# Copyright (c) 2023 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 from paddle.base import core paddle.enable_static() class XPUTestUniqueOp(XPUOpTestWrapper): def __init__(self): self.op_name = "unique" self.use_dynamic_create_class = False class TestUniqueOp(XPUOpTest): def setUp(self): self.op_type = "unique" self.init_dtype() self.init_config() def init_dtype(self): self.dtype = self.in_type def init_config(self): self.inputs = { 'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype), } self.attrs = { 'dtype': int(core.VarDesc.VarType.INT32), 'return_index': True, 'return_inverse': True, 'is_sorted': True, # is_sorted must be set to true to call paddle.unique rather than base.layers.unique } self.outputs = { 'Out': np.array([1, 2, 3, 5], dtype=self.dtype), 'Indices': np.array([3, 0, 1, 4], dtype='int32'), 'Index': np.array([1, 2, 2, 0, 3, 2]), } def test_check_output(self): self.check_output_with_place(paddle.XPUPlace(0)) class TestOne(TestUniqueOp): def init_config(self): self.inputs = { 'X': np.array([2], dtype=self.dtype), } self.attrs = { 'dtype': int(core.VarDesc.VarType.INT32), 'return_index': True, 'return_inverse': True, 'is_sorted': True, } self.outputs = { 'Out': np.array([2], dtype=self.dtype), 'Indices': np.array([0], dtype='int32'), 'Index': np.array([0], dtype='int32'), } class TestRandom(TestUniqueOp): def init_config(self): self.inputs = { 'X': (np.random.random([150]) * 100.0).astype(self.dtype) } self.attrs = { 'dtype': int(core.VarDesc.VarType.INT64), 'return_index': True, 'return_inverse': True, 'return_counts': True, 'is_sorted': True, } np_unique, np_index, reverse_index, np_counts = np.unique( self.inputs['X'], True, True, True, ) self.outputs = { 'Out': np_unique, 'Indices': np_index, 'Index': reverse_index, 'Counts': np_counts, } class TestRandom2(TestUniqueOp): def init_config(self): self.inputs = { 'X': (np.random.random([4, 7, 10]) * 100.0).astype(self.dtype) } unique, indices, inverse, counts = np.unique( self.inputs['X'], return_index=True, return_inverse=True, return_counts=True, axis=None, ) if np.lib.NumpyVersion(np.__version__) >= "2.0.0": inverse = inverse.flatten() self.attrs = { 'dtype': int(core.VarDesc.VarType.INT64), "return_index": True, "return_inverse": True, "return_counts": True, "axis": None, "is_sorted": True, } self.outputs = { 'Out': unique, 'Indices': indices, "Index": inverse, "Counts": counts, } class TestEmpty(TestUniqueOp): def init_config(self): self.inputs = {'X': np.ones([0, 4], dtype=self.dtype)} self.attrs = { 'dtype': int(core.VarDesc.VarType.INT64), 'return_index': True, 'return_inverse': True, 'return_counts': True, 'is_sorted': True, } self.outputs = { 'Out': np.ones([0], dtype=self.dtype), 'Indices': np.ones([0], dtype=self.dtype), 'Index': np.ones([0], dtype=self.dtype), 'Counts': np.ones([0], dtype=self.dtype), } class TestUniqueOpAxis1(TestUniqueOp): def init_config(self): self.inputs = { 'X': (np.random.random([3, 8, 8]) * 100.0).astype(self.dtype) } unique, indices, inverse, counts = np.unique( self.inputs['X'], return_index=True, return_inverse=True, return_counts=True, axis=1, ) if np.lib.NumpyVersion(np.__version__) >= "2.0.0": inverse = inverse.flatten() self.attrs = { 'dtype': int(core.VarDesc.VarType.INT32), "return_index": True, "return_inverse": True, "return_counts": True, "axis": [1], "is_sorted": True, } self.outputs = { 'Out': unique, 'Indices': indices, "Index": inverse, "Counts": counts, } class TestUniqueOpAxis2(TestUniqueOp): def init_config(self): self.inputs = { 'X': (np.random.random([1, 10]) * 100.0).astype(self.dtype) } unique, indices, inverse, counts = np.unique( self.inputs['X'], return_index=True, return_inverse=True, return_counts=True, axis=0, ) if np.lib.NumpyVersion(np.__version__) >= "2.0.0": inverse = inverse.flatten() self.attrs = { 'dtype': int(core.VarDesc.VarType.INT32), "return_index": True, "return_inverse": True, "return_counts": True, "axis": [0], "is_sorted": True, } self.outputs = { 'Out': unique, 'Indices': indices, "Index": inverse, "Counts": counts, } class TestUniqueOpAxisNeg(TestUniqueOp): def init_config(self): self.inputs = { 'X': (np.random.random([6, 1, 8]) * 100.0).astype(self.dtype) } unique, indices, inverse, counts = np.unique( self.inputs['X'], return_index=True, return_inverse=True, return_counts=True, axis=-1, ) if np.lib.NumpyVersion(np.__version__) >= "2.0.0": inverse = inverse.flatten() self.attrs = { 'dtype': int(core.VarDesc.VarType.INT32), "return_index": True, "return_inverse": True, "return_counts": True, "axis": [-1], "is_sorted": True, } self.outputs = { 'Out': unique, 'Indices': indices, "Index": inverse, "Counts": counts, } support_types = get_xpu_op_support_types("unique") for stype in support_types: create_test_class(globals(), XPUTestUniqueOp, stype) if __name__ == "__main__": unittest.main()