def train_pytorch(reader,
train_data,
test_data,
dev_data,
configuration: dict,
debug=False):
import torch
seed = configuration.get('seed')
# make everything deterministic
random.seed(seed)
torch.manual_seed(seed)
clip_value = configuration.get('clip_value')
batch_size = configuration.get('batch_size')
epochs = configuration.get('epochs')
l2 = configuration.get('l2')
optimizer = configuration.get('optimizer')
learning_rate = configuration.get('learning_rate')
learning_rate_decay = configuration.get('learning_rate_decay')
log_interval = configuration.get('log_interval')
validation_interval = configuration.get('validation_interval')
tensorboard_folder = configuration.get('tensorboard_folder')
model = configuration.get('reader')
save_dir = configuration.get('save_dir')
write_metrics_to = configuration.get('write_metrics_to')
# need setup here already :(
reader.setup_from_data(train_data, is_training=True)
if clip_value != 0.0:
clip_value = -abs(clip_value), abs(clip_value)
name_to_optimizer = {
'gd': torch.optim.SGD,
'adam': torch.optim.Adam,
'adagrad': torch.optim.Adagrad,
'adadelta': torch.optim.Adadelta
}
if optimizer not in name_to_optimizer:
raise ValueError('Unknown optimizer: {}'.format(optimizer))
torch_optimizer_class = name_to_optimizer[optimizer]
params = list(reader.model_module.prediction_module.parameters())
params.extend(reader.model_module.loss_module.parameters())
torch_optimizer = torch_optimizer_class(params, lr=learning_rate)
sw = None
if tensorboard_folder is not None:
if os.path.exists(tensorboard_folder):
shutil.rmtree(tensorboard_folder)
sw = tf.summary.FileWriter(tensorboard_folder)
# Hooks
iter_interval = 1 if debug else log_interval
hooks = [
LossHook(reader, iter_interval, summary_writer=sw),
ExamplesPerSecHook(reader, batch_size, iter_interval, sw)
]
preferred_metric, best_metric = readers.eval_hooks[
model].preferred_metric_and_initial_score()
def side_effect(metrics, prev_metric):
"""Returns: a state (in this case a metric) that is used as input for the next call"""
m = metrics[preferred_metric]
if prev_metric is not None and m < prev_metric:
for param_group in torch_optimizer.param_groups:
param_group['lr'] *= learning_rate_decay
logger.info("Decayed learning rate to: %.5f" %
param_group['lr'])
elif m > best_metric[0] and save_dir is not None:
best_metric[0] = m
if prev_metric is None: # store whole model only at beginning of training
reader.store(save_dir)
else:
reader.model_module.store(
os.path.join(save_dir, "model_module"))
logger.info("Saving model to: %s" % save_dir)
return m
# this is the standard hook for the model
hooks.append(readers.eval_hooks[model](
reader,
dev_data,
batch_size,
summary_writer=sw,
side_effect=side_effect,
iter_interval=validation_interval,
epoch_interval=(1 if validation_interval is None else None),
write_metrics_to=write_metrics_to))
# Train
reader.train(torch_optimizer,
train_data,
batch_size,
max_epochs=epochs,
hooks=hooks,
l2=l2,
clip=clip_value)
# Test final model
if dev_data is not None and save_dir is not None:
reader.load(save_dir)
result_dict = evaluate_reader(reader, dev_data, batch_size)
logger.info("############### Results on the Dev Set##############")
pretty_print_results(result_dict)
if test_data is not None and save_dir is not None:
reader.load(save_dir)
result_dict = evaluate_reader(reader, test_data, batch_size)
logger.info("############### Results on the Test Set##############")
pretty_print_results(result_dict)
def train_tensorflow(reader,
train_data,
test_data,
dev_data,
configuration: dict,
debug=False):
import tensorflow as tf
seed = configuration.get('seed', 0)
# make everything deterministic
random.seed(seed)
tf.set_random_seed(seed)
clip_value = configuration.get('clip_value')
batch_size = configuration.get('batch_size')
dev_batch_size = configuration.get('dev_batch_size') or batch_size
epochs = configuration.get('epochs')
l2 = configuration.get('l2')
optimizer = configuration.get('optimizer')
learning_rate = configuration.get('learning_rate')
min_learning_rate = configuration.get('min_learning_rate')
learning_rate_decay = configuration.get('learning_rate_decay')
log_interval = configuration.get('log_interval')
