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102 lines
3.3 KiB
102 lines
3.3 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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import unittest
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import numpy as np
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import math
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from op_test import OpTest
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np.random.seed(100)
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def find_latest_set(num):
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return 1 + int(math.floor(math.log(num, 2)))
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class CodeTable(object):
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def __init__(self, num_classes, code):
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self.c = num_classes + code
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def cal_index(self, bit):
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return (self.c >> (bit + 1)) - 1
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def get_length(self):
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return find_latest_set(self.c) - 1
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def cal_bit(self, bit):
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return self.c & (1 << bit)
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def hsigmoid(x, w, label, bias, num_classes):
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batch_size = x.shape[0]
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code_length = find_latest_set(num_classes - 1)
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code_table = [0 for _ in range(code_length)]
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pre_output = np.zeros((batch_size, code_length))
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pre_sum = np.zeros((batch_size, 1))
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out = np.zeros((batch_size, 1)).astype("float32")
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += bias[0][idx]
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += np.dot(w[idx], x[i])
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# clip[-40.0, 40.0]
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pre_output = np.clip(pre_output, -40.0, 40.0)
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# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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sum = 0.0
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for j in range(length):
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if code_table.cal_bit(j):
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sum += pre_output[i][j]
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out[i] = -1.0 * sum
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# soft relu
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pre_output = np.log(1 + np.exp(pre_output))
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pre_sum = pre_output.sum(1).reshape((batch_size, 1))
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out += pre_sum
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return pre_output, out
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class TestHSigmoidOp(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6
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feature_size = 8
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batch_size = 4
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x = np.random.random((batch_size, feature_size)).astype("float32")
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w = np.random.random((num_classes - 1, feature_size)).astype("float32")
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label = np.random.randint(0, num_classes, (batch_size, 1))
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bias = np.random.random((1, num_classes - 1)).astype("float32")
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self.attrs = {'num_classes': num_classes}
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self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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if __name__ == '__main__':
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unittest.main()
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