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107 lines
4.1 KiB
107 lines
4.1 KiB
# Copyright (c) 2016 Baidu, Inc. 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 py_paddle import swig_paddle
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import numpy as np
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import unittest
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class TestMatrix(unittest.TestCase):
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def test_createZero_get_set(self):
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m = swig_paddle.Matrix.createZero(32, 24)
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self.assertEqual(m.getWidth(), 24)
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self.assertEqual(m.getHeight(), 32)
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for x in xrange(24):
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for y in xrange(32):
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self.assertEqual(0.0, m.get(x, y))
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with self.assertRaises(swig_paddle.RangeError):
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m.get(51, 47)
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m.set(3, 3, 3.0)
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self.assertEqual(m.get(3, 3), 3.0)
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def test_sparse(self):
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m = swig_paddle.Matrix.createSparse(3, 3, 6, True, False, False)
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self.assertIsNotNone(m)
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self.assertTrue(m.isSparse())
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self.assertEqual(m.getSparseValueType(), swig_paddle.SPARSE_NON_VALUE)
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self.assertEqual(m.getSparseFormat(), swig_paddle.SPARSE_CSR)
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m.sparseCopyFrom([0, 2, 3, 3], [0, 1, 2], [])
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self.assertEqual(m.getSparseRowCols(0), [0, 1])
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self.assertEqual(m.getSparseRowCols(1), [2])
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self.assertEqual(m.getSparseRowCols(2), [])
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def test_sparse_value(self):
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m = swig_paddle.Matrix.createSparse(3, 3, 6, False)
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self.assertIsNotNone(m)
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m.sparseCopyFrom([0, 2, 3, 3], [0, 1, 2], [7.3, 4.2, 3.2])
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def assertKVArraySame(actual, expect):
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self.assertEqual(len(actual), len(expect))
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for i in xrange(len(actual)):
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a = actual[i]
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e = expect[i]
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self.assertIsInstance(a, tuple)
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self.assertIsInstance(e, tuple)
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self.assertEqual(len(a), 2)
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self.assertEqual(len(e), 2)
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self.assertEqual(a[0], e[0])
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self.assertTrue(abs(a[1] - e[1]) < 1e-5)
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first_row = m.getSparseRowColsVal(0)
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assertKVArraySame(first_row, [(0, 7.3), (1, 4.2)])
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def test_createDenseMat(self):
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m = swig_paddle.Matrix.createDense([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], 2, 3)
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self.assertIsNotNone(m)
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self.assertTrue(abs(m.get(1, 1) - 0.5) < 1e-5)
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def test_numpy(self):
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numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
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m = swig_paddle.Matrix.createCpuDenseFromNumpy(numpy_mat)
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self.assertEqual((int(m.getHeight()), int(m.getWidth())), numpy_mat.shape)
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# the numpy matrix and paddle matrix shared the same memory.
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numpy_mat[0, 1] = 342.23
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for h in xrange(m.getHeight()):
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for w in xrange(m.getWidth()):
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self.assertEqual(m.get(h, w), numpy_mat[h, w])
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mat2 = m.toNumpyMatInplace()
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mat2[1, 1] = 32.2
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self.assertTrue(np.array_equal(mat2, numpy_mat))
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def test_numpyGpu(self):
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if swig_paddle.isGpuVersion():
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numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype='float32')
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gpu_m = swig_paddle.Matrix.createGpuDenseFromNumpy(numpy_mat)
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assert isinstance(gpu_m, swig_paddle.Matrix)
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self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
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numpy_mat.shape)
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self.assertTrue(gpu_m.isGpu())
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numpy_mat = gpu_m.copyToNumpyMat()
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numpy_mat[0, 1] = 3.23
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for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
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self.assertAlmostEqual(a, e)
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gpu_m.copyFromNumpyMat(numpy_mat)
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for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
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self.assertAlmostEqual(a, e)
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if __name__ == "__main__":
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swig_paddle.initPaddle("--use_gpu=0")
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unittest.main()
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