def test_traineval(self):
tf.logging.set_verbosity(tf.logging.INFO)
path = PATH.decode(sys.stdout.encoding)
#2 files, 64 epochs, batchsize 32 => 2*64/32 = 4 iterations
dset = lambda: ds.dataset_with_preprocess(LISTFILE_1, path,
epochs=4,
batchsize=32,
segs_per_sample=16,
)
dset_eval = lambda: ds.dataset_with_preprocess(LISTFILE_1, path,
epochs=1,
batchsize=16,
segs_per_sample=16,
shuffle=False
)
config = self.config.replace(save_checkpoints_steps=2)
tfnet_est = TFNetEstimator(**nets.default_net(), config=config)
input_fn = lambda: dset().make_one_shot_iterator().get_next()
eval_input_fn = lambda: dset_eval().make_one_shot_iterator().get_next()
train_spec = tf.estimator.TrainSpec(input_fn)
eval_spec = tf.estimator.EvalSpec(eval_input_fn)
tf.estimator.train_and_evaluate(tfnet_est, train_spec, eval_spec)
self.assertIsNotNone(tfnet_est)
def test_train_data_pipe_default(self):
dset = ds.dataset_with_preprocess(LISTFILE_1, self.path)
itr = dset.make_one_shot_iterator().get_next()
self.assertTrue(itr[0].shape.is_compatible_with(
tf.TensorShape((None, 8192, 1))))
self.assertTrue(itr[1].shape.is_compatible_with(
tf.TensorShape((None, 8192, 1))))
#defaults with test data shoudl throw after 1 iter
with tf.Session() as sess:
sess.run(itr)
self.assertRaises(tf.errors.OutOfRangeError, lambda: sess.run(itr))
def main(_):
path = PATH.decode(sys.stdout.encoding)
dset = ds.dataset_with_preprocess(
LISTFILE_1,
path,
epochs=1,
batchsize=32,
segs_per_sample=64,
)
tfnet_est = TFNetEstimator(**nets.default_net(),
model_dir='tests/dummymodel')
tfnet_est.train(input_fn=lambda: dset.make_one_shot_iterator().get_next())
def test_train(self):
"""Runs a training pass with test data for 4 iterations
iterations of batchsize 16"""
tf.logging.set_verbosity(tf.logging.INFO)
path = PATH.decode(sys.stdout.encoding)
#2 files, 64 epochs, batchsize 32 => 2*64/32 = 4 iterations
dset = lambda: ds.dataset_with_preprocess(LISTFILE_1, path,
epochs=64,
batchsize=32,
)
tfnet_est = TFNetEstimator(**nets.default_net(), config=self.config)
tfnet_est.train(
input_fn=lambda: dset().make_one_shot_iterator().get_next())
self.assertIsNotNone(tfnet_est)
def test_train_sbr_fusion(self):
tf.logging.set_verbosity(tf.logging.INFO)
path = PATH.decode(sys.stdout.encoding)
#2 files, 64 epochs, batchsize 32 => 2*64/32 = 4 iterations
dset = lambda: ds.dataset_with_preprocess(LISTFILE_1, path,
epochs=64,
batchsize=16,
)
netcfg = nets.build_net(copies_rate=2,
time_params={},
window_length=512)
tfnet_est = TFNetEstimator(**netcfg,
weight_decay=0.1,
config=self.config)
tfnet_est.train(
input_fn=lambda: dset().make_one_shot_iterator().get_next())
self.assertIsNotNone(tfnet_est)
def test_train_data_pipe_options(self):
dset = ds.dataset_with_preprocess(
LISTFILE_1,
self.path,
epochs=10,
length=4096,
batchsize=8,
drop_remainder=True,
)
itr = dset.make_one_shot_iterator().get_next()
self.assertTrue(itr[0].shape.is_compatible_with(
tf.TensorShape((8, 4096, 1))))
self.assertTrue(itr[1].shape.is_compatible_with(
tf.TensorShape((8, 4096, 1))))
#2 files, 10 epochs => 20 samples
#batch of 8 => 2 full iters, 3rd should throw
with tf.Session() as sess:
sess.run(itr)
sess.run(itr)
self.assertRaises(tf.errors.OutOfRangeError, lambda: sess.run(itr))
def test_loadmodel(self):
"""Test running eval from with trained model"""
tf.logging.set_verbosity(tf.logging.INFO)
path = PATH.decode(sys.stdout.encoding)
#2 files, 64 epochs, batchsize 32 => 2*64/32 = 4 iterations
dset = ds.dataset_with_preprocess(
LISTFILE_1,
path,
epochs=1,
batchsize=16,
)
#RunConfig for more more printing since we are only training for very few steps
config = tf.estimator.RunConfig(log_step_count_steps=1)
tfnet_est = TFNetEstimator(**nets.default_net(),
config=config,
model_dir=DUMMY_MODEL_PATH)
tfnet_est.evaluate(
input_fn=lambda: dset.make_one_shot_iterator().get_next())
self.assertIsNotNone(tfnet_est)