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: