def test():
opt = parse_args()
opt.experiment = os.path.join(root_dir, opt.experiment)
opt.chkpt = os.path.join(opt.experiment, opt.chkpt)
opt.out_file = os.path.join(opt.experiment, opt.out_file)
opt.out_json = os.path.join(opt.experiment, opt.out_json)
sessions = json.loads(open(opt.test_file).read())['sessions']
# Model loading
model = make_model(len(opt.word2idx))
chkpt = torch.load(opt.chkpt, map_location = lambda storage, log: storage)
model.load_state_dict(chkpt)
if opt.gpuid >= 0:
model = model.cuda()
# ====== *********************** ================
model.eval()
# ===============================================
# decode
results = []
print('Decoding ...')
decode_sessions = {'sessions': []}
for session in sessions:
n_session = {}
n_session['session-id'] = session['session-id']
n_session['turns'] = []
for turn in session['turns']:
asr_hyps = turn['asr-hyps']
asr_hyp = asr_hyps[0]['asr-hyp']
string = translate(model, asr_hyp, opt.word2idx, opt.idx2word, opt.cuda)
if string == '':
classes = []
else:
classes = trp_reverse_process(string, 1)
results.append((asr_hyp, string))
slu_hyp = [slot2dic(string) for string in classes]
n_session['turns'].append(
{
'asr-hyps': asr_hyps,
'slu-hyps': [{'slu-hyp': slu_hyp, 'score': 1.0}]
}
)
decode_sessions['sessions'].append(n_session)
string = json.dumps(decode_sessions, sort_keys=True, indent=4, separators=(',', ':'))
with open(opt.out_json, 'w') as f:
f.write(string)
print('Decode results saved in {}'.format(opt.save_file))
with open(opt.out_file, 'w') as f:
for (enc, dec) in results:
f.write('{}\t<=>\t{}\n'.format(enc.strip(), dec.strip()))
def test(opt):
opt.experiment = os.path.join(root_dir, opt.experiment)
opt.load_chkpt = os.path.join(opt.experiment, opt.load_chkpt)
opt.save_decode = os.path.join(opt.experiment, opt.save_decode)
opt.test_json = os.path.join(opt.data_root, opt.test_json)
idx2class = {v:k for k,v in opt.class2idx.items()}
model = make_model(opt)
chkpt = torch.load(opt.load_chkpt, map_location=lambda storage, log: storage)
model.load_state_dict(chkpt)
# =======================================
model.eval()
# =======================================
sessions = json.loads(open(opt.test_json).read())['sessions']
print('Decoding ...')
decode_sessions = {'sessions': []}
for session in sessions:
n_session = {}
n_session['session-id'] = session['session-id']
n_session['turns'] = []
for turn in session['turns']:
asr_hyps = turn['asr-hyps']
sent = asr_hyps[0]['asr-hyp']
tokens = process_sent(sent)
if len(tokens) == 0:
slu_hyp = []
else:
sent_ids = [opt.word2idx.get(w) if w in opt.word2idx else Constants.UNK for w in tokens]
datas = torch.from_numpy(np.asarray(sent_ids, dtype='int64')).view(1, -1)
if opt.cuda:
datas = datas.cuda()
probs = model(datas, None)
scores = probs.data.cpu().view(-1,).numpy()
pred_classes = [i for i,p in enumerate(scores) if p > 0.5]
classes = [idx2class[i] for i in pred_classes]
slu_hyp = [slot2dic(string) for string in classes]
n_session['turns'].append(
{
'asr-hyps': asr_hyps,
'slu-hyps': [{'slu-hyp': slu_hyp, 'score': 1.0}]
}
)
decode_sessions['sessions'].append(n_session)
string = json.dumps(decode_sessions, sort_keys=True, indent=4, separators=(',', ':'))
with open(opt.save_decode, 'w') as f:
f.write(string)
print('Decode results saved in {}'.format(opt.save_decode))
def test(opt):
opt.experiment = os.path.join(root_dir, opt.experiment)
opt.load_chkpt = os.path.join(opt.experiment, opt.save_model)
opt.save_file = os.path.join(opt.experiment, opt.save_file)
opt.test_file = os.path.join(opt.data_root, opt.test_file)
sessions = json.loads(open(opt.test_file).read())['sessions']
# Model loading
model = make_model(opt)
chkpt = torch.load(opt.load_chkpt,
map_location=lambda storage, log: storage)
model.load_state_dict(chkpt)
if opt.deviceid >= 0:
model = model.cuda()
print(model)
# ====== *********************** ================
model.eval()
# ===============================================
# decode
print('Decoding ...')
decode_sessions = {'sessions': []}
for session in sessions:
n_session = {}
n_session['session-id'] = session['session-id']
n_session['turns'] = []
for turn in session['turns']:
asr_hyps = turn['asr-hyps']
asr_hyp = asr_hyps[0]['asr-hyp']
classes = decode_slu(model, asr_hyp, opt.memory, opt.cuda)
slu_hyp = [slot2dic(string) for string in classes]
n_session['turns'].append({
'asr-hyps':
asr_hyps,
'slu-hyps': [{
'slu-hyp': slu_hyp,
'score': 1.0
}]
})
decode_sessions['sessions'].append(n_session)
string = json.dumps(decode_sessions,
sort_keys=True,
indent=4,
separators=(',', ':'))
with open(opt.save_file, 'w') as f:
f.write(string)
print('Decode results saved in {}'.format(opt.save_file))