def average_out_pred(r=2):
model_name = 'ti-unet'
path = os.path.join(
folder_base,
f'net_weight/{data}/{model_name}_d1_k{k}_n80/w_{1}.h5')
model_i = load_model_quick(path)
neural_net_i = NeuralNet(model_i, w_ext=10, norm_x=True)
y_pred_lst = []
r = 2
for epoch_i in range(epoch - r, epoch + r + 1): # epochs
neural_net_i.load(path.rsplit('/', 1)[0], epoch_i) # Load
try:
y_pred_i = neural_net_i.predict(img_x)
except Exception as e:
print(e)
continue
y_pred_lst.append(y_pred_i[..., 1])
y_pred_avg = np.mean(y_pred_lst, axis=0)
return y_pred_avg
def eval_3outputs():
folder_base = 'C:/Users/admin/Data/ghent_altar/' if os.name == 'nt' else '/scratch/lameeus/data/ghent_altar/'
assert data == '19botrightcrack3'
k = 9
epoch = 25
model_name = 'ti-unet'
folder_base = 'C:/Users/admin/Data/ghent_altar/' if os.name == 'nt' else '/scratch/lameeus/data/ghent_altar/'
path = os.path.join(
folder_base,
f'net_weight/{data}/{model_name}_d1_k{k}_n80/w_{epoch}.h5')
model = load_model_quick(path)
neural_net = NeuralNet(model, w_ext=10, norm_x=True)
from scripts.journal_paper.comparison_sh.shared import load_data
a = load_data("19botright", 80)
img_x, y_eval = a.get_x_train(), a.get_y_test()
y_pred = neural_net.predict(img_x)
assert y_pred.shape[-1] == 3
y_pred2 = np.stack([1 - y_pred[..., 1], y_pred[..., 1]], axis=-1)
data_i = _eval_func_single(y_eval, y_pred2)
print(data_i)
return
def __init__(self):
# data
self.data()
# Load model(s)
model_name = 'unet' # ['simple', 'ti-unet', 'unet']:
folder = f'C:/Users/admin/Data/ghent_altar/net_weight/{model_name}_d1_k9_n80'
epoch = 1
path = f'C:/Users/admin/Data/ghent_altar/net_weight/{model_name}_d1_k9_n80/w_{epoch}.h5'
from scripts.scripts_performance.main_performance import load_model_quick
model = load_model_quick(path)
neural_net = NeuralNet(model, w_ext=10, norm_x=True)
model.summary()
for epoch in range(1, 10 + 1):
print('epoch', epoch)
neural_net.load(folder, epoch)
# Predict
y_pred = neural_net.predict(self.img_x)
if 0:
plt.imshow(y_pred[..., 0])
plt.show()
for val_name in self.val:
print(val_name)
y_true_val = self.val[val_name]
data_i = _eval_func_single(y_true_val, y_pred)
print(data_i)
# TODO best performing (ti-unet: 4)
neural_net.load(folder, 4)
y_pred = neural_net.predict(self.img_x)
from performance.testing import get_y_pred_thresh
y_pred_thresh = get_y_pred_thresh(y_pred, data_i['thresh'])
concurrent([
self.img_x[..., :3], self.img_y[..., 0], y_pred[..., 0],
y_pred_thresh[..., 0]
])
y_pred
def pred_epochs():
img_x, img_y_val = data_lamb()
d = 2
k = 10
model_name = 'ti-unet'
train_data = '1319_10nat'
w_ext = 10 if d == 1 else 26
y_pred_lst = []
n = []
for epoch in range(10, 101, 10):
print(epoch)
epoch_start = 50
epoch_corr = epoch + epoch_start if train_data[:5] == '1319_' else epoch
path = f'C:/Users/admin/Data/ghent_altar/net_weight/{train_data}/{model_name}_d{d}_k{k}/w_{epoch_corr}.h5'
try:
model = load_model_quick(path)
except Exception as e:
print(e)
continue
neural_net = NeuralNet(model, w_ext=w_ext, norm_x=True)
y_pred = neural_net.predict(img_x)
if 0:
data_i = _eval_func_single(img_y_val, y_pred, metric='kappa')
print(data_i)
data_i = _eval_func_single(img_y_val, y_pred, metric='jaccard')
print(data_i)
y_pred_lst.append(y_pred)
n.append(epoch)
concurrent([a[..., 1] for a in y_pred_lst], n)
plt.show()
return 1
def main():
b_encoder_fixed = False
info_enc_fixed = '_enc_fixed'
folder_weights = '/scratch/lameeus/data/ghent_altar/net_weight/10lamb_kfold_pretrained'
folder_save = '/home/lameeus/data/ghent_altar/dataframes'
filename_single = f'pretrained_unet_10lamb_kfold_single'
filename_avg_pred = f'pretrained_unet_10lamb_kfold_avgpred'
folder_weights += info_enc_fixed if b_encoder_fixed else ''
filename_single += info_enc_fixed if b_encoder_fixed else ''
filename_avg_pred += info_enc_fixed if b_encoder_fixed else ''
fold_range = range(6)
# fold_range = [0, 1]
k = 10
epoch_range = range(1, 40 + 1)
w_ext_in = 28
k_fold_train_data = get_10lamb_6patches(5) # 5 is the number of modalities
train_data_all = k_fold_train_data.get_train_data_all()
img_x = train_data_all.get_x_train()
img_x = rescale0to1(img_x)
img_clean = img_x[..., :3]
img_y_all = train_data_all.get_y_train()
b_plot = False
for i_fold in fold_range:
print(i_fold)
img_y_te = k_fold_train_data.k_split_i(i_fold).get_y_test()
# Init for range epochs
lst_data_single = []
lst_data_avg_pred = []
list_y_pred = []
