def test_oracle(sess, testset, tokenizer, oracle, cpu_pool, batch_size, logger):
oracle_dataset = OracleDataset(testset)
oracle_sources = oracle.get_sources(sess)
oracle_evaluator = Evaluator(oracle_sources, oracle.scope_name, network=oracle, tokenizer=tokenizer)
oracle_batchifier = OracleBatchifier(tokenizer, oracle_sources, status=('success',))
oracle_iterator = Iterator(oracle_dataset, pool=cpu_pool,
batch_size=batch_size,
batchifier=oracle_batchifier)
[oracle_loss, oracle_error] = oracle_evaluator.process(sess, oracle_iterator, [oracle.loss, oracle.error])
logger.info("Oracle test loss: {}".format(oracle_loss))
logger.info("Oracle test error: {}".format(oracle_error))
# CPU/GPU option
cpu_pool = Pool(args.no_thread, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_ratio)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, args.ckpt)
features = dict()
for one_set in args.set_type:
print("Load dataset -> set: {}".format(one_set))
dataset = OracleDataset.load(args.data_dir, one_set, image_loader=image_loader, crop_loader=crop_loader)
batchifier = OracleBatchifier(tokenizer=None, sources=[source])
iterator = Iterator(dataset,
batch_size=args.batch_size,
pool=cpu_pool,
batchifier=batchifier)
for batch in tqdm(iterator):
feat = sess.run(end_points[feature_name], feed_dict={images: numpy.array(batch[source])})
for f, game in zip(feat, batch["raw"]):
f = f.squeeze()
if args.mode == "crop":
id = game.object_id
else:
id = game.picture.id
sources = network.get_sources(sess)
logger.info("Sources: " + ', '.join(sources))
sess.run(tf.global_variables_initializer())
if use_resnet:
resnet_saver.restore(sess, os.path.join(args.data_dir, 'resnet_v1_{}.ckpt'.format(resnet_version)))
start_epoch = load_checkpoint(sess, saver, args, save_path)
best_val_err = 0
best_train_err = None
# create training tools
evaluator = Evaluator(sources, network.scope_name)
batchifier = OracleBatchifier(tokenizer, sources, status=config['status'])
for t in range(start_epoch, no_epoch):
logger.info('Epoch {}..'.format(t + 1))
train_iterator = Iterator(trainset,
batch_size=batch_size, pool=cpu_pool,
batchifier=batchifier,
shuffle=True)
train_loss, train_accuracy = evaluator.process(sess, train_iterator, outputs=outputs + [optimizer])
valid_iterator = Iterator(validset, pool=cpu_pool,
batch_size=batch_size*2,
batchifier=batchifier,
shuffle=False)
valid_loss, valid_accuracy = evaluator.process(sess, valid_iterator, outputs=outputs)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)) as sess:
sources = network.get_sources(sess)
logger.info("Sources: " + ', '.join(sources))
sess.run(tf.global_variables_initializer())
start_epoch = load_checkpoint(sess, saver, args, save_path)
best_val_err = 1e5
best_train_err = None
# create training tools
evaluator = Evaluator(sources, network.scope_name)
batchifier = OracleBatchifier(tokenizer,
sources,
status=config['status'],
**config['model']['crop'])
for t in range(start_epoch, no_epoch):
logger.info('Epoch {}..'.format(t + 1))
train_iterator = Iterator(trainset,
batch_size=batch_size,
pool=cpu_pool,
batchifier=batchifier,
shuffle=True)
train_loss, train_error = evaluator.process(sess,
train_iterator,
outputs=outputs +
[optimizer])