nav_env_config.py

# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ==============================================================================

"""Configs for stanford navigation environment.

Base config for stanford navigation enviornment.
"""
import numpy as np
import src.utils as utils
import datasets.nav_env as nav_env

def nav_env_base_config():
  """Returns the base config for stanford navigation environment.

  Returns:
    Base config for stanford navigation environment.
  """
  robot = utils.Foo(radius=15,
                    base=10,
                    height=140,
                    sensor_height=120,
                    camera_elevation_degree=-15)

  env = utils.Foo(padding=10,
                  resolution=5,
                  num_point_threshold=2,
                  valid_min=-10,
                  valid_max=200,
                  n_samples_per_face=200)

  camera_param = utils.Foo(width=225,
                           height=225,
                           z_near=0.05,
                           z_far=20.0,
                           fov=60.,
                           modalities=['rgb'],
                           img_channels=3)

  data_augment = utils.Foo(lr_flip=0,
                           delta_angle=0.5,
                           delta_xy=4,
                           relight=True,
                           relight_fast=False, 
                           structured=False) # if True, uses the same perturb for the whole episode.

  outputs = utils.Foo(images=True,
                      rel_goal_loc=False,
                      loc_on_map=True,
                      gt_dist_to_goal=True,
                      ego_maps=False,
                      ego_goal_imgs=False,
                      egomotion=False,
                      visit_count=False,
                      analytical_counts=False,
                      node_ids=True,
                      readout_maps=False)

  # class_map_names=['board', 'chair', 'door', 'sofa', 'table']
  class_map_names = ['chair', 'door', 'table']
  semantic_task = utils.Foo(class_map_names=class_map_names, pix_distance=16,
                            sampling='uniform')
  
  # time per iteration for cmp is 0.82 seconds per episode with 3.4s overhead per batch.
  task_params = utils.Foo(max_dist=32,
                          step_size=8,
                          num_steps=40,
                          num_actions=4,
                          batch_size=4, 
                          building_seed=0,
                          num_goals=1,
                          img_height=None,
                          img_width=None,
                          img_channels=None,
                          modalities=None,
                          outputs=outputs,
                          map_scales=[1.],
                          map_crop_sizes=[64],
                          rel_goal_loc_dim=4,
                          base_class='Building',
                          task='map+plan',
                          n_ori=4,
                          type='room_to_room_many',
                          data_augment=data_augment,
                          room_regex='^((?!hallway).)*$',
                          toy_problem=False,
                          map_channels=1,
                          gt_coverage=False,
                          input_type='maps',
                          full_information=False,
                          aux_delta_thetas=[],
                          semantic_task=semantic_task,
                          num_history_frames=0,
                          node_ids_dim=1,
                          perturbs_dim=4,
                          map_resize_method='linear_noantialiasing',
                          readout_maps_channels=1,
                          readout_maps_scales=[],
                          readout_maps_crop_sizes=[],
                          n_views=1,
                          reward_time_penalty=0.1,
                          reward_at_goal=1.,
                          discount_factor=0.99,
                          rejection_sampling_M=100,
                          min_dist=None)

  navtask_args = utils.Foo(
      building_names=['area1_gates_wingA_floor1_westpart'],
      env_class=nav_env.VisualNavigationEnv,
      robot=robot,
      task_params=task_params,
      env=env,
      camera_param=camera_param,
      cache_rooms=True)
  return navtask_args