def load_in(encoding, direction):
if encoding == "char":
print(encoding)
args = parse_args_char(direction)
else:
args = parse_args_bpe(direction)
if args["config"] is None:
logging.info("Reading parameters from config.py")
from config import load_parameters
params = load_parameters()
else:
logging.info("Loading parameters from %s" % str(args["config"]))
params = 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:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print('Error processing arguments: (', k, ",", v, ")")
exit(2)
params = check_params(params)
model, dataset = loadmodel(encoding, args)
# dit moet een functie worden
# sample_ensemble(args, params, models, dataset)
return args, params, model, dataset
def main():
args = parse_args()
server_address = (args.address, args.port)
httpd = HTTPServer(server_address, NMTHandler)
logger.setLevel(args.logging_level)
parameters = load_parameters()
if args.config is not None:
logger.info("Loading parameters from %s" % str(args.config))
parameters = update_parameters(parameters, pkl2dict(args.config))
if args.online:
online_parameters = load_parameters_online()
parameters = update_parameters(parameters, online_parameters)
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)
dataset = loadDataset(args.dataset)
# For converting predictions into sentences
# Dataset backwards compatibility
bpe_separator = dataset.BPE_separator if hasattr(
dataset,
"BPE_separator") and dataset.BPE_separator is not None else '@@'
# Build BPE tokenizer if necessary
if 'bpe' in parameters['TOKENIZATION_METHOD'].lower():
logger.info('Building BPE')
if not dataset.BPE_built:
dataset.build_bpe(parameters.get(
'BPE_CODES_PATH',
parameters['DATA_ROOT_PATH'] + '/training_codes.joint'),
separator=bpe_separator)
# Build tokenization function
tokenize_f = eval('dataset.' +
parameters.get('TOKENIZATION_METHOD', 'tokenize_bpe'))
detokenize_function = eval(
'dataset.' + parameters.get('DETOKENIZATION_METHOD', 'detokenize_bpe'))
dataset.build_moses_tokenizer(language=parameters['SRC_LAN'])
dataset.build_moses_detokenizer(language=parameters['TRG_LAN'])
tokenize_general = dataset.tokenize_moses
detokenize_general = dataset.detokenize_moses
# Prediction parameters
params_prediction = dict()
params_prediction['max_batch_size'] = parameters.get('BATCH_SIZE', 20)
params_prediction['n_parallel_loaders'] = parameters.get(
'PARALLEL_LOADERS', 1)
params_prediction['beam_size'] = parameters.get('BEAM_SIZE', 6)
params_prediction['maxlen'] = parameters.get('MAX_OUTPUT_TEXT_LEN_TEST',
100)
params_prediction['optimized_search'] = parameters['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = parameters['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = parameters['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = parameters['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = parameters['OUTPUTS_IDS_DATASET']
params_prediction['search_pruning'] = parameters.get(
'SEARCH_PRUNING', False)
params_prediction['normalize_probs'] = True
params_prediction['alpha_factor'] = parameters.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = True
params_prediction['length_penalty'] = True
params_prediction['length_norm_factor'] = parameters.get(
'LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = parameters.get(
'COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = parameters.get('POS_UNK', False)
params_prediction['heuristic'] = parameters.get('HEURISTIC', 0)
params_prediction['state_below_index'] = -1
params_prediction['output_text_index'] = 0
params_prediction['state_below_maxlen'] = -1 if parameters.get(
'PAD_ON_BATCH', True) else parameters.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = parameters.get(
'MAXLEN_GIVEN_X', True)
params_prediction[
'output_max_length_depending_on_x_factor'] = parameters.get(
'MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = parameters.get(
'MINLEN_GIVEN_X', True)
params_prediction[
'output_min_length_depending_on_x_factor'] = parameters.get(
'MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = parameters.get(
'ATTEND_ON_OUTPUT', 'transformer' in parameters['MODEL_TYPE'].lower())
# Manage pos_unk strategies
if parameters['POS_UNK']:
mapping = None if dataset.mapping == dict() else dataset.mapping
else:
mapping = None
if 'transformer' in parameters['MODEL_TYPE'].lower():
params_prediction['pos_unk'] = False
params_prediction['coverage_penalty'] = False
# Training parameters
parameters_training = dict()
if args.online:
logger.info('Loading models from %s' % str(args.models))
parameters_training = { # Traning parameters
'n_epochs': parameters['MAX_EPOCH'],
'shuffle': False,
'loss': parameters.get('LOSS', 'categorical_crossentropy'),
'batch_size': parameters.get('BATCH_SIZE', 1),
'homogeneous_batches': False,
'optimizer': parameters.get('OPTIMIZER', 'SGD'),
'lr': parameters.get('LR', 0.1),
'lr_decay': parameters.get('LR_DECAY', None),
'lr_gamma': parameters.get('LR_GAMMA', 1.),
'epochs_for_save': -1,
'verbose': args.verbose,
'eval_on_sets': parameters.get('EVAL_ON_SETS_KERAS', None),
'n_parallel_loaders': parameters['PARALLEL_LOADERS'],
'extra_callbacks': [], # callbacks,
'reload_epoch': parameters['RELOAD'],
'epoch_offset': parameters['RELOAD'],
'data_augmentation': parameters['DATA_AUGMENTATION'],
'patience': parameters.get('PATIENCE', 0),
'metric_check': parameters.get('STOP_METRIC', None),
'eval_on_epochs': parameters.get('EVAL_EACH_EPOCHS', True),
'each_n_epochs': parameters.get('EVAL_EACH', 1),
'start_eval_on_epoch': parameters.get('START_EVAL_ON_EPOCH', 0),
'additional_training_settings': {
'k': parameters.get('K', 1),
'tau': parameters.get('TAU', 1),
'lambda': parameters.get('LAMBDA', 0.5),
'c': parameters.get('C', 0.5),
'd': parameters.get('D', 0.5)
}
}
model_instances = [
TranslationModel(
parameters,
model_type=parameters['MODEL_TYPE'],
verbose=parameters['VERBOSE'],
model_name=parameters['MODEL_NAME'] + '_' + str(i),
vocabularies=dataset.vocabulary,
store_path=parameters['STORE_PATH'],
set_optimizer=False) for i in range(len(args.models))
]
models = [
updateModel(model, path, -1, full_path=True)
for (model, path) in zip(model_instances, args.models)
]
else:
models = [loadModel(m, -1, full_path=True) for m in args.models]
for nmt_model in models:
nmt_model.setParams(parameters)
nmt_model.setOptimizer()
parameters['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
parameters['INPUTS_IDS_DATASET'][0]]
parameters['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
parameters['OUTPUTS_IDS_DATASET'][0]]
# Get word2index and index2word dictionaries
index2word_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET']
[0]]['words2idx']
index2word_x = dataset.vocabulary[parameters['INPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_x = dataset.vocabulary[parameters['INPUTS_IDS_DATASET']
[0]]['words2idx']
excluded_words = None
interactive_beam_searcher = NMTSampler(models,
dataset,
parameters,
params_prediction,
parameters_training,
tokenize_f,
detokenize_function,
tokenize_general,
detokenize_general,
mapping=mapping,
word2index_x=word2index_x,
word2index_y=word2index_y,
index2word_y=index2word_y,
eos_symbol=args.eos_symbol,
excluded_words=excluded_words,
online=args.online,
verbose=args.verbose)
httpd.sampler = interactive_beam_searcher
logger.info('Server starting at %s' % str(server_address))
httpd.serve_forever()
"--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()
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, ")")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
models = args.models
logging.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
if args.config is None:
logging.info("Reading parameters from config.py")
from config import load_parameters
params = load_parameters()
else:
logging.info("Loading parameters from %s" % str(args.config))
params = 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:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print 'Error processing arguments: (', k, ",", v, ")"
exit(2)
def sample_ensemble(args, params):
"""
Use several translation models for obtaining predictions from a source text file.
