help='Train/Restore Attribute')
parser.add_argument('--TC',
type=int,
default=0,
help='Train/Restore Classify')
parser.add_argument('--PRE',
type=int,
default=5,
help='1.CNN, 2.Model, 3.Classify')
globalV.FLAGS, _ = parser.parse_known_args()
print('\nLoad Data for {0}'.format(globalV.FLAGS.KEY))
(trainClass, trainAtt, trainVec, trainX, trainY,
trainYAtt), (valClass, valAtt, valVec, valX, valY,
valYAtt), (testClass, testAtt, testVec, testX, testY,
testYAtt) = loadData.getData()
if globalV.FLAGS.KEY == 'SUN' or globalV.FLAGS.KEY == 'APY':
print(' {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(
'numClass', 'classAtt', 'classVec', 'inputX', 'outputY',
'outputAtt'))
print('Train: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(
len(trainClass), str(trainAtt.shape), str(trainVec.shape),
str(trainX.shape), str(trainY.shape), str(trainYAtt.shape)))
print('Valid: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(
len(valClass), str(valAtt.shape), str(valVec.shape),
str(valX.shape), str(valY.shape), str(valYAtt.shape)))
print('Test: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(
len(testClass), str(testAtt.shape), str(testVec.shape),
str(testX.shape), str(testY.shape), str(testYAtt.shape)))
else:
print(' {0:>10} {1:>12} {2:>10} {3:>20} {4:>10}'.format(
parser.add_argument('--KEY', type=str, default='APY',help='Choose dataset (AWA2, CUB, SUN, APY)')
parser.add_argument('--maxSteps', type=int, default=1, help='Number of steps to run trainer.')
parser.add_argument('--lr', type=float, default=1e-4, help='Initial learning rate')
parser.add_argument('--width', type=int, default=300, help='Width')
parser.add_argument('--height', type=int, default=300, help='Height')
parser.add_argument('--numClass', type=int, default=32, help='Number of class')
parser.add_argument('--numAtt', type=int, default=640, help='Dimension of Attribute')
parser.add_argument('--batchSize', type=int, default=32, help='Number of batch size')
parser.add_argument('--TD', type=int, default=0, help='Train/Restore Darknet')
parser.add_argument('--TA', type=int, default=0, help='Train/Restore Attribute')
parser.add_argument('--TC', type=int, default=0, help='Train/Restore Classify')
parser.add_argument('--PRE', type=int, default=4, help='1.CNN, 2.Model, 3.Classify')
globalV.FLAGS, _ = parser.parse_known_args()
print('\nLoad Data for {0}'.format(globalV.FLAGS.KEY))
(trainClass, trainAtt, trainVec, trainX, trainY, trainYAtt), (valClass, valAtt, valVec, valX, valY, valYAtt), (testClass, testAtt, testVec, testX, testY, testYAtt) = loadData.getData()
if globalV.FLAGS.KEY == 'SUN' or globalV.FLAGS.KEY == 'APY':
print(' {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format('numClass', 'classAtt', 'classVec','inputX', 'outputY', 'outputAtt'))
print('Train: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(len(trainClass), str(trainAtt.shape), str(trainVec.shape), str(trainX.shape), str(trainY.shape), str(trainYAtt.shape)))
print('Valid: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(len(valClass), str(valAtt.shape), str(valVec.shape), str(valX.shape), str(valY.shape), str(valYAtt.shape)))
print('Test: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10} {5:>12}'.format(len(testClass), str(testAtt.shape), str(testVec.shape), str(testX.shape), str(testY.shape), str(testYAtt.shape)))
else:
print(' {0:>10} {1:>12} {2:>10} {3:>20} {4:>10}'.format('numClass', 'classAtt', 'classVec','inputX', 'outputY'))
print('Train: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10}'.format(len(trainClass), str(trainAtt.shape), str(trainVec.shape), str(trainX.shape), str(trainY.shape)))
print('Valid: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10}'.format(len(valClass), str(valAtt.shape), str(valVec.shape), str(valX.shape), str(valY.shape)))
print('Test: {0:>10} {1:>12} {2:>10} {3:>20} {4:>10}'.format(len(testClass), str(testAtt.shape), str(testVec.shape), str(testX.shape), str(testY.shape)))
# Index with total classes
def printClassName(pos):
if pos < len(trainClass):
return trainClass[pos]