# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import six import copy import paddle import paddle.fluid as fluid import paddle.fluid.core as core class TestVarBase(unittest.TestCase): def setUp(self): self.shape = [512, 1234] self.dtype = np.float32 self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) def test_to_tensor(self): def _test_place(place): with fluid.dygraph.guard(): paddle.set_default_dtype('float32') # set_default_dtype should not take effect on int x = paddle.to_tensor(1, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1])) self.assertNotEqual(x.dtype, core.VarDesc.VarType.FP32) y = paddle.to_tensor(2, place=x.place) self.assertEqual(str(x.place), str(y.place)) # set_default_dtype should not take effect on numpy x = paddle.to_tensor( np.array([1.2]).astype('float16'), place=place, stop_gradient=False) self.assertTrue( np.array_equal(x.numpy(), np.array([1.2], 'float16'))) self.assertEqual(x.dtype, core.VarDesc.VarType.FP16) # set_default_dtype take effect on float x = paddle.to_tensor(1.2, place=place, stop_gradient=False) self.assertTrue( np.array_equal(x.numpy(), np.array([1.2]).astype( 'float32'))) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) clone_x = x.clone() self.assertTrue( np.array_equal(clone_x.numpy(), np.array([1.2]).astype('float32'))) self.assertEqual(clone_x.dtype, core.VarDesc.VarType.FP32) y = clone_x**2 y.backward() self.assertTrue( np.array_equal(x.grad, np.array([2.4]).astype('float32'))) y = x.cpu() self.assertEqual(y.place.__repr__(), "CPUPlace") if core.is_compiled_with_cuda(): y = x.pin_memory() self.assertEqual(y.place.__repr__(), "CUDAPinnedPlace") y = x.cuda(blocking=False) self.assertEqual(y.place.__repr__(), "CUDAPlace(0)") y = x.cuda(blocking=True) self.assertEqual(y.place.__repr__(), "CUDAPlace(0)") # support 'dtype' is core.VarType x = paddle.rand((2, 2)) y = paddle.to_tensor([2, 2], dtype=x.dtype) self.assertEqual(y.dtype, core.VarDesc.VarType.FP32) # set_default_dtype take effect on complex x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j])) self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX64) paddle.set_default_dtype('float64') x = paddle.to_tensor(1.2, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1.2])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP64) x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j])) self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX128) x = paddle.to_tensor( 1, dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1.])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.shape, [1]) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) x = paddle.to_tensor( (1, 2), dtype='float32', place=place, stop_gradient=False) x = paddle.to_tensor( [1, 2], dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1., 2.])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.grad, None) self.assertEqual(x.shape, [2]) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) x = paddle.to_tensor( self.array, dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), self.array)) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.shape, self.shape) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) y = paddle.to_tensor(x) y = paddle.to_tensor(y, dtype='float64', place=place) self.assertTrue(np.array_equal(y.numpy(), self.array)) self.assertEqual(y.dtype, core.VarDesc.VarType.FP64) self.assertEqual(y.shape, self.shape) self.assertEqual(y.stop_gradient, True) self.assertEqual(y.type, core.VarDesc.VarType.LOD_TENSOR) z = x + y self.assertTrue(np.array_equal(z.numpy(), 2 * self.array)) x = paddle.to_tensor( [1 + 2j, 1 - 2j], dtype='complex64', place=place) y = paddle.to_tensor(x) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j, 1 - 2j])) self.assertEqual(y.dtype, core.VarDesc.VarType.COMPLEX64) self.assertEqual(y.shape, [2]) with self.assertRaises(TypeError): paddle.to_tensor('test') with self.assertRaises(TypeError): paddle.to_tensor(1, dtype='test') with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]]) with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]], place='test') with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]], place=1) _test_place(core.CPUPlace()) _test_place("cpu") if core.is_compiled_with_cuda(): _test_place(core.CUDAPinnedPlace()) _test_place("gpu_pinned") _test_place(core.CUDAPlace(0)) _test_place("gpu:0") def test_to_tensor_change_place(self): if core.is_compiled_with_cuda(): a_np = np.random.rand(1024, 1024) with paddle.fluid.dygraph.guard(core.CPUPlace()): a = paddle.to_tensor(a_np, place=paddle.CUDAPinnedPlace()) a = paddle.to_tensor(a) self.assertEqual(a.place.