# Copyright (c) 2019 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 op_test import OpTest import numpy as np import unittest import paddle.fluid as fluid import paddle paddle.enable_static() def strided_slice_native_forward(input, axes, starts, ends, strides): dim = input.ndim start = [] end = [] stride = [] for i in range(dim): start.append(0) end.append(input.shape[i]) stride.append(1) for i in range(len(axes)): start[axes[i]] = starts[i] end[axes[i]] = ends[i] stride[axes[i]] = strides[i] result = { 1: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0] ], 2: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0], start[1] : end[1] : stride[1] ], 3: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0], start[1] : end[1] : stride[1], start[2] : end[2] : stride[2], ], 4: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0], start[1] : end[1] : stride[1], start[2] : end[2] : stride[2], start[3] : end[3] : stride[3], ], 5: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0], start[1] : end[1] : stride[1], start[2] : end[2] : stride[2], start[3] : end[3] : stride[3], start[4] : end[4] : stride[4], ], 6: lambda input, start, end, stride: input[ start[0] : end[0] : stride[0], start[1] : end[1] : stride[1], start[2] : end[2] : stride[2], start[3] : end[3] : stride[3], start[4] : end[4] : stride[4], start[5] : end[5] : stride[5], ], }[dim](input, start, end, stride) return result class TestStrideSliceOp(OpTest): def setUp(self): self.initTestCase() self.op_type = 'strided_slice' self.python_api = paddle.strided_slice self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) self.inputs = {'Input': self.input} self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, 'starts': self.starts, 'ends': self.ends, 'strides': self.strides, 'infer_flags': self.infer_flags, } def test_check_output(self): self.check_output(check_eager=True) def test_check_grad(self): self.check_grad(set(['Input']), 'Out', check_eager=True) def initTestCase(self): self.input = np.random.rand(100) self.axes = [0] self.starts = [-4] self.ends = [-3] self.strides = [1] self.infer_flags = [1] class TestStrideSliceOp1(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(100) self.axes = [0] self.starts = [3] self.ends = [8] self.strides = [1] self.infer_flags = [1] class TestStrideSliceOp2(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(100) self.axes = [0] self.starts = [5] self.ends = [0] self.strides = [-1] self.infer_flags = [1] class TestStrideSliceOp3(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(100) self.axes = [0] self.starts = [-1] self.ends = [-3] self.strides = [-1] self.infer_flags = [1] class TestStrideSliceOp4(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(3, 4, 10) self.axes = [0, 1, 2] self.starts = [0, -1, 0] self.ends = [2, -3, 5] self.strides = [1, -1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOp5(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(5, 5, 5) self.axes = [0, 1, 2] self.starts = [1, 0, 0] self.ends = [2, 1, 3] self.strides = [1, 1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOp6(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(5, 5, 5) self.axes = [0, 1, 2] self.starts = [1, -1, 0] self.ends = [2, -3, 3] self.strides = [1, -1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOp7(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(5, 5, 5) self.axes = [0, 1, 2] self.starts = [1, 0, 0] self.ends = [2, 2, 3] self.strides = [1, 1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOp8(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(1, 100, 1) self.axes = [1] self.starts = [1] self.ends = [2] self.strides = [1] self.infer_flags = [1] class TestStrideSliceOp9(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(1, 100, 1) self.axes = [1] self.starts = [-1] self.ends = [-2] self.strides = [-1] self.infer_flags = [1] class TestStrideSliceOp10(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(10, 10) self.axes = [0, 1] self.starts = [1, 0] self.ends = [2, 2] self.strides = [1, 1] self.infer_flags = [1, 1] class TestStrideSliceOp11(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 4) self.axes = [0, 1, 2, 3] self.starts = [1, 0, 0, 0] self.ends = [2, 2, 3, 4] self.strides = [1, 1, 1, 2] self.infer_flags = [1, 1, 1, 1] class TestStrideSliceOp12(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 4, 5) self.axes = [0, 