def main():
# load config
parser = argparse.ArgumentParser(description='Evaluation')
parser = load_arguments(parser)
args = vars(parser.parse_args())
config = validate_config(args)
# load src-tgt pair
test_path_src = config['test_path_src']
test_path_tgt = config['test_path_tgt']
if type(test_path_tgt) == type(None):
test_path_tgt = test_path_src
test_path_out = config['test_path_out']
test_acous_path = config['test_acous_path']
acous_norm_path = config['acous_norm_path']
load_dir = config['load']
max_seq_len = config['max_seq_len']
batch_size = config['batch_size']
beam_width = config['beam_width']
use_gpu = config['use_gpu']
seqrev = config['seqrev']
use_type = config['use_type']
# set test mode
MODE = config['eval_mode']
if MODE != 2:
if not os.path.exists(test_path_out):
os.makedirs(test_path_out)
config_save_dir = os.path.join(test_path_out, 'eval.cfg')
save_config(config, config_save_dir)
# check device:
device = check_device(use_gpu)
print('device: {}'.format(device))
# load model
latest_checkpoint_path = load_dir
resume_checkpoint = Checkpoint.load(latest_checkpoint_path)
model = resume_checkpoint.model.to(device)
vocab_src = resume_checkpoint.input_vocab
vocab_tgt = resume_checkpoint.output_vocab
print('Model dir: {}'.format(latest_checkpoint_path))
print('Model laoded')
# combine model
if type(config['combine_path']) != type(None):
model = combine_weights(config['combine_path'])
# import pdb; pdb.set_trace()
# load test_set
test_set = Dataset(
path_src=test_path_src,
path_tgt=test_path_tgt,
vocab_src_list=vocab_src,
vocab_tgt_list=vocab_tgt,
use_type=use_type,
acous_path=test_acous_path,
seqrev=seqrev,
acous_norm=config['acous_norm'],
acous_norm_path=config['acous_norm_path'],
acous_max_len=6000, # max 50k for mustc trainset
max_seq_len_src=900,
max_seq_len_tgt=900, # max 2.5k for mustc trainset
batch_size=batch_size,
mode='ST',
use_gpu=use_gpu)
print('Test dir: {}'.format(test_path_src))
print('Testset loaded')
sys.stdout.flush()
# '{AE|ASR|MT|ST}-{REF|HYP}'
if len(config['gen_mode'].split('-')) == 2:
gen_mode = config['gen_mode'].split('-')[0]
history = config['gen_mode'].split('-')[1]
elif len(config['gen_mode'].split('-')) == 1:
gen_mode = config['gen_mode']
history = 'HYP'
# add external language model
lm_mode = config['lm_mode']
# run eval:
if MODE == 1:
translate(test_set,
model,
test_path_out,
use_gpu,
max_seq_len,
beam_width,
device,
seqrev=seqrev,
gen_mode=gen_mode,
lm_mode=lm_mode,
history=history)
elif MODE == 2: # save combined model
ckpt = Checkpoint(model=model,
optimizer=None,
epoch=0,
step=0,
input_vocab=test_set.vocab_src,
output_vocab=test_set.vocab_tgt)
saved_path = ckpt.save_customise(
os.path.join(config['combine_path'].strip('/') + '-combine',
'combine'))
log_ckpts(config['combine_path'],
config['combine_path'].strip('/') + '-combine')
print('saving at {} ... '.format(saved_path))
elif MODE == 3:
plot_emb(test_set, model, test_path_out, use_gpu, max_seq_len, device)
elif MODE == 4:
gather_emb(test_set, model, test_path_out, use_gpu, max_seq_len,
device)
elif MODE == 5:
compute_kl(test_set, model, test_path_out, use_gpu, max_seq_len,
device)
def main():
# load config
parser = argparse.ArgumentParser(description='Seq2seq Evaluation')
parser = load_arguments(parser)
args = vars(parser.parse_args())
config = validate_config(args)
# load src-tgt pair
test_path_src = config['test_path_src']
test_path_tgt = test_path_src
test_path_out = config['test_path_out']
load_dir = config['load']
max_seq_len = config['max_seq_len']
batch_size = config['batch_size']
beam_width = config['beam_width']
use_gpu = config['use_gpu']
seqrev = config['seqrev']
use_type = config['use_type']
# set test mode: 1 = translate; 2 = plot; 3 = save comb ckpt
MODE = config['eval_mode']
if MODE != 3:
if not os.path.exists(test_path_out):
os.makedirs(test_path_out)
config_save_dir = os.path.join(test_path_out, 'eval.cfg')
save_config(config, config_save_dir)
# check device:
device = check_device(use_gpu)
print('device: {}'.format(device))
# load model
latest_checkpoint_path = load_dir
resume_checkpoint = Checkpoint.load(latest_checkpoint_path)
model = resume_checkpoint.model.to(device)
vocab_src = resume_checkpoint.input_vocab
vocab_tgt = resume_checkpoint.output_vocab
print('Model dir: {}'.format(latest_checkpoint_path))
print('Model laoded')
# combine model
if type(config['combine_path']) != type(None):
model = combine_weights(config['combine_path'])
# load test_set
test_set = Dataset(test_path_src, test_path_tgt,
vocab_src_list=vocab_src, vocab_tgt_list=vocab_tgt,
seqrev=seqrev,
max_seq_len=900,
batch_size=batch_size,
use_gpu=use_gpu,
use_type=use_type)
print('Test dir: {}'.format(test_path_src))
print('Testset loaded')
sys.stdout.flush()
# run eval
if MODE == 1:
translate(test_set, model, test_path_out, use_gpu,
max_seq_len, beam_width, device, seqrev=seqrev)
elif MODE == 2: # output posterior
translate_logp(test_set, model, test_path_out, use_gpu,
max_seq_len, device, seqrev=seqrev)
elif MODE == 3: # save combined model
ckpt = Checkpoint(model=model,
optimizer=None, epoch=0, step=0,
input_vocab=test_set.vocab_src,
output_vocab=test_set.vocab_tgt)
saved_path = ckpt.save_customise(
os.path.join(config['combine_path'].strip('/')+'-combine','combine'))
log_ckpts(config['combine_path'], config['combine_path'].strip('/')+'-combine')
print('saving at {} ... '.format(saved_path))