:param argparse.Namespace args: Arguments given to the method:
* dataset: Dataset instance with data.
* text: Text file with source sentences.
* splits: Splits to sample. Should be already included in the dataset object.
* dest: Output file to save scores.
* weights: Weight given to each model in the ensemble. You should provide the same number of weights than models. By default, it applies the same weight to each model (1/N).
* n_best: Write n-best list (n = beam size).
* config: Config .pkl for loading the model configuration. If not specified, hyperparameters are read from config.py.
* models: Path to the models.
* verbose: Be verbose or not.
:param params: parameters of the translation model.
"""
from data_engine.prepare_data import update_dataset_from_file
from keras_wrapper.model_ensemble import BeamSearchEnsemble
from keras_wrapper.cnn_model import loadModel
from keras_wrapper.dataset import loadDataset
from keras_wrapper.utils import decode_predictions_beam_search
logger.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
dataset = update_dataset_from_file(dataset, args.text, params, splits=args.splits, remove_outputs=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
# For converting predictions into sentences
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
if params.get('APPLY_DETOKENIZATION', False):
detokenize_function = eval('dataset.' + params['DETOKENIZATION_METHOD'])
params_prediction = dict()
params_prediction['max_batch_size'] = params.get('BATCH_SIZE', 20)
params_prediction['n_parallel_loaders'] = params.get('PARALLEL_LOADERS', 1)
params_prediction['beam_size'] = params.get('BEAM_SIZE', 6)
params_prediction['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 100)
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
params_prediction['search_pruning'] = params.get('SEARCH_PRUNING', False)
params_prediction['normalize_probs'] = params.get('NORMALIZE_SAMPLING', False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get('COVERAGE_PENALTY', False)
params_prediction['length_penalty'] = params.get('LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get('LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get('COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get('MAXLEN_GIVEN_X', True)
params_prediction['output_max_length_depending_on_x_factor'] = params.get('MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get('MINLEN_GIVEN_X', True)
params_prediction['output_min_length_depending_on_x_factor'] = params.get('MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = params.get('ATTEND_ON_OUTPUT',
'transformer' in params['MODEL_TYPE'].lower())
params_prediction['glossary'] = params.get('GLOSSARY', None)
heuristic = params.get('HEURISTIC', 0)
mapping = None if dataset.mapping == dict() else dataset.mapping
model_weights = args.weights
if args.glossary is not None:
glossary = pkl2dict(args.glossary)
elif params_prediction['glossary'] is not None:
glossary = pkl2dict(params_prediction['glossary'])
else:
glossary = None
if model_weights:
assert len(model_weights) == len(
models), 'You should give a weight to each model. You gave %d models and %d weights.' % (
len(models), len(model_weights))
model_weights = list(map(float, model_weights))
if len(model_weights) > 1:
logger.info('Giving the following weights to each model: %s' % str(model_weights))
for s in args.splits:
# Apply model predictions
params_prediction['predict_on_sets'] = [s]
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
model_weights=model_weights,
n_best=args.n_best,
verbose=args.verbose)
predictions = beam_searcher.predictBeamSearchNet()[s]
samples = predictions['samples']
alphas = predictions['alphas'] if params_prediction['pos_unk'] else None
if params_prediction['pos_unk']:
sources = [x.strip() for x in open(args.text, 'r').read().split('\n')]
sources = sources[:-1] if len(sources[-1]) == 0 else sources
else:
sources = None
decoded_predictions = decode_predictions_beam_search(samples,
index2word_y,
glossary=glossary,
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
decoded_predictions = list(map(detokenize_function, decoded_predictions))
if args.n_best:
n_best_predictions = []
for i, (n_best_preds, n_best_scores, n_best_alphas) in enumerate(predictions['n_best']):
n_best_sample_score = []
for n_best_pred, n_best_score, n_best_alpha in zip(n_best_preds, n_best_scores, n_best_alphas):
pred = decode_predictions_beam_search([n_best_pred],
index2word_y,
glossary=glossary,
alphas=[n_best_alpha] if params_prediction[
'pos_unk'] else None,
x_text=[sources[i]] if params_prediction['pos_unk'] else None,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
pred = list(map(detokenize_function, pred))
n_best_sample_score.append([i, pred, n_best_score])
n_best_predictions.append(n_best_sample_score)
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params.get('SAMPLING_SAVE_MODE', 'list'):
list2file(filepath, decoded_predictions)
if args.n_best:
nbest2file(filepath + '.nbest', n_best_predictions)
else:
raise Exception('Only "list" is allowed in "SAMPLING_SAVE_MODE"')
else:
list2stdout(decoded_predictions)
if args.n_best:
logger.info('Storing n-best sentences in ./' + s + '.nbest')
nbest2file('./' + s + '.nbest', n_best_predictions)
logger.info('Sampling finished')
"-ch",
"--changes",
nargs="*",
help="Changes to config, following the syntax Key=Value",
default="")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# Update parameters
if args.config is not None:
logger.info('Reading parameters from %s.' % args.config)
params = update_parameters({}, pkl2dict(args.config))
else:
logger.info('Reading parameters from config.py.')
params = load_parameters()
logger.info('Starting active learning with arguments: %str' % str(args))
online_parameters = load_parameters_online()
params = update_parameters(params, online_parameters)
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)
def main():
args = parse_args()
server_address = ('', args.port)
httpd = BaseHTTPServer.HTTPServer(server_address, NMTHandler)
if args.config is None:
logging.info("Reading parameters from config.py")
from config import load_parameters
params = load_parameters()
else:
logging.info("Loading parameters from %s" % str(args.config))
params = 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:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print 'Error processing arguments: (', k, ",", v, ")"
exit(2)
dataset = loadDataset(args.dataset)
# For converting predictions into sentences
# Dataset backwards compatibility
bpe_separator = dataset.BPE_separator if hasattr(
dataset,
"BPE_separator") and dataset.BPE_separator is not None else '@@'
# Build BPE tokenizer if necessary
if 'bpe' in params['TOKENIZATION_METHOD'].lower():
logger.info('Building BPE')
if not dataset.BPE_built:
dataset.build_bpe(
params.get('BPE_CODES_PATH',
params['DATA_ROOT_PATH'] + '/training_codes.joint'),
bpe_separator)
# Build tokenization function
tokenize_f = eval('dataset.' +
params.get('TOKENIZATION_METHOD', 'tokenize_none'))
detokenize_function = eval(
'dataset.' + params.get('DETOKENIZATION_METHOD', 'detokenize_none'))
dataset.build_moses_tokenizer(language=params['SRC_LAN'])
dataset.build_moses_detokenizer(language=params['TRG_LAN'])
tokenize_general = dataset.tokenize_moses
detokenize_general = dataset.detokenize_moses
params_prediction = dict()
params_prediction['max_batch_size'] = params.get('BATCH_SIZE', 20)
params_prediction['n_parallel_loaders'] = params.get('PARALLEL_LOADERS', 1)
params_prediction['beam_size'] = params.get('BEAM_SIZE', 6)
params_prediction['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 100)
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
params_prediction['search_pruning'] = params.get('SEARCH_PRUNING', False)
params_prediction['normalize_probs'] = params.get('NORMALIZE_SAMPLING',
False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get('COVERAGE_PENALTY',
False)
params_prediction['length_penalty'] = params.get('LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get(
'LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get(
'COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['heuristic'] = params.get('HEURISTIC', 0)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get(
'MAXLEN_GIVEN_X', True)
params_prediction['output_max_length_depending_on_x_factor'] = params.get(
'MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get(
'MINLEN_GIVEN_X', True)
params_prediction['output_min_length_depending_on_x_factor'] = params.get(
'MINLEN_GIVEN_X_FACTOR', 2)
# Manage pos_unk strategies
if params['POS_UNK']:
mapping = None if dataset.mapping == dict() else dataset.mapping
else:
mapping = None
if args.online:
logging.info('Loading models from %s' % str(args.models))
model_instances = [
TranslationModel(params,
model_type=params['MODEL_TYPE'],
verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'] + '_' + str(i),
vocabularies=dataset.vocabulary,
store_path=params['STORE_PATH'],
set_optimizer=False)
for i in range(len(args.models))
]
models = [
updateModel(model, path, -1, full_path=True)
for (model, path) in zip(model_instances, args.models)
]
# Set additional inputs to models if using a custom loss function
params['USE_CUSTOM_LOSS'] = True if 'PAS' in params[
'OPTIMIZER'] else False
if params['N_BEST_OPTIMIZER']:
logging.info('Using N-best optimizer')
models = build_online_models(models, params)
online_trainer = OnlineTrainer(models,
dataset,
None,
None,
params_training,
verbose=args.verbose)
else:
models = [loadModel(m, -1, full_path=True) for m in args.models]
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
# Get word2index and index2word dictionaries
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['words2idx']
index2word_x = dataset.vocabulary[params['INPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_x = dataset.vocabulary[params['INPUTS_IDS_DATASET']
[0]]['words2idx']
excluded_words = None
interactive_beam_searcher = NMTSampler(models,
dataset,
params_prediction,
tokenize_f,
detokenize_function,
tokenize_general,
detokenize_general,
mapping=mapping,
word2index_x=word2index_x,
word2index_y=word2index_y,
index2word_y=index2word_y,
excluded_words=excluded_words,
verbose=args.verbose)
# Compile Theano sampling function by generating a fake sample # TODO: Find a better way of doing this
print "Compiling sampler..."
interactive_beam_searcher.generate_sample('i')
httpd.sampler = interactive_beam_searcher
print 'Server starting at localhost:' + str(args.port)
httpd.serve_forever()
def main():
args = parse_args()
server_address = (args.address, args.port)
httpd = BaseHTTPServer.HTTPServer(server_address, NMTHandler)
logger.setLevel(args.logging_level)
if args.config is not None:
logger.info('Reading parameters from %s.' % args.config)
parameters = update_parameters({}, pkl2dict(args.config))
else:
logger.info('Reading parameters from config.py.')
parameters = load_parameters()
if args.online:
online_parameters = load_parameters_online(parameters)
parameters = update_parameters(parameters, online_parameters)
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 args.verbose:
logging.info("parameters = " + str(parameters))
dataset = loadDataset(args.dataset)
# Dataset backwards compatibility
bpe_separator = dataset.BPE_separator if hasattr(
dataset,
"BPE_separator") and dataset.BPE_separator is not None else '@@'
# Build BPE tokenizer if necessary
if 'bpe' in parameters['TOKENIZATION_METHOD'].lower():
logger.info('Building BPE')
if not dataset.BPE_built:
dataset.build_bpe(
parameters.get(
'BPE_CODES_PATH',
