def main(args):
# set the necessary list
train_list = pd.read_csv(args.train_list, header=None)
val_list = pd.read_csv(args.val_list, header=None)
# set the necessary directories
trainimg_dir = args.trainimg_dir
trainmsk_dir = args.trainmsk_dir
valimg_dir = args.valimg_dir
valmsk_dir = args.valmsk_dir
train_gen = data_gen_small(
trainimg_dir,
trainmsk_dir,
train_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels,
)
val_gen = data_gen_small(
valimg_dir,
valmsk_dir,
val_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels,
)
model = SegNet(args.n_labels, args.kernel, args.pool_size,
args.output_mode)
model.build(input_shape=(1, 256, 256, 3))
print(model.summary())
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
model.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
callbacks=[TensorBoard(log_dir="./run")])
model.save_weights(args.save_dir + str(args.n_epochs) + ".hdf5")
print("sava weight done..")
x_test = np.asarray(testImages) / 255.0
x_test = np.delete(x_test, len(x_test) - 1, axis=0)
y_train = sc.fit_transform(np.asarray(controls))
# Prepare model for training
model = SegNet((IMG_H, IMG_W))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE)
if not os.path.exists('models'):
os.mkdir('models')
model.save_weights('models/res.h5', save_format='h5')
# Predict on testing dataset
predictions = model.predict(x_test)
predictions = sc.inverse_transform(predictions)
print(predictions)
# Show results and compare
plt.plot(testControls, color='blue', label=f'Real steering')
plt.plot(predictions, color='red', label=f'Predicted steering')
plt.title(f"Steering Angle Prediction")
plt.xlabel('Frame')
plt.ylabel('Steering Angle')
plt.legend()
plt.show()
def main():
# Parse arguments.
parser = argparse.ArgumentParser()
kwargs = {
'type': int,
'default': 100,
'help': 'The number of times of learning. default: 100'
}
parser.add_argument('-e', '--epochs', **kwargs)
kwargs = {
'type': int,
'default': 10,
'help': 'The frequency of saving model. default: 10'
}
parser.add_argument('-c', '--checkpoint_interval', **kwargs)
kwargs = {
'type': int,
'default': 1,
'help': 'The number of samples contained per mini batch. default: 1'
}
parser.add_argument('-b', '--batch_size', **kwargs)
kwargs = {
'default':
False,
'action':
'store_true',
'help':
'Whether store all data to GPU. If not specified this option, use both CPU memory and GPU memory.'
}
parser.add_argument('--onmemory', **kwargs)
args = parser.parse_args()
# Prepare training data.
dataset = np.load('./temp/dataset.npz')
train_x = dataset['train_x']
train_y = dataset['train_y']
test_x = dataset['test_x']
test_y = dataset['test_y']
# Prepare tensorflow.
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
keras.backend.tensorflow_backend.set_session(session)
# Prepare model.
model = SegNet(shape=(360, 480, 3))
model.compile(loss='binary_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
# Training.
callbacks = []
timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
directory = f'./logs/{timestamp}/'
os.makedirs(directory, exist_ok=True)
callbacks.append(keras.callbacks.TensorBoard(log_dir=directory))
filename = 'model-{epoch:04d}.h5'
directory = f'./temp/{timestamp}/'
os.makedirs(directory, exist_ok=True)
callbacks.append(
keras.callbacks.ModelCheckpoint(filepath=f'{directory}{filename}',
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=args.checkpoint_interval))
model.save_weights(f'{directory}{filename}'.format(epoch=0))
if args.onmemory:
model.fit(x=train_x,
y=train_y,
validation_data=(test_x, test_y),
epochs=args.epochs,
batch_size=args.batch_size,
class_weight='balanced',
shuffle=True,
verbose=1,
callbacks=callbacks)
else:
class Generator(keras.utils.Sequence):
def __init__(self, x, y, batch_size, shuffle):
self.x = x
self.y = y
self.batch_size = batch_size
self.indices = np.arange(len(self.x))
self.shuffle = shuffle
assert len(self.x) == len(self.y)
assert len(self.x) % self.batch_size == 0
def __getitem__(self, index):
i = index * self.batch_size
indices = self.indices[i:i + self.batch_size]
x = self.x[indices]
y = self.y[indices]
return x, y
def __len__(self):
return len(self.x) // self.batch_size
def on_epoch_end(self):
if self.shuffle:
self.indices = np.random.permutation(self.indices)
model.fit_generator(generator=Generator(train_x, train_y,
args.batch_size, True),
validation_data=Generator(test_x, test_y,
args.batch_size, False),
epochs=args.epochs,
class_weight='balanced',
shuffle=True,
verbose=1,
callbacks=callbacks)