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main.py
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import argparse
import ast
from timeit import default_timer as timer
from config import load_parameters
from data_engine.prepare_data import build_dataset, update_dataset_from_file
from keras_wrapper.cnn_model import loadModel, updateModel
from keras_wrapper.dataset import loadDataset, saveDataset
from keras_wrapper.extra.callbacks import *
from model_zoo import TranslationModel
from utils.utils import update_parameters
logging.basicConfig(level=logging.DEBUG, format='[%(asctime)s] %(message)s', datefmt='%d/%m/%Y %H:%M:%S')
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser("Train or sample NMT models")
parser.add_argument("-c", "--config", required=False, help="Config pkl for loading the model configuration. "
"If not specified, hyperparameters "
"are read from config.py")
parser.add_argument("-ds", "--dataset", required=False, help="Dataset instance with data")
parser.add_argument("changes", nargs="*", help="Changes to config. "
"Following the syntax Key=Value",
default="")
return parser.parse_args()
def train_model(params, load_dataset=None):
"""
Training function. Sets the training parameters from params. Build or loads the model and launches the training.
:param params: Dictionary of network hyperparameters.
:param load_dataset: Load dataset from file or build it from the parameters.
:return: None
"""
check_params(params)
if params['RELOAD'] > 0:
logging.info('Resuming training.')
# Load data
if load_dataset is None:
if params['REBUILD_DATASET']:
logging.info('Rebuilding dataset.')
dataset = build_dataset(params)
else:
logging.info('Updating dataset.')
dataset = loadDataset(params['DATASET_STORE_PATH'] + '/Dataset_' + params['DATASET_NAME']
+ '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
for split, filename in params['TEXT_FILES'].iteritems():
dataset = update_dataset_from_file(dataset,
params['DATA_ROOT_PATH'] + '/' + filename + params['SRC_LAN'],
params,
splits=list([split]),
output_text_filename=params['DATA_ROOT_PATH'] + '/' + filename +
params['TRG_LAN'],
remove_outputs=False,
compute_state_below=True,
recompute_references=True)
dataset.name = params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN']
saveDataset(dataset, params['DATASET_STORE_PATH'])
else:
logging.info('Reloading and using dataset.')
dataset = loadDataset(load_dataset)
else:
# Load data
if load_dataset is None:
dataset = build_dataset(params)
else:
dataset = loadDataset(load_dataset)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
# Build model
if params['RELOAD'] == 0: # build new model
nmt_model = TranslationModel(params, model_type=params['MODEL_TYPE'], verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'], vocabularies=dataset.vocabulary,
store_path=params['STORE_PATH'])
dict2pkl(params, params['STORE_PATH'] + '/config')
# Define the inputs and outputs mapping from our Dataset instance to our model
inputMapping = dict()
for i, id_in in enumerate(params['INPUTS_IDS_DATASET']):
pos_source = dataset.ids_inputs.index(id_in)
id_dest = nmt_model.ids_inputs[i]
inputMapping[id_dest] = pos_source
nmt_model.setInputsMapping(inputMapping)
outputMapping = dict()
for i, id_out in enumerate(params['OUTPUTS_IDS_DATASET']):
pos_target = dataset.ids_outputs.index(id_out)
id_dest = nmt_model.ids_outputs[i]
outputMapping[id_dest] = pos_target
nmt_model.setOutputsMapping(outputMapping)
else: # resume from previously trained model
nmt_model = TranslationModel(params,
model_type=params['MODEL_TYPE'],
verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'],
vocabularies=dataset.vocabulary,
store_path=params['STORE_PATH'],
set_optimizer=False,
clear_dirs=False)
# Define the inputs and outputs mapping from our Dataset instance to our model
inputMapping = dict()
for i, id_in in enumerate(params['INPUTS_IDS_DATASET']):
pos_source = dataset.ids_inputs.index(id_in)
id_dest = nmt_model.ids_inputs[i]
inputMapping[id_dest] = pos_source
nmt_model.setInputsMapping(inputMapping)
outputMapping = dict()
for i, id_out in enumerate(params['OUTPUTS_IDS_DATASET']):
pos_target = dataset.ids_outputs.index(id_out)
id_dest = nmt_model.ids_outputs[i]
outputMapping[id_dest] = pos_target
nmt_model.setOutputsMapping(outputMapping)
nmt_model = updateModel(nmt_model, params['STORE_PATH'], params['RELOAD'], reload_epoch=params['RELOAD_EPOCH'])
nmt_model.setParams(params)
nmt_model.setOptimizer()
params['EPOCH_OFFSET'] = params['RELOAD'] if params['RELOAD_EPOCH'] else \
int(params['RELOAD'] * params['BATCH_SIZE'] / dataset.len_train)
# Callbacks
callbacks = buildCallbacks(params, nmt_model, dataset)
# Training
total_start_time = timer()
logger.debug('Starting training!')
