Add verbose mode

docs/apis/infer.md

@@ -158,7 +158,8 @@ def slider_predict(self,
                    transforms=None,
                    invalid_value=255,
                    merge_strategy='keep_last',
-                   batch_size=1):
+                   batch_size=1,
+                   quiet=False):
 ```
 
 输入参数列表：
@@ -173,6 +174,7 @@ def slider_predict(self,
 |`invalid_value`|`int`|输出影像中用于标记无效像素的数值。|`255`|
 |`merge_strategy`|`str`|合并滑窗重叠区域使用的策略。`'keep_first'`表示保留遍历顺序（从左至右，从上往下，列优先）最靠前的窗口的预测类别；`'keep_last'`表示保留遍历顺序最靠后的窗口的预测类别；`'accum'`表示通过将各窗口在重叠区域给出的预测概率累加，计算最终预测类别。需要注意的是，在对大尺寸影像进行`overlap`较大的密集推理时，使用`'accum'`策略可能导致较长的推理时间，但一般能够在窗口交界部分取得更好的表现。|`'keep_last'`|
 |`batch_size`|`int`|预测时使用的mini-batch大小。|`1`|
+|`quiet`|`bool`|若为`True`，不显示预测进度。|`False`|
 
 变化检测任务的滑窗推理API与图像分割任务类似，但需要注意的是输出结果中存储的地理变换、投影等信息以从第一时相影像中读取的信息为准，存储滑窗推理结果的文件名也与第一时相影像文件相同。
 

paddlers/deploy/predictor.py

@@ -333,7 +333,8 @@ class Predictor(object):
                        transforms=None,
                        invalid_value=255,
                        merge_strategy='keep_last',
-                       batch_size=1):
+                       batch_size=1,
+                       quiet=False):
         """
         Do inference using sliding windows. Only semantic segmentation and change detection models are supported in the 
             sliding-predicting mode.
@@ -357,6 +358,7 @@ class Predictor(object):
                 the last block in traversal order, respectively. 'accum' means determining the class of an overlapping pixel 
                 according to accumulated probabilities. Defaults to 'keep_last'.
             batch_size (int, optional): Batch size used in inference. Defaults to 1.
+            quiet (bool, optional): If True, disable the progress bar. Defaults to False.
         """
         slider_predict(
             partial(
@@ -368,7 +370,8 @@ class Predictor(object):
             transforms,
             invalid_value,
             merge_strategy,
-            batch_size)
+            batch_size,
+            not quiet)
 
     def batch_predict(self, image_list, **params):
         return self.predict(img_file=image_list, **params)

paddlers/tasks/change_detector.py

@@ -589,7 +589,8 @@ class BaseChangeDetector(BaseModel):
                        transforms=None,
                        invalid_value=255,
                        merge_strategy='keep_last',
-                       batch_size=1):
+                       batch_size=1,
+                       quiet=False):
         """
         Do inference using sliding windows.
 
@@ -613,10 +614,12 @@ class BaseChangeDetector(BaseModel):
                 order, respectively. 'accum' means determining the class of an overlapping 
                 pixel according to accumulated probabilities. Defaults to 'keep_last'.
             batch_size (int, optional): Batch size used in inference. Defaults to 1.
+            quiet (bool, optional): If True, disable the progress bar. Defaults to False.
         """
 
         slider_predict(self.predict, img_files, save_dir, block_size, overlap,
-                       transforms, invalid_value, merge_strategy, batch_size)
+                       transforms, invalid_value, merge_strategy, batch_size,
+                       not quiet)
 
     def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')

paddlers/tasks/segmenter.py

@@ -561,7 +561,8 @@ class BaseSegmenter(BaseModel):
                        transforms=None,
                        invalid_value=255,
                        merge_strategy='keep_last',
-                       batch_size=1):
+                       batch_size=1,
+                       quiet=False):
         """
         Do inference using sliding windows.
 
@@ -585,10 +586,12 @@ class BaseSegmenter(BaseModel):
                 order, respectively. 'accum' means determining the class of an overlapping 
                 pixel according to accumulated probabilities. Defaults to 'keep_last'.
             batch_size (int, optional): Batch size used in inference. Defaults to 1.
+            quiet (bool, optional): If True, disable the progress bar. Defaults to False.
         """
 
         slider_predict(self.predict, img_file, save_dir, block_size, overlap,
-                       transforms, invalid_value, merge_strategy, batch_size)
+                       transforms, invalid_value, merge_strategy, batch_size,
+                       not quiet)
 
     def preprocess(self, images, transforms, to_tensor=True):
         self._check_transforms(transforms, 'test')