Fix bugs

paddlers/datasets/voc.py

@@ -188,7 +188,7 @@ class VOCDetection(Dataset):
                     box_tag = pattern.findall(str(ET.tostringlist(obj)))
                     if len(box_tag) == 0:
                         logging.warning(
-                            "There's no field '<bndbox>' in one of object, "
+                            "There is no field '<bndbox>' in the object, "
                             "so this object will be ignored. xml file: {}".
                             format(xml_file))
                         continue

paddlers/rs_models/cd/layers/__init__.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...layers.blocks import *
+from paddlers.rs_models.layers.blocks import *
 from .attention import ChannelAttention, SpatialAttention, CBAM

paddlers/rs_models/cd/layers/attention.py

@@ -16,7 +16,7 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 
-from .blocks import Conv1x1, BasicConv
+from paddlers.rs_models.layers.blocks import Conv1x1, BasicConv
 
 
 class ChannelAttention(nn.Layer):

paddlers/rs_models/clas/condensenet_v2.py

@@ -20,7 +20,7 @@ Apache License [see LICENSE for details]
 import paddle
 import paddle.nn as nn
 
-from ...layers.blocks import make_bn
+from paddlers.rs_models.layers.blocks import make_bn
 
 __all__ = ["CondenseNetV2_a", "CondenseNetV2_b", "CondenseNetV2_c"]
 

paddlers/rs_models/seg/layers/layers_lib.py

@@ -16,7 +16,7 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 
-from ...layers.blocks import *
+from paddlers.rs_models.layers.blocks import *
 
 
 class Add(nn.Layer):

paddlers/tasks/__init__.py

@@ -15,6 +15,6 @@
 import paddlers.tasks.object_detector as det
 import paddlers.tasks.segmenter as seg
 import paddlers.tasks.change_detector as cd
-import paddlers.classifier as clas
-import paddlers.image_restorer as res
+import paddlers.tasks.classifier as clas
+import paddlers.tasks.image_restorer as res
 from .load_model import load_model

paddlers/tasks/change_detector.py

@@ -52,7 +52,7 @@ class BaseChangeDetector(BaseModel):
             del self.init_params['with_net']
         super(BaseChangeDetector, self).__init__('change_detector')
         if model_name not in __all__:
-            raise Exception("ERROR: There's no model named {}.".format(
+            raise ValueError("ERROR: There is no model named {}.".format(
                 model_name))
         self.model_name = model_name
         self.num_classes = num_classes

paddlers/tasks/classifier.py

@@ -23,7 +23,7 @@ import paddle.nn.functional as F
 from paddle.static import InputSpec
 
 import paddlers.models.ppcls as paddleclas
-import paddlers.rs_models.cls as cmcls
+import paddlers.rs_models.clas as cmcls
 import paddlers
 from paddlers.transforms import arrange_transforms
 from paddlers.utils import get_single_card_bs, DisablePrint

paddlers/tasks/object_detector.py

@@ -49,7 +49,7 @@ class BaseDetector(BaseModel):
             del self.init_params['with_net']
         super(BaseDetector, self).__init__('detector')
         if not hasattr(ppdet.modeling, model_name):
-            raise Exception("ERROR: There's no model named {}.".format(
+            raise ValueError("ERROR: There is no model named {}.".format(
                 model_name))
 
         self.model_name = model_name

paddlers/tasks/segmenter.py

@@ -49,7 +49,7 @@ class BaseSegmenter(BaseModel):
         super(BaseSegmenter, self).__init__('segmenter')
         if not hasattr(paddleseg.models, model_name) and \
            not hasattr(cmseg, model_name):
-            raise Exception("ERROR: There's no model named {}.".format(
+            raise ValueError("ERROR: There is no model named {}.".format(
                 model_name))
         self.model_name = model_name
         self.num_classes = num_classes

paddlers/transforms/operators.py

@@ -234,6 +234,7 @@ class DecodeImg(Transform):
                 raise ValueError(
                     "The height or width of the image is not same as the mask.")
         if 'aux_masks' in sample:
+            sample['aux_masks_ori'] = copy.deepcopy(sample['aux_masks'])
             sample['aux_masks'] = list(
                 map(self.apply_mask, sample['aux_masks']))
             # TODO: check the shape of auxiliary masks

tests/transforms/test_operators.py

@@ -23,7 +23,7 @@ from data import build_input_from_file
 
 __all__ = ['TestTransform', 'TestCompose', 'TestArrange']
 
-WHITE_LIST = []
+WHITE_LIST = ['ReloadMask']
 
 
 def _add_op_tests(cls):
@@ -37,6 +37,9 @@ def _add_op_tests(cls):
                                                      T.operators.Transform):
             if op_class is T.DecodeImg or op_class in WHITE_LIST or op_name in WHITE_LIST:
                 continue
+            if issubclass(op_class, T.Compose) or issubclass(
+                    op_class, T.operators.Arrange):
+                continue
             attr_name = 'test_' + op_name
             if hasattr(cls, attr_name):
                 continue