def __init__(self, resolution=None, input_shape=None):
super(Flower_DataSet, self).__init__()
if resolution is None:
resolution = [100, 100]
if input_shape is None:
input_shape = [-1]
path = '../data/flowers'
self.target_names = mu.list_dir(path)
images = []
idxs = []
for dx, dname in enumerate(self.target_names):
subpath = path + '/' + dname
filenames = mu.list_dir(subpath)
for fname in filenames:
if fname[-4:] != '.jpg':
continue
imagepath = os.path.join(subpath, fname)
pixels = mu.load_image_pixels(imagepath, resolution,
input_shape)
images.append(pixels)
idxs.append(dx)
self.image_shape = resolution + [3]
xs = np.asarray(images, np.float32)
ys = mu.onehot(idxs, len(self.target_names))
self.dataset_shuffle_data(xs, ys, 0.8)
def __init__(self, resolution=None, input_shape=None):
super(Office31Dataset, self).__init__()
if resolution is None:
resolution = [100, 100]
if input_shape is None:
input_shape = [-1]
path = '../data/domain_adaptation_images'
domain_names = mu.list_dir(path)
images = []
didxs, oidxs = [], []
object_names = None
for dx, dname in enumerate(domain_names):
domainpath = os.path.join(path, dname, 'images')
object_names = mu.list_dir(domainpath)
for ox, oname in enumerate(object_names):
objectpath = os.path.join(domainpath, oname)
filenames = mu.list_dir(objectpath)
for fname in filenames:
if fname[-4:] != '.jpg':
continue
imagepath = os.path.join(objectpath, fname)
pixels = mu.load_image_pixels(imagepath, resolution,
input_shape)
images.append(pixels)
didxs.append(dx)
oidxs.append(ox)
self.image_shape = resolution + [3]
xs = np.asarray(images, np.float32) # shape(4110, 30000)
ys0 = mu.onehot(didxs, len(domain_names)) # ys0.shape(4110, 3)
ys1 = mu.onehot(oidxs, len(object_names)) # ys1.shape(4110, 31)
ys = np.hstack([ys0, ys1]) # ys.shape(4110, 34)
self.dataset_shuffle_data(xs, ys, 0.8)
self.target_names = [domain_names, object_names]
self.cnts = [len(domain_names)]
def __init__(self, name):
super(DataSet, self).__init__(name)
# print("dataset init")
resolution = [100, 100]
input_shape = [-1]
self.initialize()
if self.name == 'abalone':
elif self.name == 'pulsar':
rows, _ = mu.load_csv('../data/pulsar_stars.csv')
data = np.asarray(rows, dtype='float32')
self.dataset_shuffle_data(data[:, :-1], data[:, -1:], 0.8)
self.target_names = ['별', '펄서']
elif self.name == 'steel':
rows, headers = mu.load_csv('../data/faults.csv')
data = np.asarray(rows, dtype='float32')
self.dataset_shuffle_data(data[:, :-7], data[:, -7:], 0.8)
self.target_names = headers[-7:]
elif self.name == 'pulsarselect':
rows, _ = mu.load_csv('../data/pulsar_stars.csv')
data = np.asarray(rows, dtype='float32')
self.dataset_shuffle_data(data[:, :-1], mu.onehot(data[:, -1], 2), 0.8)
self.target_names = ['별', '펄서']
elif self.name == 'flower':
path = '../data/flowers'
self.target_names = mu.list_dir(path)
images = []
idxs = []
for dx, dname in enumerate(self.target_names):
subpath = path + '/' + dname
filenames = mu.list_dir(subpath)
for fname in filenames:
if fname[-4:] != '.jpg':
continue
imagepath = os.path.join(subpath, fname)
pixels = mu.load_image_pixels(imagepath, resolution, input_shape)
images.append(pixels)
idxs.append(dx)
self.image_shape = resolution + [3]
xs = np.asarray(images, np.float32)
ys = mu.onehot(idxs, len(self.target_names))
self.dataset_shuffle_data(xs, ys, 0.8)
elif self.name == 'office31':
path = '../data/domain_adaptation_images'
domain_names = mu.list_dir(path)
images = []
didxs, oidxs = [], []
object_names = None
for dx, dname in enumerate(domain_names):
domainpath = os.path.join(path, dname, 'images')
object_names = mu.list_dir(domainpath)
for ox, oname in enumerate(object_names):
objectpath = os.path.join(domainpath, oname)
filenames = mu.list_dir(objectpath)
for fname in filenames:
if fname[-4:] != '.jpg':
continue
imagepath = os.path.join(objectpath, fname)
pixels = mu.load_image_pixels(imagepath, resolution, input_shape)
images.append(pixels)
didxs.append(dx)
oidxs.append(ox)
self.image_shape = resolution + [3]
xs = np.asarray(images, np.float32) # shape(4110, 30000)
ys0 = mu.onehot(didxs, len(domain_names)) # ys0.shape(4110, 3)
ys1 = mu.onehot(oidxs, len(object_names)) # ys1.shape(4110, 31)
ys = np.hstack([ys0, ys1]) # ys.shape(4110, 34)
self.dataset_shuffle_data(xs, ys, 0.8)
self.target_names = [domain_names, object_names]
print(type(self.target_names[0]))
self.cnts = [len(domain_names)]
def __init__(self):
super(Pulsar_Select_DataSet, self).__init__()
rows, _ = mu.load_csv('../data/pulsar_stars.csv')
data = np.asarray(rows, dtype='float32')
self.dataset_shuffle_data(data[:, :-1], mu.onehot(data[:, -1], 2), 0.8)
self.target_names = ['별', '펄서']