def get_default_args():
summary_args = utils.Foo(display_interval=1,
test_iters=26,
arop_full_summary_iters=14)
control_args = utils.Foo(train=False,
test=False,
force_batchnorm_is_training_at_test=False,
reset_rng_seed=False,
only_eval_when_done=False,
test_mode=None)
return summary_args, control_args
def get_default_cmp_args():
batch_norm_param = {
'center': True,
'scale': True,
'activation_fn': tf.nn.relu
}
mapper_arch_args = utils.Foo(dim_reduce_neurons=64,
fc_neurons=[1024, 1024],
fc_out_size=8,
fc_out_neurons=64,
encoder='resnet_v2_50',
deconv_neurons=[64, 32, 16, 8, 4, 2],
deconv_strides=[2, 2, 2, 2, 2, 2],
deconv_layers_per_block=2,
deconv_kernel_size=4,
fc_dropout=0.5,
combine_type='wt_avg_logits',
batch_norm_param=batch_norm_param)
readout_maps_arch_args = utils.Foo(num_neurons=[],
strides=[],
kernel_size=None,
layers_per_block=None)
arch_args = utils.Foo(vin_val_neurons=8,
vin_action_neurons=8,
vin_ks=3,
vin_share_wts=False,
pred_neurons=[64, 64],
pred_batch_norm_param=batch_norm_param,
conv_on_value_map=0,
fr_neurons=16,
fr_ver='v2',
fr_inside_neurons=64,
fr_stride=1,
crop_remove_each=30,
value_crop_size=4,
action_sample_type='sample',
action_sample_combine_type='one_or_other',
sample_gt_prob_type='inverse_sigmoid_decay',
dagger_sample_bn_false=True,
vin_num_iters=36,
isd_k=750.,
use_agent_loc=False,
multi_scale=True,
readout_maps=False,
rom_arch=readout_maps_arch_args)
return arch_args, mapper_arch_args
def get_arch_vars(arch_str):
if arch_str == '':
vals = []
else:
vals = arch_str.split('_')
ks = ['var1', 'var2', 'var3']
ks = ks[:len(vals)]
# Exp Ver.
if len(vals) == 0:
ks.append('var1')
vals.append('v0')
# custom arch.
if len(vals) == 1:
ks.append('var2')
vals.append('')
# map scape for projection baseline.
if len(vals) == 2:
ks.append('var3')
vals.append('fr2')
assert (len(vals) == 3)
vars = utils.Foo()
for k, v in zip(ks, vals):
setattr(vars, k, v)
logging.error('arch_vars: %s', vars)
return vars
def get_args_for_config(config_name):
args = utils.Foo()
args.summary, args.control = get_default_args()
exp_name, mode_str = config_name.split('+')
arch_str, solver_str, navtask_str = exp_name.split('.')
logging.error('config_name: %s', config_name)
logging.error('arch_str: %s', arch_str)
logging.error('navtask_str: %s', navtask_str)
logging.error('solver_str: %s', solver_str)
logging.error('mode_str: %s', mode_str)
args.solver = cc.process_solver_str(solver_str)
args.navtask = cc.process_navtask_str(navtask_str)
args = process_arch_str(args, arch_str)
args.arch.isd_k = args.solver.isd_k
# Train, test, etc.
mode, imset = mode_str.split('_')
args = cc.adjust_args_for_mode(args, mode)
args.navtask.building_names = args.navtask.dataset.get_split(imset)
args.control.test_name = '{:s}_on_{:s}'.format(mode, imset)
# Log the arguments
logging.error('%s', args)
return args
def get_default_baseline_args():
batch_norm_param = {
'center': True,
'scale': True,
'activation_fn': tf.nn.relu
}
arch_args = utils.Foo(pred_neurons=[],
goal_embed_neurons=[],
img_embed_neurons=[],
batch_norm_param=batch_norm_param,
dim_reduce_neurons=64,
combine_type='',
encoder='resnet_v2_50',
action_sample_type='sample',
action_sample_combine_type='one_or_other',
sample_gt_prob_type='inverse_sigmoid_decay',
dagger_sample_bn_false=True,
isd_k=750.,
use_visit_count=False,
lstm_output=False,
lstm_ego=False,
lstm_img=False,
fc_dropout=0.0,
embed_goal_for_state=False,
lstm_output_init_state_from_goal=False)
return arch_args
def process_arch_projected_map(args, arch_vars):
