本文整理汇总了Python中utility.Utility.report_memused方法的典型用法代码示例。如果您正苦于以下问题：Python Utility.report_memused方法的具体用法？Python Utility.report_memused怎么用？Python Utility.report_memused使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utility.Utility的用法示例。
在下文中一共展示了Utility.report_memused方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: load_training
# 需要导入模块: from utility import Utility [as 别名]
# 或者: from utility.Utility import report_memused [as 别名]
#.........这里部分代码省略.........
# skip if no annotations found
if n_data == 0:
continue
# add sample to the training set
if offset == 0:
x = x_data
y = y_data
p = np.ones( n_data, dtype=np.int )
else:
x = np.vstack( (x, x_data) )
y = np.hstack( (y, y_data) )
p = np.hstack( (p, np.ones( n_data, dtype=np.int )) )
# keep track of each image's data in an entry for
# easier replacement.
entries.append( Entry( image.id, offset, n_data ) )
#Utility.report_memused()
Utility.report_status('x', '(%d bytes)'%(x.nbytes))
Utility.report_status('y', '(%d bytes)'%(y.nbytes))
Utility.report_status('.','.')
# bailout if no entries found
if len(entries) == 0:
Utility.report_status('Fetching new data', 'None Found')
return
Utility.report_status( 'Loading new data', 'please wait')
# bailout if no current entries
if len(self.entries) > 0:
#append old entries after the new entries
offset = len(y)
print entries[-1].name, entries[-1].offset, entries[-1].length
mask = np.ones( len(self.y), dtype=bool)
names = [ e.name for e in entries ]
for entry in self.entries:
if entry.name in names:
mask[ entry.offset : entry.offset+entry.length ] = False
else:
entry.offset = offset
offset += entry.length
entries.append( entry )
print entry.name, entry.offset, entry.length
x_keep = self.x[ mask ]
y_keep = self.y[ mask ]
p_keep = self.p[ mask ]
x = np.vstack( (x, x_keep) )
y = np.hstack( (y, y_keep) )
p = np.hstack( (p, p_keep) )
if len( np.unique( y ) ) <= 1:
print 'not enough labels specified...'
return
n_data = len(y)
#DB.finishLoadingTrainingset( project.id )
print '=>n_data:', n_data
# save the data
self.x = x
self.y = y
self.p = p
self.n_train = min(Data.TrainSuperBatchSize, n_data)
#self.n_train = self.get_size( self.n_train )
self.n_train = min(self.n_train, Data.MaxTrainSuperBatchSize)
print 'n_train:', self.n_train, Data.TrainSuperBatchSize
self.i = np.random.choice(n_data, n_data, replace=False)
#self.i_valid = self.i[:self.ValidSuperBatchSize]
#self.n_valid = len(self.i_valid)
self.i_batch = 0
self.n_batches = n_data/self.n_train
self.entries = entries
self.i_randomize = 0
self.data_changed = True
self.i_train = []
self.avg_losses = []
self.last_avg_loss = 0
Utility.report_status('.','.')
Utility.report_status('loading complete','.')
Utility.report_status('#samples','(%d)'%(n_data))
Utility.report_status('x shape','(%d,%d)'%(self.x.shape[0], self.x.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y.nbytes))
Utility.report_memused()示例2: load_validation
# 需要导入模块: from utility import Utility [as 别名]
# 或者: from utility.Utility import report_memused [as 别名]
#.........这里部分代码省略.........
# Load training samples for each image.
for image in images:
Utility.report_status( 'loading validation image', image.id)
print 'ttime:', image.trainingTime
print 'atime:', image.annotationTime
print 'tstat:', image.trainingStatus
offset = len( entries )
# generate samples for the image
#data = self.gen_samples( project, image.id, n_samples_per_image )
data = self.gen_samples( Paths.ValidGrayscale, self.project, image.id, n_samples_per_image )
x_data = data[0]
y_data = data[1]
n_data = len( y_data )
# skip if no annotations found
if n_data == 0:
continue
# add sample to the training set
if offset == 0:
x = x_data
y = y_data
p = np.ones( n_data, dtype=np.int )
else:
x = np.vstack( (x, x_data) )
y = np.hstack( (y, y_data) )
p = np.hstack( (p, np.ones( n_data, dtype=np.int )) )
# keep track of each image's data in an entry for
# easier replacement.
entries.append( Entry( image.id, offset, n_data ) )
#Utility.report_memused()
Utility.report_status('x', '(%d bytes)'%(x.nbytes))
Utility.report_status('y', '(%d bytes)'%(y.nbytes))
Utility.report_status('.','.')
