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102 lines
3.4 KiB
102 lines
3.4 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.fluid as fluid
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import paddle.fluid.core as core
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from op_test import OpTest
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def maxout_forward_naive(input, groups, channel_axis):
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s0, s1, s2, s3 = input.shape
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if channel_axis == 3:
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return np.ndarray([s0, s1, s2, s3 // groups, groups], \
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buffer = input, dtype=input.dtype).max(axis=(4))
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return np.ndarray([s0, s1 // groups, groups, s2, s3], \
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buffer = input, dtype=input.dtype).max(axis=(2))
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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.init_test_case()
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input = np.random.random(self.shape).astype("float32")
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output = self.MaxOut_forward_naive(input, self.groups,
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self.axis).astype("float32")
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self.inputs = {'X': input}
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self.attrs = {'groups': self.groups, 'axis': self.axis}
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self.outputs = {'Out': output.astype('float32')}
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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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def init_test_case(self):
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self.MaxOut_forward_naive = maxout_forward_naive
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self.shape = [100, 6, 2, 2]
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self.groups = 2
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self.axis = 1
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class TestMaxOutOpAxis(TestMaxOutOp):
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def init_test_case(self):
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self.MaxOut_forward_naive = maxout_forward_naive
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self.shape = [100, 2, 2, 6] # NHWC format
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self.groups = 2
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self.axis = 3
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class TestMaxOutOpAxisAPI(unittest.TestCase):
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def test_axis(self):
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data1 = fluid.data(name='data1', shape=[3, 6, 2, 2], dtype='float32')
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data2 = fluid.data(name='data2', shape=[3, 2, 2, 6], dtype='float32')
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out1 = fluid.layers.maxout(data1, groups=2, axis=1)
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out2 = fluid.layers.maxout(data2, groups=2, axis=-1)
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data1_np = np.random.random((3, 6, 2, 2)).astype("float32")
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data2_np = np.transpose(data1_np, [0, 2, 3, 1])
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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results = exe.run(fluid.default_main_program(),
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feed={"data1": data1_np,
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"data2": data2_np},
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fetch_list=[out1, out2],
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return_numpy=True)
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self.assertTrue(
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np.allclose(results[0], np.transpose(results[1], (0, 3, 1, 2))))
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def test_exception(self):
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input = fluid.data(name="input", shape=[2, 4, 6, 6], dtype="float32")
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def _attr_axis():
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out = fluid.layers.maxout(input, groups=2, axis=2)
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self.assertRaises(ValueError, _attr_axis)
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if __name__ == '__main__':
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unittest.main()
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