# Single scale vision based system which does not use a mapper but instead
# uses an analytically estimated map.
ds = int(arch_vars.var3[2])
args.navtask.task_params.input_type = 'analytical_counts'
args.navtask.task_params.outputs.analytical_counts = True
assert (args.navtask.task_params.modalities[0] == 'depth')
args.navtask.camera_param.img_channels = None
analytical_counts = utils.Foo(map_sizes=[512 / ds],
xy_resolution=[5. * ds],
z_bins=[[-10, 10, 150, 200]],
non_linearity=[arch_vars.var2])
args.navtask.task_params.analytical_counts = analytical_counts
sc = 1. / ds
args.arch.vin_num_iters = 36
args.navtask.task_params.map_scales = [sc]
args.navtask.task_params.map_crop_sizes = [512 / ds]
args.arch.fr_stride = [1, 2]
args.arch.vin_action_neurons = 8
args.arch.vin_val_neurons = 3
args.arch.fr_inside_neurons = 32
map_channels = len(analytical_counts.z_bins[0]) + 1
args.navtask.task_params.map_channels = map_channels
args.solver.freeze_conv = False
return args
def adjust_args_for_mode(args, mode):
if mode == 'train':
args.control.train = True
elif mode == 'val1':
# Same settings as for training, to make sure nothing wonky is happening
# there.
args.control.test = True
args.control.test_mode = 'val'
args.navtask.task_params.batch_size = 32
elif mode == 'val2':
# No data augmentation, not sampling but taking the argmax action, not
# sampling from the ground truth at all.
args.control.test = True
args.arch.action_sample_type = 'argmax'
args.arch.sample_gt_prob_type = 'zero'
args.navtask.task_params.data_augment = \
utils.Foo(lr_flip=0, delta_angle=0, delta_xy=0, relight=False,
relight_fast=False, structured=False)
args.control.test_mode = 'val'
args.navtask.task_params.batch_size = 32
elif mode == 'bench':
# Actually testing the agent in settings that are kept same between
# different runs.
args.navtask.task_params.batch_size = 16
args.control.test = True
args.arch.action_sample_type = 'argmax'
args.arch.sample_gt_prob_type = 'zero'
args.navtask.task_params.data_augment = \
utils.Foo(lr_flip=0, delta_angle=0, delta_xy=0, relight=False,
relight_fast=False, structured=False)
args.summary.test_iters = 250
args.control.only_eval_when_done = True
args.control.reset_rng_seed = True
args.control.test_mode = 'test'
else:
logging.fatal('Unknown mode: %s.', mode)
assert False
return args
def get_solver_vars(solver_str):
if solver_str == '':
vals = []
else:
vals = solver_str.split('_')
ks = ['clip', 'dlw', 'long', 'typ', 'isdk', 'adam_eps', 'init_lr']
ks = ks[:len(vals)]
# Gradient clipping or not.
if len(vals) == 0:
ks.append('clip')
vals.append('noclip')
# data loss weight.
if len(vals) == 1:
ks.append('dlw')
vals.append('dlw20')
# how long to train for.
if len(vals) == 2:
ks.append('long')
vals.append('nolong')
# Adam
if len(vals) == 3:
ks.append('typ')
vals.append('adam2')
# reg loss wt
if len(vals) == 4:
ks.append('rlw')
vals.append('rlw1')
# isd_k
if len(vals) == 5:
ks.append('isdk')
vals.append('isdk415') # 415, inflexion at 2.5k.
# adam eps
if len(vals) == 6:
ks.append('adam_eps')
vals.append('aeps1en8')
# init lr
if len(vals) == 7:
ks.append('init_lr')
vals.append('lr1en3')
assert (len(vals) == 8)
vars = utils.Foo()
for k, v in zip(ks, vals):
setattr(vars, k, v)
logging.error('solver_vars: %s', vars)
return vars
def get_arch_vars(arch_str):
if arch_str == '':
vals = []
else:
vals = arch_str.split('_')
ks = ['ver', 'lstm_dim', 'dropout']
# Exp Ver
if len(vals) == 0: vals.append('v0')
# LSTM dimentsions
if len(vals) == 1: vals.append('lstm2048')
# Dropout
if len(vals) == 2: vals.append('noDO')
assert (len(vals) == 3)
vars = utils.Foo()
for k, v in zip(ks, vals):
setattr(vars, k, v)
logging.error('arch_vars: %s', vars)
return vars
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
def get_default_args():
robot = utils.Foo(radius=15,
base=10,
height=140,
sensor_height=120,
camera_elevation_degree=-15)
camera_param = utils.Foo(width=225,
height=225,
z_near=0.05,
z_far=20.0,
fov=60.,
modalities=['rgb', 'depth'])
env = utils.Foo(padding=10,
resolution=5,
num_point_threshold=2,
valid_min=-10,
valid_max=200,
n_samples_per_face=200)
data_augment = utils.Foo(lr_flip=0,
delta_angle=1,
delta_xy=4,
relight=False,
relight_fast=False,
structured=False)
task_params = utils.Foo(num_actions=4,
step_size=4,
num_steps=0,
batch_size=32,
room_seed=0,
base_class='Building',
task='mapping',
n_ori=6,
data_augment=data_augment,
output_transform_to_global_map=False,
output_canonical_map=False,
output_incremental_transform=False,
output_free_space=False,
move_type='shortest_path',
toy_problem=0)
buildinger_args = utils.Foo(
building_names=['area1_gates_wingA_floor1_westpart'],
env_class=None,
robot=robot,
task_params=task_params,
env=env,
camera_param=camera_param)
solver_args = utils.Foo(seed=0,
learning_rate_decay=0.1,
clip_gradient_norm=0,
max_steps=120000,
initial_learning_rate=0.001,
momentum=0.99,
steps_per_decay=40000,
logdir=None,
sync=False,
adjust_lr_sync=True,
wt_decay=0.0001,
data_loss_wt=1.0,
reg_loss_wt=1.0,
num_workers=1,
task=0,
ps_tasks=0,
master='local')
summary_args = utils.Foo(display_interval=1, test_iters=100)
control_args = utils.Foo(train=False,
test=False,
force_batchnorm_is_training_at_test=False)
arch_args = utils.Foo(rgb_encoder='resnet_v2_50', d_encoder='resnet_v2_50')
return utils.Foo(solver=solver_args,
summary=summary_args,
control=control_args,
arch=arch_args,
buildinger=buildinger_args)
def get_navtask_vars(navtask_str):
if navtask_str == '':
vals = []
else:
vals = navtask_str.split('_')
ks_all = [
'dataset_name', 'modality', 'task', 'history', 'max_dist', 'num_steps',
'step_size', 'n_ori', 'aux_views', 'data_aug'
]
ks = ks_all[:len(vals)]
