Commit 58d3ceb4 authored by Diego André Sant'Ana's avatar Diego André Sant'Ana 🤞

Otimized for Computer Low Memory

parent ac84e5ca
......@@ -76,6 +76,12 @@
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......
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......@@ -6,6 +6,7 @@ except Exception as e:
WekaClassifiers = None
print(e)
try:
from .cnn_caffe import CNNCaffe
except Exception as e:
......@@ -27,7 +28,7 @@ try:
from .cnn_pseudo_label_keras import CNNPseudoLabel
except Exception as e:
CNNPseudoLabel = None
print e.message
print(e)
try:
......
......@@ -223,10 +223,10 @@ class CNNPseudoLabel(Classifier):
new_file=os.path.splitext(file)[0] + ".png"
img.save(File.make_path(predict_directory,
'png', new_file), "PNG", quality=100)
except Exception, e:
print e
except Exception as e:
print(e)
else:
print File.make_path(predict_directory, file)
print (File.make_path(predict_directory, file))
os.symlink(File.make_path(predict_directory, file),
File.make_path(predict_directory, 'png', file))
......@@ -244,7 +244,7 @@ class CNNPseudoLabel(Classifier):
if self.pseudo_label.weight_path is not None:
self.create_model()
self.model.load_weights(self.pseudo_label.weight_path)
except Exception, e:
except Exception as e:
raise IException("Can't load the model in " +
self.pseudo_label.weight_path + str(e))
......
......@@ -433,8 +433,8 @@ class PseudoLabel:
# Mini-batch
#==========================
if (self.print_pseudo_generate):
print ''
print 'Generating pseudo-labels...'
print('')
print('Generating pseudo-labels...')
verbose = 1
else:
verbose = 0
......
......@@ -283,4 +283,3 @@ class WekaClassifiers(Classifier):
remove(directory+'/'+test_file+'.arff')
return predicted[0]
\ No newline at end of file
......@@ -106,10 +106,22 @@ class FeatureExtractor(object):
# start job for each extractor
self.job_extractor(dataset, cl, classes)
# Output is in kb, here I convert it in Mb for readability
RAM_stats = self.getRAMinfo()
RAM_total = round(int(RAM_stats[0]) / 1000,1)
RAM_used = round(int(RAM_stats[1]) / 1000,1)
print("RAM Total : "+str(RAM_total))
print("RAM Used : "+str(RAM_used))
self.print_console("Wait a moment, the threads are processing "+str(len(self.threads)) +" images, it may be delayed depending on the size or quantity of the images!")
with tqdm(total=len(self.threads)) as pbar:
for t in self.threads:
t.start()
if((RAM_total)<10000):#se menor que 10gb
RAM_stats = self.getRAMinfo()
RAM_used = round(int(RAM_stats[1]) / 1000,1)
if((RAM_total-RAM_used)<2000):
t.join()
pbar.update(1)
pbar.close()
......@@ -296,3 +308,11 @@ class FeatureExtractor(object):
self.tkParent.append_log( mensagem)
self.tkParent._root.update_idletasks()
def getRAMinfo(self):
p = os.popen('free')
i = 0
while 1:
i = i + 1
line = p.readline()
if i==2:
return(line.split()[1:4])
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