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"))
log_dir="logs",
histogram_freq=0,
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")
training_callbacks.append(tensorboard_callback)
# Compile the model.
lr = 0.0001
adam = optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss="mse", metrics=["mae"])
batch_size = 128
epochs = 500
model.fit(dataset_training.batch(batch_size),
validation_data=dataset_validate.batch(batch_size),
epochs=epochs,
callbacks=training_callbacks)
# Save the model.
logger.info('Saving and uploading weights...')
path = "gapnet_weights.h5"
model.save_weights(path)
run.upload_file(name="gapnet_weights.h5", path_or_stream=path)
# Save the model.