__repr__(), "CPUPlace") with paddle.fluid.dygraph.guard(core.CUDAPlace(0)): a = paddle.to_tensor(a_np, place=paddle.CUDAPinnedPlace()) a = paddle.to_tensor(a) self.assertEqual(a.place.__repr__(), "CUDAPlace(0)") with paddle.fluid.dygraph.guard(core.CUDAPlace(0)): a = paddle.to_tensor(a_np, place=paddle.CPUPlace()) a = paddle.to_tensor(a, place=paddle.CUDAPinnedPlace()) self.assertEqual(a.place.__repr__(), "CUDAPinnedPlace") def test_to_variable(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array, name="abc") self.assertTrue(np.array_equal(var.numpy(), self.array)) self.assertEqual(var.name, 'abc') # default value self.assertEqual(var.persistable, False) self.assertEqual(var.stop_gradient, True) self.assertEqual(var.shape, self.shape) self.assertEqual(var.dtype, core.VarDesc.VarType.FP32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) # The type of input must be 'ndarray' or 'Variable', it will raise TypeError with self.assertRaises(TypeError): var = fluid.dygraph.to_variable("test", name="abc") # test to_variable of LayerObjectHelper(LayerHelperBase) with self.assertRaises(TypeError): linear = fluid.dygraph.Linear(32, 64) var = linear._helper.to_variable("test", name="abc") def test_list_to_variable(self): with fluid.dygraph.guard(): array = [[[1, 2], [1, 2], [1.0, 2]], [[1, 2], [1, 2], [1, 2]]] var = fluid.dygraph.to_variable(array, dtype='int32') self.assertTrue(np.array_equal(var.numpy(), array)) self.assertEqual(var.shape, [2, 3, 2]) self.assertEqual(var.dtype, core.VarDesc.VarType.INT32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) def test_tuple_to_variable(self): with fluid.dygraph.guard(): array = (((1, 2), (1, 2), (1, 2)), ((1, 2), (1, 2), (1, 2))) var = fluid.dygraph.to_variable(array, dtype='float32') self.assertTrue(np.array_equal(var.numpy(), array)) self.assertEqual(var.shape, [2, 3, 2]) self.assertEqual(var.dtype, core.VarDesc.VarType.FP32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) def test_tensor_to_variable(self): with fluid.dygraph.guard(): t = fluid.Tensor() t.set(np.random.random((1024, 1024)), fluid.CPUPlace()) var = fluid.dygraph.to_variable(t) self.assertTrue(np.array_equal(t, var.numpy())) def test_leaf_tensor(self): with fluid.dygraph.guard(): x = paddle.to_tensor(np.random.uniform(-1, 1, size=[10, 10])) self.assertTrue(x.is_leaf) y = x + 1 self.assertTrue(y.is_leaf) x = paddle.to_tensor( np.random.uniform( -1, 1, size=[10, 10]), stop_gradient=False) self.assertTrue(x.is_leaf) y = x + 1 self.assertFalse(y.is_leaf) linear = paddle.nn.Linear(10, 10) input = paddle.to_tensor( np.random.uniform( -1, 1, size=[10, 10]).astype('float32'), stop_gradient=False) self.assertTrue(input.is_leaf) out = linear(input) self.assertTrue(linear.weight.is_leaf) self.assertTrue(linear.bias.is_leaf) self.assertFalse(out.is_leaf) def test_detach(self): with fluid.dygraph.guard(): x = paddle.to_tensor(1.0, dtype="float64", stop_gradient=False) detach_x = x.detach() self.assertTrue(detach_x.stop_gradient, True) detach_x[:] = 10.0 self.assertTrue(np.array_equal(x.numpy(), [10.0])) y = x**2 y.backward() self.assertTrue(np.array_equal(x.grad, [20.0])) self.assertEqual(detach_x.grad, None) detach_x.stop_gradient = False # Set stop_gradient to be False, supported auto-grad z = 3 * detach_x**2 z.backward() self.assertTrue(np.array_equal(x.grad, [20.0])) self.assertTrue(np.array_equal(detach_x.grad, [60.0])) # Due to sharing of data with origin Tensor, There are some unsafe operations: with self.assertRaises(RuntimeError): y = 2**x detach_x[:] = 5.0 y.backward() def test_write_property(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertEqual(var.name, 'generated_tensor_0') var.name = 'test' self.assertEqual(var.name, 