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