def run(experiment_id, restore_path, config_file, bit, unquant_layers):
if config_file is None and experiment_id is None:
raise Exception("config_file or experiment_id are required")
if experiment_id:
environment.init(experiment_id)
config = config_util.load_from_experiment()
if config_file:
config = config_util.merge(config, config_util.load(config_file))
if restore_path is None:
restore_file = executor.search_restore_filename(
environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR,
restore_file)
if not os.path.exists("{}.index".format(restore_path)):
raise Exception(
"restore file {} dont exists.".format(restore_path))
else:
experiment_id = "profile"
environment.init(experiment_id)
config = config_util.load(config_file)
executor.init_logging(config)
config_util.display(config)
_profile(config, restore_path, bit, unquant_layers)
def run(experiment_id,
restore_path=None,
image_size=(None, None),
image=DEFAULT_INFERENCE_TEST_DATA_IMAGE,
config_file=None):
environment.init(experiment_id)
config = config_util.load_from_experiment()
if config_file:
config = config_util.merge(config, config_util.load(config_file))
config.BATCH_SIZE = 1
config.NETWORK.BATCH_SIZE = 1
config.DATASET.BATCH_SIZE = 1
if list(image_size) != [None, None]:
config.IMAGE_SIZE = list(image_size)
config.NETWORK.IMAGE_SIZE = list(image_size)
# override pre processes image size.
if config.PRE_PROCESSOR:
config.PRE_PROCESSOR.set_image_size(image_size)
# override post processes image size.
if config.POST_PROCESSOR:
config.POST_PROCESSOR.set_image_size(image_size)
print("Override IMAGE_SIZE", config.IMAGE_SIZE)
executor.init_logging(config)
config_util.display(config)
return _export(config, restore_path, image)
def run_server(server_info, experiment_id, config_file, restore_path):
environment.init(experiment_id)
if config_file is None:
config = config_util.load_from_experiment()
else:
config = config_util.merge(config, config_util.load(config_file))
if restore_path is None:
restore_file = search_restore_filename(environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR, restore_file)
inference_model = Inference(config, restore_path)
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(server_info)
s.listen(32)
print("boot: {}:{}".format(*server_info))
while True:
client_conn, client_addr = s.accept()
print("\033[Kfrom: {}:{}".format(*client_addr), end="\r")
try:
th = threading.Thread(target=receive_and_send,
args=(client_conn, inference_model),
daemon=True)
th.start()
# th.join()
# receive_and_send(client_conn, inference_model)
except BrokenPipeError:
print("Send data aborted!")
pass
def run(config_file, tunable_id, local_dir):
register_trainable(tunable_id, TrainTunable)
lm_config = config_util.load(config_file)
def easydict_to_dict(config):
if isinstance(config, EasyDict):
config = dict(config)
for key, value in config.items():
if isinstance(value, EasyDict):
value = dict(value)
easydict_to_dict(value)
config[key] = value
return config
tune_space = easydict_to_dict(lm_config['TUNE_SPACE'])
tune_spec = easydict_to_dict(lm_config['TUNE_SPEC'])
tune_spec['run'] = tunable_id
tune_spec['config'] = {'lm_config': os.path.join(os.getcwd(), config_file)}
tune_spec['local_dir'] = local_dir
tune_spec['trial_name_creator'] = ray.tune.function(trial_str_creator)
# Expecting use of gpus to do parameter search
ray.init(num_cpus=multiprocessing.cpu_count() // 2, num_gpus=max(get_num_gpu(), 1))
algo = HyperOptSearch(tune_space, max_concurrent=4, reward_attr="mean_accuracy")
scheduler = AsyncHyperBandScheduler(time_attr="training_iteration", reward_attr="mean_accuracy", max_t=200)
trials = run_experiments(experiments={'exp_tune': tune_spec},
search_alg=algo,
scheduler=scheduler)
print("The best result is", get_best_result(trials, metric="mean_accuracy", param='config'))
def _setup(self, config):
self.lm_config = config_util.load(self.config['lm_config'])
executor.init_logging(self.lm_config)
model_class = self.lm_config.NETWORK_CLASS
network_kwargs = {key.lower(): val for key, val in self.lm_config.NETWORK.items()}
