def test_example_classification_config_yaml():
"""
Test that export config and meta yaml from example classification config python.
"""
config_file = os.path.join("configs", "example", "classification.py")
config = _load_py(config_file)
config_yaml = os.path.join("configs", "example", "classification.yaml")
config_meta = os.path.join("configs", "example",
"classification_meta.yaml")
environment.init("test_example_classification_config_yaml")
saved_config, saved_meta = save_yaml(environment.EXPERIMENT_DIR, config)
print(saved_meta)
with open(config_yaml) as f:
expected = f.read()
with open(saved_config) as f:
data = f.read()
assert expected == data
with open(config_meta) as f:
expected = f.read()
with open(saved_meta) as f:
data = f.read()
assert expected == data
def run(network, dataset, config_file, experiment_id, recreate):
environment.init(experiment_id)
config = config_util.load(config_file)
if network:
network_class = module_loader.load_network_class(network)
config.NETWORK_CLASS = network_class
if dataset:
dataset_class = module_loader.load_dataset_class(dataset)
config.DATASET_CLASS = dataset_class
config_util.display(config)
executor.init_logging(config)
executor.prepare_dirs(recreate)
config_util.copy_to_experiment_dir(config_file)
config_util.save_yaml(environment.EXPERIMENT_DIR, config)
start_training(config)
def _export(config, restore_path, image_path):
if restore_path is None:
restore_file = executor.search_restore_filename(
environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR, restore_file)
print("Restore from {}".format(restore_path))
if not os.path.exists("{}.index".format(restore_path)):
raise Exception("restore file {} dont exists.".format(restore_path))
output_root_dir = os.path.join(environment.EXPERIMENT_DIR, "export")
output_root_dir = os.path.join(output_root_dir,
os.path.basename(restore_path))
if not os.path.exists(output_root_dir):
os.makedirs(output_root_dir)
graph = tf.Graph()
ModelClass = config.NETWORK_CLASS
network_kwargs = dict(
(key.lower(), val) for key, val in config.NETWORK.items())
with graph.as_default():
model = ModelClass(
classes=config.CLASSES,
is_debug=config.IS_DEBUG,
**network_kwargs,
)
is_training = tf.constant(False, name="is_training")
images_placeholder, _ = model.placeholders()
model.inference(images_placeholder, is_training)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=50)
session_config = tf.ConfigProto()
sess = tf.Session(graph=graph, config=session_config)
sess.run(init_op)
saver.restore(sess, restore_path)
main_output_dir = os.path.join(
output_root_dir, "{}x{}".format(config.IMAGE_SIZE[0],
config.IMAGE_SIZE[1]))
if not os.path.exists(main_output_dir):
os.makedirs(main_output_dir)
# npy files for DLK debug.
if image_path:
all_ops = _minimal_operations(sess)
npy_output_dir = os.path.join(main_output_dir, "inference_test_data")
if not os.path.exists(npy_output_dir):
os.makedirs(npy_output_dir)
raw_image = load_image(*config.IMAGE_SIZE)
image = _pre_process(raw_image, config.PRE_PROCESSOR,
config.DATA_FORMAT)
images = np.expand_dims(image, axis=0)
feed_dict = {
images_placeholder: images,
}
all_outputs = []
index = 0
for op in all_ops:
for op_output in op.outputs:
val = sess.run(op_output.name, feed_dict=feed_dict)
name = '%03d' % index + '_' + op_output.name.replace('/', '_')
all_outputs.append({'val': val, 'name': name})
index += 1
_save_npy(image_path, npy_output_dir, image, raw_image, all_outputs,
config.IMAGE_SIZE)
yaml_names = config_util.save_yaml(main_output_dir, config)
pb_name = executor.save_pb_file(sess, main_output_dir)
message = """
Create pb and yaml files in: {}
pb: {}
yaml: {}, {}
""".format(main_output_dir, pb_name, *yaml_names)
if image_path:
message += "Create npy files in under `inference_test_data` folder \n"
message += "npy: {}".format([d["name"] for d in all_outputs] + [
"raw_image",
"preprocessed_image",
])
print(message)
print("finish")
return main_output_dir
