Refactoring about None, spacing and indent. (apache#5477)

example/bayesian-methods/utils.py

@@ -40,7 +40,7 @@ def get_executor(sym, ctx, data_inputs, initializer=None):
     exe = sym.bind(ctx=ctx, args=dict(params, **data_inputs),
                    args_grad=params_grad,
                    aux_states=aux_states)
-    if initializer != None:
+    if initializer is not None:
         for k, v in params.items():
             initializer(k, v)
     return exe, params, params_grad, aux_states

example/bi-lstm-sort/sort_io.py

@@ -118,11 +118,11 @@ def __init__(self, path, vocab, buckets, batch_size,
                  seperate_char=' <eos> ', text2id=None, read_content=None):
         super(BucketSentenceIter, self).__init__()
 
-        if text2id == None:
+        if text2id is None:
             self.text2id = default_text2id
         else:
             self.text2id = text2id
-        if read_content == None:
+        if read_content is None:
             self.read_content = default_read_content
         else:
             self.read_content = read_content

example/caffe/train_model.py

@@ -78,11 +78,11 @@ def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
         initializer        = mx.init.Xavier(factor_type="in", magnitude=2.34),
         **model_args)
 
-    if eval_metrics == None:
+    if eval_metrics is None:
         eval_metrics = ['accuracy']
         ## TopKAccuracy only allows top_k > 1
         for top_k in [5, 10, 20]:
-            eval_metrics.append(mx.metric.create('top_k_accuracy', top_k = top_k))
+            eval_metrics.append(mx.metric.create('top_k_accuracy', top_k=top_k))
 
     if batch_end_callback is not None:
         if not isinstance(batch_end_callback, list):

example/fcn-xs/solver.py

@@ -92,7 +92,7 @@ def fit(self, train_data, eval_data=None,
                 self.exector.outputs[0].wait_to_read()
                 batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch, eval_metric=eval_metric)
                 batch_end_callback(batch_end_params)
-            if epoch_end_callback != None:
+            if epoch_end_callback is not None:
                 epoch_end_callback(epoch, self.symbol, self.arg_params, self.aux_params)
             name, value = eval_metric.get()
             logger.info("                     --->Epoch[%d] Train-%s=%f", epoch, name, value)

example/kaggle-ndsb2/Preprocessing.py

@@ -50,7 +50,7 @@ def write_label_csv(fname, frames, label_map):
    fo = open(fname, "w")
    for lst in frames:
        index = int(lst[0].split("/")[3])
-       if label_map != None:
+       if label_map is not None:
            fo.write(label_map[index])
        else:
            fo.write("%d,0,0\n" % index)
@@ -69,7 +69,7 @@ def get_data(lst,preproc):
        data.append(img)
    data = np.array(data, dtype=np.uint8)
    data = data.reshape(data.size)
-   data = np.array(data,dtype=np.str_)
+   data = np.array(data, dtype=np.str_)
    data = data.reshape(data.size)
    return [data,result]
 

example/model-parallel-lstm/lstm.py

@@ -199,7 +199,7 @@ def setup_rnn_model(default_ctx,
                                 grad_req="add", group2ctx=group2ctx)
               max_rnn_exec = rnn_exec
         else:
-              assert max_rnn_exec != None
+              assert max_rnn_exec is not None
               rnn_exec = rnn_sym.bind(default_ctx, args=arg_arrays,
                             args_grad=args_grad,
                             grad_req="add", group2ctx=group2ctx,

example/model-parallel-lstm/lstm_ptb.py

@@ -23,7 +23,7 @@ def load_data(path, dic=None):
     content = content.split(' ')
     print("Loading %s, size of data = %d" % (path, len(content)))
     x = np.zeros(len(content))
-    if dic == None:
+    if dic is None:
         dic = {}
     idx = 0
     for i in range(len(content)):

example/multi-task/example_multi_task.py

@@ -64,7 +64,7 @@ def __init__(self, num=None):
     def update(self, labels, preds):
         mx.metric.check_label_shapes(labels, preds)
 
-        if self.num != None:
+        if self.num is not None:
             assert len(labels) == self.num
 
         for i in range(len(labels)):
@@ -73,7 +73,7 @@ def update(self, labels, preds):
 
             mx.metric.check_label_shapes(label, pred_label)
 
-            if i == None:
+            if i is None:
                 self.sum_metric += (pred_label.flat == label.flat).sum()
                 self.num_inst += len(pred_label.flat)
             else:

example/neural-style/end_to_end/data_processing.py

@@ -13,7 +13,7 @@ def PreprocessContentImage(path, short_edge, dshape=None):
     new_size = (int(img.shape[0] * factor), int(img.shape[1] * factor))
     resized_img = transform.resize(img, new_size)
     sample = np.asarray(resized_img) * 256
-    if dshape != None:
+    if dshape is not None:
         # random crop
         xx = int((sample.shape[0] - dshape[2]))
         yy = int((sample.shape[1] - dshape[3]))

example/rcnn/rcnn/pycocotools/coco.py

@@ -73,7 +73,7 @@ def __init__(self, annotation_file=None):
         self.catToImgs = {}
         self.imgs = {}
         self.cats = {}
-        if not annotation_file == None:
+        if annotation_file is not None:
             print 'loading annotations into memory...'
             tic = time.time()
             dataset = json.load(open(annotation_file, 'r'))
@@ -149,7 +149,7 @@ def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
                 anns = self.dataset['annotations']
             anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
             anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
-        if not iscrowd == None:
+        if iscrowd is not None:
             ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
         else:
             ids = [ann['id'] for ann in anns]
@@ -325,7 +325,7 @@ def loadRes(self, resFile):
         res.createIndex()
         return res
 
