rm ArrageSegmeter

dygraph/infer.py

@@ -111,7 +111,7 @@ def infer(model, data_dir=None, test_list=None, model_dir=None,
     for file in tqdm.tqdm(files):
         file = file.strip()
         im_file = osp.join(data_dir, file)
-        im, im_info = transforms(im_file)
+        im, im_info, _ = transforms(im_file)
         im = np.expand_dims(im, axis=0)
         im = to_variable(im)
 
@@ -140,17 +140,8 @@ def infer(model, data_dir=None, test_list=None, model_dir=None,
         cv2.imwrite(pred_saved_path, pred_im)
 
 
-def arrange_transform(transforms, mode='train'):
-    arrange_transform = T.ArrangeSegmenter
-    if type(transforms.transforms[-1]).__name__.startswith('Arrange'):
-        transforms.transforms[-1] = arrange_transform(mode=mode)
-    else:
-        transforms.transforms.append(arrange_transform(mode=mode))
-
-
 def main(args):
     test_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-    arrange_transform(test_transforms, mode='test')
 
     if args.model_name == 'UNet':
         model = models.UNet(num_classes=args.num_classes)

dygraph/train.py

@@ -143,7 +143,7 @@ def train(model,
     for epoch in range(num_epochs):
         for step, data in enumerate(data_generator()):
             images = np.array([d[0] for d in data])
-            labels = np.array([d[1] for d in data]).astype('int64')
+            labels = np.array([d[2] for d in data]).astype('int64')
             images = to_variable(images)
             labels = to_variable(labels)
             loss = model(images, labels, mode='train')
@@ -175,21 +175,12 @@ def train(model,
                 model.train()
 
 
-def arrange_transform(transforms, mode='train'):
-    arrange_transform = T.ArrangeSegmenter
-    if type(transforms.transforms[-1]).__name__.startswith('Arrange'):
-        transforms.transforms[-1] = arrange_transform(mode=mode)
-    else:
-        transforms.transforms.append(arrange_transform(mode=mode))
-
-
 def main(args):
     # Creat dataset reader
     train_transforms = T.Compose(
         [T.Resize(args.input_size),
          T.RandomHorizontalFlip(),
          T.Normalize()])
-    arrange_transform(train_transforms, mode='train')
     train_dataset = Dataset(
         data_dir=args.data_dir,
         file_list=args.train_list,
@@ -200,7 +191,6 @@ def main(args):
         shuffle=True)
     if args.val_list is not None:
         eval_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        arrange_transform(eval_transforms, mode='eval')
         eval_dataset = Dataset(
             data_dir=args.data_dir,
             file_list=args.val_list,

dygraph/transforms/transforms.py

@@ -74,7 +74,10 @@ class Compose:
                 im_info = outputs[1]
             if len(outputs) == 3:
                 label = outputs[2]
-        return outputs
+        im = permute(im)
+        if len(outputs) == 3:
+            label = label[np.newaxis, :, :]
+        return (im, im_info, label)
 
 
 class RandomHorizontalFlip:
@@ -873,42 +876,3 @@ class RandomDistort:
             return (im, im_info)
         else:
             return (im, im_info, label)
-
-
-class ArrangeSegmenter:
-    """获取训练/验证/预测所需的信息。
-
-    Args:
-        mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
-
-    Raises:
-        ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内
-    """
-
-    def __init__(self, mode):
-        if mode not in ['train', 'eval', 'test', 'quant']:
-            raise ValueError(
-                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
-            )
-        self.mode = mode
-
-    def __call__(self, im, im_info, label=None):
-        """
-        Args:
-            im (np.ndarray): 图像np.ndarray数据。
-            im_info (dict): 存储与图像相关的信息。
-            label (np.ndarray): 标注图像np.ndarray数据。
-
-        Returns:
-            tuple: 当mode为'train'或'eval'时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
-                当mode为'test'时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；当mode为
-                'quant'时，返回的tuple为(im,)，为图像np.ndarray数据。
-        """
-        im = permute(im)
-        if self.mode == 'train' or self.mode == 'eval':
-            label = label[np.newaxis, :, :]
-            return (im, label)
-        elif self.mode == 'test':
-            return (im, im_info)
-        else:
-            return (im, )

dygraph/val.py

@@ -21,7 +21,7 @@ from paddle.fluid.dygraph.base import to_variable
 import numpy as np
 import paddle.fluid as fluid
 
-from datasets.dataset import Dataset
+from datasets import Dataset
 import transforms as T
 import models
 import utils.logging as logging
@@ -112,7 +112,7 @@ def evaluate(model,
             eval_dataset.num_samples, total_steps))
     for step, data in enumerate(data_generator()):
         images = np.array([d[0] for d in data])
-        labels = np.array([d[1] for d in data]).astype('int64')
+        labels = np.array([d[2] for d in data]).astype('int64')
         images = to_variable(images)
         pred, _ = model(images, labels, mode='eval')
 
@@ -134,17 +134,8 @@ def evaluate(model,
     logging.info("[EVAL] Kappa:{:.4f} ".format(conf_mat.kappa()))
 
 
-def arrange_transform(transforms, mode='train'):
-    arrange_transform = T.ArrangeSegmenter
-    if type(transforms.transforms[-1]).__name__.startswith('Arrange'):
-        transforms.transforms[-1] = arrange_transform(mode=mode)
-    else:
-        transforms.transforms.append(arrange_transform(mode=mode))
-
-
 def main(args):
     eval_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-    arrange_transform(eval_transforms, mode='eval')
     eval_dataset = Dataset(
         data_dir=args.data_dir,
         file_list=args.val_list,