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145 lines
5.0 KiB
145 lines
5.0 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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from __future__ import print_function
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import unittest
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import numpy as np
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import six
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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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import paddle.fluid as fluid
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.fluid import compiler, Program, program_guard
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def ref_selu(x,
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scale=1.0507009873554804934193349852946,
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alpha=1.6732632423543772848170429916717):
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out = np.copy(x)
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out_flat = out.flatten()
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for i in range(out_flat.size):
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if out_flat[i] < 0:
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out_flat[i] = alpha * np.exp(out_flat[i]) - alpha
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out_flat[i] = scale * out_flat[i]
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out = out_flat.reshape(x.shape)
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return out
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class SeluTest(OpTest):
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def setUp(self):
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self.op_type = "selu"
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self.x_shape = [3, 5, 5, 10]
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self.dtype = np.float64
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self.init_x_shape()
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self.init_dtype()
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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x = np.random.normal(size=self.x_shape).astype(self.dtype)
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# Since zero point in selu is not differentiable, avoid randomize
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# zero.
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x[np.abs(x) < 0.005] = 0.02
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out = ref_selu(x, scale, alpha)
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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self.attrs = {
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'alpha': alpha,
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'scale': scale,
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}
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def init_x_shape(self):
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pass
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def init_dtype(self):
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pass
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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(['X'], 'Out')
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class TestSeluAPI(unittest.TestCase):
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# test paddle.nn.SELU, paddle.nn.functional.selu
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def setUp(self):
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self.scale = 1.5
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self.alpha = 2.0
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self.x_np = np.random.normal(size=[3, 5, 5, 10]).astype(np.float64)
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# Since zero point in selu is not differentiable, avoid randomize
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# zero.
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self.x_np[np.abs(self.x_np) < 0.005] = 0.02
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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 test_static_api(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.fluid.data('X', self.x_np.shape, self.x_np.dtype)
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out1 = F.selu(x, self.scale, self.alpha)
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selu = paddle.nn.SELU(self.scale, self.alpha)
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out2 = selu(x)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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for r in res:
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self.assertEqual(np.allclose(out_ref, r), True)
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def test_dygraph_api(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out1 = F.selu(x, self.scale, self.alpha)
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selu = paddle.nn.SELU(self.scale, self.alpha)
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out2 = selu(x)
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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for r in [out1, out2]:
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self.assertEqual(np.allclose(out_ref, r.numpy()), True)
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paddle.enable_static()
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def test_fluid_api(self):
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with fluid.program_guard(fluid.Program()):
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x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
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out = fluid.layers.selu(x, self.scale, self.alpha)
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exe = fluid.Executor(self.place)
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res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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self.assertEqual(np.allclose(out_ref, res[0]), True)
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def test_errors(self):
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with paddle.static.program_guard(paddle.static.Program()):
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# The input type must be Variable.
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self.assertRaises(TypeError, F.selu, 1)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
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self.assertRaises(TypeError, F.selu, x_int32)
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# The scale must be greater than 1.0
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x_fp32 = paddle.fluid.data(name='x_fp32', shape=[12, 10], dtype='float32')
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self.assertRaises(ValueError, F.selu, x_fp32, -1.0)
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# The alpha must be no less than 0
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self.assertRaises(ValueError, F.selu, x_fp32, 1.6, -1.0)
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# support the input dtype is float16
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x_fp16 = paddle.fluid.data(name='x_fp16', shape=[12, 10], dtype='float16')
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F.selu(x_fp16)
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if __name__ == "__main__":
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unittest.main()
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