Fix build test. (#1057)

* 2.2.1 release

* Fix build test.

* apply yapf

* ping yapf to 0.28.0

* fix build

* use yapf 0.29

* fix yapf

* include tests in make format.

* ping autoflake and isort version.

Author: luomai

CHANGELOG.md

@@ -80,16 +80,17 @@ To release a new version, please update the changelog as followed:
 
 ### Fixed
 
-- Fix README. (#PR 1044)
-- Fix package info. (#PR 1046)
+- Fix README. (#1044)
+- Fix package info. (#1046)
+- Fix build test (Using YAPF 0.29) (#1057)
 
 ### Removed
 
 ### Security
 
 ### Contributors
 
-- @luomai (PR #1044, 1046)
+- @luomai (#1044, #1046, #1057)
 
 
 ## [2.2.0] - 2019-09-13
@@ -150,7 +151,7 @@ This release is compatible with TensorFlow 2 RC1.
 - Replace tf.nn.func with tf.nn.func.\_\_name\_\_ in model config. (PR #994)
 - Add Reinforcement learning tutorials. (PR #995)
 - Add RNN layers with simple rnn cell, GRU cell, LSTM cell. (PR #998)
-- Update Seq2seq (#998) 
+- Update Seq2seq (#998)
 - Add Seq2seqLuongAttention model (#998)
 
 ### Fixed

Makefile

@@ -14,16 +14,17 @@ test:
 	python3 tests/files/test_utils_saveload.py
 
 format:
-	autoflake -i examples/*.py
-	autoflake -i tensorlayer/*.py
-	autoflake -i tensorlayer/**/*.py
+	autoflake -ir examples
+	autoflake -ir tensorlayer
+	autoflake -ir tests
 
 	isort -rc examples
 	isort -rc tensorlayer
+	isort -rc tests
 
-	yapf -i examples/*.py
-	yapf -i tensorlayer/*.py
-	yapf -i tensorlayer/**/*.py
+	yapf -ir examples
+	yapf -ir tensorlayer
+	yapf -ir tests
 
 install3:
 	pip3 install -U . --user

examples/basic_tutorials/tutorial_cifar10_cnn_static.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+import multiprocessing
 import time
+
 import numpy as np
-import multiprocessing
 import tensorflow as tf
 
 import tensorlayer as tl
@@ -123,7 +124,7 @@ def _map_fn_train(img, target):
 
 def _map_fn_test(img, target):
     # 1. Crop the central [height, width] of the image.
-    img = tf.image.resize_with_pad(img, 24, 24)  
+    img = tf.image.resize_with_pad(img, 24, 24)
     # 2. Subtract off the mean and divide by the variance of the pixels.
     img = tf.image.per_image_standardization(img)
     img = tf.reshape(img, (24, 24, 3))

examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py

@@ -1,4 +1,5 @@
 import time
+
 import numpy as np
 import tensorflow as tf
 

examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py

@@ -1,4 +1,5 @@
 import time
+
 import numpy as np
 import tensorflow as tf
 

examples/basic_tutorials/tutorial_mnist_mlp_static.py

@@ -1,5 +1,6 @@
 import pprint
 import time
+
 import numpy as np
 import tensorflow as tf
 

examples/basic_tutorials/tutorial_mnist_mlp_static_2.py

@@ -1,4 +1,5 @@
 import time
+
 import numpy as np
 import tensorflow as tf
 

examples/data_process/tutorial_fast_affine_transform.py

@@ -8,10 +8,10 @@
 import multiprocessing
 import time
 
-import cv2
 import numpy as np
 import tensorflow as tf
 
+import cv2
 import tensorlayer as tl
 
 # tl.logging.set_verbosity(tl.logging.DEBUG)
@@ -98,7 +98,6 @@ def _map_fn(image_path, target):
     st = time.time()
     for img, target in dataset:
         n_step += 1
-        pass
     assert n_step == n_epoch * n_data / batch_size
     print("dataset APIs took %fs for each image" % ((time.time() - st) / batch_size / n_step))  # CPU ~ 100%
 

examples/data_process/tutorial_tfrecord3.py

@@ -231,8 +231,8 @@ def distort_image(image, thread_id):
 
 
 def prefetch_input_data(
-        reader, file_pattern, is_training, batch_size, values_per_shard, input_queue_capacity_factor=16,
-        num_reader_threads=1, shard_queue_name="filename_queue", value_queue_name="input_queue"
+    reader, file_pattern, is_training, batch_size, values_per_shard, input_queue_capacity_factor=16,
+    num_reader_threads=1, shard_queue_name="filename_queue", value_queue_name="input_queue"
 ):
     """Prefetches string values from disk into an input queue.
 

examples/pretrained_cnn/tutorial_models_resnet50.py

@@ -8,8 +8,8 @@
 import time
 
 import numpy as np
-
 import tensorflow as tf
+
 import tensorlayer as tl
 from tensorlayer.models.imagenet_classes import class_names