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