network_kwargs = update_parameters_for_each_trial(network_kwargs, self.config)
# No distributed training was implemented, therefore rank set to 0
self.train_dataset = setup_dataset(self.lm_config, "train", 0)
self.validation_dataset = setup_dataset(self.lm_config, "validation", 0)
if model_class.__module__.startswith("lmnet.networks.object_detection"):
model = model_class(
classes=self.train_dataset.classes,
num_max_boxes=self.train_dataset.num_max_boxes,
is_debug=self.lm_config.IS_DEBUG,
**network_kwargs,
)
elif model_class.__module__.startswith("lmnet.networks.segmentation"):
model = model_class(
classes=self.train_dataset.classes,
label_colors=self.train_dataset.label_colors,
is_debug=self.lm_config.IS_DEBUG,
**network_kwargs,
)
else:
model = model_class(
classes=self.train_dataset.classes,
is_debug=self.lm_config.IS_DEBUG,
**network_kwargs,
)
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.is_training_placeholder = tf.placeholder(tf.bool, name="is_training_placeholder")
self.images_placeholder, self.labels_placeholder = model.placeholders()
output = model.inference(self.images_placeholder, self.is_training_placeholder)
if model_class.__module__.startswith("lmnet.networks.object_detection"):
loss = model.loss(output, self.labels_placeholder, self.is_training_placeholder)
else:
loss = model.loss(output, self.labels_placeholder)
opt = model.optimizer(self.global_step)
train_op = model.train(loss, opt, self.global_step)
metrics_ops_dict, metrics_update_op = model.metrics(output, self.labels_placeholder)
self.train_op = train_op
self.metrics_ops_dict = metrics_ops_dict
self.metrics_update_op = metrics_update_op
init_op = tf.global_variables_initializer()
self.reset_metrics_op = tf.local_variables_initializer()
session_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(allow_growth=True))
self.sess = tf.Session(config=session_config)
self.sess.run([init_op, self.reset_metrics_op])
self.iterations = 0
self.saver = tf.train.Saver()
def test_build_tfds_classification():
environment.setup_test_environment()
# Build TFDS Dataset
config_file = "tests/fixtures/configs/for_build_tfds_classification.py"
run(config_file, overwrite=True)
# Check if the builded dataset can be loaded with the same config file
expriment_id = "tfds_classification"
train_run(None, None, config_file, expriment_id, recreate=True)
# Check if the dataset was build correctly
train_data_num = 3
validation_data_num = 2
config = config_util.load(config_file)
train_dataset = setup_dataset(TFDSClassification,
subset="train",
batch_size=config.BATCH_SIZE,
pre_processor=config.PRE_PROCESSOR,
**config.DATASET.TFDS_KWARGS)
validation_dataset = setup_dataset(TFDSClassification,
subset="validation",
batch_size=config.BATCH_SIZE,
pre_processor=config.PRE_PROCESSOR,
**config.DATASET.TFDS_KWARGS)
assert train_dataset.num_per_epoch == train_data_num
assert validation_dataset.num_per_epoch == validation_data_num
for _ in range(train_data_num):
images, labels = train_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == config.BATCH_SIZE
assert images.shape[1] == config.IMAGE_SIZE[0]
assert images.shape[2] == config.IMAGE_SIZE[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == config.BATCH_SIZE
assert labels.shape[1] == train_dataset.num_classes
for _ in range(validation_data_num):
images, labels = validation_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == config.BATCH_SIZE
assert images.shape[1] == config.IMAGE_SIZE[0]
assert images.shape[2] == config.IMAGE_SIZE[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == config.BATCH_SIZE
assert labels.shape[1] == validation_dataset.num_classes
def _get_tfds_settings(config_file):
config = config_util.load(config_file)
dataset_class = config.DATASET_CLASS
dataset_kwargs = {key.lower(): val for key, val in config.DATASET.items()}
if "tfds_kwargs" not in dataset_kwargs:
raise ValueError(
"The given config file does not contain settings for building TFDS datasets.\n"
"Please see help messages (python executor/build_tfds.py -h) for detail."