parameters['DATA_ROOT_PATH'] + '/training_codes.joint'),
bpe_separator)
# Build tokenization function
tokenize_f = eval('dataset.' +
parameters.get('TOKENIZATION_METHOD', 'tokenize_bpe'))
detokenize_function = eval(
'dataset.' + parameters.get('DETOKENIZATION_METHOD', 'detokenize_bpe'))
dataset.build_moses_tokenizer(language=parameters['TRG_LAN'])
dataset.build_moses_detokenizer(language=parameters['TRG_LAN'])
tokenize_general = dataset.tokenize_moses
detokenize_general = dataset.detokenize_moses
parameters_training = dict()
if args.online:
logging.info('Loading models from %s' % str(args.models))
parameters_training = { # Traning parameters
'n_epochs': parameters['MAX_EPOCH'],
'shuffle': False,
'loss': parameters.get('LOSS', 'categorical_crossentropy'),
'batch_size': parameters.get('BATCH_SIZE', 1),
'homogeneous_batches': False,
'optimizer': parameters.get('OPTIMIZER', 'SGD'),
'lr': parameters.get('LR', 0.1),
'lr_decay': parameters.get('LR_DECAY', None),
'lr_gamma': parameters.get('LR_GAMMA', 1.),
'epochs_for_save': -1,
'verbose': args.verbose,
'eval_on_sets': parameters['EVAL_ON_SETS_KERAS'],
'n_parallel_loaders': parameters['PARALLEL_LOADERS'],
'extra_callbacks': [], # callbacks,
'reload_epoch': parameters['RELOAD'],
'epoch_offset': parameters['RELOAD'],
'data_augmentation': parameters['DATA_AUGMENTATION'],
'patience': parameters.get('PATIENCE', 0),
'metric_check': parameters.get('STOP_METRIC', None),
'eval_on_epochs': parameters.get('EVAL_EACH_EPOCHS', True),
'each_n_epochs': parameters.get('EVAL_EACH', 1),
'start_eval_on_epoch': parameters.get('START_EVAL_ON_EPOCH', 0),
'additional_training_settings': {
'k': parameters.get('K', 1),
'tau': parameters.get('TAU', 1),
'lambda': parameters.get('LAMBDA', 0.5),
'c': parameters.get('C', 0.5),
'd': parameters.get('D', 0.5)
}
}
# Load trainable model(s)
logging.info('Loading models from %s' % str(args.models))
model_instances = [
Captioning_Model(
parameters,
model_type=parameters['MODEL_TYPE'],
verbose=parameters['VERBOSE'],
model_name=parameters['MODEL_NAME'] + '_' + str(i),
vocabularies=dataset.vocabulary,
store_path=parameters['STORE_PATH'],
set_optimizer=False) for i in range(len(args.models))
]
models = [
updateModel(model, path, -1, full_path=True)
for (model, path) in zip(model_instances, args.models)
]
for model in models:
model.setParams(parameters)
model.setOptimizer()
else:
# Otherwise, load regular model(s)
models = [loadModel(m, -1, full_path=True) for m in args.models]
parameters['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
parameters['OUTPUTS_IDS_DATASET'][0]]
# Get word2index and index2word dictionaries
index2word_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_y = dataset.vocabulary[parameters['OUTPUTS_IDS_DATASET']
[0]]['words2idx']
unk_id = dataset.extra_words['<unk>']
parameters_prediction = {
'max_batch_size':
parameters['BATCH_SIZE'],
'n_parallel_loaders':
parameters['PARALLEL_LOADERS'],
'predict_on_sets': [args.split],
'beam_size':
parameters['BEAM_SIZE'],
'maxlen':
parameters['MAX_OUTPUT_TEXT_LEN_TEST'],
'optimized_search':
parameters['OPTIMIZED_SEARCH'],
'model_inputs':
parameters['INPUTS_IDS_MODEL'],
'model_outputs':
parameters['OUTPUTS_IDS_MODEL'],
'dataset_inputs':
parameters['INPUTS_IDS_DATASET'],
'dataset_outputs':
parameters['OUTPUTS_IDS_DATASET'],
'normalize_probs':
parameters['NORMALIZE_SAMPLING'],
'alpha_factor':
parameters['ALPHA_FACTOR'],
'normalize':
parameters.get('NORMALIZATION', False),
'normalization_type':
parameters.get('NORMALIZATION_TYPE', None),
'data_augmentation':
parameters.get('DATA_AUGMENTATION', False),
'mean_substraction':
parameters.get('MEAN_SUBTRACTION', False),
'wo_da_patch_type':
parameters.get('WO_DA_PATCH_TYPE', 'whole'),
'da_patch_type':
parameters.get('DA_PATCH_TYPE', 'resize_and_rndcrop'),
'da_enhance_list':
parameters.get('DA_ENHANCE_LIST', None),
'pos_unk':
parameters.get('POS_UNK', None),
'heuristic':
parameters.get('HEURISTIC', None),
'search_pruning':
parameters.get('SEARCH_PRUNING', False),
'state_below_index':
-1,
'output_text_index':
0,
'apply_tokenization':
parameters.get('APPLY_TOKENIZATION', False),
'tokenize_f':
eval('dataset.' +
parameters.get('TOKENIZATION_METHOD', 'tokenize_none')),
'apply_detokenization':
parameters.get('APPLY_DETOKENIZATION', True),
'detokenize_f':
eval('dataset.' +
parameters.get('DETOKENIZATION_METHOD', 'detokenize_none')),
'coverage_penalty':
parameters.get('COVERAGE_PENALTY', False),
'length_penalty':
parameters.get('LENGTH_PENALTY', False),
'length_norm_factor':
parameters.get('LENGTH_NORM_FACTOR', 0.0),
'coverage_norm_factor':
parameters.get('COVERAGE_NORM_FACTOR', 0.0),
'output_max_length_depending_on_x':
parameters.get('MAXLEN_GIVEN_X', False),
'output_max_length_depending_on_x_factor':
parameters.get('MAXLEN_GIVEN_X_FACTOR', 3),
'output_min_length_depending_on_x':
parameters.get('MINLEN_GIVEN_X', False),
'output_min_length_depending_on_x_factor':
parameters.get('MINLEN_GIVEN_X_FACTOR', 2),
'attend_on_output':
parameters.get('ATTEND_ON_OUTPUT', 'transformer'
in parameters['MODEL_TYPE'].lower()),
'n_best_optimizer':
parameters.get('N_BEST_OPTIMIZER', False)
}
excluded_words = None
interactive_beam_searcher = VideoDescSampler(models,
dataset,
parameters,
parameters_prediction,
parameters_training,
tokenize_f,
detokenize_function,
tokenize_general,
detokenize_general,
split=args.split,
word2index_y=word2index_y,
index2word_y=index2word_y,
eos_symbol=args.eos_symbol,
excluded_words=excluded_words,
unk_id=unk_id,
online=args.online,
verbose=args.verbose)
httpd.sampler = interactive_beam_searcher
logger.info('Server starting at %s' % str(server_address))
httpd.serve_forever()
def interactive_simulation():
args = parse_args()
# Update parameters
if args.config is not None:
logger.info('Reading parameters from %s.' % args.config)
params = update_parameters({}, pkl2dict(args.config))
else:
logger.info('Reading parameters from config.py.')