'test') self.assertEqual(var.persistable, False) var.persistable = True self.assertEqual(var.persistable, True) self.assertEqual(var.stop_gradient, True) var.stop_gradient = False self.assertEqual(var.stop_gradient, False) def test_deep_copy(self): with fluid.dygraph.guard(): empty_var = core.VarBase() empty_var_copy = copy.deepcopy(empty_var) self.assertEqual(empty_var.stop_gradient, empty_var_copy.stop_gradient) self.assertEqual(empty_var.persistable, empty_var_copy.persistable) self.assertEqual(empty_var.type, empty_var_copy.type) self.assertEqual(empty_var.dtype, empty_var_copy.dtype) x = paddle.to_tensor([2.], stop_gradient=False) y = paddle.to_tensor([3.], stop_gradient=False) z = x * y memo = {} x_copy = copy.deepcopy(x, memo) y_copy = copy.deepcopy(y, memo) self.assertEqual(x_copy.stop_gradient, y_copy.stop_gradient) self.assertEqual(x_copy.persistable, y_copy.persistable) self.assertEqual(x_copy.type, y_copy.type) self.assertEqual(x_copy.dtype, y_copy.dtype) self.assertTrue(np.array_equal(x.numpy(), x_copy.numpy())) self.assertTrue(np.array_equal(y.numpy(), y_copy.numpy())) self.assertNotEqual(id(x), id(x_copy)) x_copy[:] = 5. self.assertTrue(np.array_equal(x_copy.numpy(), [5.])) self.assertTrue(np.array_equal(x.numpy(), [2.])) with self.assertRaises(RuntimeError): copy.deepcopy(z) x_copy2 = copy.deepcopy(x, memo) y_copy2 = copy.deepcopy(y, memo) self.assertEqual(id(x_copy), id(x_copy2)) self.assertEqual(id(y_copy), id(y_copy2)) # test copy selected rows x = core.VarBase(core.VarDesc.VarType.FP32, [3, 100], "selected_rows", core.VarDesc.VarType.SELECTED_ROWS, True) selected_rows = x.value().get_selected_rows() selected_rows.get_tensor().set( np.random.rand(3, 100), core.CPUPlace()) selected_rows.set_height(10) selected_rows.set_rows([3, 5, 7]) x_copy = copy.deepcopy(x) self.assertEqual(x_copy.stop_gradient, x.stop_gradient) self.assertEqual(x_copy.persistable, x.persistable) self.assertEqual(x_copy.type, x.type) self.assertEqual(x_copy.dtype, x.dtype) copy_selected_rows = x_copy.value().get_selected_rows() self.assertEqual(copy_selected_rows.height(), selected_rows.height()) self.assertEqual(copy_selected_rows.rows(), selected_rows.rows()) self.assertTrue( np.array_equal( np.array(copy_selected_rows.get_tensor()), np.array(selected_rows.get_tensor()))) # test some patched methods def test_set_value(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) tmp1 = np.random.uniform(0.1, 1, [2, 2, 3]).astype(self.dtype) self.assertRaises(AssertionError, var.set_value, tmp1) tmp2 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) var.set_value(tmp2) self.assertTrue(np.array_equal(var.numpy(), tmp2)) def test_to_string(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertTrue(isinstance(str(var), str)) def test_backward(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) var.stop_gradient = False loss = fluid.layers.relu(var) loss.backward() grad_var = var._grad_ivar() self.assertEqual(grad_var.shape, self.shape) def test_gradient(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) var.stop_gradient = False loss = fluid.layers.relu(var) loss.backward() grad_var = var.gradient() self.assertEqual(grad_var.shape, self.array.shape) def test_block(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertEqual(var.block, fluid.default_main_program().global_block()) def _test_slice(self): w = fluid.dygraph.to_variable( np.random.random((784, 100, 100)).astype('float64')) for i in range(3): nw = w[i] self.assertEqual((100, 100), tuple(nw.shape)) nw = w[:] self.assertEqual((784, 100, 100), tuple(nw.shape)) nw = w[:, :] self.assertEqual((784, 100, 100), tuple(nw.shape)) nw = w[:, :, -1] self.assertEqual((784, 100), tuple(nw.shape)) nw = w[1, 1, 1] self.assertEqual(len(nw.shape), 1) self.assertEqual(nw.shape[0], 1) nw = w[:, :, :-1] self.assertEqual((784, 100, 99), tuple(nw.shape)) tensor_array = np.array( [[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]).astype('float32') var = fluid.dygraph.to_variable(tensor_array) var1 = var[0, 1, 1] var2 = var[1:] var3 = var[0:1] var4 = var[::-1] var5 = var[1, 1:, 1:] var_reshape = fluid.layers.reshape(var, [3, -1, 3]) var6 = var_reshape[:, :, -1] var7 = var[:, :, :-1] var8 = var[:1, :1, :1] var9 = var[:-1, :-1, :-1] var10 = var[::-1, :1, :-1] var11 = var[:-1, ::-1, -1:] var12 = var[1:2, 2:, ::-1] var13 = var[2:10, 2:, -2:-1] var14 = var[1:-1, 0:2, ::-1] var15 = var[::-1, ::-1, ::-1] var16 = var[-4:4] vars = [ var, var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16 ] local_out = [var.numpy() for var in vars] self.assertTrue(np.array_equal(local_out[1], tensor_array[0, 1, 1:2])) self.assertTrue(np.array_equal(local_out[2], tensor_array[1:])) self.assertTrue(np.array_equal(local_out[3], tensor_array[0:1])) self.assertTrue(np.array_equal(local_out[4], tensor_array[::-1])) self.assertTrue(np.array_equal(local_out[5], tensor_array[1, 1:, 1:])) self.assertTrue( np.array_equal(local_out[6], tensor_array.reshape((3, -1, 3))[:, :, -1])) self.assertTrue(np.array_equal(local_out[7], tensor_array[:, :, :-1])) self.assertTrue(np.array_equal(local_out[8], tensor_array[:1, :1, :1])) self.assertTrue( np.array_equal(local_out[9], tensor_array[:-1, :-1, :-1])) self.assertTrue( np.array_equal(local_out[10], tensor_array[::-1, :1, :-1])) self.assertTrue( np.array_equal(local_out[11], tensor_array[:-1, ::-1, -1:])) self.assertTrue( np.array_equal(local_out[12], tensor_array[1:2, 2:, ::-1])) self.assertTrue( np.array_equal(local_out[13], tensor_array[2:10, 2:, -2:-1])) self.assertTrue( np.array_equal(local_out[14], tensor_array[1:-1, 0:2, ::-1])) self.assertTrue( np.array_equal(local_out[15], tensor_array[::-1, ::-1, ::-1])) self.assertTrue(np.array_equal(local_out[16], tensor_array[-4:4])) def _test_for_var(self): np_value = np.random.random((30, 100, 100)).astype('float32') w = fluid.dygraph.to_variable(np_value) for i, e in enumerate(w): self.assertTrue(np.array_equal(e.numpy(), np_value[i])) def test_slice(self): with fluid.dygraph.guard(): self._test_slice() self._test_for_var() var = fluid.dygraph.to_variable(self.array) self.assertTrue(np.array_equal(var[1, :].numpy(), self.array[1, :])) self.assertTrue(np.array_equal(var[::-1].numpy(), self.array[::-1])) with self.assertRaises(IndexError): y = var[self.shape[0]] with self.assertRaises(IndexError): y = var[0 - self.shape[0] - 1] def test_var_base_to_np(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertTrue( np.array_equal(var.numpy(), fluid.framework._var_base_to_np(var))) def test_if(self): with fluid.dygraph.guard(): var1 = fluid.dygraph.to_variable(np.array([[[0]]])) var2 = fluid.dygraph.to_variable(np.array([[[1]]])) var1_bool = False var2_bool = False if var1: var1_bool = True if var2: var2_bool = True assert var1_bool == False, "if var1 should be false" assert var2_bool == True, "if var2 should be true" assert bool(var1) == False, "bool(var1) is False" assert bool(var2) == True, "bool(var2) is True" def test_to_static_var(self): with fluid.dygraph.guard(): # Convert VarBase into Variable or Parameter var_base = fluid.dygraph.to_variable(self.array, name="var_base_1") static_var = var_base._to_static_var() self._assert_to_static(var_base, static_var) var_base = fluid.dygraph.to_variable(self.array, name="var_base_2") static_param = var_base._to_static_var(to_parameter=True) self._assert_to_static(var_base, static_param, True) # Convert ParamBase into Parameter fc = fluid.dygraph.Linear( 10, 20, param_attr=fluid.ParamAttr( learning_rate=0.001, do_model_average=True, regularizer=fluid.regularizer.L1Decay())) weight = fc.parameters()[0] static_param = weight._to_static_var() self._assert_to_static(weight, static_param, True) def _assert_to_static(self, var_base, static_var, is_param=False): if is_param: self.assertTrue(isinstance(static_var, fluid.framework.Parameter)) self.assertTrue(static_var.persistable, True) if isinstance(var_base, fluid.framework.ParamBase): for attr in ['trainable', 'is_distributed', 'do_model_average']: self.assertEqual( getattr(var_base, attr), getattr(static_var, attr)) self.assertEqual(static_var.optimize_attr['learning_rate'], 0.001) self.assertTrue( isinstance(static_var.regularizer, fluid.regularizer.L1Decay)) else: self.assertTrue(isinstance(static_var, fluid.framework.Variable)) attr_keys = ['block', 'dtype', 'type', 'name'] for attr in attr_keys: