def main():
nb_classes = 40
train_file = './ModelNet40/ply_data_train.h5'
test_file = './ModelNet40/ply_data_test.h5'
epochs = 100
batch_size = 32
train = DataGenerator(train_file, batch_size, nb_classes, train=True)
val = DataGenerator(test_file, batch_size, nb_classes, train=False)
model = PointNet(nb_classes)
model.summary()
lr = 0.0001
adam = Adam(lr=lr)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
if not os.path.exists('./results/'):
os.mkdir('./results/')
checkpoint = ModelCheckpoint('./results/pointnet.h5', monitor='val_acc',
save_weights_only=True, save_best_only=True,
verbose=1)
history = model.fit_generator(train.generator(),
steps_per_epoch=9840 // batch_size,
epochs=epochs,
validation_data=val.generator(),
validation_steps=2468 // batch_size,
callbacks=[checkpoint, onetenth_50_75(lr)],
verbose=1)
plot_history(history, './results/')
save_history(history, './results/')
model.save_weights('./results/pointnet_weights.h5')
def main():
nb_classes = 40
train_file = './ModelNet40/ply_data_train.h5'
test_file = './ModelNet40/ply_data_test.h5'
# train_num_points = 9840
# test_num_points = 2468
# below variables are for dev propose
train_num_points = 1648
test_num_points = 420
epochs = 100
batch_size = 32
train = DataGenerator(train_file, batch_size, nb_classes, train=True)
val = DataGenerator(test_file, batch_size, nb_classes, train=False)
model = pointnet2(nb_classes)
model.summary()
lr = 0.0001
adam = Adam(lr=lr)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
if not os.path.exists('./results/'):
os.mkdir('./results/')
last_epoch, last_meta = get_last_status(model)
checkpoint = MetaCheckpoint('./results/pointnet.h5',
monitor='val_acc',
save_weights_only=True,
save_best_only=True,
verbose=1,
meta=last_meta)
history = model.fit_generator(
train.generator(),
steps_per_epoch=train_num_points // batch_size,
epochs=epochs,
validation_data=val.generator(),
validation_steps=test_num_points // batch_size,
callbacks=[checkpoint, onetenth_50_75(lr)],
verbose=1,
initial_epoch=last_epoch + 1)
plot_history(history, './results/')
save_history(history, './results/')
model.save_weights('./results/pointnet_weights.h5')
def main():
nb_classes = 40
train_file = 'PATH/ModelNet40/ply_data_train.h5'
val_file = 'PATH/ModelNet40/ply_data_test.h5'
num_samples_train = len(h5py.File(train_file, mode='r')['data'])
num_samples_val = len(h5py.File(val_file, mode='r')['data'])
epochs = 500
batch_size = 32
train = DataGenerator(train_file, batch_size, nb_classes, train=True)
val = DataGenerator(val_file, batch_size, nb_classes, train=False)
model = point_mask(nb_classes)
model.summary()
lr = 0.001
adam = Adam(lr=lr)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
if not os.path.exists('PATH/results/'):
os.mkdir('PATH/results/')
checkpoint = ModelCheckpoint('PATH/results/pointmask.h5',
monitor='val_acc',
mode='max',
save_weights_only=True,
save_best_only=True,
verbose=1)
history = model.fit_generator(
train.generator(),
steps_per_epoch=num_samples_train // batch_size,
validation_data=val.generator(),
validation_steps=num_samples_val // batch_size,
epochs=epochs,
callbacks=[checkpoint, onetenth_50_75(lr)],
verbose=1)
plot_history(history, 'PATH/results/')
save_history(history, 'PATH/results/')
train_file = '/home/changetest/datasets/Modelnet40/ply_data_train.h5'
test_file = '/home/changetest/datasets/Modelnet40/ply_data_test.h5'
epochs = 100
batch_size = 32
train = DataGenerator(train_file, batch_size, nb_classes, train=True)
val = DataGenerator(test_file, batch_size, nb_classes, train=False)
model.summary()
lr = 0.0001
adam = Adam(lr=lr)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
if not os.path.exists('./results/'):
os.mkdir('./results/')
checkpoint = ModelCheckpoint('./results/pointnet.h5',
monitor='val_acc',
save_weights_only=True,
save_best_only=True,
verbose=1)
model.fit_generator(train.generator(),
steps_per_epoch=9840 // batch_size,
epochs=epochs,
validation_data=val.generator(),
callbacks=[checkpoint, onetenth_50_75(lr)],
validation_steps=2468 // batch_size,
verbose=1)
def main():
# Check command line arguments.
#if len(sys.argv) != 2 or sys.argv[1] not in model_names:
# print("Must provide name of model.")
# print("Options: " + " ".join(model_names))
# exit(0)
#model_name = sys.argv[1]
# Data preparation.
nb_classes = 40
train_file = './ModelNet40/ply_data_train.h5'
test_file = './ModelNet40/ply_data_test.h5'
# Hyperparameters.
number_of_points = 1024
epochs = 100
batch_size = 32
# Data generators for training and validation.
train = DataGenerator(train_file,
batch_size,
number_of_points,
nb_classes,
train=True)
val = DataGenerator(test_file,
batch_size,
number_of_points,
nb_classes,
train=False)
# Create the model.
if model_name == "pointnet":
model = create_pointnet(number_of_points, nb_classes)
elif model_name == "gapnet":
model = GAPNet()
model.summary()
# Ensure output paths.
output_path = "logs"
if not os.path.exists(output_path):
os.mkdir(output_path)
output_path = os.path.join(output_path, model_name)
if not os.path.exists(output_path):
os.mkdir(output_path)
output_path = os.path.join(output_path, training_name)
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.mkdir(output_path)
# Compile the model.
lr = 0.0001
adam = Adam(lr=lr)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
# Checkpoint callback.
checkpoint = ModelCheckpoint(os.path.join(output_path, "model.h5"),
monitor="val_acc",
save_weights_only=True,
save_best_only=True,
verbose=1)
# Logging training progress with tensorboard.
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=output_path,
histogram_freq=0,
batch_size=32,
write_graph=True,
write_grads=False,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None,
embeddings_data=None,
update_freq="epoch")
callbacks = []
#callbacks.append(checkpoint)
callbacks.append(onetenth_50_75(lr))
callbacks.append(tensorboard_callback)
# Train the model.
history = model.fit_generator(train.generator(),
steps_per_epoch=9840 // batch_size,
epochs=epochs,
validation_data=val.generator(),
validation_steps=2468 // batch_size,
callbacks=callbacks,
verbose=1)
# Save history and model.
plot_history(history, output_path)
save_history(history, output_path)
model.save_weights(os.path.join(output_path, "model_weights.h5"))