params = load_parameters()
if args.online:
from config_online import load_parameters as load_parameters_online
online_parameters = load_parameters_online(params)
params = update_parameters(params, online_parameters)
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:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print('Error processing arguments: (', k, ",", v, ")")
exit(2)
check_params(params)
if args.verbose:
logging.info("params = " + str(params))
dataset = loadDataset(args.dataset)
# dataset = update_dataset_from_file(dataset, args.source, params, splits=args.splits, remove_outputs=True)
# Dataset backwards compatibility
bpe_separator = dataset.BPE_separator if hasattr(
dataset,
"BPE_separator") and dataset.BPE_separator is not None else u'@@'
# Set tokenization method
params[
'TOKENIZATION_METHOD'] = 'tokenize_bpe' if args.tokenize_bpe else params.get(
'TOKENIZATION_METHOD', 'tokenize_none')
# Build BPE tokenizer if necessary
if 'bpe' in params['TOKENIZATION_METHOD'].lower():
logger.info('Building BPE')
if not dataset.BPE_built:
dataset.build_bpe(params.get(
'BPE_CODES_PATH',
params['DATA_ROOT_PATH'] + '/training_codes.joint'),
separator=bpe_separator)
# Build tokenization function
tokenize_f = eval('dataset.' +
params.get('TOKENIZATION_METHOD', 'tokenize_none'))
if args.online:
# Traning params
params_training = { # Traning params
'n_epochs': params['MAX_EPOCH'],
'shuffle': False,
'loss': params.get('LOSS', 'categorical_crossentropy'),
'batch_size': params.get('BATCH_SIZE', 1),
'homogeneous_batches': False,
'optimizer': params.get('OPTIMIZER', 'SGD'),
'lr': params.get('LR', 0.1),
'lr_decay': params.get('LR_DECAY', None),
'lr_gamma': params.get('LR_GAMMA', 1.),
'epochs_for_save': -1,
'verbose': args.verbose,
'eval_on_sets': params['EVAL_ON_SETS_KERAS'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'extra_callbacks': [], # callbacks,
'reload_epoch': 0,
'epoch_offset': 0,
'data_augmentation': params['DATA_AUGMENTATION'],
'patience': params.get('PATIENCE', 0),
'metric_check': params.get('STOP_METRIC', 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),
'additional_training_settings': {
'k': params.get('K', 1),
'tau': params.get('TAU', 1),
'lambda': params.get('LAMBDA', 0.5),
'c': params.get('C', 0.5),
'd': params.get('D', 0.5)
}
}
else:
params_training = dict()
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
logger.info("<<< Using an ensemble of %d models >>>" % len(args.models))
if args.online:
# Load trainable model(s)
logging.info('Loading models from %s' % str(args.models))
model_instances = [
Captioning_Model(params,
model_type=params['MODEL_TYPE'],
verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'] + '_' + str(i),
vocabularies=dataset.vocabulary,
store_path=params['STORE_PATH'],
clear_dirs=False,
set_optimizer=False)
for i in range(len(args.models))
]
models = [
updateModel(model, path, -1, full_path=True)
for (model, path) in zip(model_instances, args.models)
]
# Set additional inputs to models if using a custom loss function
params['USE_CUSTOM_LOSS'] = True if 'PAS' in params[
'OPTIMIZER'] else False
if params['N_BEST_OPTIMIZER']:
logging.info('Using N-best optimizer')
models = build_online_models(models, params)
online_trainer = OnlineTrainer(models,
dataset,
None,
None,
params_training,
verbose=args.verbose)
else:
# Otherwise, load regular model(s)
models = [loadModel(m, -1, full_path=True) for m in args.models]
# Load text files
logger.info("<<< Storing corrected hypotheses into: %s >>>" %
str(args.dest))
ftrans = open(args.dest, 'w')
ftrans.close()
# Do we want to save the original sentences?
if args.original_dest is not None:
logger.info("<<< Storing original hypotheses into: %s >>>" %
str(args.original_dest))
ftrans_ori = open(args.original_dest, 'w')
ftrans_ori.close()
if args.references is not None:
ftrg = codecs.open(args.references, 'r', encoding='utf-8'
) # File with post-edited (or reference) sentences.
all_references = ftrg.read().split('\n')
if all_references[-1] == u'':
all_references = all_references[:-1]
# Get word2index and index2word dictionaries
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['words2idx']
unk_id = dataset.extra_words['<unk>']
# Initialize counters
total_errors = 0
total_words = 0
total_chars = 0
total_mouse_actions = 0
try:
for s in args.splits:
# Apply model predictions
params_prediction = {
'max_batch_size':
params['BATCH_SIZE'],
'n_parallel_loaders':
params['PARALLEL_LOADERS'],
'predict_on_sets': [s],
'beam_size':
params['BEAM_SIZE'],
'maxlen':
params['MAX_OUTPUT_TEXT_LEN_TEST'],
'optimized_search':
params['OPTIMIZED_SEARCH'],
'model_inputs':
params['INPUTS_IDS_MODEL'],
'model_outputs':
params['OUTPUTS_IDS_MODEL'],
'dataset_inputs':
params['INPUTS_IDS_DATASET'],
'dataset_outputs':
params['OUTPUTS_IDS_DATASET'],
'normalize_probs':
params.get('NORMALIZE_SAMPLING', False),
'alpha_factor':
params.get('ALPHA_FACTOR', 1.0),
'normalize':
params.get('NORMALIZATION', False),
'normalization_type':
params.get('NORMALIZATION_TYPE', None),
'data_augmentation':
params.get('DATA_AUGMENTATION', False),
'mean_substraction':
params.get('MEAN_SUBTRACTION', False),
'wo_da_patch_type':
params.get('WO_DA_PATCH_TYPE', 'whole'),
'da_patch_type':
params.get('DA_PATCH_TYPE', 'resize_and_rndcrop'),
'da_enhance_list':
params.get('DA_ENHANCE_LIST', None),
'heuristic':
params.get('HEURISTIC', None),
'search_pruning':
params.get('SEARCH_PRUNING', False),
'state_below_index':
-1,
'output_text_index':
0,
'apply_tokenization':
params.get('APPLY_TOKENIZATION', False),
'tokenize_f':
eval('dataset.' +
params.get('TOKENIZATION_METHOD', 'tokenize_none')),
'apply_detokenization':
params.get('APPLY_DETOKENIZATION', True),
'detokenize_f':
eval('dataset.' +
params.get('DETOKENIZATION_METHOD', 'detokenize_none')),
'coverage_penalty':
params.get('COVERAGE_PENALTY', False),
'length_penalty':
params.get('LENGTH_PENALTY', False),
'length_norm_factor':
params.get('LENGTH_NORM_FACTOR', 0.0),
'coverage_norm_factor':
params.get('COVERAGE_NORM_FACTOR', 0.0),
'pos_unk':
False,
'state_below_maxlen':
-1 if params.get('PAD_ON_BATCH', True) else params.get(
'MAX_OUTPUT_TEXT_LEN_TEST', 50),
'output_max_length_depending_on_x':
params.get('MAXLEN_GIVEN_X', False),
'output_max_length_depending_on_x_factor':
params.get('MAXLEN_GIVEN_X_FACTOR', 3),
'output_min_length_depending_on_x':
params.get('MINLEN_GIVEN_X', False),
'output_min_length_depending_on_x_factor':
params.get('MINLEN_GIVEN_X_FACTOR', 2),
'attend_on_output':
params.get('ATTEND_ON_OUTPUT', 'transformer'
in params['MODEL_TYPE'].lower()),
'n_best_optimizer':
params.get('N_BEST_OPTIMIZER', False)
}
# Build interactive sampler
interactive_beam_searcher = InteractiveBeamSearchSampler(
models,
dataset,
params_prediction,
excluded_words=None,
verbose=args.verbose)
start_time = time.time()
if args.verbose:
logging.info("Params prediction = " + str(params_prediction))
if args.online:
logging.info("Params training = " + str(params_training))
n_samples = getattr(dataset, 'len_' + s)
if args.references is None:
all_references = dataset.extra_variables[s][
params['OUTPUTS_IDS_DATASET'][0]]
# Start to translate the source file interactively
for n_sample in range(n_samples):
