# 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) class BadInputTestCTCAlignr(unittest.TestCase): def test_error(self): with fluid.program_guard(fluid.Program()): def test_bad_x(): x = fluid.layers.data(name='x', shape=[8], dtype='int64') cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0) self.assertRaises(TypeError, test_bad_x) if __name__ == "__main__": unittest.main()