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Paddle/python/paddle/fluid/tests/unittests/test_linear.py

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# Copyright (c) 2020 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 paddle.fluid.core as core
from op_test import OpTest
import paddle
from paddle import fluid, nn
import paddle.fluid.dygraph as dg
import paddle.nn.functional as F
import paddle.fluid.initializer as I
class LinearTestCase(unittest.TestCase):
def setUp(self):
self.dtype = 'float32'
self.input = np.ones((3, 1, 2)).astype(self.dtype)
self.weight = np.ones((2, 2)).astype(self.dtype)
self.bias = np.ones((2)).astype(self.dtype)
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
) else paddle.CPUPlace()
def functional(self, place):
paddle.disable_static(place)
input = paddle.to_tensor(self.input)
weight = paddle.to_tensor(self.weight)
bias = paddle.to_tensor(self.bias)
out = F.linear(input, weight, bias)
return out.numpy()
def paddle_nn_layer(self, place):
paddle.disable_static(place)
input = paddle.to_tensor(self.input)
weight_attr = fluid.ParamAttr(
name="linear_weight",
learning_rate=1.0,
trainable=False,
regularizer=None,
initializer=paddle.fluid.initializer.ConstantInitializer(value=1.0))
bias_attr = fluid.ParamAttr(
name="linear_bias",
learning_rate=1.0,
trainable=False,
regularizer=None,
initializer=paddle.fluid.initializer.ConstantInitializer(value=1.0))
linear = paddle.nn.Linear(
2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
y = linear(input)
return y.numpy()
def numpy_cal(self):
res = np.matmul(self.input, self.weight) + self.bias
return res
def test_error(self, place=paddle.CPUPlace()):
res_f = self.functional(place)
res_nn = self.paddle_nn_layer(place)
res_np = self.numpy_cal()
np.testing.assert_array_almost_equal(res_f, res_nn)
np.testing.assert_array_almost_equal(res_nn, res_np)
if __name__ == "__main__":
unittest.main()