-    def download( self, tarDir = None, imgIds = [] ):
+    def download(self, tarDir=None, imgIds=[]):
         '''
         Download COCO images from mscoco.org server.
         :param tarDir (str): COCO results directory name

example/rnn-time-major/bucket_io.py

@@ -114,11 +114,11 @@ def __init__(self, path, vocab, buckets, batch_size,
                  time_major=True):
         super(BucketSentenceIter, self).__init__()
 
-        if text2id == None:
+        if text2id is None:
             self.text2id = default_text2id
         else:
             self.text2id = text2id
-        if read_content == None:
+        if read_content is None:
             self.read_content = default_read_content
         else:
             self.read_content = read_content

example/rnn/old/bucket_io.py

@@ -117,11 +117,11 @@ def __init__(self, path, vocab, buckets, batch_size,
                  seperate_char=' <eos> ', text2id=None, read_content=None, model_parallel=False):
         super(BucketSentenceIter, self).__init__()
 
-        if text2id == None:
+        if text2id is None:
             self.text2id = default_text2id
         else:
             self.text2id = text2id
-        if read_content == None:
+        if read_content is None:
             self.read_content = default_read_content
         else:
             self.read_content = read_content

example/speech-demo/io_func/feat_readers/stats.py

@@ -50,7 +50,7 @@ def GetMean(self):
         return self.mean
 
     def GetVariance(self):
-        return numpy.power(self.GetStd(),2)
+        return numpy.power(self.GetStd(), 2)
 
     def GetStd(self):
         return 1.0/self.invStd
@@ -64,7 +64,7 @@ def GetStatsFromList(self,fileList,featureFileHandler):
         stats = None
 
         for featureFile,label in featureList.FeatureList(fileList):
-            if stats == None:
+            if stats is None:
                 self.dim = self.getDimFromFile(featureFile,featureFileHandler)
                 stats    = _StreamVariance(self.dim)
 
@@ -117,9 +117,9 @@ def Save(self,filename):
         with open(filename,'wb') as f:
             dt = numpy.dtype([('magicNumber',(numpy.int32,1)),('numSamples',(numpy.int32,1)),('dim',(numpy.int32,1))])
             header=numpy.zeros((1,),dtype=dt)
-            header[0]['magicNumber']=21812
-            header[0]['numSamples']=self.populationSize
-            header[0]['dim']=self.mean.shape[0]
+            header[0]['magicNumber'] = 21812
+            header[0]['numSamples'] = self.populationSize
+            header[0]['dim'] = self.mean.shape[0]
             header.tofile(f)
 
             self.mean.astype(numpy.float32).tofile(f)
@@ -130,7 +130,7 @@ def Save(self,filename):
     import argparse
 
     parser = argparse.ArgumentParser(description='Print the mean and standard deviation from a stat file',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('filename',help="Name of the stat file")
+    parser.add_argument('filename', help="Name of the stat file")
     args = parser.parse_args()
     featureStats = FeatureStats()
     featureStats.Load(args.filename)

example/speech-demo/io_func/feat_readers/writer_kaldi.py

@@ -18,21 +18,21 @@ def __init__(self, scp_path, ark_path):
             raise Exception("output file needs to be little endian")
 
     def open(self):
-        self.out_ark = open(self.ark_path,"w")
-        self.out_scp = open(self.scp_path,"w")
+        self.out_ark = open(self.ark_path, "w")
+        self.out_scp = open(self.scp_path, "w")
 
     def open_or_fd(self):
         offset = None
         if self.ark_path[0] == '|':
             #self.out_ark = os.popen(sys.stdout, 'wb')
             self.out_ark = sys.stdout
         else:
-            self.out_ark = open(self.ark_path,"w")
+            self.out_ark = open(self.ark_path, "w")
     def write(self, uttID, data):
         assert data.dtype == numpy.float32
 
         self.out_ark.write(uttID + ' ')
-        if self.out_scp != None:
+        if self.out_scp is not None:
             start_offset = self.out_ark.tell()
 
         # write out ark
@@ -47,11 +47,11 @@ def write(self, uttID, data):
         self.out_ark.flush()
 
         # write out scp
-        if self.out_scp != None:
+        if self.out_scp is not None:
             scp_out = uttID + ' ' + self.ark_path + ':' + str(start_offset)
             self.out_scp.write(scp_out + '\n')
 
     def close(self):
         self.out_ark.close()
-        if self.out_scp != None:
+        if self.out_scp is not None:
             self.out_scp.close()