1, 2, 3, 4] self.starts = [1, 0, 0, 0, 0] self.ends = [2, 2, 3, 4, 4] self.strides = [1, 1, 1, 1, 1] self.infer_flags = [1, 1, 1, 1] class TestStrideSliceOp13(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 6, 7, 8) self.axes = [0, 1, 2, 3, 4, 5] self.starts = [1, 0, 0, 0, 1, 2] self.ends = [2, 2, 3, 1, 2, 8] self.strides = [1, 1, 1, 1, 1, 2] self.infer_flags = [1, 1, 1, 1, 1] class TestStrideSliceOp14(TestStrideSliceOp): def initTestCase(self): self.input = np.random.rand(4, 4, 4, 4) self.axes = [1, 2, 3] self.starts = [-5, 0, -7] self.ends = [-1, 2, 4] self.strides = [1, 1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOpBool(TestStrideSliceOp): def test_check_grad(self): pass class TestStrideSliceOpBool1D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(100).astype("bool") self.axes = [0] self.starts = [3] self.ends = [8] self.strides = [1] self.infer_flags = [1] class TestStrideSliceOpBool2D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(10, 10).astype("bool") self.axes = [0, 1] self.starts = [1, 0] self.ends = [2, 2] self.strides = [1, 1] self.infer_flags = [1, 1] class TestStrideSliceOpBool3D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(3, 4, 10).astype("bool") self.axes = [0, 1, 2] self.starts = [0, -1, 0] self.ends = [2, -3, 5] self.strides = [1, -1, 1] self.infer_flags = [1, 1, 1] class TestStrideSliceOpBool4D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 4).astype("bool") self.axes = [0, 1, 2, 3] self.starts = [1, 0, 0, 0] self.ends = [2, 2, 3, 4] self.strides = [1, 1, 1, 2] self.infer_flags = [1, 1, 1, 1] class TestStrideSliceOpBool5D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 4, 5).astype("bool") self.axes = [0, 1, 2, 3, 4] self.starts = [1, 0, 0, 0, 0] self.ends = [2, 2, 3, 4, 4] self.strides = [1, 1, 1, 1, 1] self.infer_flags = [1, 1, 1, 1] class TestStrideSliceOpBool6D(TestStrideSliceOpBool): def initTestCase(self): self.input = np.random.rand(3, 3, 3, 6, 7, 8).astype("bool") self.axes = [0, 1, 2, 3, 4, 5] self.starts = [1, 0, 0, 0, 1, 2] self.ends = [2, 2, 3, 1, 2, 8] self.strides = [1, 1, 1, 1, 1, 2] self.infer_flags = [1, 1, 1, 1, 1] class TestStridedSliceOp_starts_ListTensor(OpTest): def setUp(self): self.op_type = "strided_slice" self.config() starts_tensor = [] for index, ele in enumerate(self.starts): starts_tensor.append( ("x" + str(index), np.ones((1)).astype('int32') * ele) ) self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor} self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, 'starts': self.starts_infer, 'ends': self.ends, 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, 0, 2] self.ends = [3, 3, 4] self.axes = [0, 1, 2] self.strides = [1, 1, 1] self.infer_flags = [1, -1, 1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) self.starts_infer = [1, 10, 2] def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) class TestStridedSliceOp_ends_ListTensor(OpTest): def setUp(self): self.op_type = "strided_slice" self.config() ends_tensor = [] for index, ele in enumerate(self.ends): ends_tensor.append( ("x" + str(index), np.ones((1)).astype('int32') * ele) ) self.inputs = {'Input': self.input, 'EndsTensorList': ends_tensor} self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, 'starts': self.starts, 'ends': self.ends_infer, 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, 0, 0] self.ends = [3, 3, 4] self.axes = [0, 1, 2] self.strides = [1, 1, 2] self.infer_flags = [1, -1, 1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) self.ends_infer = [3, 1, 4] def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) class TestStridedSliceOp_starts_Tensor(OpTest): def setUp(self): self.op_type = "strided_slice" self.config() self.inputs = { 'Input': self.input, "StartsTensor": np.array(self.starts, dtype="int32"), } self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, # 'starts': self.starts, 'ends': self.ends, 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, 0, 2] self.ends = [2, 3, 4] self.axes = [0, 1, 2] self.strides = [1, 1, 1] self.infer_flags = [-1, -1, -1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) class TestStridedSliceOp_ends_Tensor(OpTest): def setUp(self): self.op_type = "strided_slice" self.config() self.inputs = { 'Input': self.input, "EndsTensor": np.array(self.ends, dtype="int32"), } self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, 