# bailout if no entries found
if len(entries) == 0:
Utility.report_status('Fetching new data', 'None Found')
return
Utility.report_status( 'Loading new data', 'please wait')
# bailout if no current entries
if len(self.entries_valid) > 0:
#append old entries after the new entries
offset = len(y)
print entries[-1].name, entries[-1].offset, entries[-1].length
mask = np.ones( len(self.y_valid), dtype=bool)
names = [ e.name for e in entries ]
for entry in self.entries_valid:
if entry.name in names:
mask[ entry.offset : entry.offset+entry.length ] = False
else:
entry.offset = offset
offset += entry.length
entries.append( entry )
print entry.name, entry.offset, entry.length
x_keep = self.x_valid[ mask ]
y_keep = self.y_valid[ mask ]
p_keep = self.p_valid[ mask ]
x = np.vstack( (x, x_keep) )
y = np.hstack( (y, y_keep) )
p = np.hstack( (p, p_keep) )
if len( np.unique( y ) ) <= 1:
print 'not enough labels specified...'
return
n_data = len(y)
# save the data
self.x_valid = x
self.y_valid = y
self.p_valid = p
self.entries_valid = entries
#self.i_valid = np.random.choice(n_data, Data.ValidSuperBatchSize, replace=False)
self.n_valid = min(Data.ValidSuperBatchSize, n_data)
self.i_valid = np.random.choice(n_data, self.n_valid)
Utility.report_status('.','.')
Utility.report_status('loading complete','.')
Utility.report_status('#samples','(%d)'%(n_data))
Utility.report_status('x shape','(%d,%d)'%(self.x_valid.shape[0], self.x_valid.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x_valid.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x_valid.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y_valid.nbytes))
Utility.report_memused()
self.data_changed = True示例3: aload
# 需要导入模块: from utility import Utility [as 别名]
# 或者: from utility.Utility import report_memused [as 别名]
def aload(self, project):
self.projecft = project
# LOAD TRAINING DATA
train_new = len(self.entries) > 0
images = DB.getImages( project.id, purpose=0, new=train_new)
out = self.load_data(
Paths.TrainGrayscale,
images,
project,
self.x,
self.y,
self.p,
self.entries)
self.x = out[0]
self.y = out[1]
self.p = out[2]
self.entries = out[3]
n_samples = len(self.y)
self.i = np.arange( n_samples )
self.n_superbatch = int(n_samples/(Data.TrainSuperBatchSize + Data.ValidSuperBatchSize))
self.i_randomize = 0
self.data_changed = True
self.i_train = []
self.avg_losses = []
self.last_avg_loss = 0
if n_samples > 0:
Utility.report_status('---------training---------', '')
Utility.report_status('#samples','(%d)'%len(self.y))
Utility.report_status('x shape','(%d,%d)'%(self.x.shape[0], self.x.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y.nbytes))
print 'min:', np.min( self.x )
print 'max:', np.max( self.x )
print 'uy:', np.unique( self.y )
print 'x:', self.x[:5]
print 'y:', self.y[:5]
# LOAD VALIDATION IMAGES
valid_new = len(self.entries_valid) > 0
images = DB.getImages( project.id, purpose=1, new=valid_new )
out = self.load_data(
Paths.ValidGrayscale,
images,
project,
self.x_valid,
self.y_valid,
self.p_valid,
self.entries_valid)
self.x_valid = out[0]
self.y_valid = out[1]
self.p_valid = out[2]
self.entries_valid = out[3]
n_samples = len(self.y_valid)
self.i_valid = np.arange( n_samples )
if n_samples > 0:
Utility.report_status('---------validation---------', '')
Utility.report_status('#samples','(%d)'%len(self.y_valid))
Utility.report_status('x shape','(%d,%d)'%(self.x_valid.shape[0], self.x_valid.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x_valid.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x_valid.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y_valid.nbytes))
print 'min:', np.min( self.x_valid )
print 'max:', np.max( self.x_valid )
print 'uy:', np.unique( self.y_valid )
print 'x:', self.x_valid[:5]
print 'y:', self.y_valid[:5]
Utility.report_memused()
DB.finishLoadingTrainingset( project.id )