_preprocess and _postprocess as public methods

paddlers/deploy/predictor.py

@@ -146,7 +146,7 @@ class Predictor(object):
         return predictor
 
     def preprocess(self, images, transforms):
-        preprocessed_samples = self._model._preprocess(
+        preprocessed_samples = self._model.preprocess(
             images, transforms, to_tensor=False)
         if self._model.model_type == 'classifier':
             preprocessed_samples = {'image': preprocessed_samples[0]}
@@ -172,12 +172,12 @@ class Predictor(object):
     def postprocess(self, net_outputs, topk=1, ori_shape=None, transforms=None):
         if self._model.model_type == 'classifier':
             true_topk = min(self._model.num_classes, topk)
-            if self._model._postprocess is None:
+            if self._model.postprocess is None:
                 self._model.build_postprocess_from_labels(topk)
-            # XXX: Convert ndarray to tensor as self._model._postprocess requires
+            # XXX: Convert ndarray to tensor as self._model.postprocess requires
             assert len(net_outputs) == 1
             net_outputs = paddle.to_tensor(net_outputs[0])
-            outputs = self._model._postprocess(net_outputs)
+            outputs = self._model.postprocess(net_outputs)
             class_ids = map(itemgetter('class_ids'), outputs)
             scores = map(itemgetter('scores'), outputs)
             label_names = map(itemgetter('label_names'), outputs)
@@ -187,7 +187,7 @@ class Predictor(object):
                 'label_names_map': n,
             } for l, s, n in zip(class_ids, scores, label_names)]
         elif self._model.model_type in ('segmenter', 'change_detector'):
-            label_map, score_map = self._model._postprocess(
+            label_map, score_map = self._model.postprocess(
                 net_outputs,
                 batch_origin_shape=ori_shape,
                 transforms=transforms.transforms)
@@ -200,7 +200,7 @@ class Predictor(object):
                 k: v
                 for k, v in zip(['bbox', 'bbox_num', 'mask'], net_outputs)
             }
-            preds = self._model._postprocess(net_outputs)
+            preds = self._model.postprocess(net_outputs)
         else:
             logging.error(
                 "Invalid model type {}.".format(self._model.model_type),

paddlers/tasks/change_detector.py

@@ -111,10 +111,10 @@ class BaseChangeDetector(BaseModel):
         if mode == 'test':
             origin_shape = inputs[2]
             if self.status == 'Infer':
-                label_map_list, score_map_list = self._postprocess(
+                label_map_list, score_map_list = self.postprocess(
                     net_out, origin_shape, transforms=inputs[3])
             else:
-                logit_list = self._postprocess(
+                logit_list = self.postprocess(
                     logit, origin_shape, transforms=inputs[3])
                 label_map_list = []
                 score_map_list = []
@@ -142,7 +142,7 @@ class BaseChangeDetector(BaseModel):
                 raise ValueError("Expected label.ndim == 4 but got {}".format(
                     label.ndim))
             origin_shape = [label.shape[-2:]]
-            pred = self._postprocess(
+            pred = self.postprocess(
                 pred, origin_shape, transforms=inputs[3])[0]  # NCHW
             intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
                 pred, label, self.num_classes)
@@ -553,7 +553,7 @@ class BaseChangeDetector(BaseModel):
             images = [img_file]
         else:
             images = img_file
-        batch_im1, batch_im2, batch_origin_shape = self._preprocess(
+        batch_im1, batch_im2, batch_origin_shape = self.preprocess(
             images, transforms, self.model_type)
         self.net.eval()
         data = (batch_im1, batch_im2, batch_origin_shape, transforms.transforms)
@@ -664,7 +664,7 @@ class BaseChangeDetector(BaseModel):
         dst_data = None
         print("GeoTiff saved in {}.".format(save_file))
 
-    def _preprocess(self, images, transforms, to_tensor=True):
+    def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')
         batch_im1, batch_im2 = list(), list()
         batch_ori_shape = list()
@@ -736,7 +736,7 @@ class BaseChangeDetector(BaseModel):
             batch_restore_list.append(restore_list)
         return batch_restore_list
 
