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ThreadTrainer.py
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ThreadTrainer.py
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# Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from threading import Thread
import numpy as np
from Config import Config
class ThreadTrainer(Thread):
def __init__(self, server, id):
super(ThreadTrainer, self).__init__()
self.setDaemon(True)
self.id = id
self.server = server
self.exit_flag = False
def run(self):
while not self.exit_flag:
batch_size = 0
while batch_size <= Config.TRAINING_MIN_BATCH_SIZE:
x_, r_, a_ = self.server.training_q.get()
if batch_size == 0:
x__ = x_; r__ = r_; a__ = a_
else:
np.concatenate((x__, x_))
np.concatenate((r__, r_))
np.concatenate((a__, a_))
batch_size += x_.shape[0]
if Config.TRAIN_MODELS:
self.server.train_model(x__, r__, a__, self.id)