def test_gpu_numpy(self):
if swig_paddle.isGpuVersion():
vec = swig_paddle.IVector.create(range(0, 10), True)
assert isinstance(vec, swig_paddle.IVector)
self.assertTrue(vec.isGpu())
self.assertEqual(vec.getData(), range(0, 10))
num_arr = vec.copyToNumpyArray()
assert isinstance(num_arr, np.ndarray) # for code hint.
num_arr[4] = 7
self.assertEquals(vec.getData(), range(0, 10))
vec.copyFromNumpyArray(num_arr)
expect_vec = range(0, 10)
expect_vec[4] = 7
self.assertEqual(vec.getData(), expect_vec)
def test_gpu_numpy(self):
if swig_paddle.isGpuVersion():
vec = swig_paddle.IVector.create(range(0, 10), True)
assert isinstance(vec, swig_paddle.IVector)
self.assertTrue(vec.isGpu())
self.assertEqual(vec.getData(), range(0, 10))
num_arr = vec.copyToNumpyArray()
assert isinstance(num_arr, np.ndarray) # for code hint.
num_arr[4] = 7
self.assertEquals(vec.getData(), range(0, 10))
vec.copyFromNumpyArray(num_arr)
expect_vec = range(0, 10)
expect_vec[4] = 7
self.assertEqual(vec.getData(), expect_vec)
def test_numpyGpu(self):
if swig_paddle.isGpuVersion():
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype='float32')
gpu_m = swig_paddle.Matrix.createGpuDenseFromNumpy(numpy_mat)
assert isinstance(gpu_m, swig_paddle.Matrix)
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpyGpu(self):
if swig_paddle.isGpuVersion():
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype='float32')
gpu_m = swig_paddle.Matrix.createGpuDenseFromNumpy(numpy_mat)
assert isinstance(gpu_m, swig_paddle.Matrix)
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
if __name__ == "__main__":
swig_paddle.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(TestMatrix)
unittest.TextTestRunner().run(suite)
if swig_paddle.isGpuVersion():
swig_paddle.setUseGpu(True)
unittest.main()
def gen_data(batch_size, shape):
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(np.random.random(shape))
data.append(each_sample)
return data
feeder = DataFeeder([('image', data_type.dense_array(2352))],
{'image': 0})
arg = feeder(gen_data(32, (3, 28, 28)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 28)
self.assertEqual(w, 28)
arg = feeder(gen_data(32, (3, 30, 32)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 30)
self.assertEqual(w, 32)
if __name__ == '__main__':
api.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest)
unittest.TextTestRunner().run(suite)
if api.isGpuVersion():
api.setUseGpu(True)
unittest.main()
self.assertTrue(util.doubleEqual(n, v))
numpy_2 = vec.toNumpyArrayInplace()
vec[0] = 1.3
for x, y in zip(numpy_arr, numpy_2):
self.assertTrue(util.doubleEqual(x, y))
for x, y in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(x, y))
numpy_3 = vec.copyToNumpyArray()
numpy_3[0] = 0.4
self.assertTrue(util.doubleEqual(vec[0], 1.3))
self.assertTrue(util.doubleEqual(numpy_3[0], 0.4))
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1)
arr = np.array([1.3, 3.2, 2.4], dtype="float32")
vec.copyFromNumpyArray(arr)
for i in xrange(len(vec)):
self.assertTrue(util.doubleEqual(vec[i], arr[i]))
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=1"
if swig_paddle.isGpuVersion() else "--use_gpu=0")
unittest.main()
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
if __name__ == "__main__":
swig_paddle.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(TestMatrix)
unittest.TextTestRunner().run(suite)
if swig_paddle.isGpuVersion():
swig_paddle.setUseGpu(True)
unittest.main()
data = []
for i in xrange(batch_size):
a = np.random.randint(10)
b = self.sparse_binary_reader(20000, 40, non_empty=True)
c = self.dense_reader(100)
each_sample = (a, b, c)
data.append(each_sample)
# test multiple features
data_types = [('fea0', data_type.dense_vector(100)),
('fea1', data_type.sparse_binary_vector(20000)),
('fea2', data_type.integer_value(10))]
feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
arg = feeder(data)
out_dense = arg.getSlotValue(0).copyToNumpyMat()
out_sparse = arg.getSlotValue(1)
out_index = arg.getSlotIds(2).copyToNumpyArray()
for i in xrange(batch_size):
self.assertEqual(out_dense[i].all(), data[i][2].all())
self.assertEqual(out_sparse.getSparseRowCols(i), data[i][1])
self.assertEqual(out_index[i], data[i][0])
if __name__ == '__main__':
api.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest)
unittest.TextTestRunner().run(suite)
if api.isGpuVersion():
api.setUseGpu(True)
unittest.main()
self.assertTrue(util.doubleEqual(n, v))
numpy_2 = vec.toNumpyArrayInplace()
vec[0] = 1.3
for x, y in zip(numpy_arr, numpy_2):
self.assertTrue(util.doubleEqual(x, y))
for x, y in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(x, y))
numpy_3 = vec.copyToNumpyArray()
numpy_3[0] = 0.4
self.assertTrue(util.doubleEqual(vec[0], 1.3))
self.assertTrue(util.doubleEqual(numpy_3[0], 0.4))
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1)
arr = np.array([1.3, 3.2, 2.4], dtype="float32")
vec.copyFromNumpyArray(arr)
for i in xrange(len(vec)):
self.assertTrue(util.doubleEqual(vec[i], arr[i]))
if __name__ == '__main__':
swig_paddle.initPaddle(
"--use_gpu=1" if swig_paddle.isGpuVersion() else "--use_gpu=0")
unittest.main()