errors_sentence = 0
mouse_actions_sentence = 0
hypothesis_number = 0
# Load data from dataset
current_input = dataset.getX_FromIndices(
s, [n_sample],
normalization_type=params_prediction.get(
'normalization_type'),
normalization=params_prediction.get('normalize', False),
dataAugmentation=params_prediction.get(
'data_augmentation', False),
wo_da_patch_type=params_prediction.get(
'wo_da_patch_type', 'whole'),
da_patch_type=params_prediction.get(
'da_patch_type', 'resize_and_rndcrop'),
da_enhance_list=params_prediction.get(
'da_enhance_list', None))[0][0]
# Load references
references = all_references[n_sample]
tokenized_references = list(map(
tokenize_f,
references)) if args.tokenize_references else references
# Get reference as desired by the user, i.e. detokenized if necessary
reference = list(map(params_prediction['detokenize_f'], tokenized_references)) if \
args.detokenize_bpe else tokenized_references
# Detokenize line for nicer logging :)
logger.debug(u'\n\nProcessing sample %d' % (n_sample + 1))
logger.debug(u'Target: %s' % reference)
# 1. Get a first hypothesis
trans_indices, costs, alphas = interactive_beam_searcher.sample_beam_search_interactive(
current_input)
# 1.2 Decode hypothesis
hypothesis = decode_predictions_beam_search([trans_indices],
index2word_y,
pad_sequences=True,
verbose=0)[0]
# 1.3 Store result (optional)
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if params_prediction.get('apply_detokenization', False) else hypothesis
if args.original_dest is not None:
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(args.original_dest, [hypothesis],
permission='a')
else:
raise Exception(
'Only "list" is allowed in "SAMPLING_SAVE_MODE"')
logger.debug(u'Hypo_%d: %s' % (hypothesis_number, hypothesis))
# 2.0 Interactive translation
if hypothesis in tokenized_references:
# 2.1 If the sentence is correct, we validate it
pass
else:
# 2.2 Wrong hypothesis -> Interactively translate the sentence
correct_hypothesis = False
last_correct_pos = 0
while not correct_hypothesis:
# 2.2.1 Empty data structures for the next sentence
fixed_words_user = OrderedDict()
unk_words_dict = OrderedDict()
isle_indices = []
unks_in_isles = []
if args.prefix:
# 2.2.2 Compute longest common character prefix (LCCP)
reference_idx, next_correction_pos, validated_prefix = common_prefixes(
hypothesis, tokenized_references)
else:
# 2.2.2 Compute common character segments
#TODO
next_correction_pos, validated_prefix, validated_segments = common_segments(
hypothesis, reference)
reference = tokenized_references[reference_idx]
if next_correction_pos == len(reference):
correct_hypothesis = True
break
# 2.2.3 Get next correction by checking against the reference
next_correction = reference[next_correction_pos]
# 2.2.4 Tokenize the prefix properly (possibly applying BPE)
tokenized_validated_prefix = tokenize_f(
validated_prefix + next_correction)
# 2.2.5 Validate words
for pos, word in enumerate(
tokenized_validated_prefix.split()):
fixed_words_user[pos] = word2index_y.get(
word, unk_id)
if word2index_y.get(word) is None:
unk_words_dict[pos] = word
# 2.2.6 Constrain search for the last word
last_user_word_pos = list(fixed_words_user.keys())[-1]
if next_correction != u' ':
last_user_word = tokenized_validated_prefix.split(
)[-1]
filtered_idx2word = dict(
(word2index_y[candidate_word], candidate_word)
for candidate_word in word2index_y
if candidate_word[:len(last_user_word)] ==
last_user_word)
if filtered_idx2word != dict():
del fixed_words_user[last_user_word_pos]
if last_user_word_pos in unk_words_dict.keys():
del unk_words_dict[last_user_word_pos]
else:
filtered_idx2word = dict()
logger.debug(u'"%s" to character %d.' %
(next_correction, next_correction_pos))
# 2.2.7 Generate a hypothesis compatible with the feedback provided by the user
hypothesis = generate_constrained_hypothesis(
interactive_beam_searcher, current_input,
fixed_words_user, params_prediction, args,
isle_indices, filtered_idx2word, index2word_y,
None, None, None, unk_words_dict.keys(),
unk_words_dict.values(), unks_in_isles)
hypothesis_number += 1
hypothesis = u' '.join(
hypothesis) # Hypothesis is unicode
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if args.detokenize_bpe else hypothesis
logger.debug(u'Target: %s' % reference)
logger.debug(u"Hypo_%d: %s" %
(hypothesis_number, hypothesis))
# 2.2.8 Add a keystroke
errors_sentence += 1
# 2.2.9 Add a mouse action if we moved the pointer
if next_correction_pos - last_correct_pos > 1:
mouse_actions_sentence += 1
last_correct_pos = next_correction_pos
# 2.3 Final check: The reference is a subset of the hypothesis: Cut the hypothesis
if len(reference) < len(hypothesis):
hypothesis = hypothesis[:len(reference)]
errors_sentence += 1
logger.debug(u"Cutting hypothesis")
# 2.4 Security assertion
assert hypothesis in references, "Error: The final hypothesis does not match with the reference! \n" \
"\t Split: %s \n" \
"\t Sentence: %d \n" \
"\t Hypothesis: %s\n" \
"\t Reference: %s" % (s, n_sample + 1,
hypothesis,
reference)
# 3. Update user effort counters
mouse_actions_sentence += 1 # This +1 is the validation action
chars_sentence = len(hypothesis)
total_errors += errors_sentence
total_words += len(hypothesis.split())
total_chars += chars_sentence
total_mouse_actions += mouse_actions_sentence
# 3.1 Log some info
logger.debug(u"Final hypotesis: %s" % hypothesis)
logger.debug(
u"%d errors. "
u"Sentence WSR: %4f. "
u"Sentence mouse strokes: %d "
u"Sentence MAR: %4f. "
u"Sentence MAR_c: %4f. "
u"Sentence KSMR: %4f. "
u"Accumulated (should only be considered for debugging purposes!) "
u"WSR: %4f. "
u"MAR: %4f. "
u"MAR_c: %4f. "
u"KSMR: %4f.\n\n\n\n" %
(errors_sentence, float(errors_sentence) / len(hypothesis),
mouse_actions_sentence,
float(mouse_actions_sentence) / len(hypothesis),
float(mouse_actions_sentence) / chars_sentence,
float(errors_sentence + mouse_actions_sentence) /
chars_sentence, float(total_errors) / total_words,
float(total_mouse_actions) / total_words,
float(total_mouse_actions) / total_chars,
float(total_errors + total_mouse_actions) / total_chars))
# 4. If we are performing OL after each correct sample:
if args.online:
# 4.1 Compute model inputs
# 4.1.1 Source text -> Already computed (used for the INMT process)
# 4.1.2 State below
state_below = dataset.loadText(
[reference],
vocabularies=dataset.vocabulary[
params['OUTPUTS_IDS_DATASET'][0]],
max_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
offset=1,
fill=dataset.fill_text[params['INPUTS_IDS_DATASET']
[-1]],
pad_on_batch=dataset.pad_on_batch[
params['INPUTS_IDS_DATASET'][-1]],
words_so_far=False,
loading_X=True)[0]
# 4.1.3 Ground truth sample -> Interactively translated sentence
trg_seq = dataset.loadTextOneHot(
[reference],
vocabularies=dataset.vocabulary[
params['OUTPUTS_IDS_DATASET'][0]],
vocabulary_len=dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]],
max_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
offset=0,
fill=dataset.fill_text[params['OUTPUTS_IDS_DATASET']
[0]],
pad_on_batch=dataset.pad_on_batch[
params['OUTPUTS_IDS_DATASET'][0]],
words_so_far=False,
sample_weights=params['SAMPLE_WEIGHTS'],
loading_X=False)
