def _data10nat():
# Sparse annotations
train_data = get_10lamb_old(5)
from datasets.examples import get_10lamb
from data.conversion_tools import annotations2y
train_data.y_tr = annotations2y(get_10lamb().get("annot_tflearning"))
return train_data
def get_10lamb_6patches(mod):
img_x = x_from_df(get_10lamb(), mod)
df_kfold_annot = get_10lamb_kfold()
lst_names = [f'annot_{i+1}' for i in range(6)]
y_img_lst = list(map(annotations2y, df_kfold_annot.get(lst_names)))
return KFoldTrainData(img_x, y_img_lst)
def get_10lamb_old(mod):
"""
Face of the lamb on panel 10
:param mod:
:return:
"""
img_x, img_y = xy_from_df(get_10lamb(), mod)
train_data = TrainData(img_x, img_y, np.zeros(img_y.shape))
return train_data
def data_lamb():
img_x, _ = xy_from_df(get_10lamb(), 5)
if 0:
img_y_val = annotations2y(get_10lamb_kfold().get("annot_clean_comb"))
else:
# "Improved" evaluation set
from datasets.default_trainingsets import get_10lamb_6patches
kFoldTrainData = get_10lamb_6patches(5)
img_y_val_lst = []
for i in [1, 2, 3, 5]:
y_i = kFoldTrainData.k_split_i(i).get_y_test()
img_y_val_lst.append(y_i)
img_y_val = np.sum(img_y_val_lst, axis=0)
return img_x, img_y_val
def load_data(data_name, n_per_class, seed=None):
def _data10nat():
# Sparse annotations
train_data = get_10lamb_old(5)
from datasets.examples import get_10lamb
from data.conversion_tools import annotations2y
train_data.y_tr = annotations2y(get_10lamb().get("annot_tflearning"))
return train_data
if data_name == '13botright':
train_data = get_13botleftshuang(5, n_per_class=n_per_class)
elif data_name == '19botright':
train_data = get_19SE_shuang(5, n_per_class=n_per_class, seed=seed)
elif data_name == '19botrightcrack':
train_data = get_19SE_shuang_crack(5, n_per_class=n_per_class)
elif data_name == '19botrightcrack3':
train_data = get_19SE_shuang_crack(5,
n_per_class=n_per_class,
n_outputs=3)
elif data_name == '1319':
train_data = get_1319(5)
elif data_name == '1319botright':
from datasets.training import TrainData
a13 = load_data("13botright", n_per_class)
a19 = load_data("19botright", n_per_class)
img_x = [a13.get_x_train(), a19.get_x_train()]
img_y_train = [a13.get_y_train(), a19.get_y_train()]
img_y_val = [a13.get_y_test(), a19.get_y_test()]
train_data = TrainData(img_x, img_y_train, img_y_val)
elif data_name.split('_')[-1] == '10':
# Sparse annotations
train_data = get_10lamb_old(5)
elif data_name.split('_')[-1] == '101319':
from datasets.default_trainingsets import xy_from_df, get_13zach, get_19hand, get_10lamb, TrainData
img_x10, img_y10 = xy_from_df(get_10lamb(), 5)
img_x13, img_y13 = xy_from_df(get_13zach(), 5)
img_x19, img_y19 = xy_from_df(get_19hand(), 5)
img_x = [img_x10, img_x13, img_x19]
img_y = [img_y10, img_y13, img_y19]
# No test data
train_data = TrainData(
img_x, img_y, [np.zeros(shape=img_y_i.shape) for img_y_i in img_y])
elif data_name.split('_')[-1] == '10nat':
train_data = _data10nat()
elif data_name.split('_')[-1] == '10nat1319':
from datasets.default_trainingsets import TrainData
train_data10 = _data10nat()
train_data1319 = get_1319(5)
img_x = [train_data10.get_x_train()] + train_data1319.get_x_train()
img_y = [train_data10.get_y_train()] + train_data1319.get_y_train()
train_data = TrainData(
img_x, img_y, [np.zeros(shape=img_y_i.shape) for img_y_i in img_y])
else:
raise ValueError(data_name)
from data.preprocessing import rescale0to1
train_data.x = rescale0to1(train_data.x)
return train_data