls_transforms.py

@@ -70,8 +70,8 @@ class Compose(ClsTransform):
         if isinstance(im, np.ndarray):
             if len(im.shape) != 3:
                 raise Exception(
-                    "im should be 3-dimension, but now is {}-dimensions".
-                    format(len(im.shape)))
+                    "im should be 3-dimension, but now is {}-dimensions".format(
+                        len(im.shape)))
         else:
             try:
                 im = cv2.imread(im).astype('float32')
@@ -100,7 +100,9 @@ class Compose(ClsTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                logging.error(
+                    "{} is already in ComposedTransforms, need to remove it from add_augmenters().".
+                    format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
@@ -139,8 +141,8 @@ class RandomCrop(ClsTransform):
             tuple: 当label为空时，返回的tuple为(im, )，对应图像np.ndarray数据；
                    当label不为空时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、图像类别id。
         """
-        im = random_crop(im, self.crop_size, self.lower_scale,
-                         self.lower_ratio, self.upper_ratio)
+        im = random_crop(im, self.crop_size, self.lower_scale, self.lower_ratio,
+                         self.upper_ratio)
         if label is None:
             return (im, )
         else:
@@ -270,14 +272,12 @@ class ResizeByShort(ClsTransform):
         im_short_size = min(im.shape[0], im.shape[1])
         im_long_size = max(im.shape[0], im.shape[1])
         scale = float(self.short_size) / im_short_size
-        if self.max_size > 0 and np.round(scale *
-                                          im_long_size) > self.max_size:
+        if self.max_size > 0 and np.round(scale * im_long_size) > self.max_size:
             scale = float(self.max_size) / float(im_long_size)
         resized_width = int(round(im.shape[1] * scale))
         resized_height = int(round(im.shape[0] * scale))
         im = cv2.resize(
-            im, (resized_width, resized_height),
-            interpolation=cv2.INTER_LINEAR)
+            im, (resized_width, resized_height), interpolation=cv2.INTER_LINEAR)
 
         if label is None:
             return (im, )
@@ -490,13 +490,15 @@ class ComposedClsTransforms(Compose):
             crop_size(int|list): 输入模型里的图像大小
             mean(list): 图像均值
             std(list): 图像方差
+            random_horizontal_flip(bool): 是否以0.5的概率使用随机水平翻转增强，该仅在mode为`train`时生效，默认为True
     """
 
     def __init__(self,
                  mode,
                  crop_size=[224, 224],
                  mean=[0.485, 0.456, 0.406],
-                 std=[0.229, 0.224, 0.225]):
+                 std=[0.229, 0.224, 0.225],
+                 random_horizontal_flip=True):
         width = crop_size
         if isinstance(crop_size, list):
             if crop_size[0] != crop_size[1]:
@@ -512,10 +514,11 @@ class ComposedClsTransforms(Compose):
         if mode == 'train':
             # 训练时的transforms，包含数据增强
             transforms = [
-                RandomCrop(crop_size=width), RandomHorizontalFlip(prob=0.5),
-                Normalize(
+                RandomCrop(crop_size=width), Normalize(
                     mean=mean, std=std)
             ]
+            if random_horizontal_flip:
+                transforms.insert(0, RandomHorizontalFlip())
         else:
             # 验证/预测时的transforms
             transforms = [

