def __init__(self, which_set, numOfClasses,
numOfExamplesPerClass, axes=('c', 0, 1, 'b')):
self.height = 32
self.width = 100
self.axes = axes
self.dtype = 'uint8'
self.examples = []
self.img_shape = (1, self.height, self.width)
self.img_size = numpy.prod(self.img_shape)
self.numOfClasses = numOfClasses
self.numOfExamplesPerClass = numOfExamplesPerClass
self.examplesPerClassCount = {}
self.which_set = which_set
if which_set == "train":
self.fileToLoadFrom = "annotation_train.txt"
elif which_set == "test":
self.fileToLoadFrom = "annotation_test.txt"
elif which_set == "valid":
self.fileToLoadFrom = "annotation_val.txt"
else:
raise ValueError("Set not recognized")
self.datapath = Config.getDatapath()
self.preprocess = Config.doPreprocess()
self.loadData()
random.seed()
view_converter = dense_design_matrix.DefaultViewConverter((self.height, self.width, 1),
axes)
super(MJSYNTH, self).__init__(X=numpy.cast['float32'](self.x), y=self.y, view_converter=view_converter,
y_labels=self.numOfClasses)
def __init__(self,
which_set,
numOfClasses,
numOfExamplesPerClass,
axes=('c', 0, 1, 'b')):
self.height = 32
self.width = 100
self.axes = axes
self.dtype = 'uint8'
self.examples = []
self.img_shape = (1, self.height, self.width)
self.img_size = numpy.prod(self.img_shape)
self.numOfClasses = numOfClasses
self.numOfExamplesPerClass = numOfExamplesPerClass
self.examplesPerClassCount = {}
self.which_set = which_set
if which_set == "train":
self.fileToLoadFrom = "annotation_train.txt"
elif which_set == "test":
self.fileToLoadFrom = "annotation_test.txt"
elif which_set == "valid":
self.fileToLoadFrom = "annotation_val.txt"
else:
raise ValueError("Set not recognized")
self.datapath = Config.getDatapath()
self.preprocess = Config.doPreprocess()
self.loadData()
random.seed()
view_converter = dense_design_matrix.DefaultViewConverter(
(self.height, self.width, 1), axes)
super(MJSYNTH, self).__init__(X=numpy.cast['float32'](self.x),
y=self.y,
view_converter=view_converter,
y_labels=self.numOfClasses)