training_params = {'n_epochs': params['MAX_EPOCH'],
'batch_size': params['BATCH_SIZE'],
'homogeneous_batches': params['HOMOGENEOUS_BATCHES'],
'maxlen': params['MAX_OUTPUT_TEXT_LEN'],
'joint_batches': params['JOINT_BATCHES'],
'lr_decay': params.get('LR_DECAY', None), # LR decay parameters
'reduce_each_epochs': params.get('LR_REDUCE_EACH_EPOCHS', True),
'start_reduction_on_epoch': params.get('LR_START_REDUCTION_ON_EPOCH', 0),
'lr_gamma': params.get('LR_GAMMA', 0.9),
'lr_reducer_type': params.get('LR_REDUCER_TYPE', 'linear'),
'lr_reducer_exp_base': params.get('LR_REDUCER_EXP_BASE', 0),
'lr_half_life': params.get('LR_HALF_LIFE', 50000),
'epochs_for_save': params['EPOCHS_FOR_SAVE'],
'verbose': params['VERBOSE'],
'eval_on_sets': params['EVAL_ON_SETS_KERAS'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'extra_callbacks': callbacks,
'reload_epoch': params['RELOAD'],
'epoch_offset': params.get('EPOCH_OFFSET', 0),
'data_augmentation': params['DATA_AUGMENTATION'],
'patience': params.get('PATIENCE', 0), # early stopping parameters
'metric_check': params.get('STOP_METRIC', None) if params.get('EARLY_STOP', False) else None,
'eval_on_epochs': params.get('EVAL_EACH_EPOCHS', True),
'each_n_epochs': params.get('EVAL_EACH', 1),
'start_eval_on_epoch': params.get('START_EVAL_ON_EPOCH', 0)}
nmt_model.trainNet(dataset, training_params)
total_end_time = timer()
time_difference = total_end_time - total_start_time
logging.info('In total is {0:.2f}s = {1:.2f}m'.format(time_difference, time_difference / 60.0))
def apply_NMT_model(params, load_dataset=None):
"""
Sample from a previously trained model.
:param params: Dictionary of network hyperparameters.
:param load_dataset: Load dataset from file or build it from the parameters.
:return: None
"""
# Load data
if load_dataset is None:
dataset = build_dataset(params)
else:
dataset = loadDataset(load_dataset)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
# Load model
nmt_model = loadModel(params['STORE_PATH'], params['RELOAD'], reload_epoch=params['RELOAD_EPOCH'])
nmt_model.setOptimizer()
for s in params["EVAL_ON_SETS"]:
# Evaluate training
extra_vars = {'language': params.get('TRG_LAN', 'en'),
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'tokenize_f': eval('dataset.' + params['TOKENIZATION_METHOD']),
'detokenize_f': eval('dataset.' + params['DETOKENIZATION_METHOD']),
'apply_detokenization': params['APPLY_DETOKENIZATION'],
'tokenize_hypotheses': params['TOKENIZE_HYPOTHESES'],
'tokenize_references': params['TOKENIZE_REFERENCES']}
vocab = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
extra_vars[s] = dict()
extra_vars[s]['references'] = dataset.extra_variables[s][params['OUTPUTS_IDS_DATASET'][0]]
input_text_id = None
vocab_src = None
if params['BEAM_SEARCH']:
extra_vars['beam_size'] = params.get('BEAM_SIZE', 6)
extra_vars['state_below_index'] = params.get('BEAM_SEARCH_COND_INPUT', -1)
extra_vars['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 30)
extra_vars['optimized_search'] = params.get('OPTIMIZED_SEARCH', True)
extra_vars['model_inputs'] = params['INPUTS_IDS_MODEL']
extra_vars['model_outputs'] = params['OUTPUTS_IDS_MODEL']
extra_vars['dataset_inputs'] = params['INPUTS_IDS_DATASET']
extra_vars['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
extra_vars['normalize_probs'] = params.get('NORMALIZE_SAMPLING', False)
extra_vars['search_pruning'] = params.get('SEARCH_PRUNING', False)
extra_vars['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
