def testCreateZero(self): v = swig_paddle.Vector.createZero(10) self.assertIsNotNone(v) for i in xrange(len(v)): self.assertTrue(util.doubleEqual(v[i], 0)) v[i] = i self.assertTrue(util.doubleEqual(v[i], i))
def testCreateZero(self): v = swig_paddle.Vector.createZero(10, False) self.assertIsNotNone(v) for i in xrange(len(v)): self.assertTrue(util.doubleEqual(v[i], 0)) v[i] = i self.assertTrue(util.doubleEqual(v[i], i)) v = swig_paddle.Vector.createZero(10) self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu()) self.assertEqual(v.getData(), [0] * 10)
def testCreate(self): v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False) self.assertIsNotNone(v) for i in xrange(len(v)): self.assertTrue(util.doubleEqual(v[i], i / 100.0)) self.assertEqual(100, len(v)) v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)]) self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu()) self.assertEqual(100, len(v)) vdata = v.getData() for i in xrange(len(v)): self.assertTrue(util.doubleEqual(vdata[i], i / 100.0))
def testNumpy(self): numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32") vec = swig_paddle.Vector.createVectorFromNumpy(numpy_arr) self.assertEqual(vec.isGpu(), swig_paddle.isUsingGpu()) vecData = vec.getData() for n, v in zip(numpy_arr, vecData): self.assertTrue(util.doubleEqual(n, v))
def backward_callback(param_): self.isCalled = isinstance(param_, swig_paddle.Parameter) assert isinstance(param_, swig_paddle.Parameter) vec = param_.getBuf(swig_paddle.PARAMETER_VALUE) assert isinstance(vec, swig_paddle.Vector) vec = vec.copyToNumpyArray() for val_ in vec: self.assertTrue(util.doubleEqual( val_, 0.1)) # Assert All Value is 0.1 vecs = list(param_.getBufs()) opt_ = optimizers[param_.getID()] opt_.update(vecs, param_.getConfig())
def backward_callback(param_): self.isCalled = isinstance(param_, swig_paddle.Parameter) assert isinstance(param_, swig_paddle.Parameter) vec = param_.getBuf(swig_paddle.PARAMETER_VALUE) assert isinstance(vec, swig_paddle.Vector) vec = vec.copyToNumpyArray() for val_ in vec: self.assertTrue( util.doubleEqual(val_, 0.1)) # Assert All Value is 0.1 vecs = list(param_.getBufs()) opt_ = optimizers[param_.getID()] opt_.update(vecs, param_.getConfig())
def testNumpy(self): numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32") vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr) assert isinstance(vec, swig_paddle.Vector) numpy_arr[0] = 0.1 for n, v in zip(numpy_arr, vec): 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 testCpuNumpy(self): numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32") vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, False) assert isinstance(vec, swig_paddle.Vector) numpy_arr[0] = 0.1 for n, v in zip(numpy_arr, vec): 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, False) 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]))
def testCreate(self): v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)]) self.assertIsNotNone(v) for i in xrange(len(v)): self.assertTrue(util.doubleEqual(v[i], i / 100.0)) self.assertEqual(100, len(v))
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]))