# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import unittest
import numpy as np
from op_test import OpTest
from test_softmax_op import stable_softmax
import paddle.fluid as fluid
def CTCAlign(input, lod, blank, merge_repeated, padding=0, input_length=None):
if input_length is None:
lod0 = lod[0]
result = []
cur_offset = 0
for i in range(len(lod0)):
prev_token = -1
for j in range(cur_offset, cur_offset + lod0[i]):
token = input[j][0]
if (token != blank) and not (merge_repeated and
token == prev_token):
result.append(token)
prev_token = token
cur_offset += lod0[i]
result = np.array(result).reshape([len(result), 1]).astype("int32")
if len(result) == 0:
result = np.array([-1])
return result
else:
result = [[] for i in range(len(input))]
output_length = []
for i in range(len(input)):
prev_token = -1
for j in range(input_length[i][0]):
token = input[i][j]
if (token != blank) and not (merge_repeated and
token == prev_token):
result[i].append(token)
prev_token = token
start = len(result[i])
output_length.append([start])
for j in range(start, len(input[i])):
result[i].append(padding)
result = np.array(result).reshape(
[len(input), len(input[0])]).astype("int32")
output_length = np.array(output_length).reshape(
[len(input), 1]).astype("int32")
return result, output_length
class TestCTCAlignOp(OpTest):
def config(self):
self.op_type = "ctc_align"
self.input_lod = [[11, 7]]
self.blank = 0
self.merge_repeated = False
self.input = np.array(
[0, 1, 2, 2, 0, 4, 0, 4, 5, 0, 6, 6, 0, 0, 7, 7, 7, 0]).reshape(
[18, 1]).astype("int32")
def setUp(self):
self.config()
output = CTCAlign(self.input, self.input_lod, self.blank,
self.merge_repeated)
self.inputs = {"Input": (self.input, self.input_lod), }
self.outputs = {"Output": output}
self.attrs = {
"blank": self.blank,
"merge_repeated": self.merge_repeated
}
def test_check_output(self):
self.check_output()
pass
class TestCTCAlignOpCase1(TestCTCAlignOp):
def config(self):
self.op_type = "ctc_align"
self.input_lod = [[11, 8]]
self.blank = 0
self.merge_repeated = True
self.input = np.array(
[0, 1, 2, 2, 0, 4, 0, 4, 5, 0, 6, 6, 0, 0, 7, 7, 7, 0, 0]).reshape(
[19, 1]).astype("int32")
class TestCTCAlignOpCase2(TestCTCAlignOp):
def config(self):
self.op_type = "ctc_align"
self.input_lod = [[4]]
self.blank = 0
self.merge_repeated = True
self.input = np.array([0, 0, 0, 0]).reshape([4, 1]).astype("int32")
class TestCTCAlignPaddingOp(OpTest):
def config(self):
self.op_type = "ctc_align"
self.input_lod = []
self.blank = 0
self.padding_value = 0
self.merge_repeated = True
self.input = np.array([[0, 2, 4, 4, 0, 6, 3, 6, 6, 0, 0],
[1, 1, 3, 0, 0, 4, 5, 6, 0, 0, 0]]).reshape(
[2, 11]).astype("int32")
self.input_length = np.array([[9], [8]]).reshape([2, 1]).astype("int32")
def setUp(self):
self.config()
output, output_length = CTCAlign(self.input, self.input_lod, self.blank,
self.merge_repeated,
self.padding_value, self.input_length)
self.inputs = {
"Input": (self.input, self.input_lod),
"InputLength": self.input_length
}
self.outputs = {"Output": output, "OutputLength": output_length}
self.attrs = {
"blank": self.blank,
"merge_repeated": self.merge_repeated,
"padding_value": self.padding_value
}
def test_check_output(self):
self.check_output()
class TestCTCAlignOpCase3(TestCTCAlignPaddingOp):
def config(self):
self.op_type = "ctc_align"
self.blank = 0
self.input_lod = []
self.merge_repeated = True
self.padding_value = 0
self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
[0, 7, 7, 7, 0, 0]]).reshape(
[3, 6]).astype("int32")
self.input_length = np.array([[6], [5],
[4]]).reshape([3, 1]).astype("int32")
class TestCTCAlignOpCase4(TestCTCAlignPaddingOp):
'''
# test tensor input which has attr input padding_value
'''
def config(self):
self.op_type = "ctc_align"
self.blank = 0
self.input_lod = []
self.merge_repeated = False
self.padding_value = 0
self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
[0, 7, 7, 7, 0, 0]]).reshape(
[3, 6]).astype("int32")
self.input_length = np.array([[6], [5],
[4]]).reshape([3, 1]).astype("int32")
class TestCTCAlignOpCase5(TestCTCAlignPaddingOp):
def config(self):
self.op_type = "ctc_align"
self.blank = 0
self.input_lod = []
self.merge_repeated = False
self.padding_value = 1
self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
[0, 7, 1, 7, 0, 0]]).reshape(
[3, 6]).astype("int32")
self.input_length = np.array([[6], [5],
[4]]).reshape([3, 1]).astype("int32")
class TestCTCAlignOpApi(unittest.TestCase):
def test_api(self):
x = fluid.layers.data('x', shape=[4], dtype='float32')
y = fluid.layers.ctc_greedy_decoder(x, blank=0)
x_pad = fluid.layers.data('x_pad', shape=[4, 4], dtype='float32')
x_pad_len = fluid.layers.data('x_pad_len', shape=[1], dtype='int64')
y_pad, y_pad_len = fluid.layers.ctc_greedy_decoder(
x_pad, blank=0, input_length=x_pad_len)
place = fluid.CPUPlace()
x_tensor = fluid.create_lod_tensor(
np.random.rand(8, 4).astype("float32"), [[4, 4]], place)
x_pad_tensor = np.random.rand(2, 4, 4).astype("float32")
x_pad_len_tensor = np.array([[4], [4]]).reshape([2, 1]).astype("int64")
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
ret = exe.run(feed={
'x': x_tensor,
'x_pad': x_pad_tensor,
'x_pad_len': x_pad_len_tensor
},
fetch_list=[y, y_pad, y_pad_len],
return_numpy=False)
if __name__ == "__main__":
unittest.main()