def create_rgb_trainer_params():
from params.simulator.sbpd_simulator_params import create_params as create_simulator_params
from params.visual_navigation_trainer_params import create_params as create_trainer_params
from params.waypoint_grid.sbpd_image_space_grid import create_params as create_waypoint_params
from params.model.resnet50_arch_v1_params import create_params as create_model_params
from params.reachability_map.reachability_map_params import create_params as create_reachability_map_params
# Load the dependencies
simulator_params = create_simulator_params()
# Seed
simulator_params.seed = 10
# Ensure the waypoint grid is projected SBPD Grid
simulator_params.planner_params.control_pipeline_params.waypoint_params = create_waypoint_params(
)
# Ensure the renderer modality is rgb
simulator_params.obstacle_map_params.renderer_params.camera_params.modalities = [
'rgb'
]
simulator_params.obstacle_map_params.renderer_params.camera_params.img_channels = 3
simulator_params.obstacle_map_params.renderer_params.camera_params.width = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.height = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.im_resize = 0.21875
# Change episode horizon
simulator_params.episode_horizon_s = 80.0
# Ensure the renderer is using area3
# TODO: When generating our own data, choose area 3, area4, area5
# When testing, choosing area 1
simulator_params.obstacle_map_params.renderer_params.building_name = 'area4'
# TODO: area3: thread='v1'; area4: thread='v2'; area5a: thread='v3'
simulator_params.reachability_map_params.thread = 'v2'
# specify reachability data dir name according to building_name and thread
create_reachability_data_dir_params(
simulator_params.reachability_map_params)
# Save trajectory data
simulator_params.save_trajectory_data = True
# Choose cost function
# TODO: in training, always use 'heuristics'
# simulator_params.cost = 'heuristics' # heuristics cost.
simulator_params.cost = 'reachability'
p = create_trainer_params(simulator_params=simulator_params)
# Create the model params
p.model = create_model_params()
return p
def create_rgb_trainer_params():
from params.simulator.sbpd_simulator_params import create_params as create_simulator_params
from params.visual_navigation_trainer_params import create_params as create_trainer_params
from params.waypoint_grid.sbpd_image_space_grid import create_params as create_waypoint_params
from params.model.resnet50_arch_v1_params import create_params as create_model_params
# Load the dependencies
simulator_params = create_simulator_params()
# Ensure the waypoint grid is projected SBPD Grid
simulator_params.planner_params.control_pipeline_params.waypoint_params = create_waypoint_params(
)
# Ensure the renderer modality is rgb
simulator_params.obstacle_map_params.renderer_params.camera_params.modalities = [
'rgb'
]
simulator_params.obstacle_map_params.renderer_params.camera_params.img_channels = 3
simulator_params.obstacle_map_params.renderer_params.camera_params.width = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.height = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.im_resize = 0.21875
# Ensure the renderer is using area1
simulator_params.obstacle_map_params.renderer_params.building_name = 'area1'
# Change the episode horizon
simulator_params.episode_horizon_s = 80.0
# Save trajectory data
simulator_params.save_trajectory_data = True
p = create_trainer_params(simulator_params=simulator_params)
# Create the model params
p.model = create_model_params()
return p
def create_params(simulator_params):
p = DotMap()
p.simulator_params = simulator_params
# Model parameters
p.model = create_model_params()
# Data processing parameters
p.data_processing = DotMap(
# NN Input processing function
input_processing_function=None,
# NN output processing function
output_processing_function=None
)
# Loss function parameters
p.loss = DotMap(
# Type of the loss function
loss_type='mse',
# Weight regularization co-efficient
regn=1e-6,
# Velocity Smoothing co-efficient for end-to-end networks
smoothing_coeff=0.0
)
# Trainer parameters
p.trainer = DotMap(
# Trainer seed
seed=10,
# Number of epochs
num_epochs=400,
# Total number of samples in the dataset
num_samples=int(20e3),
# The percentage of the dataset that corresponds to the training set
training_set_size=0.8,
# Batch size
batch_size=64,
# The training optimizer
optimizer=tf.train.AdamOptimizer,
# Learning rate
lr=1e-4,
# Learning schedule
learning_schedule=1,
# Learning schedule adjustment parameters
lr_decay_frequency=None,
lr_decay_factor=None,
# Checkpoint settings
max_num_ckpts_to_keep=int(1e2),
ckpt_save_frequency=20,
ckpt_path='',
restore_from_ckpt=False,
# Callback settings
callback_frequency=20,
callback_number_tests=50,
callback_seed=10,
# For models which regress to waypoints in the image space
# True: Supervision for model outputs is normalized to a 0, 1 range
# False: Supervision for model outputs is kept in its normal range
rescale_imageframe_coordinates = False,
# Custom Simulator Parameters for Training. Add more as needed.
simulator_params=deepcopy(p.simulator_params)
)
# Data creation parameters
# Custom Simulator Parameters for Data Creation
simulator_params = deepcopy(p.simulator_params)
# Change the reset parameters for the simulator
reset_params = simulator_params.reset_params
reset_params.obstacle_map.params = DotMap(min_n=5, max_n=5,
min_r=.3, max_r=.8)
reset_params.start_config.heading.reset_type = 'random'
reset_params.start_config.speed.reset_type = 'zero'
reset_params.start_config.ang_speed.reset_type = 'zero'
p.data_creation = DotMap(
# Number of data points
data_points=int(100e3),
# Number of data points per file
data_points_per_file=1000,
# Data directory
data_dir='./REPLACE_ME',
# Custom Simulator Params
simulator_params = simulator_params
)
# Custom Simulator Parameters for Testing
simulator_params = deepcopy(p.simulator_params)
# Test parameters
p.test = DotMap(
# Test seed
seed=10,
simulate_expert=True,
expert_success_goals=DotMap(use=False,
dirname = ''),
number_tests=100,
# If true the velocity and angular velocity profiles of the robot
# are plotted along with the x, y, theta trajectory
plot_controls=True,
# If true, the observed images (topview/occupancy grid, rgb, or depth)
# for each episode are plotted as well
plot_images=True,
# Custom Simulator Parameters for Testing. Add more as needed
simulator_params=simulator_params
)
return p
