From b3f9e5e0079f22cdab7fa60025ab04bbee1e7827 Mon Sep 17 00:00:00 2001 From: weixing02 Date: Thu, 31 May 2018 11:21:15 +0800 Subject: [PATCH] make test_hsigmoid_op.py right --- .../fluid/tests/unittests/test_hsigmoid_op.py | 51 +++++++++---------- 1 file changed, 23 insertions(+), 28 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py b/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py index 178f56aeb8..226ce8b904 100644 --- a/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py +++ b/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# 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. @@ -14,8 +14,8 @@ import unittest import numpy as np -from op_test import OpTest import math +from op_test import OpTest def find_latest_set(num): @@ -37,40 +37,36 @@ class CodeTable(object): def hsigmoid(x, w, ids, bias, num_classes): - # code length = - # initialize pre out with dims={batch_size, code_length} + global pre_output batch_size = x.shape[0] code_length = find_latest_set(num_classes - 1) code_table = [0 for _ in range(code_length)] pre_output = np.zeros((batch_size, code_length)) pre_sum = np.zeros((batch_size, 1)) out = np.zeros((batch_size, 1)).astype("float32") - # pre_out += code(bias) - for i in xrange(batch_size): + for i in range(batch_size): code_table = CodeTable(num_classes, ids[i]) length = code_table.get_length() - for j in xrange(length): + for j in range(length): idx = code_table.cal_index(j) pre_output[i][j] += bias[0][idx] - # pre_out += code(w) * x - for i in xrange(batch_size): - for j in xrange(batch_size): - code_table = CodeTable(num_classes, ids[j]) - length = code_table.get_length() - for k in xrange(length): - idx = code_table.cal_index(k) - sum = 0.0 - for l in xrange(x.shape[1]): - sum += w[i][idx][l] * x[j][l] - pre_output[j][k] += sum + for j in range(batch_size): + code_table = CodeTable(num_classes, ids[j]) + length = code_table.get_length() + for k in range(length): + idx = code_table.cal_index(k) + sum = 0.0 + for l in range(x.shape[1]): + sum += w[idx][l] * x[j][l] + pre_output[j][k] += sum # clip[-40.0, 40.0] np.clip(pre_output, -40.0, 40.0) # out(i, 0) = \sum_j bit(i, j) * preout(i, j) - for i in xrange(batch_size): + for i in range(batch_size): code_table = CodeTable(num_classes, ids[i]) length = code_table.get_length() sum = 0.0 - for j in xrange(length): + for j in range(length): if code_table.cal_bit(j): sum += pre_output[i][j] out[i] = -1.0 * sum @@ -85,24 +81,23 @@ def hsigmoid(x, w, ids, bias, num_classes): class TestHSigmoidOp(OpTest): def setUp(self): self.op_type = "hierarchical_sigmoid" - num_classes = 6 - embded_size = 10 - batch_size = 5 + num_classes = 4 + embded_size = 1 + batch_size = 1 x = np.random.random((batch_size, embded_size)).astype("float32") - w = np.random.random( - (batch_size, num_classes - 1, embded_size)).astype("float32") + w = np.random.random((num_classes - 1, embded_size)).astype("float32") ids = np.random.randint(0, num_classes, batch_size) bias = np.random.random((1, num_classes - 1)).astype("float32") - self.inputs = {'X': x, 'W': w, 'Ids': ids, 'Bias': bias} self.attrs = {'num_classes': num_classes} + self.inputs = {'X': x, 'W': w, 'Ids': ids, 'Bias': bias} out = hsigmoid(x, w, ids, bias, num_classes) - self.outputs = {'Out': out} + self.outputs = {'PreOut': pre_output, 'Out': out} def test_check_output(self): self.check_output() def test_check_grad(self): - self.check_grad(['X', 'W', 'Bias'], 'Out', no_grad_set=set('Ids')) + self.check_grad(['Bias', 'X', 'W'], 'Out', no_grad_set=set('Ids')) if __name__ == '__main__': -- GitLab