extra_vars['coverage_penalty'] = params.get('COVERAGE_PENALTY', False)
extra_vars['length_penalty'] = params.get('LENGTH_PENALTY', False)
extra_vars['length_norm_factor'] = params.get('LENGTH_NORM_FACTOR', 0.0)
extra_vars['coverage_norm_factor'] = params.get('COVERAGE_NORM_FACTOR', 0.0)
extra_vars['pos_unk'] = params['POS_UNK']
extra_vars['output_max_length_depending_on_x'] = params.get('MAXLEN_GIVEN_X', True)
extra_vars['output_max_length_depending_on_x_factor'] = params.get('MAXLEN_GIVEN_X_FACTOR', 3)
extra_vars['output_min_length_depending_on_x'] = params.get('MINLEN_GIVEN_X', True)
extra_vars['output_min_length_depending_on_x_factor'] = params.get('MINLEN_GIVEN_X_FACTOR', 2)
if params['POS_UNK']:
extra_vars['heuristic'] = params['HEURISTIC']
input_text_id = params['INPUTS_IDS_DATASET'][0]
vocab_src = dataset.vocabulary[input_text_id]['idx2words']
if params['HEURISTIC'] > 0:
extra_vars['mapping'] = dataset.mapping
callback_metric = PrintPerformanceMetricOnEpochEndOrEachNUpdates(nmt_model,
dataset,
gt_id=params['OUTPUTS_IDS_DATASET'][0],
metric_name=params['METRICS'],
set_name=params['EVAL_ON_SETS'],
batch_size=params['BATCH_SIZE'],
each_n_epochs=params['EVAL_EACH'],
extra_vars=extra_vars,
reload_epoch=params['RELOAD'],
is_text=True,
input_text_id=input_text_id,
save_path=nmt_model.model_path,
index2word_y=vocab,
index2word_x=vocab_src,
sampling_type=params['SAMPLING'],
beam_search=params['BEAM_SEARCH'],
start_eval_on_epoch=params[
'START_EVAL_ON_EPOCH'],
write_samples=True,
write_type=params['SAMPLING_SAVE_MODE'],
eval_on_epochs=params['EVAL_EACH_EPOCHS'],
save_each_evaluation=False,
verbose=params['VERBOSE'])
callback_metric.evaluate(params['RELOAD'], counter_name='epoch' if params['EVAL_EACH_EPOCHS'] else 'update')
def buildCallbacks(params, model, dataset):
"""
Builds the selected set of callbacks run during the training of the model.
:param params: Dictionary of network hyperparameters.
:param model: Model instance on which to apply the callback.
:param dataset: Dataset instance on which to apply the callback.
:return:
"""
callbacks = []
if params['METRICS'] or params['SAMPLE_ON_SETS']:
# Evaluate training
extra_vars = {'language': params.get('TRG_LAN', 'en'),
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'tokenize_f': eval('dataset.' + params.get('TOKENIZATION_METHOD', 'tokenize_none')),
'detokenize_f': eval('dataset.' + params.get('DETOKENIZATION_METHOD', 'detokenize_none')),
'apply_detokenization': params.get('APPLY_DETOKENIZATION', False),
'tokenize_hypotheses': params.get('TOKENIZE_HYPOTHESES', True),
'tokenize_references': params.get('TOKENIZE_REFERENCES', True)
}
input_text_id = params['INPUTS_IDS_DATASET'][0]
vocab_x = dataset.vocabulary[input_text_id]['idx2words']
vocab_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
if params['BEAM_SEARCH']:
extra_vars['beam_size'] = params.get('BEAM_SIZE', 6)
extra_vars['state_below_index'] = params.get('BEAM_SEARCH_COND_INPUT', -1)
extra_vars['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 30)
extra_vars['optimized_search'] = params.get('OPTIMIZED_SEARCH', True)
extra_vars['model_inputs'] = params['INPUTS_IDS_MODEL']
extra_vars['model_outputs'] = params['OUTPUTS_IDS_MODEL']
extra_vars['dataset_inputs'] = params['INPUTS_IDS_DATASET']
extra_vars['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
extra_vars['search_pruning'] = params.get('SEARCH_PRUNING', False)
extra_vars['normalize_probs'] = params.get('NORMALIZE_SAMPLING', False)
extra_vars['alpha_factor'] = params.get('ALPHA_FACTOR', 1.)