# 4.2 Train online!
online_trainer.train_online(
[np.asarray([current_input]), state_below],
trg_seq,
trg_words=[reference])
# 5 Write correct sentences into a file
list2file(args.dest, [hypothesis], permission='a')
if (n_sample + 1) % 50 == 0:
logger.info(u"%d sentences processed" % (n_sample + 1))
logger.info(u"Current speed is {} per sentence".format(
(time.time() - start_time) / (n_sample + 1)))
logger.info(u"Current WSR is: %f" %
(float(total_errors) / total_words))
logger.info(u"Current MAR is: %f" %
(float(total_mouse_actions) / total_words))
logger.info(u"Current MAR_c is: %f" %
(float(total_mouse_actions) / total_chars))
logger.info(u"Current KSMR is: %f" %
(float(total_errors + total_mouse_actions) /
total_chars))
# 6. Final!
# 6.1 Log some information
print(u"Total number of errors:", total_errors)
print(u"Total number selections", total_mouse_actions)
print(u"WSR: %f" % (float(total_errors) / total_words))
print(u"MAR: %f" % (float(total_mouse_actions) / total_words))
print(u"MAR_c: %f" % (float(total_mouse_actions) / total_chars))
print(u"KSMR: %f" %
(float(total_errors + total_mouse_actions) / total_chars))
except KeyboardInterrupt:
print(u'Interrupted!')
print(u"Total number of corrections (up to now):", total_errors)
print(u"WSR: %f" % (float(total_errors) / total_words))
print(u"MAR: %f" % (float(total_mouse_actions) / total_words))
print(u"MAR_c: %f" % (float(total_mouse_actions) / total_chars))
print(u"KSMR: %f" %
(float(total_errors + total_mouse_actions) / total_chars))
def build_dataset(params):
if params['REBUILD_DATASET']: # We build a new dataset instance
if params['VERBOSE'] > 0:
silence = False
logging.info('Building ' + params['DATASET_NAME'] + ' dataset')
else:
silence = True
base_path = params['DATA_ROOT_PATH']
name = params['DATASET_NAME']
ds = Dataset(name, base_path, silence=silence)
if not '-vidtext-embed' in params['DATASET_NAME']:
# OUTPUT DATA
# Let's load the train, val and test splits of the descriptions (outputs)
# the files include a description per line. In this dataset a variable number
# of descriptions per video are provided.
ds.setOutput(base_path + '/' + params['DESCRIPTION_FILES']['train'],
'train',
type='text',
id=params['OUTPUTS_IDS_DATASET'][0],
build_vocabulary=True,
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
sample_weights=params['SAMPLE_WEIGHTS'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
ds.setOutput(base_path + '/' + params['DESCRIPTION_FILES']['val'],
'val',
type='text',
id=params['OUTPUTS_IDS_DATASET'][0],
build_vocabulary=True,
pad_on_batch=True,
tokenization=params['TOKENIZATION_METHOD'],
sample_weights=params['SAMPLE_WEIGHTS'],
max_text_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
ds.setOutput(base_path + '/' + params['DESCRIPTION_FILES']['test'],
'test',
type='text',
id=params['OUTPUTS_IDS_DATASET'][0],
build_vocabulary=True,
pad_on_batch=True,
tokenization=params['TOKENIZATION_METHOD'],
sample_weights=params['SAMPLE_WEIGHTS'],
max_text_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
else:
