loss = 'binary_crossentropy'
metrics = 'binary_accuracy'
else:
trainLabels = to_categorical(trainLabels)
validationLabels = to_categorical(validationLabels)
activation = 'softmax'
nClasses = len(classNames)
loss = 'categorical_crossentropy'
metrics = 'categorical_accuracy'
else:
nClasses = len(classNames)
metrics = IOUScore(threshold=0.5)
if nClasses == 1:
activation = 'sigmoid'
loss = binary_focal_dice_loss
else:
activation = 'softmax'
loss = categorical_focal_dice_loss
# Configura el generador de datos para usar data augmentation y redimensión
trainData = DataGenerator(trainImages,
trainLabels,
batchSize=batchSize,
resize=resize(imageDimensions[:-1]),
augmenters=augmenters,
shuffle=True)
classes=cfg.CLASSES,
augmentation=get_validation_augmentation(
cfg.image_size),
preprocessing=train_preprocessing)
train_dataloader = Dataloader(train_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=True)
valid_dataloader = Dataloader(valid_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False)
loss = cfg.loss
optim = tf.keras.optimizers.SGD(lr=cfg.LR, momentum=0.9, nesterov=True)
metrics = [
IOUScore(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
FScore(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
Precision(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
Recall(threshold=cfg.metric_threshold, per_image=cfg.metric_per_image),
TruePositives(thresholds=cfg.metric_threshold),
TrueNegatives(thresholds=cfg.metric_threshold),
FalsePositives(thresholds=cfg.metric_threshold),
FalseNegatives(thresholds=cfg.metric_threshold)
]
# with mirrored_strategy.scope():
model = sm.Unet(backbone_name=cfg.backbone_name,
input_shape=cfg.input_shape,
classes=n_classes,
activation='sigmoid' if n_classes == 1 else 'softmax',
weights=None,
#assert np.max(X_val) == 1
#assert np.min(X_val) == -1
X_tst = preprocess_input(X_tst)
#assert np.max(X_tst) == 1
#assert np.min(X_tst) == -1
print(len(X_trn), len(X_val), len(X_tst))
# training
print(':: training')
print("dataset: ", dataset, "model_name: ", model_name, "seed: ", seed_id,
" is training")
model.compile(optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=[IOUScore(threshold=0.5,
per_image=True)]) # model.summary()
earlystopper = EarlyStopping(monitor='val_loss',
patience=10,
restore_best_weights=True,
verbose=1)
# mcp_save = ModelCheckpoint('mnnet_seg_'+str(data_id)+'.h5', save_best_only=True, monitor='val_loss', mode='min')#monitor='val_iou_score', mode='max')
mcp_save = ModelCheckpoint(dataset + '/models/' + dataset + '_' + model_name +
'_' + "seed_" + str(seed_id) + '.h5',
save_best_only=True,
monitor='val_loss',
mode='min') # monitor='val_iou_score', mode='max'))
model.fit_generator(
data_flow,
steps_per_epoch=np.ceil(len(X_trn) / batch_size),
epochs=100,
from segmentation_models.metrics import FScore, IOUScore
from segmentation_models.losses import DiceLoss
import numpy as np
'''
This file is used to train and evaluate model
'''
IMG_WIDTH, IMG_HEIGHT = 256, 256
BATCH_SIZE = 4
EPOCHS = 15
IMG_CHANNELS = 1
dice_loss = DiceLoss(beta=1)
dice_metric = FScore(beta=1)
iou_metric = IOUScore()
'''
Training kidney
'''
def train_network(X_train, Y_train, X_test, Y_test, type_):
'''
Function trains model in regard to type_ (kidney model or tumour model)
Takes:
X_train -> arrays with training images
Y_train -> array with training masks
X_test -> array with test images
Y_test -> array with test masks
type_ -> string, 'kidney' or 'tumour'
#assert np.min(X_trn) == -1
X_val = preprocess_input(X_val)
#assert np.max(X_val) == 1
#assert np.min(X_val) == -1
X_tst = preprocess_input(X_tst)
#assert np.max(X_tst) == 1
#assert np.min(X_tst) == -1
print(len(X_trn), len(X_val), len(X_tst))
# prediction
print(':: prediction')
model = load_model(dataset + '/models/' + dataset + '_' + model_name + '_' + "seed_" + str(seed_id) + '.h5',
custom_objects={'iou_score': IOUScore(threshold=0.5, per_image=True)})
decay = decay_list[decay_id]
lam = lam_list[lam_id]
combine = combine_list[combine_id]
T = 5
a = 0
b = 0
single_result_iou = []
ensemble_result_iou = []
single_result_auroc = []
ensemble_result_auroc = []
for it in range(T + 1):
X_val = preprocess_input(X_val)
assert np.max(X_val) == 1
assert np.min(X_val) == -1
X_tst = preprocess_input(X_tst)
assert np.max(X_tst) == 1
assert np.min(X_tst) == -1
print(tmpbatchx.shape, tmpbatchy.shape)
print(X_tst.shape, Y_tst.shape)
# training
print(':: training')
model.compile(optimizer=Adam(lr=1e-4),
loss=bce_jaccard_loss,
metrics=[IOUScore(threshold=0.5,
per_image=True)]) #loss='binary_crossentropy'
#model.summary()
earlystopper = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10,
min_lr=1e-7,
verbose=1)
mcp_save = ModelCheckpoint('mnnet_seg_' + str(model_id) + '.h5',
save_best_only=True,
monitor='val_iou_score',
mode='max')