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150 lines
5.0 KiB
150 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 paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.nn.functional as F
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from op_test import OpTest
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paddle.enable_static()
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np.random.seed(1)
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def maxout_forward_naive(x, groups, channel_axis):
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s0, s1, s2, s3 = x.shape
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if channel_axis == 1:
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return np.ndarray([s0, s1 // groups, groups, s2, s3], \
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buffer = x, dtype=x.dtype).max(axis=2)
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return np.ndarray([s0, s1, s2, s3 // groups, groups], \
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buffer = x, dtype=x.dtype).max(axis=4)
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class TestMaxOutOp(OpTest):
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def setUp(self):
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self.op_type = "maxout"
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self.dtype = 'float64'
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self.shape = [3, 6, 2, 4]
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self.groups = 2
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self.axis = 1
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self.set_attrs()
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x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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out = maxout_forward_naive(x, self.groups, self.axis)
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self.inputs = {'X': x}
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self.attrs = {'groups': self.groups, 'axis': self.axis}
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self.outputs = {'Out': out}
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def set_attrs(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 TestMaxOutOpAxis0(TestMaxOutOp):
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def set_attrs(self):
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self.axis = -1
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class TestMaxOutOpAxis1(TestMaxOutOp):
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def set_attrs(self):
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self.axis = 3
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class TestMaxOutOpFP32(TestMaxOutOp):
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def set_attrs(self):
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self.dtype = 'float32'
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class TestMaxOutOpGroups(TestMaxOutOp):
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def set_attrs(self):
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self.groups = 3
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class TestMaxoutAPI(unittest.TestCase):
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# test paddle.nn.Maxout, paddle.nn.functional.maxout
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def setUp(self):
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self.x_np = np.random.uniform(-1, 1, [2, 6, 5, 4]).astype(np.float64)
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self.groups = 2
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self.axis = 1
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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.maxout(x, self.groups, self.axis)
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m = paddle.nn.Maxout(self.groups, self.axis)
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out2 = m(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 = maxout_forward_naive(self.x_np, self.groups, self.axis)
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for r in res:
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self.assertTrue(np.allclose(out_ref, r))
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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.maxout(x, self.groups, self.axis)
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m = paddle.nn.Maxout(self.groups, self.axis)
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out2 = m(x)
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out_ref = maxout_forward_naive(self.x_np, self.groups, self.axis)
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for r in [out1, out2]:
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self.assertTrue(np.allclose(out_ref, r.numpy()))
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out3 = F.maxout(x, self.groups, -1)
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out3_ref = maxout_forward_naive(self.x_np, self.groups, -1)
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self.assertTrue(np.allclose(out3_ref, out3.numpy()))
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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.maxout(x, groups=self.groups, axis=self.axis)
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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 = maxout_forward_naive(self.x_np, self.groups, self.axis)
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self.assertTrue(np.allclose(out_ref, res[0]))
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out = paddle.fluid.layers.maxout(x, groups=self.groups, axis=self.axis)
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self.assertTrue(np.allclose(out_ref, out.numpy()))
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paddle.enable_static()
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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.maxout, 1)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.fluid.data(
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name='x_int32', shape=[2, 4, 6, 8], dtype='int32')
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self.assertRaises(TypeError, F.maxout, x_int32)
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x_float32 = paddle.fluid.data(name='x_float32', shape=[2, 4, 6, 8])
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self.assertRaises(ValueError, F.maxout, x_float32, 2, 2)
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if __name__ == '__main__':
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unittest.main()
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