# Use descriptions as inputs instead --> 'matching'/'non-matching' as output
ds.setInput(base_path + '/' + params['DESCRIPTION_FILES']['train'],
'train',
type='text',
id=params['INPUTS_IDS_DATASET'][1],
build_vocabulary=True,
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
ds.setInput(base_path + '/' + params['DESCRIPTION_FILES']['val'],
'val',
type='text',
id=params['INPUTS_IDS_DATASET'][1],
build_vocabulary=True,
pad_on_batch=True,
tokenization=params['TOKENIZATION_METHOD'],
max_text_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
ds.setInput(base_path + '/' + params['DESCRIPTION_FILES']['test'],
'test',
type='text',
id=params['INPUTS_IDS_DATASET'][1],
build_vocabulary=True,
pad_on_batch=True,
tokenization=params['TOKENIZATION_METHOD'],
max_text_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
# INPUT DATA
# Let's load the associated videos (inputs)
# we must take into account that in this dataset we have a different number of sentences per video,
# for this reason we introduce the parameter 'repeat_set'=num_captions, where num_captions is a list
# containing the number of captions in each video.
num_captions_train = np.load(base_path + '/' + params['DESCRIPTION_COUNTS_FILES']['train'])
num_captions_val = np.load(base_path + '/' + params['DESCRIPTION_COUNTS_FILES']['val'])
num_captions_test = np.load(base_path + '/' + params['DESCRIPTION_COUNTS_FILES']['test'])
for feat_type in params['FEATURE_NAMES']:
for split, num_cap in zip(['train', 'val', 'test'],
[num_captions_train, num_captions_val, num_captions_test]):
list_files = base_path + '/' + params['FRAMES_LIST_FILES'][split] % feat_type
counts_files = base_path + '/' + params['FRAMES_COUNTS_FILES'][split] % feat_type
ds.setInput([list_files, counts_files],
split,
type=params['INPUT_DATA_TYPE'],
id=params['INPUTS_IDS_DATASET'][0],
repeat_set=num_cap,
max_video_len=params['NUM_FRAMES'],
feat_len=params['IMG_FEAT_SIZE'],
data_augmentation_types=params['DATA_AUGMENTATION_TYPE'])
if not '-vidtext-embed' in params['DATASET_NAME'] and len(params['INPUTS_IDS_DATASET']) > 1:
ds.setInput(base_path + '/' + params['DESCRIPTION_FILES']['train'],
'train',
type='text',
id=params['INPUTS_IDS_DATASET'][1],
required=False,
tokenization=params['TOKENIZATION_METHOD'],
pad_on_batch=True,
build_vocabulary=params['OUTPUTS_IDS_DATASET'][0],
offset=1,
fill=params['FILL'],
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
max_words=params['OUTPUT_VOCABULARY_SIZE'],
min_occ=params['MIN_OCCURRENCES_VOCAB'])
ds.setInput(None, 'val', type='ghost', id=params['INPUTS_IDS_DATASET'][1], required=False)
ds.setInput(None, 'test', type='ghost', id=params['INPUTS_IDS_DATASET'][1], required=False)
# Set inputs for temporally-linked samples
if not '-vidtext-embed' in params['DATASET_NAME'] and '-linked' in params['DATASET_NAME']:
# Set input captions from previous event/video
if '-upperbound' not in params['DATASET_NAME']:
if '-vidtext' in params['DATASET_NAME']: # use both previous video and previous description
ds, repeat_images = insertTemporallyLinkedCaptionsVidText(ds, params,
vidtext_set_names={
'video': ['train', 'val', 'test'],
'text': ['train']})
del repeat_images['test']
del repeat_images['val']
# Insert empty prev_descriptions on val and test sets
ds.setInput([],
'val',
type='text',
id=params['INPUTS_IDS_DATASET'][2],
build_vocabulary=params['OUTPUTS_IDS_DATASET'][0],
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
min_occ=params['MIN_OCCURRENCES_VOCAB'],
required=False,
overwrite_split=True)
ds.setInput([],
'test',
type='text',
id=params['INPUTS_IDS_DATASET'][2],
build_vocabulary=params['OUTPUTS_IDS_DATASET'][0],
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
min_occ=params['MIN_OCCURRENCES_VOCAB'],
required=False,
overwrite_split=True)
elif '-video' in params['DATASET_NAME']:
ds, repeat_images = insertTemporallyLinkedCaptions(ds, params,
set_names=['train', 'val', 'test'],
video=True)
num_captions_val = repeat_images['val']
num_captions_test = repeat_images['test']
else:
ds, repeat_images = insertTemporallyLinkedCaptions(ds, params)
# Insert empty prev_descriptions on val and test sets
ds.setInput([],
'val',
type='text',
id=params['INPUTS_IDS_DATASET'][2],
build_vocabulary=params['OUTPUTS_IDS_DATASET'][0],
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
min_occ=params['MIN_OCCURRENCES_VOCAB'],
required=False,
overwrite_split=True)
ds.setInput([],
'test',
type='text',
id=params['INPUTS_IDS_DATASET'][2],
build_vocabulary=params['OUTPUTS_IDS_DATASET'][0],
tokenization=params['TOKENIZATION_METHOD'],
fill=params['FILL'],
pad_on_batch=True,
max_text_len=params['MAX_OUTPUT_TEXT_LEN'],
min_occ=params['MIN_OCCURRENCES_VOCAB'],
required=False,
overwrite_split=True)
else:
ds, repeat_images = insertTemporallyLinkedCaptions(ds,
params,
set_names=['train', 'val', 'test'],
upperbound=True,
video='-video' in params['DATASET_NAME'],
copy='-copy' in params['DATASET_NAME'],
force_nocopy='-nocopy' in params['DATASET_NAME'],
prev='-prev' in params['DATASET_NAME'])
num_captions_val = repeat_images['val']
num_captions_test = repeat_images['test']
if not '-vidtext-embed' in params['DATASET_NAME']:
# Process dataset for keeping only one caption per video and storing the rest in a dict() with the following format:
# ds.extra_variables[set_name][id_output][img_position] = [cap1, cap2, cap3, ..., capN]
keep_n_captions(ds, repeat=[num_captions_val, num_captions_test], n=1, set_names=['val', 'test'])
else:
# Set outputs for -vidtext-embed model
insertVidTextEmbedNegativeSamples(ds, params,
repeat=[num_captions_train, num_captions_val, num_captions_test])
if not '-vidtext-embed' in params['DATASET_NAME'] and \
'-linked' in params['DATASET_NAME'] and \
'-upperbound' not in params['DATASET_NAME'] and \
'-video' not in params['DATASET_NAME']:
# Set previous data indices
for s, file in params['LINK_SAMPLE_FILES'].iteritems():
if s in repeat_images:
rep = repeat_images[s]
else:
rep = 1
ds.setInput(base_path + '/' + file,
s,
type='id',
id=params['INPUTS_IDS_DATASET'][-1],
repeat_set=rep)
# We have finished loading the dataset, now we can store it for using it in the future
saveDataset(ds, params['DATASET_STORE_PATH'])
else:
# We can easily recover it with a single line
ds = loadDataset(params['DATASET_STORE_PATH'] + '/Dataset_' + params['DATASET_NAME'] + '.pkl')
# Load vocabulary-related parameters of dataset used for pre-training
if params['PRE_TRAINED_DATASET_NAME'] is not None:
logging.info('Re-using previous dataset vocabulary ' + params['PRE_TRAINED_DATASET_NAME'])
dataset_pretrained = loadDataset(
params['DATASET_STORE_PATH'] + 'Dataset_' + params['PRE_TRAINED_DATASET_NAME'] + '.pkl')
for id_new, id_old in params['VOCABULARIES_MAPPING'].iteritems():
ds.vocabulary[id_new] = copy.deepcopy(dataset_pretrained.vocabulary[id_old])
ds.vocabulary_len[id_new] = copy.deepcopy(dataset_pretrained.vocabulary_len[id_old])
elif params['PRE_TRAINED_VOCABULARY_NAME'] is not None:
logging.info('Re-using previous vocabulary ' + params['PRE_TRAINED_VOCABULARY_NAME'])
dataset_pretrained_vocabulary = pkl2dict(
params['DATASET_STORE_PATH'] + params['PRE_TRAINED_VOCABULARY_NAME'] + '.pkl')
for id_new, id_old in params['VOCABULARIES_MAPPING'].iteritems():
ds.vocabulary[id_new] = copy.deepcopy(dataset_pretrained_vocabulary[id_old])
ds.vocabulary_len[id_new] = len(dataset_pretrained_vocabulary[id_old]['idx2words'])
return ds