'starts': self.starts, # 'ends': self.ends, 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, 0, 2] self.ends = [2, 3, 4] self.axes = [0, 1, 2] self.strides = [1, 1, 1] self.infer_flags = [-1, -1, -1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) class TestStridedSliceOp_listTensor_Tensor(OpTest): def setUp(self): self.config() ends_tensor = [] for index, ele in enumerate(self.ends): ends_tensor.append( ("x" + str(index), np.ones((1)).astype('int32') * ele) ) self.op_type = "strided_slice" self.inputs = { 'Input': self.input, "StartsTensor": np.array(self.starts, dtype="int32"), "EndsTensorList": ends_tensor, } self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, # 'starts': self.starts, # 'ends': self.ends, 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, 0, 2] self.ends = [2, 3, 4] self.axes = [0, 1, 2] self.strides = [1, 1, 1] self.infer_flags = [-1, -1, -1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) class TestStridedSliceOp_strides_Tensor(OpTest): def setUp(self): self.op_type = "strided_slice" self.config() self.inputs = { 'Input': self.input, "StridesTensor": np.array(self.strides, dtype="int32"), } self.outputs = {'Out': self.output} self.attrs = { 'axes': self.axes, 'starts': self.starts, 'ends': self.ends, # 'strides': self.strides, 'infer_flags': self.infer_flags, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype("float64") self.starts = [1, -1, 2] self.ends = [2, 0, 4] self.axes = [0, 1, 2] self.strides = [1, -1, 1] self.infer_flags = [-1, -1, -1] self.output = strided_slice_native_forward( self.input, self.axes, self.starts, self.ends, self.strides ) def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['Input'], 'Out', max_relative_error=0.006) # Test python API class TestStridedSliceAPI(unittest.TestCase): def test_1(self): input = np.random.random([3, 4, 5, 6]).astype("float64") minus_1 = fluid.layers.fill_constant([1], "int32", -1) minus_3 = fluid.layers.fill_constant([1], "int32", -3) starts = fluid.layers.data( name='starts', shape=[3], dtype='int32', append_batch_size=False ) ends = fluid.layers.data( name='ends', shape=[3], dtype='int32', append_batch_size=False ) strides = fluid.layers.data( name='strides', shape=[3], dtype='int32', append_batch_size=False ) x = fluid.layers.data( name="x", shape=[3, 4, 5, 6], append_batch_size=False, dtype="float64", ) out_1 = paddle.strided_slice( x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 100, -1], strides=[1, 1, 1], ) out_2 = paddle.strided_slice( x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1], strides=[1, 1, 1], ) out_3 = paddle.strided_slice( x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1], strides=[1, 1, 1], ) out_4 = paddle.strided_slice( x, axes=[0, 1, 2], starts=starts, ends=ends, strides=strides ) out_5 = x[-3:3, 0:100:2, -1:2:-1] out_6 = x[minus_3:3:1, 0:100:2, :, minus_1:2:minus_1] out_7 = x[minus_1, 0:100:2, :, -1:2:-1] exe = fluid.Executor(place=fluid.CPUPlace()) res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run( fluid.default_main_program(), feed={ "x": input, 'starts': np.array([-3, 0, 2]).astype("int32"), 'ends': np.array([3, 2147483648, -1]).astype("int64"), 'strides': np.array([1, 1, 1]).astype("int32"), }, fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7], ) assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :]) assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1]) assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1]) assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :]) assert np.array_equal(res_5, input[-3:3, 0:100:2, -1:2:-1, :]) assert np.array_equal(res_6, input[-3:3, 0:100:2, :, -1:2:-1]) assert np.array_equal(res_7, input[-1, 0:100:2, :, -1:2:-1]) def test_dygraph_op(self): x = paddle.zeros(shape=[3, 4, 5, 6], dtype="float32") axes = [1, 2, 3] starts = [-3, 0, 2] ends = [3, 2, 4] strides_1 = [1, 1, 1] sliced_1 = paddle.strided_slice( x, axes=axes, starts=starts, ends=ends, strides=strides_1 ) assert sliced_1.shape == (3, 2, 2, 2) @unittest.skipIf( not paddle.is_compiled_with_cuda(), "Cannot use CUDAPinnedPlace in CPU only version", ) def test_cuda_pinned_place(self): with paddle.fluid.dygraph.guard(): x = paddle.to_tensor( np.random.randn(2, 10), place=paddle.CUDAPinnedPlace() ) self.assertTrue(x.place.is_cuda_pinned_place()) y = x[:, ::2] self.assertFalse(x.place.is_cuda_pinned_place()) self.assertFalse(y.place.is_cuda_pinned_place()) class ArrayLayer(paddle.nn.Layer): def __init__(self, input_size=224, output_size=10, array_size=1): super().