def test_image_decoder(self):
# Make random image.
image_size = 84
image = np.random.randint(low=0,
high=255,
size=[image_size, image_size,
3]).astype(np.ubyte)
# Encode
image_bytes = dataset_to_records.encode_image(image,
image_format='PNG')
label = np.zeros(1).astype(np.int64)
image_example = dataset_to_records.make_example(image_bytes,
label,
input_key='image',
label_key='label')
# Decode
image_decoder = decoder.ImageDecoder(image_size=image_size)
image_decoded = image_decoder(image_example)
# Assert perfect reconstruction.
with self.session(use_gpu=False) as sess:
image_rec_numpy = sess.run(image_decoded)
self.assertAllClose(2 * (image.astype(np.float32) / 255.0 - 0.5),
image_rec_numpy)
def test_trainer(self):
# PrototypicalNetworkLearner is built automatically and this test does not
# have the opportunity to pass values to its constructor except through gin.
gin.bind_parameter('PrototypicalNetworkLearner.weight_decay', 1e-4)
gin.bind_parameter(
'PrototypicalNetworkLearner.backprop_through_moments', True)
gin.bind_parameter(
'PrototypicalNetworkLearner.transductive_batch_norm', False)
gin.bind_parameter('PrototypicalNetworkLearner.embedding_fn',
'four_layer_convnet')
# Values that can't be passed directly to EpisodeDescriptionConfig
gin.bind_parameter('process_episode.support_decoder',
decoder.ImageDecoder())
gin.bind_parameter('process_episode.query_decoder',
decoder.ImageDecoder())
episode_config = config.EpisodeDescriptionConfig(
num_ways=None,
num_support=None,
num_query=None,
min_ways=5,
max_ways_upper_bound=50,
max_num_query=10,
max_support_set_size=500,
max_support_size_contrib_per_class=100,
min_log_weight=np.log(0.5),
max_log_weight=np.log(2),
ignore_dag_ontology=False,
ignore_bilevel_ontology=False)
# Inspired from `learn/gin/default/debug_proto_mini_imagenet.gin`, but
# building the objects explicitly.
data_config = config.DataConfig(
image_height=84,
shuffle_buffer_size=20,
read_buffer_size_bytes=(1024**2),
num_prefetch=2,
)
trainer_instance = trainer.Trainer(
train_learner_class=learner_lib.PrototypicalNetworkLearner,
eval_learner_class=learner_lib.PrototypicalNetworkLearner,
is_training=True,
train_dataset_list=['mini_imagenet'],
eval_dataset_list=['mini_imagenet'],
restrict_classes={},
restrict_num_per_class={},
checkpoint_dir='',
summary_dir='',
records_root_dir=FLAGS.records_root_dir,
eval_split=trainer.VALID_SPLIT,
eval_finegrainedness=False,
eval_finegrainedness_split='',
eval_imbalance_dataset='',
omit_from_saving_and_reloading='',
train_episode_config=episode_config,
eval_episode_config=episode_config,
data_config=data_config,
num_updates=100,
batch_size=8, # unused
num_eval_episodes=10,
checkpoint_every=10,
validate_every=5,
log_every=1,
checkpoint_to_restore=None,
learning_rate=1e-4,
decay_learning_rate=True,
decay_every=5000,
decay_rate=0.5,
experiment_name='test',
pretrained_source='',
)
# Get the next train / valid / test episodes.
train_episode = trainer_instance.next_data[trainer.TRAIN_SPLIT]
self.assertIsInstance(train_episode, providers.EpisodeDataset)
# This isn't really a test. It just checks that things don't crash...
print(
trainer_instance.sess.run([
trainer_instance.train_op,
trainer_instance.losses[trainer.TRAIN_SPLIT],
trainer_instance.accuracies[trainer.TRAIN_SPLIT]
]))