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()
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()
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()