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')
type=float,
default=0.0001,
help="learning rate",
)
parser.add_argument(
"--logs",
type=str,
default="./logs",
help="Log folder",
)
args = parser.parse_args()
model = Unet()
model.build(input_shape=(None, 256, 256, 3))
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=dice_coef_loss,
metrics=['accuracy', dice_coef])
# Loading Dataset
X_train, Y_train, X_test, Y_test = get_db(args.data_dir)
# Set tf.keras.callbacks.ModelCheckpoint callback to automatically save the model
checkpoint_path = "weight/ep{epoch:03d}-val_dice_coef{val_dice_coef:.3f}-val_acc{val_accuracy:.3f}.ckpt"
modelCheckpoint = callbacks.ModelCheckpoint(
filepath=checkpoint_path, # Path to save the model
verbose=1, # Whether to output information
save_weights_only=True,
period=1, # Save the model every few rounds
)
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', # Monitored data
min_delta=0.001,
dtype=np.float32)
print(str(xtrain.shape), 'images and', str(ytrain.shape), 'masks')
unet = Unet(DESIRED_SIZE,
DESIRED_SIZE,
nclasses=NUM_CLASSES,
filters=UNET_FILTERS)
print(unet.output_shape)
unet.summary()
# Setting BCE to False to use focal loss
BCE = False
print(xtrain.shape, train_images.shape)
if BCE:
unet.compile(optimizer='adam',
loss="sparse_categorical_crossentropy",
metrics=['accuracy'])
history = unet.fit(train_images,
train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(valid_images, valid_labels))
else:
unet.compile(optimizer='adam',
loss=SparseCategoricalFocalLoss(gamma=2),
metrics=['accuracy'])
history = unet.fit(xtrain,
ytrain,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(xval, yval))
'--generate_images',
help=
'saves representative image of mask in addition to the mask prediction',
action="store_true")
args = parser.parse_args()
assert os.path.isfile(args.weights), "Could not find weights file!"
assert os.path.isdir(args.images), "Could not find images directory!"
unet = Unet(DESIRED_SIZE,
DESIRED_SIZE,
nclasses=NUM_CLASSES,
filters=UNET_FILTERS)
unet.load_weights(args.weights)
unet.compile(optimizer='adam',
loss=SparseCategoricalFocalLoss(gamma=2),
metrics=['accuracy'])
image_paths = glob.glob(args.images + '/*.jpg')
images = [
x[0] for x in [
get_resized_image_and_mask_label(cv2.imread(p), NEGATIVE)
for p in image_paths
]
]
mpreds = unet.predict(np.array(images, dtype=np.uint8))
preds = maskpred(mpreds)
cmap = copy.copy(plt.cm.get_cmap("gist_rainbow", 20))
cmap.set_under(color="black")
os.mkdir(logdir)
output_model_file = os.path.join(logdir, 'slate_models2.h5')
callbacks = [
tf.keras.callbacks.TensorBoard(logdir),
tf.keras.callbacks.ModelCheckpoint(output_model_file,
monitor='val_acc',
save_best_only=True),
# tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',patience =10,min_delta = 0.1)
]
def focal(y_ture, y_pre):
loss = -tf.reduce_mean(
y_ture * tf.multiply(1 - y_pre, 1 - y_pre) *
tf.math.log(tf.clip_by_value(y_pre, 1e-10, 1.0))) * 0.25
return loss
# def meanIOU(y_ture,y_pre):
#
#
#
# #'categorical_crossentropy'
model.compile(loss=focal, optimizer=optimizers.Adam(lr=1e-4), metrics=['acc'])
model.fit(train_db,
epochs=100,
validation_data=test_db,
callbacks=callbacks,
steps_per_epoch=3)