)
tfds_kwargs = dataset_kwargs.pop("tfds_kwargs")
dataset_name = tfds_kwargs["name"]
data_dir = os.path.expanduser(tfds_kwargs["data_dir"])
return dataset_class, dataset_kwargs, dataset_name, data_dir
def main(network, dataset, config_file, experiment_id, restore_path):
environment.init(experiment_id)
config = config_util.load_from_experiment()
if config_file:
config = config_util.merge(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
executor.init_logging(config)
config_util.display(config)
evaluate(config, restore_path)
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 main(model):
if model == "yolov2":
weight_file = 'inputs/yolo-voc.weights'
experiment_id = "convert_weight_from_darknet/yolo_v2"
config_file = "configs/convert_weight_from_darknet/yolo_v2.py"
if model == "darknet19":
weight_file = 'inputs/darknet19_448.weights'
experiment_id = "convert_weight_from_darknet/darknet19"
config_file = "configs/convert_weight_from_darknet/darknet19.py"
recreate = True
environment.init(experiment_id)
executor.prepare_dirs(recreate)
config = config_util.load(config_file)
config_util.display(config)
config_util.copy_to_experiment_dir(config_file)
convert(config, weight_file)
def run(input_dir, output_dir, experiment_id, config_file, restore_path,
save_images):
environment.init(experiment_id)
config = config_util.load_from_experiment()
if config_file:
config = config_util.merge(config, config_util.load(config_file))
if not os.path.isdir(input_dir):
raise Exception("Input directory {} does not exist.".format(input_dir))
if restore_path is None:
restore_file = 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))
print("---- start predict ----")
_run(input_dir, output_dir, config, restore_path, save_images)
print("---- end predict ----")
self.sess = tf.Session(graph=graph, config=session_config)
self.sess.run(init_op)
saver.restore(self.sess, restore_path)
def __call__(self, input_data):
feed_dict = {self.images_placeholder: input_data * (1 / 255.0)}
t_begin = time.time()
output = self.sess.run(self.output_op, feed_dict=feed_dict)
calc_time = time.time() - t_begin
return output, calc_time
if __name__ == '__main__':
environment.init(args.experiment_id)
config = config_util.load_from_experiment()
print(config)
if args.config_file is not None:
config = config_util.merge(config, config_util.load(args.config_file))
if args.restore_path is None:
restore_file = search_restore_filename(environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR, restore_file)
else:
restore_path = args.restore_path
print("Restore from {}".format(restore_path))
inference_model = Inference(config, restore_path)
window_name = os.path.basename(restore_path)
run_demo(inference_model,
diff_step=args.diff_step,
window_name=window_name)
def _run(config_file, experiment_id, restore_path, image_size, step_size, cpu):
if experiment_id:
environment.init(experiment_id)
config = config_util.load_from_experiment()
if config_file:
config = config_util.merge(config, config_util.load(config_file))
if restore_path is None:
restore_file = executor.search_restore_filename(
environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR,
restore_file)
if not os.path.exists("{}.index".format(restore_path)):
raise Exception(
"restore file {} dont exists.".format(restore_path))
else:
experiment_id = "measure_latency"
environment.init(experiment_id)
config = config_util.load(config_file)
config.BATCH_SIZE = 1
config.NETWORK.BATCH_SIZE = 1
config.DATASET.BATCH_SIZE = 1
if list(image_size) != [None, None]:
config.IMAGE_SIZE = list(image_size)
config.NETWORK.IMAGE_SIZE = list(image_size)
# override pre processes image size.
if config.PRE_PROCESSOR:
config.PRE_PROCESSOR.set_image_size(image_size)
# override post processes image size.
if config.POST_PROCESSOR:
config.POST_PROCESSOR.set_image_size(image_size)
print("Override IMAGE_SIZE", config.IMAGE_SIZE)
executor.init_logging(config)
config_util.display(config)
overall_times, only_network_times = _measure_time(config, restore_path,
step_size)
overall_times = np.array(overall_times)
only_network_times = np.array(only_network_times)
# list of physical_device_desc
devices = [
device.physical_device_desc
for device in device_lib.list_local_devices()
if device.physical_device_desc
]
message = """
---- measure latency result ----
total number of execution (number of samples): {}
network: {}
use gpu by network: {}
image size: {}
devices: {}
* overall (include pre-post-process which execute on cpu)
total time: {:.4f} msec
latency
mean (SD=standard deviation): {:.4f} (SD={:.4f}) msec, min: {:.4f} msec, max: {:.4f} msec
FPS
mean (SD=standard deviation): {:.4f} (SD={:.4f}), min: {:.4f}, max: {:.4f}
* network only (exclude pre-post-process):
total time: {:.4f} msec
latency
mean (SD=standard deviation): {:.4f} (SD={:.4f}) msec, min: {:.4f} msec, max: {:.4f} msec
FPS
mean (SD=standard deviation): {:.4f} (SD={:.4f}), min: {:.4f}, max: {:.4f}
---- measure latency result ----
""".format(
step_size,
config.NETWORK_CLASS.__name__,
not cpu,
config.IMAGE_SIZE,
devices,
# overall
np.sum(overall_times) * 1000,
# latency
np.mean(overall_times) * 1000,
np.std(overall_times) * 1000,
np.min(overall_times) * 1000,
np.max(overall_times) * 1000,
# FPS
np.mean(1 / overall_times),
np.std(1 / overall_times),
np.min(1 / overall_times),
np.max(1 / overall_times),
# network only
np.sum(only_network_times) * 1000,
# latency
np.mean(only_network_times) * 1000,
np.std(only_network_times) * 1000,
np.min(only_network_times) * 1000,
np.max(only_network_times) * 1000,
# FPS
np.mean(1 / only_network_times),
np.std(1 / only_network_times),
np.min(1 / only_network_times),
np.max(1 / only_network_times),
)
print(message)