__init__() self.input_size = input_size self.output_size = output_size self.array_size = array_size for i in range(self.array_size): setattr( self, self.create_name(i), paddle.nn.Linear(input_size, output_size), ) def create_name(self, index): return 'linear_' + str(index) def forward(self, inps): array = [] for i in range(self.array_size): linear = getattr(self, self.create_name(i)) array.append(linear(inps)) tensor_array = self.create_tensor_array(array) tensor_array = self.array_slice(tensor_array) array1 = paddle.concat(tensor_array) array2 = paddle.concat(tensor_array[::-1]) return array1 + array2 * array2 def get_all_grads(self, param_name='weight'): grads = [] for i in range(self.array_size): linear = getattr(self, self.create_name(i)) param = getattr(linear, param_name) g = param.grad if g is not None: g = g.numpy() grads.append(g) return grads def clear_all_grad(self): param_names = ['weight', 'bias'] for i in range(self.array_size): linear = getattr(self, self.create_name(i)) for p in param_names: param = getattr(linear, p) param.clear_gradient() def array_slice(self, array): return array def create_tensor_array(self, tensors): tensor_array = None for i, tensor in enumerate(tensors): index = paddle.full(shape=[1], dtype='int64', fill_value=i) if tensor_array is None: tensor_array = paddle.tensor.array_write(tensor, i=index) else: paddle.tensor.array_write(tensor, i=index, array=tensor_array) return tensor_array class TestStridedSliceTensorArray(unittest.TestCase): def setUp(self): paddle.disable_static() def grad_equal(self, g1, g2): if g1 is None: g1 = np.zeros_like(g2) if g2 is None: g2 = np.zeros_like(g1) return np.array_equal(g1, g2) def is_grads_equal(self, g1, g2): for i, g in enumerate(g1): self.assertTrue( self.grad_equal(g, g2[i]), msg="gradient_1:\n{} \ngradient_2:\n{}".format(g, g2), ) def is_grads_equal_zeros(self, grads): for g in grads: self.assertTrue( self.grad_equal(np.zeros_like(g), g), msg="The gradient should be zeros, but received \n{}".format(g), ) def create_case(self, net): inps1 = paddle.randn([1, net.input_size], dtype='float32') inps2 = inps1.detach().clone() l1 = net(inps1) s1 = l1.numpy() l1.sum().backward() grads_dy = net.get_all_grads() net.clear_all_grad() grads_zeros = net.get_all_grads() self.is_grads_equal_zeros(grads_zeros) func = paddle.jit.to_static(net.forward) l2 = func(inps2) s2 = l2.numpy() l2.sum().backward() grads_static = net.get_all_grads() net.clear_all_grad() # compare result of dygraph and static self.is_grads_equal(grads_static, grads_dy) np.testing.assert_array_equal( s1, s2, err_msg='dygraph graph result:\n{} \nstatic dygraph result:\n{}'.format( l1.numpy(), l2.numpy() ), ) def test_strided_slice_tensor_array_cuda_pinned_place(self): if paddle.device.is_compiled_with_cuda(): with paddle.fluid.dygraph.guard(): class Simple(paddle.nn.Layer): def __init__(self): super().__init__() def forward(self, inps): tensor_array = None for i, tensor in enumerate(inps): index = paddle.full( shape=[1], dtype='int64', fill_value=i ) if tensor_array is None: tensor_array = paddle.tensor.array_write( tensor, i=index ) else: paddle.tensor.array_write( tensor, i=index, array=tensor_array ) array1 = paddle.concat(tensor_array) array2 = paddle.concat(tensor_array[::-1]) return array1 + array2 * array2 net = Simple() func = paddle.jit.to_static(net.forward) inps1 = paddle.to_tensor( np.random.randn(2, 10), place=paddle.CUDAPinnedPlace(), stop_gradient=False, ) inps2 = paddle.to_tensor( np.random.randn(2, 10), place=paddle.CUDAPinnedPlace(), stop_gradient=False, ) self.assertTrue(inps1.place.is_cuda_pinned_place()) self.assertTrue(inps2.place.is_cuda_pinned_place()) result = func([inps1, inps2]) self.assertFalse(result.place.is_cuda_pinned_place()) def test_strided_slice_tensor_array(self): class Net01(ArrayLayer): def array_slice(self, tensors): return tensors[::-1] self.create_case(Net01(array_size=10)) class Net02(ArrayLayer): def array_slice(self, tensors): return tensors[::-2] self.create_case(Net02(input_size=112, array_size=11)) class Net03(ArrayLayer): def array_slice(self, tensors): return tensors[::-3] self.create_case(Net03(input_size=112, array_size=9)) class Net04(ArrayLayer): def