# 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. import unittest import numpy as np from get_test_cover_info import ( XPUOpTestWrapper, create_test_class, get_xpu_op_support_types, ) from op_test_xpu import XPUOpTest import paddle paddle.enable_static() # Situation 1: starts(list, no tensor), ends(list, no tensor) # 1.1 without attr(decrease) class XPUTestSliceOp(XPUOpTestWrapper): def __init__(self): self.op_name = 'slice' self.use_dynamic_create_class = False class TestSliceOp(XPUOpTest): def setUp(self): self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.op_type = "slice" self.config() self.inputs = {'Input': self.input} self.outputs = {'Out': self.out} self.attrs = { 'axes': self.axes, 'starts': self.starts, 'ends': self.ends, 'infer_flags': self.infer_flags, "use_xpu": True, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [1, 0, 2] self.ends = [3, 3, 4] self.axes = [0, 1, 2] self.infer_flags = [1, 1, 1] self.out = self.input[1:3, 0:3, 2:4, :] def test_check_grad_normal(self): if self.dtype == np.float16: self.check_grad_with_place(self.place, ['Input'], 'Out') else: user_defined_grad_outputs = np.random.random( self.out.shape ).astype(self.dtype) self.check_grad_with_place( self.place, ['Input'], 'Out', user_defined_grad_outputs=user_defined_grad_outputs, ) class TestCase1(TestSliceOp): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [-3, 0, 2] self.ends = [3, 100, -1] self.axes = [0, 1, 2] self.infer_flags = [1, 1, 1] self.out = self.input[-3:3, 0:100, 2:-1, :] class TestCase2(TestSliceOp): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [-3, 0, 2] self.ends = [3, 100, -1] self.axes = [0, 1, 3] self.infer_flags = [1, 1, 1] self.out = self.input[-3:3, 0:100, :, 2:-1] # 1.2 with attr(decrease) class XPUTestSliceOp_decs_dim(XPUOpTestWrapper): def __init__(self): self.op_name = 'slice' self.use_dynamic_create_class = False class TestSliceOp_decs_dim(XPUOpTest): def setUp(self): self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.op_type = "slice" self.config() self.inputs = {'Input': self.input} self.outputs = {'Out': self.out} self.attrs = { 'axes': self.axes, 'starts': self.starts, 'ends': self.ends, 'infer_flags': self.infer_flags, 'decrease_axis': self.decrease_axis, "use_xpu": True, } def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [1, 0, 2] self.ends = [2, 3, 4] self.axes = [0, 1, 2] self.decrease_axis = [0] self.infer_flags = [1, 1, 1] self.out = self.input[1, 0:3, 2:4, :] def test_check_output(self): self.check_output_with_place(self.place) def test_check_grad_normal(self): if self.dtype == np.float16: self.check_grad_with_place(self.place, ['Input'], 'Out') else: user_defined_grad_outputs = np.random.random( self.out.shape ).astype(self.dtype) self.check_grad_with_place( self.place, ['Input'], 'Out', user_defined_grad_outputs=user_defined_grad_outputs, ) class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [1, 0, 2] self.ends = [2, 1, 4] self.axes = [0, 1, 2] self.decrease_axis = [0, 1] self.infer_flags = [1, 1, 1] self.out = self.input[1, 0, 2:4, :] class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [-1, 0, 2] self.ends = [1000000, 1, 4] self.axes = [0, 1, 2] self.decrease_axis = [0, 1] self.infer_flags = [1, 1, 1] self.out = self.input[-1, 0, 2:4, :] class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim): def config(self): self.input = np.random.random([3, 4, 5, 7]).astype(self.dtype) self.starts = [0, 1, 2, 3] self.ends = [1, 2, 3, 4] self.axes = [0, 1, 2, 3] self.decrease_axis = [0, 1, 2] self.infer_flags = [1, 1, 1] self.out = self.input[0, 1, 2, 3:4] class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [-1] self.ends = [1000000] self.axes = [3] self.decrease_axis = [3] self.infer_flags = [1, 1, 1] self.out = self.input[:, :, :, -1] class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim): def config(self): self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) self.starts = [0, 1, 2, 3] self.ends = [1, 2, 3, 4] self.axes = [0, 1, 2, 3] self.decrease_axis = [0, 1, 2, 3] self.infer_flags = [1, 1, 1] self.out = self.input[0, 1, 2, 3] support_types = get_xpu_op_support_types('slice') for stype in support_types: create_test_class(globals(), XPUTestSliceOp, stype) create_test_class(globals(), XPUTestSliceOp_decs_dim, stype) if __name__ == '__main__': unittest.main()