model = None
for epoch in np.sort(epoch_range)[::-1]:
filepath_model = os.path.join(
folder_weights, f'unet_enc_k{k}_ifold{i_fold}/w_{epoch}.h5')
model = load_model_quick(filepath_model, model=model)
n = NeuralNet(model, w_ext=w_ext_in)
y_pred = n.predict(img_x)
"""
Average out predictions
"""
list_y_pred.append(y_pred)
y_avg_pred = np.mean(list_y_pred, axis=0)
thresh_single = optimal_test_thresh_equal_distribution(
img_y_all, y_pred)
thresh_avg_pred = optimal_test_thresh_equal_distribution(
img_y_all, y_avg_pred)
y_pred_bin = np.greater_equal(y_pred[..., 1], thresh_single)
dict_perf = foo_performance(img_y_te, y_pred, thresh_single)
print(dict_perf)
if b_plot:
concurrent([
y_pred_bin, img_clean,
semi_transparant(img_clean, y_pred_bin),
semi_transparant(img_clean, img_y_te[..., 1].astype(bool))
])
data_single_i = {'k': k, 'i_fold': i_fold, 'epoch': epoch}
data_avg_pred_i = {
'k': k,
'i_fold': i_fold,
'epoch_start': epoch,
'epoch_end': max(epoch_range)
}
data_single_i.update(dict_perf)
data_avg_pred_i.update(
foo_performance(img_y_te, y_avg_pred, thresh_avg_pred))
lst_data_single.append(data_single_i)
lst_data_avg_pred.append(data_avg_pred_i)
df_single = pd.DataFrame(lst_data_single)
df_avg_pred = pd.DataFrame(lst_data_avg_pred)
path_single = os.path.join(folder_save, filename_single + '.csv')
path_avg_pred = os.path.join(folder_save, filename_avg_pred + '.csv')
pandas_save(path_single, df_single, append=True)
pandas_save(path_avg_pred, df_avg_pred, append=True)
return
def transfer_learning(
epoch=25, # Could check a few
b_plot=False):
d = 2 # 1, 2
img_x, img_y_val = data_lamb()
k = 10
model_name = 'ti-unet'
w_ext = 10 if d == 1 else 26
# train_data:
y_pred_lst = []
n = ['clean']
# train_data_lst = ['1319_10', '10', '1319', '1319_101319']
train_data_lst = ['10nat', '1319_10nat', '1319_10nat1319', '1319']
data_i_lst = {}
for train_data in train_data_lst:
print(train_data)
epoch_start = 50
epoch_corr = epoch + epoch_start if train_data[:5] == '1319_' else epoch
if train_data == '1319':
epoch_corr = 50
path = f'C:/Users/admin/Data/ghent_altar/net_weight/{train_data}/{model_name}_d{d}_k{k}/w_{epoch_corr}.h5'
try:
model = load_model_quick(path)
except Exception as e:
print(e)
continue
neural_net = NeuralNet(model, w_ext=w_ext, norm_x=True)
y_pred = neural_net.predict(img_x)
# baseline
data_i = _eval_func_single(img_y_val, y_pred, metric='kappa')
print(data_i)
if 0:
"""
Checking which
baseline ~ .22
i = 0: .268, Remove huge improvement ( a lot of "green" background annotated as paint loss)
i = 1: .228 Keep!
i = 2: .179 keep! Drop (keep!!
i = 3: .159 keep! Even more important
i = 4: .252 Remove (huge problem right top)
i = 5: .233 Keep, quit relevant
"""
from datasets.default_trainingsets import get_10lamb_6patches
kFoldTrainData = get_10lamb_6patches(5)
_eval_func_single(
kFoldTrainData.k_split_i(0).get_y_train(),
y_pred,
metric='kappa') # Check what is influence without!
data_i_lst[train_data] = data_i
data_i = _eval_func_single(img_y_val, y_pred, metric='jaccard')
print(data_i)
y_pred_lst.append(y_pred)
n.append(train_data)
# plt.imshow(neural_net.predict(img_x[::2,::2,:])[..., 1])
if b_plot:
concurrent([img_x[..., :3]] + [a[..., 1] for a in y_pred_lst], n)
if 0:
from figures_paper.overlay import semi_transparant
from data.datatools import imread, imsave
t = [data_i_lst[n_i]['thresh'] for n_i in train_data_lst]
p = []
for i, train_data in enumerate(train_data_lst):
b = np.greater_equal(y_pred_lst[i][..., 1], t[i])
k = semi_transparant(img_x[..., :3], b, 0)
p.append(k)
imsave(
os.path.join(
"C:/Users/admin/OneDrive - ugentbe/data/images_paper",
train_data + '.png'), k)
concurrent(p)
return data_i_lst
f'{data}_{"ti-unet"}_n_per_class.csv'),
delimiter=';')
i_max = df['kappa'].idxmax()
k, epoch = map(int, df.iloc[i_max][['k', 'epoch']])
model_name = 'ti-unet'
folder_base = 'C:/Users/admin/Data/ghent_altar/' if os.name == 'nt' else '/scratch/lameeus/data/ghent_altar/'
path = os.path.join(
folder_base,
f'net_weight/{data}/{model_name}_d1_k{k}_n80/w_{epoch}.h5')
model = load_model_quick(path)
neural_net = NeuralNet(model, w_ext=10, norm_x=True)
# Image
from scripts.journal_paper.comparison_sh.shared import load_data
if data == '1319botright':
a = load_data("19botright", n_per_class=80)
else:
a = load_data(data, n_per_class=80)
img_x, img_y = a.get_x_train(), a.get_y_test()
y_pred = neural_net.predict(img_x)
if 1: