def main():
""" function called when starting the code via command-line
"""
args = argparse.parse_cmd(arguments)
args['server'] = alchemy_api.start_training(args['server'])
# Use weight and biases only use_wandb is true and framework is tensorflow
if args['wandb_params']['use_wandb'] and "tensorflow" in args['framework']:
import wandb
wandb.init(name= args['wandb_params']['run_name'], project=args['wandb_params']['project'], config=args)
args = wandb.config
train(args)
def main():
""" function called when starting the code via command-line
"""
args = argparse.parse_cmd(arguments)
args['server'] = alchemy_api.start_training(args['server'])
train(args)
def train(config):
"""
This function setup:
1- Tensorflow (XLA, GPU configuration, mixed precision, execution strategies)
2- The datasets
3- The model
4- The execution environment
5- The monitoring (Upstride plateform and tensorboard)
Then
6- start the training
7- Export the model
"""
# 1
global_conf.config_tf2(config['config'])
global_conf.setup_mp(config['config'])
ds_strategy = global_conf.setup_strategy(config['config']['strategy'])
if config['model']['channels_first']: # if True set keras backend to channels_first
tf.keras.backend.set_image_data_format('channels_first')
# 2
train_dataset = dataloader.get_dataset(config['dataloader'], transformation_list=config['dataloader']['train_list'],
num_classes=config['model']["num_classes"], split=config['dataloader']['train_split_id'])
val_dataset = dataloader.get_dataset(config['dataloader'], transformation_list=config['dataloader']['val_list'],
num_classes=config['model']["num_classes"], split=config['dataloader']['val_split_id'])
# 3
with ds_strategy.scope():
model, optimizer = get_compiled_model(config)
# 4
checkpoint_dir, log_dir, export_dir = create_env_directories(get_experiment_name(config), config['checkpoint_dir'], config['log_dir'], config['export']['dir'])
if not os.path.exists(log_dir):
os.makedirs(log_dir)
with open(os.path.join(log_dir, "conf.yml"), 'w') as file:
yaml.dump(config, file)
# 5
config['server'] = alchemy_api.start_training(config['server'])
alchemy_api.send_model_info(model, config['server'])
callbacks = get_callbacks(config, log_dir)
with ds_strategy.scope(): # checkpoints needs to be in the same scope.
model_checkpoint_cb, latest_epoch = init_custom_checkpoint_callbacks({'model': model}, checkpoint_dir, config['max_checkpoints'], config['checkpoint_freq'])
callbacks.append(model_checkpoint_cb)
if config['server']['id'] != '':
callbacks.append(alchemy_api.send_metric_callbacks(config['server']))
if config['model']['name'] == 'Pdart':
from src.models.pdart import callback_epoch
callbacks.append(tf.keras.callbacks.LambdaCallback(on_epoch_begin=lambda epoch, logs: callback_epoch(epoch, config['num_epochs'], config['drop_path_prob'])))
# 6 training
if config['progressive_resizing']:
progressive_training(model=model,
config=config,
train_dataset=train_dataset,
val_dataset=val_dataset,
callbacks=callbacks,
latest_epoch=latest_epoch,
max_queue_size=16,
optimizer=optimizer)
else:
model.fit(x=train_dataset,
validation_data=val_dataset,
epochs=config['num_epochs'],
callbacks=callbacks,
max_queue_size=16,
initial_epoch=latest_epoch
)
# 7 training
print("export model")
export.export(model, export_dir, config)
print("Training Completed!!")