YAPF Update

CHANGELOG.md

@@ -76,6 +76,7 @@ To release a new version, please update the changelog as followed:
 ### Dependencies Update
 - nltk>=3.3,<3.4 => nltk>=3.3,<3.5 (PR #892)
 - pytest>=3.6,<3.11 => pytest>=3.6,<4.1 (PR #889)
+- yapf>=0.22,<0.25 => yapf==0.25.0 (PR #896)
 
 ### Deprecated
 

requirements/requirements_test.txt

@@ -6,4 +6,4 @@ pytest-cache>=1.0,<1.1
 pytest-cov>=2.5,<2.7
 pytest-xdist>=1.22,<1.25
 sphinx>=1.7,<1.9
-yapf>=0.22,<0.25
+yapf==0.25.0

tensorlayer/db.py

@@ -219,9 +219,8 @@ def find_top_model(self, sess, sort=None, model_name='model', **kwargs):
 
             pc = self.db.Model.find(kwargs)
             print(
-                "[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".format(
-                    kwargs, sort, _datetime, round(time.time() - s, 2)
-                )
+                "[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".
+                format(kwargs, sort, _datetime, round(time.time() - s, 2))
             )
 
             # put all informations of model into the TL layer
@@ -656,10 +655,9 @@ def run_top_task(self, task_name=None, sort=None, **kwargs):
                 }}, return_document=pymongo.ReturnDocument.AFTER
             )
             logging.info(
-                "[Database] Finished Task: task_name - {} sort: {} push time: {} took: {}s".format(
-                    task_name, sort, _datetime,
-                    time.time() - s
-                )
+                "[Database] Finished Task: task_name - {} sort: {} push time: {} took: {}s".
+                format(task_name, sort, _datetime,
+                       time.time() - s)
             )
             return True
         except Exception as e:

tensorlayer/files/dataset_loaders/voc_dataset.py

@@ -204,8 +204,9 @@ def _recursive_parse_xml_to_dict(xml):
     imgs_file_list = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False)
     logging.info("[VOC] {} images found".format(len(imgs_file_list)))
 
-    imgs_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                       )  # 2007_000027.jpg --> 2007000027
+    imgs_file_list.sort(
+        key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+    )  # 2007_000027.jpg --> 2007000027
 
     imgs_file_list = [os.path.join(folder_imgs, s) for s in imgs_file_list]
     # logging.info('IM',imgs_file_list[0::3333], imgs_file_list[-1])
@@ -215,17 +216,19 @@ def _recursive_parse_xml_to_dict(xml):
         folder_semseg = os.path.join(path, extracted_filename, "SegmentationClass")
         imgs_semseg_file_list = load_file_list(path=folder_semseg, regx='\\.png', printable=False)
         logging.info("[VOC] {} maps for semantic segmentation found".format(len(imgs_semseg_file_list)))
-        imgs_semseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                                  )  # 2007_000032.png --> 2007000032
+        imgs_semseg_file_list.sort(
+            key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+        )  # 2007_000032.png --> 2007000032
         imgs_semseg_file_list = [os.path.join(folder_semseg, s) for s in imgs_semseg_file_list]
         # logging.info('Semantic Seg IM',imgs_semseg_file_list[0::333], imgs_semseg_file_list[-1])
         ##======== 3. instance segmentation maps path list
         # folder_insseg = path+"/"+extracted_filename+"/SegmentationObject/"
         folder_insseg = os.path.join(path, extracted_filename, "SegmentationObject")
         imgs_insseg_file_list = load_file_list(path=folder_insseg, regx='\\.png', printable=False)
         logging.info("[VOC] {} maps for instance segmentation found".format(len(imgs_semseg_file_list)))
-        imgs_insseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                                  )  # 2007_000032.png --> 2007000032
+        imgs_insseg_file_list.sort(
+            key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+        )  # 2007_000032.png --> 2007000032
         imgs_insseg_file_list = [os.path.join(folder_insseg, s) for s in imgs_insseg_file_list]
         # logging.info('Instance Seg IM',imgs_insseg_file_list[0::333], imgs_insseg_file_list[-1])
     else:
@@ -238,8 +241,9 @@ def _recursive_parse_xml_to_dict(xml):
     logging.info(
         "[VOC] {} XML annotation files for bounding box and object class found".format(len(imgs_ann_file_list))
     )
-    imgs_ann_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                           )  # 2007_000027.xml --> 2007000027
+    imgs_ann_file_list.sort(
+        key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+    )  # 2007_000027.xml --> 2007000027
     imgs_ann_file_list = [os.path.join(folder_ann, s) for s in imgs_ann_file_list]
     # logging.info('ANN',imgs_ann_file_list[0::3333], imgs_ann_file_list[-1])
 

tensorlayer/files/utils.py

@@ -1189,8 +1189,9 @@ def _recursive_parse_xml_to_dict(xml):
     imgs_file_list = load_file_list(path=folder_imgs, regx='\\.jpg', printable=False)
     logging.info("[VOC] {} images found".format(len(imgs_file_list)))
 