# All data or not.
if len(vals) == 0:
ks.append('dataset_name')
vals.append('sbpd')
# modality
if len(vals) == 1:
ks.append('modality')
vals.append('rgb')
# semantic task?
if len(vals) == 2:
ks.append('task')
vals.append('r2r')
# number of history frames.
if len(vals) == 3:
ks.append('history')
vals.append('h0')
# max steps
if len(vals) == 4:
ks.append('max_dist')
vals.append('32')
# num steps
if len(vals) == 5:
ks.append('num_steps')
vals.append('40')
# step size
if len(vals) == 6:
ks.append('step_size')
vals.append('8')
# n_ori
if len(vals) == 7:
ks.append('n_ori')
vals.append('4')
# Auxiliary views.
if len(vals) == 8:
ks.append('aux_views')
vals.append('nv0')
# Normal data augmentation as opposed to structured data augmentation (if set
# to straug.
if len(vals) == 9:
ks.append('data_aug')
vals.append('straug')
assert (len(vals) == 10)
for i in range(len(ks)):
assert (ks[i] == ks_all[i])
vars = utils.Foo()
for k, v in zip(ks, vals):
setattr(vars, k, v)
logging.error('navtask_vars: %s', vals)
return vars
def process_solver_str(solver_str):
solver = utils.Foo(seed=0,
learning_rate_decay=None,
clip_gradient_norm=None,
max_steps=None,
initial_learning_rate=None,
momentum=None,
steps_per_decay=None,
logdir=None,
sync=False,
adjust_lr_sync=True,
wt_decay=0.0001,
data_loss_wt=None,
reg_loss_wt=None,
freeze_conv=True,
num_workers=1,
task=0,
ps_tasks=0,
master='local',
typ=None,
momentum2=None,
adam_eps=None)
# Clobber with overrides from solver str.
solver_vars = get_solver_vars(solver_str)
solver.data_loss_wt = float(solver_vars.dlw[3:].replace('x', '.'))
solver.adam_eps = float(solver_vars.adam_eps[4:].replace('x', '.').replace(
'n', '-'))
solver.initial_learning_rate = float(solver_vars.init_lr[2:].replace(
'x', '.').replace('n', '-'))
solver.reg_loss_wt = float(solver_vars.rlw[3:].replace('x', '.'))
solver.isd_k = float(solver_vars.isdk[4:].replace('x', '.'))
long = solver_vars.long
if long == 'long':
solver.steps_per_decay = 40000
solver.max_steps = 120000
elif long == 'long2':
solver.steps_per_decay = 80000
solver.max_steps = 120000
elif long == 'nolong' or long == 'nol':
solver.steps_per_decay = 20000
solver.max_steps = 60000
else:
logging.fatal('solver_vars.long should be long, long2, nolong or nol.')
assert False
clip = solver_vars.clip
if clip == 'noclip' or clip == 'nocl':
solver.clip_gradient_norm = 0
elif clip[:4] == 'clip':
solver.clip_gradient_norm = float(clip[4:].replace('x', '.'))
else:
logging.fatal('Unknown solver_vars.clip: %s', clip)
assert False
typ = solver_vars.typ
if typ == 'adam':
solver.typ = 'adam'
solver.momentum = 0.9
solver.momentum2 = 0.999
solver.learning_rate_decay = 1.0
elif typ == 'adam2':
solver.typ = 'adam'
solver.momentum = 0.9
solver.momentum2 = 0.999
solver.learning_rate_decay = 0.1
elif typ == 'sgd':
solver.typ = 'sgd'
solver.momentum = 0.99
solver.momentum2 = None
solver.learning_rate_decay = 0.1
else:
logging.fatal('Unknown solver_vars.typ: %s', typ)
assert False
logging.error('solver: %s', solver)
return solver
