wehub-resource-sync
158 lines
4.6 KiB
Python
158 lines
4.6 KiB
Python
# 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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from op_test import OpTest
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def nce(
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input, weight, bias, sample_weight, labels, num_classes, num_sample_class
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):
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samples = []
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sample_labels = []
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batch_size = input.shape[0]
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num_true_class = labels.shape[1]
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for i in range(batch_size):
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w = 1 if sample_weight is None else sample_weight[i]
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for label in labels[i]:
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samples.append((i, label, True, w))
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sample_labels.append(label)
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for num in range(num_sample_class):
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samples.append((i, num, False, w))
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sample_labels.append(num)
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# forward bias
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sample_out = np.zeros(len(samples)).astype(np.float32)
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if bias is not None:
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for i in range(len(samples)):
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sample_out[i] = bias[samples[i][1]]
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# forward weight
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for i in range(len(samples)):
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sample_out[i] += np.dot(input[samples[i][0]], weight[samples[i][1]])
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# forward activation
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sample_out = 1.0 / (1.0 + np.exp(-sample_out))
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# forward cost
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out = np.zeros(batch_size).astype(np.float32)
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b = 1.0 / num_classes * num_sample_class
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for i in range(len(samples)):
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o = sample_out[i]
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cost = -np.log(o / (o + b)) if samples[i][2] else -np.log(b / (o + b))
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out[samples[i][0]] += cost * samples[i][3]
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return (
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out[:, np.newaxis],
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np.array(sample_out).reshape(
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batch_size, num_sample_class + num_true_class
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),
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np.array(sample_labels).reshape(
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batch_size, num_sample_class + num_true_class
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),
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)
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class TestNCE(OpTest):
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def generate_data(
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self,
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dim,
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batch_size,
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num_classes,
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num_true_class,
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num_neg_samples,
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is_sparse,
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):
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input = np.random.randn(batch_size, dim).astype(np.float32)
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weight = np.random.randn(num_classes, dim).astype(np.float32)
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bias = np.random.randn(num_classes).astype(np.float32)
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sample_weight = np.random.randn(batch_size).astype(np.float32)
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labels = np.random.randint(
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0, num_classes, (batch_size, num_true_class)
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).astype("int64")
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self.attrs = {
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'num_total_classes': num_classes,
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'num_neg_samples': num_neg_samples,
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'custom_neg_classes': list(range(num_neg_samples)),
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'seed': 0,
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'sampler': 0,
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'is_sparse': is_sparse,
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'is_test': self.is_test,
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}
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self.inputs = {
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'Input': input,
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'Label': labels,
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'Weight': weight,
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'Bias': bias,
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'SampleWeight': sample_weight,
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}
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def set_is_test(self):
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self.is_test = False
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def set_data(self):
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self.generate_data(5, 25, 100, 1, 2, False)
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def compute(self):
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out = nce(
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self.inputs['Input'],
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self.inputs['Weight'],
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self.inputs['Bias'],
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self.inputs['SampleWeight'],
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self.inputs['Label'],
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self.attrs['num_total_classes'],
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self.attrs['num_neg_samples'],
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)
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if self.is_test:
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self.outputs = {'Cost': out[0]}
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else:
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self.outputs = {
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'Cost': out[0],
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'SampleLogits': out[1],
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'SampleLabels': out[2],
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}
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def setUp(self):
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self.op_type = 'nce'
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self.set_is_test()
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self.set_data()
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self.compute()
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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self.check_grad(
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["Input", "Weight", "Bias"],
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"Cost",
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max_relative_error=0.02,
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check_dygraph=False,
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)
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class TestNCECase1Tensor(TestNCE):
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def set_data(self):
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self.generate_data(10, 20, 100, 2, 5, False)
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class TestNCETensorIsTest(TestNCE):
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# if is_test = True, there's no need to calculate grad
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def set_is_test(self):
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self.is_test = True
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def test_check_grad(self):
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pass
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if __name__ == '__main__':
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unittest.main()
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