paddlex/cv/transforms/det_transforms.py

@@ -160,7 +160,9 @@ class Compose(DetTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                logging.error(
+                    "{} is already in ComposedTransforms, need to remove it from add_augmenters().".
+                    format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
@@ -220,15 +222,13 @@ class ResizeByShort(DetTransform):
         im_short_size = min(im.shape[0], im.shape[1])
         im_long_size = max(im.shape[0], im.shape[1])
         scale = float(self.short_size) / im_short_size
-        if self.max_size > 0 and np.round(scale *
-                                          im_long_size) > self.max_size:
+        if self.max_size > 0 and np.round(scale * im_long_size) > self.max_size:
             scale = float(self.max_size) / float(im_long_size)
         resized_width = int(round(im.shape[1] * scale))
         resized_height = int(round(im.shape[0] * scale))
         im_resize_info = [resized_height, resized_width, scale]
         im = cv2.resize(
-            im, (resized_width, resized_height),
-            interpolation=cv2.INTER_LINEAR)
+            im, (resized_width, resized_height), interpolation=cv2.INTER_LINEAR)
         im_info['im_resize_info'] = np.array(im_resize_info).astype(np.float32)
         if label_info is None:
             return (im, im_info)
@@ -268,8 +268,7 @@ class Padding(DetTransform):
                 if not isinstance(target_size, tuple) and not isinstance(
                         target_size, list):
                     raise TypeError(
-                        "Padding: Type of target_size must in (int|list|tuple)."
-                    )
+                        "Padding: Type of target_size must in (int|list|tuple).")
                 elif len(target_size) != 2:
                     raise ValueError(
                         "Padding: Length of target_size must equal 2.")
@@ -454,8 +453,7 @@ class RandomHorizontalFlip(DetTransform):
             ValueError: 数据长度不匹配。
         """
         if not isinstance(im, np.ndarray):
-            raise TypeError(
-                "RandomHorizontalFlip: image is not a numpy array.")
+            raise TypeError("RandomHorizontalFlip: image is not a numpy array.")
         if len(im.shape) != 3:
             raise ValueError(
                 "RandomHorizontalFlip: image is not 3-dimensional.")
@@ -736,7 +734,7 @@ class MixupImage(DetTransform):
             gt_poly2 = im_info['mixup'][2]['gt_poly']
         is_crowd1 = label_info['is_crowd']
         is_crowd2 = im_info['mixup'][2]['is_crowd']
-        
+
         if 0 not in gt_class1 and 0 not in gt_class2:
             gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
             gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
@@ -785,9 +783,7 @@ class RandomExpand(DetTransform):
         fill_value (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
     """
 
-    def __init__(self,
-                 ratio=4.,
-                 prob=0.5,
+    def __init__(self, ratio=4., prob=0.5,
                  fill_value=[123.675, 116.28, 103.53]):
         super(RandomExpand, self).__init__()
         assert ratio > 1.01, "expand ratio must be larger than 1.01"
@@ -1281,21 +1277,25 @@ class ComposedRCNNTransforms(Compose):
             min_max_size(list): 图像在缩放时，最小边和最大边的约束条件
             mean(list): 图像均值
             std(list): 图像方差
+            random_horizontal_flip(bool): 是否以0.5的概率使用随机水平翻转增强，该仅在mode为`train`时生效，默认为True
     """
 
     def __init__(self,
                  mode,
                  min_max_size=[800, 1333],
                  mean=[0.485, 0.456, 0.406],
-                 std=[0.229, 0.224, 0.225]):
+                 std=[0.229, 0.224, 0.225],
+                 random_horizontal_flip=True):
         if mode == 'train':
             # 训练时的transforms，包含数据增强
             transforms = [
-                RandomHorizontalFlip(prob=0.5), Normalize(
+                Normalize(
                     mean=mean, std=std), ResizeByShort(
                         short_size=min_max_size[0], max_size=min_max_size[1]),
                 Padding(coarsest_stride=32)
             ]
+            if random_horizontal_flip:
+                transforms.insert(0, RandomHorizontalFlip())
         else:
             # 验证/预测时的transforms
             transforms = [
@@ -1325,9 +1325,14 @@ class ComposedYOLOv3Transforms(Compose):
         Args:
             mode(str): 图像处理流程所处阶段，训练/验证/预测，分别对应'train', 'eval', 'test'
             shape(list): 输入模型中图像的大小，输入模型的图像会被Resize成此大小
-            mixup_epoch(int): 模型训练过程中，前mixup_epoch会使用mixup策略
+            mixup_epoch(int): 模型训练过程中，前mixup_epoch会使用mixup策略, 若设为-1，则表示不使用该策略
             mean(list): 图像均值
             std(list): 图像方差
+            random_distort(bool): 数据增强方式，参数仅在mode为`train`时生效，表示是否在训练过程中随机扰动图像，默认为True
+            random_expand(bool): 数据增强方式，参数仅在mode为`train`时生效，表示是否在训练过程中随机扩张图像，默认为True
+            random_crop(bool): 数据增强方式，参数仅在mode为`train`时生效，表示是否在训练过程中随机裁剪图像，默认为True
+            random_horizontal_flip(bool): 数据增强方式，参数仅在mode为`train`时生效，表示是否在训练过程中随机水平翻转图像，默认为True
+
     """
 
