from pathlib import Path
from models.rll_model_fcgf import feature_reg_model
from parameter_settings.registration.dare_settings import get_default_feature_dare_parameters
from parameter_settings.dataset_settings import get_default_kitti_dataset_parameters
from datasets import kitti
from easydict import EasyDict as edict
from training.trainer import trainer
import torch.optim as optim
from actors import reg_loss_actors
from datasets.processing import RandomDownsampler, NoProcessing
from datasets.data_reader import collate_tensorlist
import math
import os, sys
import config
envsettings = config.EnvironmentSettings()
filename = os.path.split(sys.argv[0])[1].split('.')[0]
workspace = envsettings.workspace_dir + filename
def configure_methods():
params_feat = get_default_feature_dare_parameters()
params_feat.name = "RLLReg_kitti_multi"
params_feat.device = "cuda:0"
params_feat.feature_distr_parameters = edict(
num_channels=16,
s=float(0.4),
model="vonmises",
)
params_feat.layer = "lin3_out"
import os
from flask import Flask, redirect, render_template, url_for
import config
from sros_rootifier.sros_rootifier import sros_rootifier_bp
root_folder_path = os.path.dirname(os.path.abspath(__file__))
# get env_settings list
env_settings = config.EnvironmentSettings(root_folder_path)
# initialize Flask app
app = Flask(__name__)
app.register_blueprint(sros_rootifier_bp)
@app.route('/')
def index():
return redirect('/sros_rootifier')
if __name__ == '__main__':
# configure Flask app from a class, stored in PLAZA_SETTINGS variable
app.config.from_object(env_settings['PLAZA_SETTINGS'])
# if we are in Prod, use HOST and PORT specified
try:
app.run(host=str(env_settings['HOST']), port=80)
except config.ConfigurationError:
