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test_cvm_op.py
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test_cvm_op.py
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# Copyright (c) 2019 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.
import numpy as np
from math import log
from math import exp
from op_test import OpTest
import unittest
def cvm_compute(X, item_width, use_cvm):
cvm_offset = 0 if use_cvm else 2
batch_size = X.shape[0]
Y = np.ones([batch_size, item_width - cvm_offset], np.float32)
for idx in range(batch_size):
if use_cvm:
Y[idx] = X[idx]
Y[idx][0] = log(Y[idx][0] + 1)
Y[idx][1] = log(Y[idx][1] + 1) - Y[idx][0]
else:
Y[idx] = X[idx][2:]
return Y
def cvm_grad_compute(DY, CVM, item_width, use_cvm):
batch_size = DY.shape[0]
DX = np.ones([batch_size, item_width], np.float32)
for idx in range(batch_size):
DX[idx][0] = CVM[idx][0]
DX[idx][1] = CVM[idx][1]
if use_cvm:
DX[idx][2:] = DY[idx][2:]
else:
DX[idx][2:] = DY[idx]
return DX
class TestCVMOpWithLodTensor(OpTest):
"""
Test cvm op with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cvm"
self.use_cvm = True
self.batch_size = 1
self.item_width = 11
lod = [[1]]
self.inputs = {
'X': (np.random.uniform(
0, 1, [self.batch_size, self.item_width]).astype("float32"),
lod),
'CVM': np.array([[0.6, 0.4]]).astype("float32"),
}
self.attrs = {'use_cvm': False}
out = []
for index, emb in enumerate(self.inputs["X"][0]):
out.append(emb[2:])
self.outputs = {'Y': (np.array(out), lod)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
user_grads = np.array(
[1.0 / (self.item_width - 2)] * self.item_width).reshape(
(self.batch_size, self.item_width)).astype("float32")
user_grads[:, :2] = self.inputs['CVM'].reshape(self.batch_size, 2)
user_grads = [user_grads]
self.check_grad(['X'], 'Y', user_defined_grads=user_grads)
class TestCVMOpWithOutLodTensor1(OpTest):
"""
Test cvm op with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cvm"
self.use_cvm = True
self.batch_size = 2
self.item_width = 11
input = np.random.uniform(
0, 1, (self.batch_size, self.item_width)).astype('float32')
output = cvm_compute(input, self.item_width, self.use_cvm)
cvm = np.array([[0.6, 0.4] * self.batch_size]).reshape(
(self.batch_size, 2)).astype("float32")
self.inputs = {'X': input, 'CVM': cvm}
self.attrs = {'use_cvm': self.use_cvm}
self.outputs = {'Y': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
numel = self.batch_size * self.item_width
user_grads = np.array([1.0 / numel] * numel).reshape(
(self.batch_size, self.item_width)).astype("float32")
user_grads[:, :2] = self.inputs['CVM'].reshape(self.batch_size, 2)
user_grads = [user_grads]
self.check_grad(['X'], 'Y', user_defined_grads=user_grads)
class TestCVMOpWithOutLodTensor2(OpTest):
"""
Test cvm op with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cvm"
self.use_cvm = False
self.batch_size = 2
self.item_width = 11
input = np.random.uniform(
0, 1, (self.batch_size, self.item_width)).astype('float32')
output = cvm_compute(input, self.item_width, self.use_cvm)
cvm = np.array([[0.6, 0.4] * self.batch_size]).reshape(
(self.batch_size, 2)).astype("float32")
self.inputs = {'X': input, 'CVM': cvm}
self.attrs = {'use_cvm': self.use_cvm}
self.outputs = {'Y': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
numel = self.batch_size * self.item_width
user_grads = np.array(
[1.0 / (self.batch_size * (self.item_width - 2))] * numel).reshape(
(self.batch_size, self.item_width)).astype("float32")
user_grads[:, :2] = self.inputs['CVM'].reshape(self.batch_size, 2)
user_grads = [user_grads]
self.check_grad(['X'], 'Y', user_defined_grads=user_grads)
if __name__ == '__main__':
unittest.main()