def eval_svm(iv_file, class2int_file, test_file,
preproc_file,
model_file, score_file, vector_score_file,
eval_type, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, class2int_file, test_file, preproc, **tdr_args)
x, ndx = tdr.read()
model = SVM.load(model_file)
t1 = time.time()
scores = model.predict(x, eval_type)
dt = time.time() - t1
num_trials = scores.shape[0]*scores.shape[1]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(ndx.model_set, ndx.seg_set, scores.T)
s.save(score_file)
if vector_score_file is not None:
h5 = HDW(vector_score_file)
h5.write(ndx.seg_set, '', scores)
def eval_plda(iv_file, ndx_file, enroll_file, test_file,
preproc_file,
model_file, score_file, plda_type, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
model = F.load_plda(plda_type, model_file)
t1 = time.time()
scores = model.llr_1vs1(x_e, x_t)
dt = time.time() - t1
num_trials = x_e.shape[0] * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def eval_cos(iv_file, ndx_file, enroll_file, test_file,
preproc_file, score_file, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
lnorm = LNorm()
x_e = lnorm.predict(x_e)
x_t = lnorm.predict(x_t)
t1 = time.time()
scores = np.dot(x_e, x_t.T)
dt = time.time() - t1
num_trials = x_e.shape[0] * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def eval_plda(iv_file, ndx_file, enroll_file, test_file, preproc_file,
model_file, score_file, pool_method, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
enroll, ids_e = np.unique(enroll, return_inverse=True)
model = F.load_plda(plda_type, model_file)
t1 = time.time()
scores = model.llr_Nvs1(x_e, x_t, method=pool_method, ids1=ids_e)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.' %
(dt, dt / num_trials * 1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def convert(input_file, output_file, class_file):
r = DRF.create(input_file)
seg_set, score_mat = r.read(0, squeeze=True)
with open(class_file, 'r') as f:
model_set = [line.rstrip().split()[0] for line in f]
scores = TrialScores(model_set, seg_set, score_mat.T)
scores.save(output_file)
def eval_pdda(iv_file, ndx_file, enroll_file, test_file,
preproc_file,
model_file, score_file,
pool_method, eval_method,
num_samples_y, num_samples_z, num_samples_elbo, qy_only,
**kwargs):
set_float_cpu('float32')
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdt_args)
x_e, x_t, enroll, ndx = tdr.read()
enroll, ids_e = np.unique(enroll, return_inverse=True)
if qy_only:
model = TVAEY.load(model_file)
model.build(max_seq_length=2, num_samples=num_samples_y)
else:
model = TVAEYZ.load(model_file)
model.build(max_seq_length=2,
num_samples_y=num_samples_y, num_samples_z=num_samples_z)
t1 = time.time()
scores = model.eval_llr_Nvs1(x_e, ids_e, x_t,
pool_method=pool_method,
eval_method=eval_method,
num_samples=num_samples_elbo)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.' %
(dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)