     def __init__(self,
@@ -1335,7 +1340,11 @@ class ComposedYOLOv3Transforms(Compose):
                  shape=[608, 608],
                  mixup_epoch=250,
                  mean=[0.485, 0.456, 0.406],
-                 std=[0.229, 0.224, 0.225]):
+                 std=[0.229, 0.224, 0.225],
+                 random_distort=True,
+                 random_expand=True,
+                 random_crop=True,
+                 random_horizontal_flip=True):
         width = shape
         if isinstance(shape, list):
             if shape[0] != shape[1]:
@@ -1350,12 +1359,18 @@ class ComposedYOLOv3Transforms(Compose):
         if mode == 'train':
             # 训练时的transforms，包含数据增强
             transforms = [
-                MixupImage(mixup_epoch=mixup_epoch), RandomDistort(),
-                RandomExpand(), RandomCrop(), Resize(
-                    target_size=width,
-                    interp='RANDOM'), RandomHorizontalFlip(), Normalize(
+                MixupImage(mixup_epoch=mixup_epoch), Resize(
+                    target_size=width, interp='RANDOM'), Normalize(
                         mean=mean, std=std)
             ]
+            if random_horizontal_flip:
+                transforms.insert(1, RandomHorizontalFlip())
+            if random_crop:
+                transforms.insert(1, RandomCrop())
+            if random_expand:
+                transforms.insert(1, RandomExpand())
+            if random_distort:
+                transforms.insert(1, RandomDistort())
         else:
             # 验证/预测时的transforms
             transforms = [

paddlex/cv/transforms/seg_transforms.py

@@ -116,7 +116,9 @@ class Compose(SegTransform):
         transform_names = [type(x).__name__ for x in self.transforms]
         for aug in augmenters:
             if type(aug).__name__ in transform_names:
-                logging.error("{} is already in ComposedTransforms, need to remove it from add_augmenters().".format(type(aug).__name__))
+                logging.error(
+                    "{} is already in ComposedTransforms, need to remove it from add_augmenters().".
+                    format(type(aug).__name__))
         self.transforms = augmenters + self.transforms
 
 
@@ -401,8 +403,7 @@ class ResizeByShort(SegTransform):
         im_short_size = min(im.shape[0], im.shape[1])
         im_long_size = max(im.shape[0], im.shape[1])
         scale = float(self.short_size) / im_short_size
-        if self.max_size > 0 and np.round(scale *
-                                          im_long_size) > self.max_size:
+        if self.max_size > 0 and np.round(scale * im_long_size) > self.max_size:
             scale = float(self.max_size) / float(im_long_size)
         resized_width = int(round(im.shape[1] * scale))
         resized_height = int(round(im.shape[0] * scale))
@@ -1113,25 +1114,35 @@ class ComposedSegTransforms(Compose):
 
         Args:
             mode(str): 图像处理所处阶段，训练/验证/预测，分别对应'train', 'eval', 'test'
-            train_crop_size(list): 模型训练阶段，随机从原图crop的大小
+            min_max_size(list): 训练过程中，图像的最长边会随机resize至此区间（短边按比例相应resize)；预测阶段，图像最长边会resize至此区间中间值，即(min_size+max_size)/2。默认为[400, 600]
+            train_crop_size(list): 仅在mode为'train`时生效，训练过程中，随机从图像中裁剪出对应大小的子图（如若原图小于此大小，则会padding到此大小)，默认为[400, 600]
             mean(list): 图像均值
             std(list): 图像方差
+            random_horizontal_flip(bool): 数据增强方式，仅在mode为`train`时生效，表示训练过程是否随机水平翻转图像，默认为True
     """
 
     def __init__(self,
                  mode,
-                 train_crop_size=[769, 769],
+                 min_max_size=[400, 600],
+                 train_crop_size=[512, 512],
                  mean=[0.5, 0.5, 0.5],
-                 std=[0.5, 0.5, 0.5]):
+                 std=[0.5, 0.5, 0.5],
+                 random_horizontal_flip=True):
         if mode == 'train':
             # 训练时的transforms，包含数据增强
             transforms = [
-                RandomHorizontalFlip(prob=0.5), ResizeStepScaling(),
+                ResizeRangeScaling(
+                    min_value=min(min_max_size), max_value=max(min_max_size)),
                 RandomPaddingCrop(crop_size=train_crop_size), Normalize(
                     mean=mean, std=std)
             ]
+            if random_horizontal_flip:
+                transforms.insert(0, RandomHorizontalFlip())
         else:
             # 验证/预测时的transforms
-            transforms = [Normalize(mean=mean, std=std)]
-
+            long_size = (min(min_max_size) + max(min_max_size)) // 2
+            transforms = [
+                ResizeByLong(long_size=long_size), Normalize(
+                    mean=mean, std=std)
+            ]
         super(ComposedSegTransforms, self).__init__(transforms)