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.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=50)
session_config = tf.compat.v1.ConfigProto()
sess = tf.compat.v1.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)
# save inference values as npy files for runtime inference test and debug.
if image_path:
all_ops = _minimal_operations(sess)
inference_values_output_dir = os.path.join(main_output_dir,
"inference_test_data")
if not os.path.exists(inference_values_output_dir):
os.makedirs(inference_values_output_dir)
raw_image = load_image(image_path)
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:
# HACK: This is for TensorFlow bug workaround.
# We can remove following 4 lines once it's been resolved in TensorFlow
# Issue link: https://github.com/tensorflow/tensorflow/issues/36456
if (not tf.config.experimental.list_physical_devices('GPU')
and "FusedBatchNormV3" in op_output.name and int(
op_output.name.split(":")[1]) in set(range(1, 6))):
continue
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_all_operation_outputs(image_path, inference_values_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
def start_training(config):
use_horovod = horovod_util.is_enabled()
print("use_horovod:", use_horovod)
if use_horovod:
hvd = horovod_util.setup()
rank = hvd.rank()
local_rank = hvd.local_rank()
else:
rank = 0
local_rank = -1
ModelClass = config.NETWORK_CLASS
network_kwargs = {key.lower(): val for key, val in config.NETWORK.items()}
train_dataset = setup_dataset(config, "train", rank, local_rank)
print("train dataset num:", train_dataset.num_per_epoch)
validation_dataset = setup_dataset(config, "validation", rank, local_rank)
print("validation dataset num:", validation_dataset.num_per_epoch)
graph = tf.Graph()
with graph.as_default():
if config.TASK == Tasks.OBJECT_DETECTION:
model = ModelClass(
classes=train_dataset.classes,
num_max_boxes=train_dataset.num_max_boxes,
is_debug=config.IS_DEBUG,
**network_kwargs,
)
else:
model = ModelClass(
classes=train_dataset.classes,
is_debug=config.IS_DEBUG,
**network_kwargs,
)
is_training_placeholder = tf.compat.v1.placeholder(tf.bool, name="is_training_placeholder")
images_placeholder, labels_placeholder = model.placeholders()
output = model.inference(images_placeholder, is_training_placeholder)
loss = model.loss(output, labels_placeholder)
opt = model.optimizer()
if use_horovod:
# add Horovod Distributed Optimizer
opt = hvd.DistributedOptimizer(opt)
train_op = model.train(loss, opt)
metrics_ops_dict, metrics_update_op = model.metrics(output, labels_placeholder)
# TODO(wakisaka): Deal with many networks.
model.summary(output, labels_placeholder)
summary_op = tf.compat.v1.summary.merge_all()
metrics_summary_op = executor.metrics_summary_op(metrics_ops_dict)
init_op = tf.compat.v1.global_variables_initializer()
reset_metrics_op = tf.compat.v1.local_variables_initializer()
if use_horovod:
# add Horovod broadcasting variables from rank 0 to all
bcast_global_variables_op = hvd.broadcast_global_variables(0)
saver = tf.compat.v1.train.Saver(max_to_keep=config.KEEP_CHECKPOINT_MAX)
if config.IS_PRETRAIN:
all_vars = tf.compat.v1.global_variables()
pretrain_var_list = [
var for var in all_vars if var.name.startswith(tuple(config.PRETRAIN_VARS))
]
print("pretrain_vars", [
var.name for var in pretrain_var_list
])
pretrain_saver = tf.compat.v1.train.Saver(pretrain_var_list, name="pretrain_saver")
if use_horovod:
# For distributed training
session_config = tf.compat.v1.ConfigProto(
gpu_options=tf.compat.v1.GPUOptions(
allow_growth=True,
visible_device_list=str(hvd.local_rank())
)
)
else:
