# 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 unittest import numpy as np import random from op_test import OpTest class TestSeqProject(OpTest): def setUp(self): self.init_test_case() self.op_type = 'sequence_conv' if self.context_length == 1 \ and self.context_start == 0 \ and self.padding_trainable: print("If context_start is 0 " \ "and context_length is 1," \ " padding_trainable should be false.") return # one level, batch size x = np.random.uniform(0.1, 1, [self.input_size[0], self.input_size[1]]).astype('float32') w = np.random.uniform(0.1, 1, [ self.context_length * self.input_size[1], self.output_represention ]).astype('float32') begin_pad = np.max([0, -self.context_start]) end_pad = np.max([0, self.context_start + self.context_length - 1]) total_pad = begin_pad + end_pad padding_data = np.random.uniform( 0.1, 1, [total_pad, self.input_size[1]]).astype('float32') self.pad_data = padding_data self.inputs = { 'X': (x, self.lod), 'Filter': w, } self.inputs_val = ['X', 'Filter'] self.inputs_val_no_x = ['Filter'] self.inputs_val_no_f = ['X'] if total_pad != 0: self.inputs['PaddingData'] = padding_data self.inputs_val = ['X', 'PaddingData', 'Filter'] self.inputs_val_no_x = ['PaddingData', 'Filter'] self.inputs_val_no_f = ['PaddingData', 'X'] self.attrs = { 'contextStart': self.context_start, 'contextLength': self.context_length, 'paddingTrainable': self.padding_trainable, 'contextStride': self.context_stride } out = np.zeros( (self.input_size[0], self.output_represention)).astype('float32') self.outputs = {'Out': out} self.compute() def compute(self): x, lod = self.inputs['X'] filter = self.inputs['Filter'] pading_data = self.pad_data out = np.zeros((self.input_size[0], self.context_length * self.input_size[1])).astype('float32') offset = [0] for seq_len in lod[0]: offset.append(offset[-1] + seq_len) begin_pad = np.max([0, -self.context_start]) for i in range(len(offset) - 1): for j in range(self.context_length): in_begin = offset[i] + self.context_start + j in_end = offset[i + 1] + self.context_start + j out_begin = offset[i] out_end = offset[i + 1] if in_begin < offset[i]: pad_size = np.min( [offset[i] - in_begin, offset[i + 1] - offset[i]]) if self.padding_trainable: sub_w = pading_data[j:j + pad_size, :] out[offset[i]:offset[i] + pad_size, j * self.input_size[ 1]:(j + 1) * self.input_size[1]] = sub_w out_begin = offset[i] + pad_size in_begin = offset[i] if in_end > offset[i + 1]: pad_size = np.min( [in_end - offset[i + 1], offset[i + 1] - offset[i]]) if self.padding_trainable: sub_w = pading_data[begin_pad + self.context_start + j - pad_size:begin_pad + self.context_start + j, :] out[offset[i + 1] - pad_size:offset[i + 1], j * self. input_size[1]:(j + 1) * self.input_size[1]] = sub_w in_end = offset[i + 1] out_end = offset[i + 1] - pad_size if in_end <= in_begin: continue in_sub = x[in_begin:in_end, :] out[out_begin:out_end, j * self.input_size[1]:(j + 1) * self.input_size[1]] += in_sub np.dot(out, filter, out=self.outputs['Out']) def test_check_output(self): self.check_output() def test_check_grad(self): if self.padding_trainable: self.check_grad( set(self.inputs_val), 'Out', max_relative_error=0.05) def test_check_grad_input(self): self.check_grad( ['X'], 'Out', max_relative_error=0.05, no_grad_set=set(self.inputs_val_no_x)) def test_check_grad_padding_data(self): if self.padding_trainable: self.check_grad( ['PaddingData'], 'Out', max_relative_error=0.05, no_grad_set=set(['X', 'Filter'])) def test_check_grad_Filter(self): self.check_grad( ['Filter'], 'Out', max_relative_error=0.05, no_grad_set=set(self.inputs_val_no_f)) def test_check_grad_input_filter(self): if self.padding_trainable: self.check_grad( ['X', 'Filter'], 'Out', max_relative_error=0.05, no_grad_set=set(['PaddingData'])) def test_check_grad_padding_input(self): if self.padding_trainable: self.check_grad( self.inputs_val_no_f, 'Out', max_relative_error=0.05, no_grad_set=set(['Filter'])) def test_check_grad_padding_filter(self): if self.padding_trainable: self.check_grad( self.inputs_val_no_x, 'Out', max_relative_error=0.05, no_grad_set=set(['X'])) def init_test_case(self): self.input_row = 11 self.context_start = 0 self.context_length = 1 self.padding_trainable = False self.context_stride = 1 self.input_size = [self.input_row, 23] offset_lod = [[0, 4, 5, 8, self.input_row]] self.lod = [[]] # convert from offset-based lod to length-based lod for i in range(len(offset_lod[0]) - 1): self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i]) self.output_represention = 8 # output feature size class TestSeqProjectCase1(TestSeqProject): def init_test_case(self): self.input_row = 11 self.context_start = -1 self.context_length = 3 self.padding_trainable = True self.context_stride = 1 self.input_size = [self.input_row, 23] offset_lod = [[0, 4, 5, 8, self.input_row]] self.lod = [[]] # convert from offset-based lod to length-based lod for i in range(len(offset_lod[0]) - 1): self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i]) self.output_represention = 8 # output feature size class TestSeqProjectCase2(TestSeqProject): def init_test_case(self): self.input_row = 25 self.context_start = 2 self.context_length = 3 self.padding_trainable = True self.context_stride = 1 self.input_size = [self.input_row, 23] idx = list(range(self.input_size[0])) del idx[0] offset_lod = [[0] + np.sort(random.sample(idx, 8)).tolist() + [self.input_size[0]]] self.lod = [[]] # convert from offset-based lod to length-based lod for i in range(len(offset_lod[0]) - 1): self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i]) self.output_represention = 8 # output feature size if __name__ == '__main__': unittest.main()