array_slice(self, tensors): return tensors[1::-4] self.create_case(Net04(input_size=112, array_size=9)) class Net05(ArrayLayer): def array_slice(self, tensors): return tensors[:7:-4] self.create_case(Net05(input_size=112, array_size=9)) class Net06(ArrayLayer): def array_slice(self, tensors): return tensors[8:0:-4] self.create_case(Net06(input_size=112, array_size=9)) class Net07(ArrayLayer): def array_slice(self, tensors): return tensors[8:1:-4] self.create_case(Net07(input_size=112, array_size=9)) class Net08(ArrayLayer): def array_slice(self, tensors): return tensors[::2] self.create_case(Net08(input_size=112, array_size=11)) class Net09(ArrayLayer): def array_slice(self, tensors): return tensors[::3] self.create_case(Net09(input_size=112, array_size=9)) class Net10(ArrayLayer): def array_slice(self, tensors): return tensors[1::4] self.create_case(Net10(input_size=112, array_size=9)) class Net11(ArrayLayer): def array_slice(self, tensors): return tensors[:8:4] self.create_case(Net11(input_size=112, array_size=9)) class Net12(ArrayLayer): def array_slice(self, tensors): return tensors[1:8:4] self.create_case(Net12(input_size=112, array_size=9)) class Net13(ArrayLayer): def array_slice(self, tensors): return tensors[8:10:4] self.create_case(Net13(input_size=112, array_size=13)) class Net14(ArrayLayer): def array_slice(self, tensors): return tensors[3:10:4] self.create_case(Net14(input_size=112, array_size=13)) class Net15(ArrayLayer): def array_slice(self, tensors): return tensors[2:10:4] self.create_case(Net15(input_size=112, array_size=13)) class Net16(ArrayLayer): def array_slice(self, tensors): return tensors[3:10:3] self.create_case(Net16(input_size=112, array_size=13)) class Net17(ArrayLayer): def array_slice(self, tensors): return tensors[3:15:3] self.create_case(Net17(input_size=112, array_size=13)) class Net18(ArrayLayer): def array_slice(self, tensors): return tensors[0:15:3] self.create_case(Net18(input_size=112, array_size=13)) class Net19(ArrayLayer): def array_slice(self, tensors): return tensors[-1:-5:-3] self.create_case(Net19(input_size=112, array_size=13)) class Net20(ArrayLayer): def array_slice(self, tensors): return tensors[-1:-6:-3] self.create_case(Net20(input_size=112, array_size=13)) class Net21(ArrayLayer): def array_slice(self, tensors): return tensors[-3:-6:-3] self.create_case(Net21(input_size=112, array_size=13)) class Net22(ArrayLayer): def array_slice(self, tensors): return tensors[-5:-1:3] self.create_case(Net22(input_size=112, array_size=13)) class Net23(ArrayLayer): def array_slice(self, tensors): return tensors[-6:-1:3] self.create_case(Net23(input_size=112, array_size=13)) class Net24(ArrayLayer): def array_slice(self, tensors): return tensors[-6:-3:3] self.create_case(Net24(input_size=112, array_size=13)) class Net25(ArrayLayer): def array_slice(self, tensors): return tensors[0::3] self.create_case(Net25(input_size=112, array_size=13)) class Net26(ArrayLayer): def array_slice(self, tensors): return tensors[-60:20:3] self.create_case(Net26(input_size=112, array_size=13)) class Net27(ArrayLayer): def array_slice(self, tensors): return tensors[-3:-60:-3] self.create_case(Net27(input_size=112, array_size=13)) @unittest.skipIf( not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestStridedSliceFloat16(unittest.TestCase): def init_test_case(self): self.op_type = 'strided_slice' self.input_shape = [3, 3, 3, 6, 7, 8] self.axes = [0, 1, 2, 3, 4, 5] self.starts = [1, 0, 0, 0, 1, 2] self.ends = [2, 2, 3, 1, 2, 8] self.strides = [1, 1, 1, 1, 1, 2] self.infer_flags = [1, 1, 1, 1, 1] def check_main(self, x_np, dtype): paddle.disable_static() x_np = x_np.astype(dtype) x = paddle.to_tensor(x_np) x.stop_gradient = False output = strided_slice_native_forward( x, self.axes, self.starts, self.ends, self.strides ) x_grad = paddle.grad(output, x) output_np = output[0].numpy().astype('float32') x_grad_np = x_grad[0].numpy().astype('float32') paddle.enable_static() return output_np, x_grad_np def test_check(self): self.init_test_case() x_np = np.random.random(self.input_shape).astype("float16") output_np_fp16, x_grad_np_fp16 = self.check_main(x_np, 'float16') output_np_fp32, x_grad_np_fp32 = self.check_main(x_np, 'float32') np.testing.assert_allclose(output_np_fp16, output_np_fp32) np.testing.assert_allclose(x_grad_np_fp16, x_grad_np_fp32) if __name__ == "__main__": unittest.main()