[CodeStyle][W291] trim trailing whitespace in python file (PaddlePadd…

…le#45937)

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

.cmake-format.py

@@ -1,11 +1,11 @@
 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

paddle/fluid/eager/auto_code_generator/generate_file_structures.py

@@ -22,7 +22,7 @@ def GenerateFileStructureForFinalDygraph(eager_dir):
     |- generated
     |  |- CMakeLists.txt
     |  |  "add_subdirectory(forwards), add_subdirectory(backwards)"
-    |  
+    |
     |  |- forwards
     |     |- "dygraph_functions.cc"
     |     |- "dygraph_functions.h"
@@ -59,7 +59,7 @@ def GenerateFileStructureForIntermediateDygraph(eager_dir, split_count):
     |- generated
     |  |- CMakeLists.txt
     |  |  "add_subdirectory(forwards), add_subdirectory(nodes)"
-    |  
+    |
     |  |- forwards
     |     |- "dygraph_forward_functions.cc"
     |     |- CMakeLists.txt
@@ -70,7 +70,7 @@ def GenerateFileStructureForIntermediateDygraph(eager_dir, split_count):
     |     |- "nodes.h"
     |     |- CMakeLists.txt
     |     |  "cc_library(dygraph_node SRCS nodes.cc DEPS ${eager_deps} ${fluid_deps})"
-    | 
+    |
     |  |- dygraph_forward_api.h
     """
     # Directory Generation

paddle/fluid/eager/auto_code_generator/generator/eager_gen.py

@@ -403,9 +403,9 @@ class {} : public egr::GradNodeBase {{
   if (paddle::imperative::LayoutAutoTune::Instance().UseLayoutAutoTune()) {{
     VLOG(5) << "Check and Prepare For LAYOUT";
     paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> tensors_vector = {};
-    {} 
     {}
-    paddle::imperative::LayoutAutoTune::Instance().DisableLayoutAutoTune(); 
+    {}
+    paddle::imperative::LayoutAutoTune::Instance().DisableLayoutAutoTune();
     {}
     {}
     paddle::imperative::LayoutAutoTune::Instance().EnableLayoutAutoTune();
@@ -1772,7 +1772,7 @@ def GenerateNodeDefinition(self, next_grad_node_creation_str,
             autograd_api = self.grad_api_contents['invoke'].replace(
                 forward_api_name, forward_api_name + '_dygraph_function', 1)
             grad_function_call_str = f"""
-  if (trace_backward) {{            
+  if (trace_backward) {{
   {indent}{autograd_api_out} api_output = {autograd_api};
   {out_assign_str}}} else {{
   {indent}{autograd_api_out} api_output = paddle::experimental::{self.namespace}{self.grad_api_contents['invoke']};

paddle/fluid/inference/api/demo_ci/untar_model.py

@@ -20,7 +20,7 @@ def untar(fname, dirs):
     """
     extract the tar.gz file
     :param fname: the name of tar.gz file
-    :param dirs: the path of decompressed file 
+    :param dirs: the path of decompressed file
     :return: bool
     """
     try:

paddle/fluid/inference/tests/api/full_pascalvoc_test_preprocess.py

@@ -106,8 +106,8 @@ def convert_pascalvoc_local2bin(args):
         for object in objects:
             bbox_sample = []
             # start from 1
-            bbox_sample.append(
-                float(label_list.index(object.find('name').text)))
+            bbox_sample.append(float(label_list.index(
+                object.find('name').text)))
             bbox = object.find('bndbox')
             difficult = float(object.find('difficult').text)
             bbox_sample.append(float(bbox.find('xmin').text) / im_width)
@@ -131,7 +131,7 @@ def convert_pascalvoc_local2bin(args):
     f1.close()
 
     object_nums_sum = sum(object_nums)
-    # The data should be contains 
+    # The data should be contains
     # number of images + all images data + an array that represent object numbers of each image
     # + labels of all objects in images + bboxes of all objects + difficulties of all objects
     # so the target size should be as follows:
@@ -210,8 +210,8 @@ def convert_pascalvoc_tar2bin(tar_path, data_out_path):
 
         for object in objects:
             bbox_sample = []
-            bbox_sample.append(
-                float(label_list.index(object.find('name').text)))
+            bbox_sample.append(float(label_list.index(
+                object.find('name').text)))
             bbox = object.find('bndbox')
             difficult = float(object.find('difficult').text)
             bbox_sample.append(float(bbox.find('xmin').text) / im_width)
@@ -230,7 +230,7 @@ def convert_pascalvoc_tar2bin(tar_path, data_out_path):
         if line_idx % per_percentage:
             print_processbar(line_idx / per_percentage)
 