-    def _postprocess(self, batch_pred, batch_origin_shape, transforms):
+    def postprocess(self, batch_pred, batch_origin_shape, transforms):
         batch_restore_list = BaseChangeDetector.get_transforms_shape_info(
             batch_origin_shape, transforms)
         if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':

paddlers/tasks/classifier.py

@@ -61,7 +61,7 @@ class BaseClassifier(BaseModel):
         self.metrics = None
         self.losses = None
         self.labels = None
-        self._postprocess = None
+        self.postprocess = None
         if params.get('with_net', True):
             params.pop('with_net', None)
             self.net = self.build_net(**params)
@@ -121,13 +121,12 @@ class BaseClassifier(BaseModel):
         net_out = net(inputs[0])
 
         if mode == 'test':
-            return self._postprocess(net_out)
+            return self.postprocess(net_out)
 
         outputs = OrderedDict()
         label = paddle.to_tensor(inputs[1], dtype="int64")
 
         if mode == 'eval':
-            # print(self._postprocess(net_out)[0])  # for test
             label = paddle.unsqueeze(label, axis=-1)
             metric_dict = self.metrics(net_out, label)
             outputs['top1'] = metric_dict["top1"]
@@ -176,13 +175,13 @@ class BaseClassifier(BaseModel):
         label_dict = dict()
         for i, label in enumerate(self.labels):
             label_dict[i] = label
-        self._postprocess = build_postprocess({
+        self.postprocess = build_postprocess({
             "name": "Topk",
             "topk": topk,
             "class_id_map_file": None
         })
         # Add class_id_map from model.yml
-        self._postprocess.class_id_map = label_dict
+        self.postprocess.class_id_map = label_dict
 
     def train(self,
               num_epochs,
@@ -247,8 +246,7 @@ class BaseClassifier(BaseModel):
         if self.losses is None:
             self.losses = self.default_loss()
         self.metrics = self.default_metric()
-        self._postprocess = self.default_postprocess(train_dataset.label_list)
-        # print(self._postprocess.class_id_map)
+        self.postprocess = self.default_postprocess(train_dataset.label_list)
 
         if optimizer is None:
             num_steps_each_epoch = train_dataset.num_samples // train_batch_size
@@ -454,12 +452,12 @@ class BaseClassifier(BaseModel):
             images = [img_file]
         else:
             images = img_file
-        batch_im, batch_origin_shape = self._preprocess(images, transforms,
-                                                        self.model_type)
+        batch_im, batch_origin_shape = self.preprocess(images, transforms,
+                                                       self.model_type)
         self.net.eval()
         data = (batch_im, batch_origin_shape, transforms.transforms)
 
-        if self._postprocess is None:
+        if self.postprocess is None:
             self.build_postprocess_from_labels()
 
         outputs = self.run(self.net, data, 'test')
@@ -480,7 +478,7 @@ class BaseClassifier(BaseModel):
             }
         return prediction
 
-    def _preprocess(self, images, transforms, to_tensor=True):
+    def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')
         batch_im = list()
         batch_ori_shape = list()

paddlers/tasks/object_detector.py

@@ -580,16 +580,16 @@ class BaseDetector(BaseModel):
         else:
             images = img_file
 
-        batch_samples = self._preprocess(images, transforms)
+        batch_samples = self.preprocess(images, transforms)
         self.net.eval()
         outputs = self.run(self.net, batch_samples, 'test')
-        prediction = self._postprocess(outputs)
+        prediction = self.postprocess(outputs)
 
         if isinstance(img_file, (str, np.ndarray)):
             prediction = prediction[0]
         return prediction
 
-    def _preprocess(self, images, transforms, to_tensor=True):
+    def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')
         batch_samples = list()
         for im in images:
@@ -606,7 +606,7 @@ class BaseDetector(BaseModel):
 
         return batch_samples
 
-    def _postprocess(self, batch_pred):
+    def postprocess(self, batch_pred):
         infer_result = {}
         if 'bbox' in batch_pred:
             bboxes = batch_pred['bbox']