# TODO(wakisaka): For debug.
# session_config = tf.ConfigProto(
# gpu_options=tf.GPUOptions(
# allow_growth=True,
# per_process_gpu_memory_fraction=0.1
# )
# )
session_config = tf.compat.v1.ConfigProto() # tf.ConfigProto(log_device_placement=True)
# TODO(wakisaka): XLA JIT
# session_config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
sess = tf.compat.v1.Session(graph=graph, config=session_config)
sess.run([init_op, reset_metrics_op])
executor.save_pb_file(sess, environment.CHECKPOINTS_DIR)
if rank == 0:
train_writer = tf.compat.v1.summary.FileWriter(environment.TENSORBOARD_DIR + "/train", sess.graph)
val_writer = tf.compat.v1.summary.FileWriter(environment.TENSORBOARD_DIR + "/validation")
if config.IS_PRETRAIN:
print("------- Load pretrain data ----------")
pretrain_saver.restore(sess, os.path.join(config.PRETRAIN_DIR, config.PRETRAIN_FILE))
# for recovery
ckpt = tf.train.get_checkpoint_state(environment.CHECKPOINTS_DIR)
if ckpt and ckpt.model_checkpoint_path:
print("--------- Restore last checkpoint -------------")
saver.restore(sess, ckpt.model_checkpoint_path)
# saver.recover_last_checkpoints(ckpt.model_checkpoint_path)
last_step = sess.run(model.global_step)
# TODO(wakisaka): tensorflow v1.3 remain previous event log in tensorboard.
# https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/training/supervisor.py#L1072
train_writer.add_session_log(SessionLog(status=SessionLog.START), global_step=last_step + 1)
val_writer.add_session_log(SessionLog(status=SessionLog.START), global_step=last_step + 1)
print("recovered. last step", last_step)
if use_horovod:
# broadcast variables from rank 0 to all other processes
sess.run(bcast_global_variables_op)
last_step = sess.run(model.global_step)
# Calculate max steps. The priority of config.MAX_EPOCHS is higher than config.MAX_STEPS.
if "MAX_EPOCHS" in config:
max_steps = int(train_dataset.num_per_epoch / config.BATCH_SIZE * config.MAX_EPOCHS)
else:
max_steps = config.MAX_STEPS
progbar = Progbar(max_steps)
if rank == 0:
progbar.update(last_step)
for step in range(last_step, max_steps):
images, labels = train_dataset.feed()
feed_dict = {
is_training_placeholder: True,
images_placeholder: images,
labels_placeholder: labels,
}
if step * ((step + 1) % config.SUMMARISE_STEPS) == 0 and rank == 0:
# Runtime statistics for develop.
# run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# run_metadata = tf.RunMetadata()
sess.run(reset_metrics_op)
_, summary, _ = sess.run(
[train_op, summary_op, metrics_update_op], feed_dict=feed_dict,
# options=run_options,
# run_metadata=run_metadata,
)
# train_writer.add_run_metadata(run_metadata, "step: {}".format(step + 1))
train_writer.add_summary(summary, step + 1)
metrics_summary = sess.run(metrics_summary_op)
train_writer.add_summary(metrics_summary, step + 1)
train_writer.flush()
else:
sess.run([train_op], feed_dict=feed_dict)
to_be_saved = step == 0 or (step + 1) == max_steps or (step + 1) % config.SAVE_CHECKPOINT_STEPS == 0
if to_be_saved and rank == 0:
_save_checkpoint(saver, sess, model.global_step)
if step == 0 or (step + 1) % config.TEST_STEPS == 0:
# init metrics values
sess.run(reset_metrics_op)
test_step_size = int(math.ceil(validation_dataset.num_per_epoch / config.BATCH_SIZE))
for test_step in range(test_step_size):
images, labels = validation_dataset.feed()
feed_dict = {
is_training_placeholder: False,
images_placeholder: images,
labels_placeholder: labels,
}
if test_step % config.SUMMARISE_STEPS == 0:
summary, _ = sess.run([summary_op, metrics_update_op], feed_dict=feed_dict)
if rank == 0:
val_writer.add_summary(summary, step + 1)
val_writer.flush()
else:
sess.run([metrics_update_op], feed_dict=feed_dict)
metrics_summary = sess.run(metrics_summary_op)
if rank == 0:
val_writer.add_summary(metrics_summary, step + 1)
val_writer.flush()
if rank == 0:
progbar.update(step + 1)
# training loop end.
train_dataset.close()
validation_dataset.close()
print("Done")