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Engine.py
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import glob
import time
import tensorflow as tf
from tensorflow.contrib.framework import list_variables
import Measures
from logging_utils import logger
from Log import log
from Network import Network
from Trainer import Trainer
from Util import load_wider_or_deeper_mxnet_model
from datasets.Forward import forward, oneshot_forward, online_forward
from datasets.Loader import load_dataset
class Engine(object):
def __init__(self, config):
self.config = config
self.dataset = config.unicode("dataset").lower()
self.load_init = config.unicode("load_init", "")
self.load = config.unicode("load", "")
self.task = config.unicode("task", "train")
self.use_partialflow = config.bool("use_partialflow", False)
self.do_oneshot_or_online_or_offline = self.task in ("oneshot_forward", "oneshot", "online", "offline")
if self.do_oneshot_or_online_or_offline:
assert config.int("batch_size_eval", 1) == 1
self.need_train = self.task == "train" or self.do_oneshot_or_online_or_offline or self.task == "forward_train"
self.session = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
self.coordinator = tf.train.Coordinator()
self.valid_data = load_dataset(config, "valid", self.session, self.coordinator)
if self.need_train:
self.train_data = load_dataset(config, "train", self.session, self.coordinator)
self.num_epochs = config.int("num_epochs", 1000)
self.model = config.unicode("model")
self.model_base_dir = config.dir("model_dir", "models")
self.model_dir = self.model_base_dir + self.model + "/"
self.save = config.bool("save", True)
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.start_epoch = 0
reuse_variables = None
if self.need_train:
freeze_batchnorm = config.bool("freeze_batchnorm", False)
self.train_network = Network(config, self.train_data, self.global_step, training=True,
use_partialflow=self.use_partialflow,
do_oneshot=self.do_oneshot_or_online_or_offline,
freeze_batchnorm=freeze_batchnorm, name="trainnet")
reuse_variables = True
else:
self.train_network = None
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):
self.test_network = Network(config, self.valid_data, self.global_step, training=False,
do_oneshot=self.do_oneshot_or_online_or_offline, use_partialflow=False,
freeze_batchnorm=True, name="testnet")
logger.info("number of parameters:", "{:,}".format(self.test_network.n_params))
self.trainer = Trainer(config, self.train_network, self.test_network, self.global_step, self.session)
self.saver = tf.train.Saver(max_to_keep=0, pad_step_number=True)
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
tf.train.start_queue_runners(self.session)
self.load_init_saver = self._create_load_init_saver()
if not self.do_oneshot_or_online_or_offline:
self.try_load_weights()
# put this in again later
# self.session.graph.finalize()
def _create_load_init_saver(self):
if self.load_init != "" and not self.load_init.endswith(".pickle"):
vars_file = [x[0] for x in list_variables(self.load_init)]
vars_model = tf.global_variables()
assert all([x.name.endswith(":0") for x in vars_model])
vars_intersection = [x for x in vars_model if x.name[:-2] in vars_file]
vars_missing = [x for x in vars_model if x.name[:-2] not in vars_file]
if len(vars_missing) > 0:
logger.info(log.v1, "the following variables will not be initialized since they are not present in the",
" initialization model", [v.name for v in vars_missing])
return tf.train.Saver(var_list=vars_intersection)
else:
return None
def try_load_weights(self):
fn = None
if self.load != "":
fn = self.load.replace(".index", "")
else:
files = sorted(glob.glob(self.model_dir + self.model + "-*.index"))
if len(files) > 0:
fn = files[-1].replace(".index", "")
if fn is not None:
logger.info(log.v1, "loading model from", fn)
self.saver.restore(self.session, fn)
if self.model == fn.split("/")[-2]:
self.start_epoch = int(fn.split("-")[-1])
logger.info(log.v1, "starting from epoch", self.start_epoch + 1)
elif self.load_init != "":
if self.load_init.endswith(".pickle"):
logger.info(log.v1, "trying to initialize model from wider-or-deeper mxnet model", self.load_init)
load_wider_or_deeper_mxnet_model(self.load_init, self.session)
else:
fn = self.load_init
logger.info(log.v1, "initializing model from", fn)
assert self.load_init_saver is not None
self.load_init_saver.restore(self.session, fn)
def reset_optimizer(self):
self.trainer.reset_optimizer()
@staticmethod
def run_epoch(step_fn, data, epoch):
loss_total = 0.0
n_imgs_per_epoch = data.num_examples_per_epoch()
measures_accumulated = {}
n_imgs_processed = 0
while n_imgs_processed < n_imgs_per_epoch:
start = time.time()
loss_summed, measures, n_imgs = step_fn(epoch)
loss_total += loss_summed
measures_accumulated = Measures.calc_measures_sum(measures_accumulated, measures)
n_imgs_processed += n_imgs
loss_avg = loss_summed / n_imgs
measures_avg = Measures.calc_measures_avg(measures, n_imgs, data.ignore_classes)
end = time.time()
elapsed = end - start
# TODO: Print proper averages for the measures
logger.debug(n_imgs_processed, '/', n_imgs_per_epoch, loss_avg, measures_avg, "elapsed", elapsed)
loss_total /= n_imgs_processed
measures_accumulated = Measures.calc_measures_avg(measures_accumulated, n_imgs_processed, data.ignore_classes)
return loss_total, measures_accumulated
def train(self):
assert self.need_train
logger.info(log.v1, "starting training")
for epoch in range(self.start_epoch, self.num_epochs):
start = time.time()
train_loss, train_measures = self.run_epoch(self.trainer.train_step, self.train_data, epoch)
valid_loss, valid_measures = self.run_epoch(self.trainer.validation_step, self.valid_data, epoch)
end = time.time()
elapsed = end - start
train_error_string = Measures.get_error_string(train_measures, "train")
valid_error_string = Measures.get_error_string(valid_measures, "valid")
logger.info("epoch", epoch + 1, "finished. elapsed:", "%.5f" % elapsed, "train_score:", "%.5f" % train_loss,
train_error_string, "valid_score:", valid_loss, valid_error_string)
if self.save:
self.save_model(epoch + 1)
def eval(self):
start = time.time()
valid_loss, measures = self.run_epoch(self.trainer.validation_step, self.valid_data, 0)
end = time.time()
elapsed = end - start
valid_error_string = Measures.get_error_string(measures, "valid")
logger.info(log.v1, "eval finished. elapsed:", elapsed, "valid_score:", valid_loss, valid_error_string)
def run(self):
if self.task == "train":
self.train()
elif self.task == "eval":
self.eval()
elif self.task in ("forward", "forward_train"):
if self.task == "forward_train":
network = self.train_network
data = self.train_data
else:
network = self.test_network
data = self.valid_data
save_logits = self.config.bool("save_logits", False)
save_results = self.config.bool("save_results", True)
forward(self, network, data, self.dataset, save_results=save_results, save_logits=save_logits)
elif self.do_oneshot_or_online_or_offline:
save_logits = self.config.bool("save_logits", False)
save_results = self.config.bool("save_results", False)
if self.task == "oneshot":
oneshot_forward(self, save_results=save_results, save_logits=save_logits)
elif self.task == "online":
online_forward(self, save_results=save_results, save_logits=save_logits)
else:
assert False, "Unknown task " + str(self.task)
else:
assert False, "Unknown task " + str(self.task)
def save_model(self, epoch):
tf.gfile.MakeDirs(self.model_dir)
self.saver.save(self.session, self.model_dir + self.model, epoch)