self.assertEqual(getattr(var_base, attr), getattr(static_var, attr)) self.assertListEqual(list(var_base.shape), list(static_var.shape)) def test_tensor_str(self): paddle.enable_static() paddle.disable_static(paddle.CPUPlace()) paddle.seed(10) a = paddle.rand([10, 20]) paddle.set_printoptions(4, 100, 3) a_str = str(a) expected = '''Tensor(shape=[10, 20], dtype=float32, place=CPUPlace, stop_gradient=True, [[0.2727, 0.5489, 0.8655, ..., 0.2916, 0.8525, 0.9000], [0.3806, 0.8996, 0.0928, ..., 0.9535, 0.8378, 0.6409], [0.1484, 0.4038, 0.8294, ..., 0.0148, 0.6520, 0.4250], ..., [0.3426, 0.1909, 0.7240, ..., 0.4218, 0.2676, 0.5679], [0.5561, 0.2081, 0.0676, ..., 0.9778, 0.3302, 0.9559], [0.2665, 0.8483, 0.5389, ..., 0.4956, 0.6862, 0.9178]])''' self.assertEqual(a_str, expected) paddle.enable_static() def test_tensor_str2(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor([[1.5111111, 1.0], [0, 0]]) a_str = str(a) expected = '''Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=True, [[1.5111, 1. ], [0. , 0. ]])''' self.assertEqual(a_str, expected) paddle.enable_static() def test_tensor_str3(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor([[-1.5111111, 1.0], [0, -0.5]]) a_str = str(a) expected = '''Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=True, [[-1.5111, 1. ], [ 0. , -0.5000]])''' self.assertEqual(a_str, expected) paddle.enable_static() def test_tensor_str_scaler(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor(np.array(False)) a_str = str(a) expected = '''Tensor(shape=[], dtype=bool, place=CPUPlace, stop_gradient=True, False)''' self.assertEqual(a_str, expected) paddle.enable_static() def test_print_tensor_dtype(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.rand([1]) a_str = str(a.dtype) expected = 'paddle.float32' self.assertEqual(a_str, expected) paddle.enable_static() class TestVarBaseSetitem(unittest.TestCase): def setUp(self): paddle.disable_static() self.set_dtype() self.tensor_x = paddle.to_tensor(np.ones((4, 2, 3)).astype(self.dtype)) self.np_value = np.random.random((2, 3)).astype(self.dtype) self.tensor_value = paddle.to_tensor(self.np_value) def set_dtype(self): self.dtype = "int32" def _test(self, value): paddle.disable_static() self.assertEqual(self.tensor_x.inplace_version, 0) id_origin = id(self.tensor_x) self.tensor_x[0] = value self.assertEqual(self.tensor_x.inplace_version, 1) if isinstance(value, (six.integer_types, float)): result = np.zeros((2, 3)).astype(self.dtype) + value else: result = self.np_value self.assertTrue(np.array_equal(self.tensor_x[0].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) self.tensor_x[1:2] = value self.assertEqual(self.tensor_x.inplace_version, 2) self.assertTrue(np.array_equal(self.tensor_x[1].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) self.tensor_x[...] = value self.assertEqual(self.tensor_x.inplace_version, 3) self.assertTrue(np.array_equal(self.tensor_x[3].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) def test_value_tensor(self): paddle.disable_static() self._test(self.tensor_value) def test_value_numpy(self): paddle.disable_static() self._test(self.np_value) def test_value_int(self): paddle.disable_static() self._test(10) class TestVarBaseSetitemInt64(TestVarBaseSetitem): def set_dtype(self): self.dtype = "int64" class TestVarBaseSetitemFp32(TestVarBaseSetitem): def set_dtype(self): self.dtype = "float32" def test_value_float(self): paddle.disable_static() self._test(3.3) class TestVarBaseSetitemFp64(TestVarBaseSetitem): def set_dtype(self): self.dtype = "float64" class TestVarBaseInplaceVersion(unittest.TestCase): def test_setitem(self): paddle.disable_static() var = paddle.ones(shape=[4, 2, 3], dtype="float32") self.assertEqual(var.inplace_version, 0) var[1] = 1 self.assertEqual(var.inplace_version, 1) var[1:2] = 1 self.assertEqual(var.inplace_version, 2) def test_bump_inplace_version(self): paddle.disable_static() var = paddle.ones(shape=[4, 2, 3], dtype="float32") self.assertEqual(var.inplace_version, 0) var._bump_inplace_version() self.assertEqual(var.inplace_version, 1) var._bump_inplace_version() self.assertEqual(var.inplace_version, 2) if __name__ == '__main__': unittest.main()