def decode_predict_ctc(out, top_paths = 1):
results = []
beam_width = 5
if beam_width < top_paths:
beam_width = top_paths
for i in range(top_paths):
lables = K.get_value(K.ctc_decode(out, input_length=np.ones(out.shape[0])*out.shape[1],
greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i])[0]
text = GSQL.labels_to_text(lables)
results.append(text)
return results
def decode_batch(test_func, word_batch):
out = test_func([word_batch])[0]
ret = []
for j in range(out.shape[0]):
out_best = list(np.argmax(out[j, 2:], 1))
out_best = [k for k, g in itertools.groupby(out_best)]
outstr = GENERATOR.labels_to_text(out_best)
ret.append(outstr)
return ret
def decode_batch(test_func, word_batch):
out = test_func([word_batch])[0]
ret = []
for j in range(out.shape[0]):
out_best = list(np.argmax(out[j, 2:], 1))
out_best = [k for k, g in itertools.groupby(out_best)]
if generator_choice == "GSQL":
outstr = GSQL.labels_to_text(out_best)
else:
assert(False)
ret.append(outstr)
return ret
def decode_predict_ctc(out, top_paths = 1):
results = []
beam_width = 5
if beam_width < top_paths:
beam_width = top_paths
for i in range(top_paths):
t0 = time.time()
v1 = np.ones(out.shape[0])
t1 = time.time()
v2 = v1*out.shape[1]
t2 = time.time()
v3 = K.ctc_decode(out, input_length=v2, greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i]
t3 = time.time()
lables = K.get_value(v3)[0]
t4 = time.time()
#lables = K.get_value(K.ctc_decode(out, input_length=np.ones(out.shape[0])*out.shape[1],
# greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i])[0]
text = GSQL.labels_to_text(lables)
results.append(text)
print("why slow down" t4-t3)
return results