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 _auto_eval(self):
data_i = _eval_func_single(self.y_true,
self.y_pred,
metric=self.metric)
self.t = data_i["thresh"]
self.kappa = data_i["kappa"]
self.acc = data_i["accuracy"]
self.jaccard = data_i["jaccard"]
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 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
y_pred_lst.append(y_pred_i[..., 1])
y_pred_avg = np.mean(y_pred_lst, axis=0)
return y_pred_avg
if 1:
# Average out prediction
y_pred_avg = average_out_pred()
if 0:
concurrent([y_pred_avg])
y_pred_avg2 = np.stack([1 - y_pred_avg, y_pred_avg], axis=-1)
data_i = _eval_func_single(get_crop(img_y), get_crop(y_pred_avg2))
print(data_i)
from performance.testing import get_y_pred_thresh
thresh = df.iloc[i_max]['thresh']
y_thresh = get_y_pred_thresh(y_pred, thresh=thresh)
concurrent([img_x[..., :3], y_pred[..., 0], y_thresh[..., 0]])
im_pred = get_crop(y_thresh)
b_pred = im_pred[..., 1]
# For U-Net
model_name = 'unet'