paddlers/tasks/segmenter.py

@@ -110,10 +110,10 @@ class BaseSegmenter(BaseModel):
         if mode == 'test':
             origin_shape = inputs[1]
             if self.status == 'Infer':
-                label_map_list, score_map_list = self._postprocess(
+                label_map_list, score_map_list = self.postprocess(
                     net_out, origin_shape, transforms=inputs[2])
             else:
-                logit_list = self._postprocess(
+                logit_list = self.postprocess(
                     logit, origin_shape, transforms=inputs[2])
                 label_map_list = []
                 score_map_list = []
@@ -141,7 +141,7 @@ class BaseSegmenter(BaseModel):
                 raise ValueError("Expected label.ndim == 4 but got {}".format(
                     label.ndim))
             origin_shape = [label.shape[-2:]]
-            pred = self._postprocess(
+            pred = self.postprocess(
                 pred, origin_shape, transforms=inputs[2])[0]  # NCHW
             intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
                 pred, label, self.num_classes)
@@ -526,8 +526,8 @@ class BaseSegmenter(BaseModel):
             images = [img_file]
         else:
             images = img_file
-        batch_im, batch_origin_shape = self._preprocess(images, transforms,
-                                                        self.model_type)
+        batch_im, batch_origin_shape = self.preprocess(images, transforms,
+                                                       self.model_type)
         self.net.eval()
         data = (batch_im, batch_origin_shape, transforms.transforms)
         outputs = self.run(self.net, data, 'test')
@@ -631,7 +631,7 @@ class BaseSegmenter(BaseModel):
         dst_data = None
         print("GeoTiff saved in {}.".format(save_file))
 
-    def _preprocess(self, images, transforms, to_tensor=True):
+    def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')
         batch_im = list()
         batch_ori_shape = list()
@@ -698,7 +698,7 @@ class BaseSegmenter(BaseModel):
             batch_restore_list.append(restore_list)
         return batch_restore_list
 
-    def _postprocess(self, batch_pred, batch_origin_shape, transforms):
+    def postprocess(self, batch_pred, batch_origin_shape, transforms):
         batch_restore_list = BaseSegmenter.get_transforms_shape_info(
             batch_origin_shape, transforms)
         if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':

test_tipc/infer.py

@@ -141,7 +141,7 @@ class TIPCPredictor(object):
         return config
 
     def preprocess(self, images, transforms):
-        preprocessed_samples = self._model._preprocess(
+        preprocessed_samples = self._model.preprocess(
             images, transforms, to_tensor=False)
         if self._model.model_type == 'classifier':
             preprocessed_samples = {'image': preprocessed_samples[0]}
@@ -167,12 +167,12 @@ class TIPCPredictor(object):
     def postprocess(self, net_outputs, topk=1, ori_shape=None, transforms=None):
         if self._model.model_type == 'classifier':
             true_topk = min(self._model.num_classes, topk)
-            if self._model._postprocess is None:
+            if self._model.postprocess is None:
                 self._model.build_postprocess_from_labels(topk)
-            # XXX: Convert ndarray to tensor as self._model._postprocess requires
+            # XXX: Convert ndarray to tensor as self._model.postprocess requires
             assert len(net_outputs) == 1
             net_outputs = paddle.to_tensor(net_outputs[0])
-            outputs = self._model._postprocess(net_outputs)
+            outputs = self._model.postprocess(net_outputs)
             class_ids = map(itemgetter('class_ids'), outputs)
             scores = map(itemgetter('scores'), outputs)
             label_names = map(itemgetter('label_names'), outputs)
@@ -182,7 +182,7 @@ class TIPCPredictor(object):
                 'label_names_map': n,
             } for l, s, n in zip(class_ids, scores, label_names)]
         elif self._model.model_type in ('segmenter', 'change_detector'):
-            label_map, score_map = self._model._postprocess(
+            label_map, score_map = self._model.postprocess(
                 net_outputs,
                 batch_origin_shape=ori_shape,
                 transforms=transforms.transforms)
@@ -195,7 +195,7 @@ class TIPCPredictor(object):
                 k: v
                 for k, v in zip(['bbox', 'bbox_num', 'mask'], net_outputs)
             }
-            preds = self._model._postprocess(net_outputs)
+            preds = self._model.postprocess(net_outputs)
         else:
             logging.error(
                 "Invalid model type {}.".format(self._model.model_type),