validation_interval = configuration.get('validation_interval')
tensorboard_folder = configuration.get('tensorboard_folder')
reader_type = configuration.get('reader')
save_dir = configuration.get('save_dir')
write_metrics_to = configuration.get('write_metrics_to')
if clip_value != 0.0:
clip_value = -abs(clip_value), abs(clip_value)
learning_rate = tf.get_variable("learning_rate",
initializer=learning_rate,
dtype=tf.float32,
trainable=False)
lr_decay_op = learning_rate.assign(
tf.maximum(learning_rate_decay * learning_rate, min_learning_rate))
name_to_optimizer = {
'gd': tf.train.GradientDescentOptimizer,
'adam': tf.train.AdamOptimizer,
'adagrad': tf.train.AdagradOptimizer,
'adadelta': tf.train.AdadeltaOptimizer,
'rmsprop': tf.train.RMSPropOptimizer
}
if optimizer not in name_to_optimizer:
raise ValueError('Unknown optimizer: {}'.format(optimizer))
tf_optimizer_class = name_to_optimizer[optimizer]
tf_optimizer = tf_optimizer_class(learning_rate=learning_rate)
sw = None
if tensorboard_folder is not None:
if os.path.exists(tensorboard_folder):
shutil.rmtree(tensorboard_folder)
sw = tf.summary.FileWriter(tensorboard_folder)
# Hooks
iter_interval = 1 if debug else log_interval
hooks = [
LossHook(reader, iter_interval, summary_writer=sw),
ETAHook(reader, iter_interval,
int(math.ceil(len(train_data) / batch_size)), epochs),
ExamplesPerSecHook(reader, batch_size, iter_interval, sw)
]
preferred_metric, best_metric = readers.eval_hooks[
reader_type].preferred_metric_and_initial_score()
def side_effect(metrics, prev_metric):
"""Returns: a state (in this case a metric) that is used as input for the next call"""
m = metrics[preferred_metric]
if prev_metric is not None and m < prev_metric:
reader.session.run(lr_decay_op)
logger.info("Decayed learning rate to: %.5f" %
reader.session.run(learning_rate))
elif m > best_metric[0] and save_dir is not None:
best_metric[0] = m
if prev_metric is None: # store whole reader_type only at beginning of training
reader.store(save_dir)
else:
reader.model_module.store(
os.path.join(save_dir, "model_module"))
logger.info("Saving reader_type to: %s" % save_dir)
return m
# this is the standard hook for the reader_type
hooks.append(readers.eval_hooks[reader_type](
reader,
dev_data,
dev_batch_size,
summary_writer=sw,
side_effect=side_effect,
iter_interval=validation_interval,
epoch_interval=(1 if validation_interval is None else None),
write_metrics_to=write_metrics_to))
# Train
reader.train(tf_optimizer,
train_data,
batch_size,
max_epochs=epochs,
hooks=hooks,
l2=l2,
clip=clip_value,
clip_op=tf.clip_by_value,
summary_writer=sw)
# Test final reader_type
if dev_data is not None and save_dir is not None:
reader.load(save_dir)
result_dict = evaluate_reader(reader, dev_data, batch_size)
logger.info("############### Results on the Dev Set##############")
pretty_print_results(result_dict)
if test_data is not None and save_dir is not None:
reader.load(save_dir)
result_dict = evaluate_reader(reader, test_data, batch_size)
logger.info("############### Results on the Test Set##############")
pretty_print_results(result_dict)
from jack.readers import reader_from_file
logger = logging.getLogger(os.path.basename(sys.argv[0]))
logging.basicConfig(level=logging.INFO)
tf.app.flags.DEFINE_string('dataset', None, 'dataset file')
tf.app.flags.DEFINE_string('loader', 'jack', 'name of loader')
tf.app.flags.DEFINE_string('load_dir', None, 'directory to saved model')
tf.app.flags.DEFINE_integer('batch_size', 64, 'batch size')
tf.app.flags.DEFINE_integer('max_examples', None,
'maximum number of examples to evaluate')
tf.app.flags.DEFINE_string('overwrite', '{}',
'json string that overwrites configuration.')
FLAGS = tf.app.flags.FLAGS
logger.info("Creating and loading reader from {}...".format(FLAGS.save_dir))
kwargs = json.loads(FLAGS.overwrite)
reader = reader_from_file(FLAGS.save_dir, **kwargs)
dataset = loaders[FLAGS.loader](FLAGS.dataset)
if FLAGS.max_examples:
dataset = dataset[:FLAGS.max_examples]
logger.info("Start!")
result_dict = evaluate_reader(reader, dataset, FLAGS.batch_size)
logger.info("############### RESULTS ##############")
pretty_print_results(result_dict)