-    imgs_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                       )  # 2007_000027.jpg --> 2007000027
+    imgs_file_list.sort(
+        key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+    )  # 2007_000027.jpg --> 2007000027
 
     imgs_file_list = [os.path.join(folder_imgs, s) for s in imgs_file_list]
     # logging.info('IM',imgs_file_list[0::3333], imgs_file_list[-1])
@@ -1200,17 +1201,19 @@ def _recursive_parse_xml_to_dict(xml):
         folder_semseg = os.path.join(path, extracted_filename, "SegmentationClass")
         imgs_semseg_file_list = load_file_list(path=folder_semseg, regx='\\.png', printable=False)
         logging.info("[VOC] {} maps for semantic segmentation found".format(len(imgs_semseg_file_list)))
-        imgs_semseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                                  )  # 2007_000032.png --> 2007000032
+        imgs_semseg_file_list.sort(
+            key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+        )  # 2007_000032.png --> 2007000032
         imgs_semseg_file_list = [os.path.join(folder_semseg, s) for s in imgs_semseg_file_list]
         # logging.info('Semantic Seg IM',imgs_semseg_file_list[0::333], imgs_semseg_file_list[-1])
         # ======== 3. instance segmentation maps path list
         # folder_insseg = path+"/"+extracted_filename+"/SegmentationObject/"
         folder_insseg = os.path.join(path, extracted_filename, "SegmentationObject")
         imgs_insseg_file_list = load_file_list(path=folder_insseg, regx='\\.png', printable=False)
         logging.info("[VOC] {} maps for instance segmentation found".format(len(imgs_semseg_file_list)))
-        imgs_insseg_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                                  )  # 2007_000032.png --> 2007000032
+        imgs_insseg_file_list.sort(
+            key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+        )  # 2007_000032.png --> 2007000032
         imgs_insseg_file_list = [os.path.join(folder_insseg, s) for s in imgs_insseg_file_list]
         # logging.info('Instance Seg IM',imgs_insseg_file_list[0::333], imgs_insseg_file_list[-1])
     else:
@@ -1223,8 +1226,9 @@ def _recursive_parse_xml_to_dict(xml):
     logging.info(
         "[VOC] {} XML annotation files for bounding box and object class found".format(len(imgs_ann_file_list))
     )
-    imgs_ann_file_list.sort(key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
-                           )  # 2007_000027.xml --> 2007000027
+    imgs_ann_file_list.sort(
+        key=lambda s: int(s.replace('.', ' ').replace('_', '').split(' ')[-2])
+    )  # 2007_000027.xml --> 2007000027
     imgs_ann_file_list = [os.path.join(folder_ann, s) for s in imgs_ann_file_list]
     # logging.info('ANN',imgs_ann_file_list[0::3333], imgs_ann_file_list[-1])
 

tensorlayer/layers/convolution/deformable_conv.py

@@ -107,8 +107,9 @@ def __init__(
             input_h = int(self.inputs.get_shape()[1])
             input_w = int(self.inputs.get_shape()[2])
             kernel_n = shape[0] * shape[1]
-            initial_offsets = tf.stack(tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]),
-                                                   indexing='ij'))  # initial_offsets --> (kh, kw, 2)
+            initial_offsets = tf.stack(
+                tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]), indexing='ij')
+            )  # initial_offsets --> (kh, kw, 2)
             initial_offsets = tf.reshape(initial_offsets, (-1, 2))  # initial_offsets --> (n, 2)
             initial_offsets = tf.expand_dims(initial_offsets, 0)  # initial_offsets --> (1, n, 2)
             initial_offsets = tf.expand_dims(initial_offsets, 0)  # initial_offsets --> (1, 1, n, 2)

tensorlayer/utils.py

@@ -573,8 +573,9 @@ def exit_tensorflow(sess=None, port=6006):
 
     elif _platform == "darwin":
         tl.logging.info('OS X: %s' % text)
-        subprocess.Popen("lsof -i tcp:" + str(port) + "  | grep -v PID | awk '{print $2}' | xargs kill",
-                         shell=True)  # kill tensorboard
+        subprocess.Popen(
+            "lsof -i tcp:" + str(port) + "  | grep -v PID | awk '{print $2}' | xargs kill", shell=True
+        )  # kill tensorboard
     elif _platform == "win32":
         raise NotImplementedError("this function is not supported on the Windows platform")
 

tensorlayer/visualize.py

@@ -645,8 +645,9 @@ def draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig
             #     feature = np.zeros_like(feature)
             # if np.mean(feature) < -0.015:      # condition threshold
             #     feature = np.zeros_like(feature)
-            plt.imshow(np.reshape(feature, (shape[0], shape[1])), cmap='gray',
-                       interpolation="nearest")  # , vmin=np.min(feature), vmax=np.max(feature))
+            plt.imshow(
+                np.reshape(feature, (shape[0], shape[1])), cmap='gray', interpolation="nearest"
+            )  # , vmin=np.min(feature), vmax=np.max(feature))
             # plt.title(name)
             # ------------------------------------------------------------
             # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")

tests/test_tf_layers.py

@@ -162,8 +162,9 @@ def get_network_3d(inputs, reuse=False):
             with tf.variable_scope("3D_network", reuse=reuse):
                 net = tl.layers.InputLayer(inputs)
 
-                net1 = tl.layers.Conv3dLayer(net, shape=(2, 2, 2, 3, 32), strides=(1, 2, 2, 2, 1),
-                                             name="Conv3dLayer")  # 2 params
+                net1 = tl.layers.Conv3dLayer(
+                    net, shape=(2, 2, 2, 3, 32), strides=(1, 2, 2, 2, 1), name="Conv3dLayer"
+                )  # 2 params
                 net2 = tl.layers.DeConv3d(net1, name="DeConv3d")  # 2 params
                 net3 = tl.layers.MaxPool3d(net2, (1, 1, 1), name="MaxPool3d")  # 0 params
                 net4 = tl.layers.MeanPool3d(net3, (1, 1, 1), name="MeanPool3d")  # 0 params