add yolo with iou aware loss

paddlex/cv/transforms/det_transforms.py

@@ -1422,3 +1422,105 @@ class GenerateYoloTarget(DetTransform):
             batch_data[data_id] = tuple(data_list)
         return batch_data           
 
+
+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
+        2. 图像归一化
+        3. 图像按比例Resize，scale计算方式如下
+            scale = min_max_size[0] / short_size_of_image
+            if max_size_of_image * scale > min_max_size[1]:
+                scale = min_max_size[1] / max_size_of_image
+        4. 将3步骤的长宽进行padding，使得长宽为32的倍数
+        验证阶段：
+        1. 图像归一化
+        2. 图像按比例Resize，scale计算方式同上训练阶段
+        3. 将2步骤的长宽进行padding，使得长宽为32的倍数
+        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'
+            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
+    """
+
+    def __init__(self,
+                 mode,
+                 min_max_size=[800, 1333],
+                 mean=[0.485, 0.456, 0.406],
+                 std=[0.229, 0.224, 0.225]):
+        if mode == 'train':
+            # 训练时的transforms，包含数据增强
+            transforms = [
+                RandomHorizontalFlip(prob=0.5), Normalize(
+                    mean=mean, std=std), ResizeByShort(
+                        short_size=min_max_size[0], max_size=min_max_size[1]),
+                Padding(coarsest_stride=32)
+            ]
+        else:
+            # 验证/预测时的transforms
+            transforms = [
+                Normalize(
+                    mean=mean, std=std), ResizeByShort(
+                        short_size=min_max_size[0], max_size=min_max_size[1]),
+                Padding(coarsest_stride=32)
+            ]
+
+        super(ComposedRCNNTransforms, self).__init__(transforms)
+
+
+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
+        2. 对图像进行随机扰动，包括亮度，对比度，饱和度和色调
+        3. 随机扩充图像，见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#randomexpand
+        4. 随机裁剪图像
+        5. 将4步骤的输出图像Resize成shape参数的大小
+        6. 随机0.5的概率水平翻转图像
+        7. 图像归一化
+        验证/预测阶段：
+        1. 将图像Resize成shape参数大小
+        2. 图像归一化
+        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
+            mean(list): 图像均值
+            std(list): 图像方差
+    """
+
+    def __init__(self,
+                 mode,
+                 shape=[608, 608],
+                 mixup_epoch=250,
+                 mean=[0.485, 0.456, 0.406],
+                 std=[0.229, 0.224, 0.225]):
+        width = shape
+        if isinstance(shape, list):
+            if shape[0] != shape[1]:
+                raise Exception(
+                    "In YOLOv3 model, width and height should be equal")
+            width = shape[0]
+        if width % 32 != 0:
+            raise Exception(
+                "In YOLOv3 model, width and height should be multiple of 32, e.g 224、256、320...."
+            )
+
+        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(
+                        mean=mean, std=std)
+            ]
+        else:
+            # 验证/预测时的transforms
+            transforms = [
+                Resize(
+                    target_size=width, interp='CUBIC'), Normalize(
+                        mean=mean, std=std)
+            ]
+        super(ComposedYOLOTransforms, self).__init__(transforms)
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