-    # The data should be stored in binary in following sequence: 
+    # The data should be stored in binary in following sequence:
     # number of images->all images data->an array that represent object numbers in each image
     # ->labels of all objects in images->bboxes of all objects->difficulties of all objects
     f1.write(np.array(object_nums).astype('uint64').tobytes())
@@ -258,9 +258,9 @@ def download_pascalvoc(data_url, data_dir, tar_targethash, tar_path):
 def run_convert():
     try_limit = 2
     retry = 0
-    while not (os.path.exists(DATA_OUT_PATH) and
-               os.path.getsize(DATA_OUT_PATH) == BIN_FULLSIZE and BIN_TARGETHASH
-               == hashlib.md5(open(DATA_OUT_PATH, 'rb').read()).hexdigest()):
+    while not (os.path.exists(DATA_OUT_PATH) and os.path.getsize(DATA_OUT_PATH)
+               == BIN_FULLSIZE and BIN_TARGETHASH == hashlib.md5(
+                   open(DATA_OUT_PATH, 'rb').read()).hexdigest()):
         if os.path.exists(DATA_OUT_PATH):
             sys.stderr.write(
                 "The existing binary file is broken. It is being removed...\n")
@@ -275,52 +275,52 @@ def run_convert():
 
 def main_pascalvoc_preprocess(args):
     parser = argparse.ArgumentParser(
-        description="Convert the full pascalvoc val set or local data to binary file.",
+        description=
+        "Convert the full pascalvoc val set or local data to binary file.",
         usage=None,
         add_help=True)
     parser.add_argument(
         '--local',
         action="store_true",
         help="If used, user need to set --data_dir and then convert file")
-    parser.add_argument(
-        "--data_dir", default="", type=str, help="Dataset root directory")
+    parser.add_argument("--data_dir",
+                        default="",
+                        type=str,
+                        help="Dataset root directory")
     parser.add_argument(
         "--img_annotation_list",
         type=str,
         default="test_100.txt",
-        help="A file containing the image file path and corresponding annotation file path"
+        help=
+        "A file containing the image file path and corresponding annotation file path"
     )
     parser.add_argument(
         "--label_file",
         type=str,
         default="label_list",
-        help="List of object labels with same sequence as denoted in the annotation file"
+        help=
+        "List of object labels with same sequence as denoted in the annotation file"
     )
-    parser.add_argument(
-        "--output_file",
-        type=str,
-        default="pascalvoc_small.bin",
-        help="File path of the output binary file")
-    parser.add_argument(
-        "--resize_h",
-        type=int,
-        default=RESIZE_H,
-        help="Image preprocess with resize_h")
-    parser.add_argument(
-        "--resize_w",
-        type=int,
-        default=RESIZE_W,
-        help="Image prerocess with resize_w")
-    parser.add_argument(
-        "--mean_value",
-        type=str,
-        default=MEAN_VALUE,
-        help="Image preprocess with mean_value")
-    parser.add_argument(
-        "--ap_version",
-        type=str,
-        default=AP_VERSION,
-        help="Image preprocess with ap_version")
+    parser.add_argument("--output_file",
+                        type=str,
+                        default="pascalvoc_small.bin",
+                        help="File path of the output binary file")
+    parser.add_argument("--resize_h",
+                        type=int,
+                        default=RESIZE_H,
+                        help="Image preprocess with resize_h")
+    parser.add_argument("--resize_w",
+                        type=int,
+                        default=RESIZE_W,
+                        help="Image prerocess with resize_w")
+    parser.add_argument("--mean_value",
+                        type=str,
+                        default=MEAN_VALUE,
+                        help="Image preprocess with mean_value")
+    parser.add_argument("--ap_version",
+                        type=str,
+                        default=AP_VERSION,
+                        help="Image preprocess with ap_version")
     args = parser.parse_args()
     if args.local:
         convert_pascalvoc_local2bin(args)

paddle/infrt/tests/models/abs_model.py

@@ -1,11 +1,11 @@
 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -20,6 +20,7 @@
 
 
 class AbsNet(paddle.nn.Layer):
+
     def __init__(self):
         super(AbsNet, self).__init__()
 
@@ -32,7 +33,6 @@ def forward(self, x):
     # build network
     model = AbsNet()
     # save inferencing format model
-    net = to_static(
-        model, input_spec=[InputSpec(
-            shape=[None, 1, 28, 28], name='x')])
+    net = to_static(model,
+                    input_spec=[InputSpec(shape=[None, 1, 28, 28], name='x')])
     paddle.jit.save(net, sys.argv[1])

paddle/infrt/tests/models/efficientnet-b4/model.py

@@ -1,11 +1,11 @@
 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -20,7 +20,6 @@
 import sys
 
 model = EfficientNet.from_name('efficientnet-b4')
-net = to_static(
-    model, input_spec=[InputSpec(
-        shape=[None, 3, 256, 256], name='x')])
+net = to_static(model,
+                input_spec=[InputSpec(shape=[None, 3, 256, 256], name='x')])
 paddle.jit.save(net, sys.argv[1])

paddle/infrt/tests/models/efficientnet-b4/net/__init__.py

@@ -1,11 +1,11 @@
 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.