def reshape_image(self):
if self.x_train.shape[0] > 0:
self.x_train = reshape_func(self.x_train, self.subcarrier_spacing)
if self.x_test.shape[0] > 0:
self.x_test = reshape_func(self.x_test, self.subcarrier_spacing)
if self.no_label_test is not None:
for key in self.no_label_test:
for idx in self.no_label_test[key]:
self.no_label_test[key][idx] = reshape_func(self.no_label_test[key][idx], self.subcarrier_spacing)
def load_image(self,
training_mode,
from_file,
train_data_class={},
test_data_class={}):
x_train, x_test, y_train, y_test = np.array([]), np.array(
[]), np.array([]), np.array([])
self.classes_num = {}
for label_name, o in self.label.items():
if o not in self.classes_num.keys():
self.classes_num[o] = {'train_num': 0, 'test_num': 0}
if training_mode:
if from_file:
filename = self.file_prefix + 'training_' + str(o) + '.dat'
print('train filename ' + filename)
temp_image = np.fromfile(filename, dtype=np.complex64)
temp_image = np.reshape(temp_image,
(-1, ) + self.data_shape)
else:
temp_image = train_data_class[o]
temp_image = reshape_func(temp_image, self.subcarrier_spacing)
temp_label = np.full((temp_image.shape[0], 1), o, np.int8)
self.classes_num[o]['train_num'] += temp_label.shape[0]
x_train = append_array(x_train, temp_image)
y_train = append_array(y_train, temp_label)
if from_file:
test_filename = self.file_prefix + 'training_test_' + str(
o) + '.dat'
else:
if from_file:
test_filename = self.file_prefix + 'test_' + str(
o) + '.dat'
if from_file:
print('test filename ' + test_filename)
temp_image = np.fromfile(test_filename, dtype=np.complex64)
temp_image = np.reshape(temp_image, (-1, ) + self.data_shape)
else:
temp_image = test_data_class[o]
if temp_image.shape[0] == 0:
continue
temp_image = reshape_func(temp_image, self.subcarrier_spacing)
temp_label = np.full((temp_image.shape[0], 1), o, np.int8)
self.classes_num[o]['test_num'] += temp_label.shape[0]
x_test = append_array(x_test, temp_image)
y_test = append_array(y_test, temp_label)
self.x_train, self.y_train, self.x_test, self.y_test = x_train, y_train, x_test, y_test
print(self.classes_num)
if self.x_train.shape[0] != self.y_train.shape[0]:
raise ValueError('x_train and y_train size mismatch')
if self.x_test.shape[0] != self.y_test.shape[0]:
raise ValueError('x_test and y_test size mismatch')
def add_image_no_label(self, test_data_class):
for key in test_data_class:
for idx in test_data_class[key]:
test_data_class[key][idx] = reshape_func(test_data_class[key][idx], self.subcarrier_spacing)
self.no_label_test = test_data_class