def main():
print("loading data...")
ds = dataset.Dataset(classes=classes)
train_X, train_y = ds.load_data('train')
train_X = ds.preprocess_inputs(train_X)
train_Y = ds.reshape_labels(train_y)
print("input data shape...", train_X.shape)
print("input label shape...", train_Y.shape)
test_X, test_y = ds.load_data('test')
test_X = ds.preprocess_inputs(test_X)
test_Y = ds.reshape_labels(test_y)
print("creating model...")
model = SegNet(input_shape=input_shape, classes=classes)
model.compile(loss="categorical_crossentropy",
optimizer='adadelta',
metrics=["accuracy"])
model.fit(train_X,
train_Y,
batch_size=batch_size,
epochs=epochs,
verbose=1,
class_weight=class_weighting,
validation_data=(test_X, test_Y),
shuffle=True)
model.save('seg.h5')
def main():
input_shape = (360, 480, 3)
classes = 12
epochs = 100
batch_size = 1
log_path = './logs/'
class_weighting = [
0.2595, 0.1826, 4.5640, 0.1417, 0.5051, 0.3826, 9.6446, 1.8418, 6.6823,
6.2478, 3.0, 7.3614
]
# set gpu usage
config = tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True, per_process_gpu_memory_fraction=0.8))
session = tf.Session(config=config)
set_session(session)
print("loading data...")
ds = dataset.Dataset(classes=classes,
train_file="CamVid/train.txt",
test_file="CamVid/test.txt")
# need to implement, y shape is (None, 360, 480, classes)
train_x, train_y = ds.load_data(root_path="CamVid", mode='train')
train_x = ds.preprocess_inputs(train_x)
train_y = ds.reshape_labels(train_y)
print("input data shape...", train_x.shape)
print("input label shape...", train_y.shape)
# need to implement, y shape is (None, 360, 480, classes)
test_x, test_y = ds.load_data(root_path="CamVid", mode='test')
test_x = ds.preprocess_inputs(test_x)
test_y = ds.reshape_labels(test_y)
tb_cb = TensorBoard(log_dir=log_path,
histogram_freq=1,
write_graph=True,
write_images=True)
print("creating model...")
model = SegNet(input_shape=input_shape, classes=classes)
model.compile(loss="categorical_crossentropy",
optimizer='adadelta',
metrics=["accuracy"])
model.fit(train_x,
train_y,
batch_size=batch_size,
epochs=epochs,
verbose=1,
class_weight=class_weighting,
validation_data=(test_x, test_y),
shuffle=True,
callbacks=[tb_cb])
model.save('seg.h5')
def main():
print("loading data...")
ds = dataset.DataSet(classes=classes)
train_X, train_y = ds.load_data(
'train') # need to implement, y shape is (None, 360, 480, classes)
train_X = ds.preprocess_inputs(train_X)
train_Y = ds.reshape_labels(train_y)
print("input data shape...", train_X.shape)
print("input label shape...", train_Y.shape)
test_X, test_y = ds.load_data(
'test') # need to implement, y shape is (None, 360, 480, classes)
test_X = ds.preprocess_inputs(test_X)
test_Y = ds.reshape_labels(test_y)
tb_cb = keras.callbacks.TensorBoard(log_dir=log_filepath,
histogram_freq=1,
write_graph=True,
write_images=True)
fpath = 'weights.{epoch:02d}.hdf5'
mc_cb = keras.callbacks.ModelCheckpoint(fpath,
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=3)
print("creating model...")
model = SegNet(input_shape=input_shape, classes=classes)
model.compile(loss="categorical_crossentropy",
optimizer='adadelta',
metrics=["accuracy"])
model.fit(train_X,
train_Y,
batch_size=batch_size,
epochs=epochs,
verbose=1,
class_weight=class_weighting,
validation_data=(test_X[0:5], test_Y[0:5]),
shuffle=True,
callbacks=[mc_cb, tb_cb])
model.save('seg.h5')
def main():
print("loading data...")
ds = dataset.Dataset(test_file='test_5.txt', classes=classes)
train_X, train_y = ds.load_data('train') # need to implement, y shape is (None, 360, 480, classes)
train_X = ds.preprocess_inputs(train_X)
train_Y = ds.reshape_labels(train_y)
print("input data shape...", train_X.shape)
print("input label shape...", train_Y.shape)
test_X, test_y = ds.load_data('test') # need to implement, y shape is (None, 360, 480, classes)
test_X = ds.preprocess_inputs(test_X)
test_Y = ds.reshape_labels(test_y)
tb_cb = keras.callbacks.TensorBoard(log_dir=log_filepath, histogram_freq=1, write_graph=True, write_images=True)
print("creating model...")
model = SegNet(input_shape=input_shape, classes=classes)
model.compile(loss="categorical_crossentropy", optimizer='adadelta', metrics=["accuracy"])
model.fit(train_X, train_Y, batch_size=batch_size, epochs=epochs,
verbose=1, class_weight=class_weighting , validation_data=(test_X, test_Y), shuffle=True
, callbacks=[tb_cb])
model.save('s.h5')