# 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. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r""" Script to setup the grid moving agent. blaze build --define=ION_GFX_OGLES20=1 -c opt --copt=-mavx --config=cuda_clang \ learning/brain/public/tensorflow_std_server{,_gpu} \ experimental/users/saurabhgupta/navigation/cmp/scripts/script_distill.par \ experimental/users/saurabhgupta/navigation/cmp/scripts/script_distill ./blaze-bin/experimental/users/saurabhgupta/navigation/cmp/scripts/script_distill \ --logdir=/cns/iq-d/home/saurabhgupta/output/stanford-distill/local/v0/ \ --config_name 'v0+train' --gfs_user robot-intelligence-gpu """ import sys, os, numpy as np import copy import argparse, pprint import time import cProfile import tensorflow as tf from tensorflow.contrib import slim from tensorflow.python.framework import ops from tensorflow.contrib.framework.python.ops import variables import logging from tensorflow.python.platform import gfile from tensorflow.python.platform import app from tensorflow.python.platform import flags from cfgs import config_distill from tfcode import tf_utils import src.utils as utils import src.file_utils as fu import tfcode.distillation as distill import datasets.nav_env as nav_env FLAGS = flags.FLAGS flags.DEFINE_string('master', 'local', 'The name of the TensorFlow master to use.') flags.DEFINE_integer('ps_tasks', 0, 'The number of parameter servers. If the ' 'value is 0, then the parameters are handled locally by ' 'the worker.') flags.DEFINE_integer('task', 0, 'The Task ID. This value is used when training ' 'with multiple workers to identify each worker.') flags.DEFINE_integer('num_workers', 1, '') flags.DEFINE_string('config_name', '', '') flags.DEFINE_string('logdir', '', '') def main(_): args = config_distill.get_args_for_config(FLAGS.config_name) args.logdir = FLAGS.logdir args.solver.num_workers = FLAGS.num_workers args.solver.task = FLAGS.task args.solver.ps_tasks = FLAGS.ps_tasks args.solver.master = FLAGS.master args.buildinger.env_class = nav_env.MeshMapper fu.makedirs(args.logdir) args.buildinger.logdir = args.logdir R = nav_env.get_multiplexor_class(args.buildinger, args.solver.task) if False: pr = cProfile.Profile() pr.enable() rng = np.random.RandomState(0) for i in range(1): b, instances_perturbs = R.sample_building(rng) inputs = b.worker(*(instances_perturbs)) for j in range(inputs['imgs'].shape[0]): p = os.path.join('tmp', '{:d}.png'.format(j)) img = inputs['imgs'][j,0,:,:,:3]*1 img = (img).astype(np.uint8) fu.write_image(p, img) print(inputs['imgs'].shape) inputs = R.pre(inputs) pr.disable() pr.print_stats(2) if args.control.train: if not gfile.Exists(args.logdir): gfile.MakeDirs(args.logdir) m = utils.Foo() m.tf_graph = tf.Graph() config = tf.ConfigProto() config.device_count['GPU'] = 1 config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = 0.8 with m.tf_graph.as_default(): with tf.device(tf.train.replica_device_setter(args.solver.ps_tasks)): m = distill.setup_to_run(m, args, is_training=True, batch_norm_is_training=True) train_step_kwargs = distill.setup_train_step_kwargs_mesh( m, R, os.path.join(args.logdir, 'train'), rng_seed=args.solver.task, is_chief=args.solver.task==0, iters=1, train_display_interval=args.summary.display_interval) final_loss = slim.learning.train( train_op=m.train_op, logdir=args.logdir, master=args.solver.master, is_chief=args.solver.task == 0, number_of_steps=args.solver.max_steps, train_step_fn=tf_utils.train_step_custom, train_step_kwargs=train_step_kwargs, global_step=m.global_step_op, init_op=m.init_op, init_fn=m.init_fn, sync_optimizer=m.sync_optimizer, saver=m.saver_op, summary_op=None, session_config=config) if args.control.test: m = utils.Foo() m.tf_graph = tf.Graph() checkpoint_dir = os.path.join(format(args.logdir)) with m.tf_graph.as_default(): m = distill.setup_to_run(m, args, is_training=False, batch_norm_is_training=args.control.force_batchnorm_is_training_at_test) train_step_kwargs = distill.setup_train_step_kwargs_mesh( m, R, os.path.join(args.logdir, args.control.test_name), rng_seed=args.solver.task+1, is_chief=args.solver.task==0, iters=args.summary.test_iters, train_display_interval=None) sv = slim.learning.supervisor.Supervisor( graph=ops.get_default_graph(), logdir=None, init_op=m.init_op, summary_op=None, summary_writer=None, global_step=None, saver=m.saver_op) last_checkpoint = None while True: last_checkpoint = slim.evaluation.wait_for_new_checkpoint(checkpoint_dir, last_checkpoint) checkpoint_iter = int(os.path.basename(last_checkpoint).split('-')[1]) start = time.time() logging.info('Starting evaluation at %s using checkpoint %s.', time.strftime('%Y-%m-%d-%H:%M:%S', time.localtime()), last_checkpoint) config = tf.ConfigProto() config.device_count['GPU'] = 1 config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = 0.8 with sv.managed_session(args.solver.master,config=config, start_standard_services=False) as sess: sess.run(m.init_op) sv.saver.restore(sess, last_checkpoint) sv.start_queue_runners(sess) vals, _ = tf_utils.train_step_custom( sess, None, m.global_step_op, train_step_kwargs, mode='val') if checkpoint_iter >= args.solver.max_steps: break if __name__ == '__main__': app.run()