def main():
# 1936 x 1216
input_size = (320, 480, 3)
classes = 20
train_dataset_x = '../seg_train_images/seg_train_images'
train_dataset_y = '../seg_train_annotations/seg_train_annotations'
test_size = 0.2
batch_size = 8
datasets_paths = get_data_paths(train_dataset_x, train_dataset_y)
train_data, test_data = train_test_split(datasets_paths, test_size=test_size)
net = Unet(input_size, classes)
#net = SegNet(input_size, classes)
net.summary()
train_gen = DataGenerator(train_data, input_size, classes, batch_size)
val_gen = DataGenerator(test_data, input_size, classes, batch_size)
callbacks = [
ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1),
EarlyStopping(monitor='val_loss', min_delta=0, patience=9, verbose=1),
ModelCheckpoint('checkpoint/ep{epoch:03d}-loss{loss:.5f}-val_loss{val_loss:.5f}.h5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')
]
net.compile(optimizer=Adam(1e-3),loss=categorical_crossentropy)
history = net.fit_generator(
train_gen,
steps_per_epoch=train_gen.num_batches_per_epoch,
validation_data=val_gen,
validation_steps=val_gen.num_batches_per_epoch,
initial_epoch=0,
epochs=50,
callbacks=callbacks
)
net.save_weights('checkpoint/first_stage.h5')
train_data, test_data = train_test_split(datasets_paths, test_size=test_size)
train_gen = DataGenerator(train_data, input_size, classes, batch_size)
val_gen = DataGenerator(test_data, input_size, classes, batch_size)
net.compile(optimizer=Adam(1e-4),loss=categorical_crossentropy)
history = net.fit_generator(
train_gen,
steps_per_epoch=train_gen.num_batches_per_epoch,
validation_data=val_gen,
validation_steps=val_gen.num_batches_per_epoch,
initial_epoch=50,
epochs=100,
callbacks=callbacks
)
net.save_weights('checkpoint/final_stage.h5')
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
Environment(training=[2, 5, 6, 10, 14, 16, 19],
testing=[1, 8, 18],
training_every=10,
testing_every=10).configure()
train_gene = TrainGenerator(batch_size=9)
model = Unet().create()
checkpoint = ModelCheckpoint('weights/{epoch:03d}.liver_weights.hdf5',
monitor='loss',
verbose=1,
period=1)
plateau = ReduceLROnPlateau(factor=0.2,
min_delta=1e-8,
mode='min',
monitor='loss',
patience=10,
cooldown=20,
verbose=1)
model.fit_generator(train_gene,
epochs=4,
callbacks=[checkpoint, plateau],
validation_data=ValidationGenerator())
Predictor().save()
Measurer().show_metrics()
Visualizer().show_animation()
class_mode=None,
batch_size=batch_size,
seed=1)
train_generator = zip(image_generator, mask_generator)
for (img, mask) in train_generator:
img = img / 255.
mask = mask / 255.
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
yield (img, mask)
model = Unet(1, image_size=512)
trainset = DataGenerator("dataset/train", batch_size=2)
model.fit_generator(trainset, steps_per_epoch=100, epochs=1)
model.save_weights("model.h5")
testSet = DataGenerator("dataset/test", batch_size=1)
alpha = 0.3
model.load_weights("model.h5")
if not os.path.exists("./results"): os.mkdir("./results")
for idx, (img, mask) in enumerate(testSet):
oring_img = img[0]
# 开始用模型进行预测
pred_mask = model.predict(img)[0]
pred_mask[pred_mask > 0.5] = 1
pred_mask[pred_mask <= 0.5] = 0
# 如果这里展示的预测结果一片黑，请调整lr，同时注意图片的深度是否为8
input_size = (512, 512, 1)
batch = 8
model = Unet(input_size=input_size)
model.summary()
target_size = input_size[:2] # 前两项
train_generator = load_data.trainGenerator(batch,
train_dir,
data_gen_args,
image_folder='image',
mask_folder='label',
target_size=target_size)
valid_generator = load_data.trainGenerator(1,
validation_dir,
valid_data_gen_args,
image_folder='image',
mask_folder='label',
target_size=target_size)
history = LossHistory()
model.fit_generator(
train_generator,
steps_per_epoch=len(glob.glob('trainset_2d/train/image/' + '*.png')) //
batch,
validation_data=valid_generator,
validation_steps=1,
epochs=30,
verbose=1,
callbacks=[history],
)
#history.loss_plot('epoch')
