def full_bootstrap(batch_size):
bootstrap_flow = bootstrap.bootstrap_net(batch_size)
bootstrap_flow._make_predict_function()
bootstrap_out = bootstrap_flow.outputs
image_pair = bootstrap_flow.inputs[0]
flow_out = bootstrap_out[2]
conf_out = bootstrap_out[3]
bootstrap_motion_input = [image_pair, flow_out, conf_out]
bootstrap_motion_outputs = bootstrap.bootstrap_net_depth(batch_size)(
bootstrap_motion_input)
bootstrap_full = Model(inputs=image_pair, outputs=bootstrap_motion_outputs)
bootstrap_full._make_predict_function()
return bootstrap_full
def train_bootstrap_depth_motion(data_loader):
checkpoint_path = TRAINING_SAVE_FOLDER + "cp_depth_motion_bootstrap.ckpt"
flow_checkpoint_path = TRAINING_SAVE_FOLDER + "cp.ckpt"
# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1,
period=1000)
# create terminate on nan callback
nan_callback = tf.keras.callbacks.TerminateOnNaN()
bootstrap_flow = bootstrap.bootstrap_net(32)
# Load weights
bootstrap_flow.load_weights(flow_checkpoint_path)
bootstrap_flow._make_predict_function()
# Fix weights
for layer in bootstrap_flow.layers:
layer.trainable = False
print(bootstrap_flow.summary())
bootstrap_out = bootstrap_flow.outputs
image_pair = bootstrap_flow.inputs[0]
flow_out = bootstrap_out[2]
conf_out = bootstrap_out[3]
bootstrap_motion_input = [image_pair, flow_out, conf_out]
bootstrap_motion_outputs = bootstrap.bootstrap_net_depth(32)(
bootstrap_motion_input)
bootstrap_full = Model(inputs=image_pair, outputs=bootstrap_motion_outputs)
bootstrap_full._make_predict_function()
# Compile model
adam = Adam(decay=0.0004)
loss_list = [
custom_losses.mean_abs_error,
custom_losses.euclidean_with_gradient_loss,
custom_losses.normals_loss_from_depth_gt
]
weights = [15, 300, 100]
bootstrap_full.compile(loss=loss_list,
optimizer=adam,
loss_weights=weights)
print(bootstrap_full.summary())
# plot_model(bootstrap_net, to_file='bootstrap.png', show_shapes=True)
# Train model for 250k epochs
history = bootstrap_full.fit_generator(
generate_motion_depth_normals(data_loader),
steps_per_epoch=1,
epochs=250000,
callbacks=[cp_callback, nan_callback])
print("Training complete!")
with open(
TRAINING_SAVE_FOLDER +
'train_history_bootstrap_depth_motion.pickle', 'wb+') as file_pi:
pickle.dump(history.history, file_pi)
print("Saved weights to file")
