def add_default_summaries(mode, arop_full_summary_iters, summarize_ops,
summarize_names, to_aggregate, action_prob_op,
input_tensors, scope_name):
assert(mode == 'train_bkp' or mode == 'val' or mode == 'test'), \
'add_default_summaries mode is neither train_bkp or val or test.'
s_ops = tf_utils.get_default_summary_ops()
if mode == 'train_bkp':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
summarize_ops, summarize_names, mode, to_aggregate=False,
scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
elif mode == 'val':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
summarize_ops, summarize_names, mode, to_aggregate=to_aggregate,
scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
elif mode == 'test':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
[], [], mode, to_aggregate=[], scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
if mode == 'val':
arop = s_ops.additional_return_ops
arop += [[action_prob_op, input_tensors['train_bkp']['action']]]
arop += [[
input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal']
]]
arop += [[
input_tensors['step']['loc_on_map'],
input_tensors['common']['orig_maps'],
input_tensors['common']['goal_loc']
]]
s_ops.arop_summary_iters += [
-1, arop_full_summary_iters, arop_full_summary_iters
]
s_ops.arop_eval_fns += [eval_ap, eval_dist, plot_trajectories]
elif mode == 'test':
arop = s_ops.additional_return_ops
arop += [[
input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal']
]]
arop += [[input_tensors['step']['gt_dist_to_goal']]]
arop += [[
input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal'],
input_tensors['step']['node_ids'],
input_tensors['step']['perturbs']
]]
arop += [[
input_tensors['step']['loc_on_map'],
input_tensors['common']['orig_maps'],
input_tensors['common']['goal_loc']
]]
s_ops.arop_summary_iters += [-1, -1, -1, arop_full_summary_iters]
s_ops.arop_eval_fns += [
eval_dist, save_d_at_t, save_all, plot_trajectories
]
return s_ops
def add_default_summaries(mode, arop_full_summary_iters, summarize_ops,
summarize_names, to_aggregate, action_prob_op,
input_tensors, scope_name):
assert(mode == 'train' or mode == 'val' or mode == 'test'), \
'add_default_summaries mode is neither train or val or test.'
s_ops = tf_utils.get_default_summary_ops()
if mode == 'train':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
summarize_ops, summarize_names, mode, to_aggregate=False,
scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
elif mode == 'val':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
summarize_ops, summarize_names, mode, to_aggregate=to_aggregate,
scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
elif mode == 'test':
s_ops.summary_ops, s_ops.print_summary_ops, additional_return_ops, \
arop_summary_iters, arop_eval_fns = tf_utils.simple_summaries(
[], [], mode, to_aggregate=[], scope_name=scope_name)
s_ops.additional_return_ops += additional_return_ops
s_ops.arop_summary_iters += arop_summary_iters
s_ops.arop_eval_fns += arop_eval_fns
if mode == 'val':
arop = s_ops.additional_return_ops
arop += [[action_prob_op, input_tensors['train']['action']]]
arop += [[input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal']]]
arop += [[input_tensors['step']['loc_on_map'],
input_tensors['common']['orig_maps'],
input_tensors['common']['goal_loc']]]
s_ops.arop_summary_iters += [-1, arop_full_summary_iters,
arop_full_summary_iters]
s_ops.arop_eval_fns += [eval_ap, eval_dist, plot_trajectories]
elif mode == 'test':
arop = s_ops.additional_return_ops
arop += [[input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal']]]
arop += [[input_tensors['step']['gt_dist_to_goal']]]
arop += [[input_tensors['step']['loc_on_map'],
input_tensors['common']['goal_loc'],
input_tensors['step']['gt_dist_to_goal'],
input_tensors['step']['node_ids'],
input_tensors['step']['perturbs']]]
arop += [[input_tensors['step']['loc_on_map'],
input_tensors['common']['orig_maps'],
input_tensors['common']['goal_loc']]]
s_ops.arop_summary_iters += [-1, -1, -1, arop_full_summary_iters]
s_ops.arop_eval_fns += [eval_dist, save_d_at_t, save_all,
plot_trajectories]
return s_ops