extra_vars['coverage_penalty'] = params.get('COVERAGE_PENALTY', False)
extra_vars['length_penalty'] = params.get('LENGTH_PENALTY', False)
extra_vars['length_norm_factor'] = params.get('LENGTH_NORM_FACTOR', 0.0)
extra_vars['coverage_norm_factor'] = params.get('COVERAGE_NORM_FACTOR', 0.0)
extra_vars['pos_unk'] = params['POS_UNK']
extra_vars['output_max_length_depending_on_x'] = params.get('MAXLEN_GIVEN_X', True)
extra_vars['output_max_length_depending_on_x_factor'] = params.get('MAXLEN_GIVEN_X_FACTOR', 3)
extra_vars['output_min_length_depending_on_x'] = params.get('MINLEN_GIVEN_X', True)
extra_vars['output_min_length_depending_on_x_factor'] = params.get('MINLEN_GIVEN_X_FACTOR', 2)
if params['POS_UNK']:
extra_vars['heuristic'] = params['HEURISTIC']
if params['HEURISTIC'] > 0:
extra_vars['mapping'] = dataset.mapping
if params['METRICS']:
for s in params['EVAL_ON_SETS']:
extra_vars[s] = dict()
extra_vars[s]['references'] = dataset.extra_variables[s][params['OUTPUTS_IDS_DATASET'][0]]
callback_metric = PrintPerformanceMetricOnEpochEndOrEachNUpdates(model,
dataset,
gt_id=params['OUTPUTS_IDS_DATASET'][0],
metric_name=params['METRICS'],
set_name=params['EVAL_ON_SETS'],
batch_size=params['BATCH_SIZE'],
each_n_epochs=params['EVAL_EACH'],
extra_vars=extra_vars,
reload_epoch=params['RELOAD'],
is_text=True,
input_text_id=input_text_id,
index2word_y=vocab_y,
index2word_x=vocab_x,
sampling_type=params['SAMPLING'],
beam_search=params['BEAM_SEARCH'],
save_path=model.model_path,
start_eval_on_epoch=params[
'START_EVAL_ON_EPOCH'],
write_samples=True,
write_type=params['SAMPLING_SAVE_MODE'],
eval_on_epochs=params['EVAL_EACH_EPOCHS'],
save_each_evaluation=params[
'SAVE_EACH_EVALUATION'],
verbose=params['VERBOSE'])
callbacks.append(callback_metric)
if params['SAMPLE_ON_SETS']:
callback_sampling = SampleEachNUpdates(model,
dataset,
gt_id=params['OUTPUTS_IDS_DATASET'][0],
set_name=params['SAMPLE_ON_SETS'],
n_samples=params['N_SAMPLES'],
each_n_updates=params['SAMPLE_EACH_UPDATES'],
extra_vars=extra_vars,
reload_epoch=params['RELOAD'],
batch_size=params['BATCH_SIZE'],
is_text=True,
index2word_x=vocab_x,
index2word_y=vocab_y,
print_sources=True,
in_pred_idx=params['INPUTS_IDS_DATASET'][0],
sampling_type=params['SAMPLING'], # text info
beam_search=params['BEAM_SEARCH'],
start_sampling_on_epoch=params['START_SAMPLING_ON_EPOCH'],
verbose=params['VERBOSE'])
callbacks.append(callback_sampling)
return callbacks
def check_params(params):
"""
Checks some typical parameters and warns if something wrong was specified.
:param params: Model instance on which to apply the callback.
:return: None
"""
if params['POS_UNK']:
assert params['OPTIMIZED_SEARCH'], 'Unknown words replacement requires ' \
'to use the optimized search ("OPTIMIZED_SEARCH" parameter).'
if params['COVERAGE_PENALTY']:
assert params['OPTIMIZED_SEARCH'], 'The application of "COVERAGE_PENALTY" requires ' \
'to use the optimized search ("OPTIMIZED_SEARCH" parameter).'
if params['SRC_PRETRAINED_VECTORS'] and params['SRC_PRETRAINED_VECTORS'][:-1] != '.npy':
warnings.warn('It seems that the pretrained word vectors provided for the target text are not in npy format.'
'You should preprocess the word embeddings with the "utils/preprocess_*_word_vectors.py script.')
if params['TRG_PRETRAINED_VECTORS'] and params['TRG_PRETRAINED_VECTORS'][:-1] != '.npy':
warnings.warn('It seems that the pretrained word vectors provided for the target text are not in npy format.'
'You should preprocess the word embeddings with the "utils/preprocess_*_word_vectors.py script.')
if __name__ == "__main__":
args = parse_args()
parameters = load_parameters()
if args.config is not None:
parameters = update_parameters(parameters, pkl2dict(args.config))
try:
for arg in args.changes:
try:
k, v = arg.split('=')
except ValueError:
print 'Overwritten arguments must have the form key=Value. \n Currently are: %s' % str(args.changes)
exit(1)
try:
parameters[k] = ast.literal_eval(v)
except ValueError:
parameters[k] = v
except ValueError:
print 'Error processing arguments: (', k, ",", v, ")"
exit(2)
check_params(parameters)
if parameters['MODE'] == 'training':
logging.info('Running training.')
train_model(parameters, args.dataset)
elif parameters['MODE'] == 'sampling':
logging.info('Running sampling.')
apply_NMT_model(parameters, args.dataset)
logging.info('Done!')