wehub-resource-sync
250 lines
8.0 KiB
Python
250 lines
8.0 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle.base import core
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paddle.enable_static()
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class XPUTestUniqueOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = "unique"
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self.use_dynamic_create_class = False
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class TestUniqueOp(XPUOpTest):
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def setUp(self):
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self.op_type = "unique"
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self.init_dtype()
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self.init_config()
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def init_dtype(self):
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self.dtype = self.in_type
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def init_config(self):
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self.inputs = {
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'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype),
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}
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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'return_index': True,
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'return_inverse': True,
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'is_sorted': True, # is_sorted must be set to true to call paddle.unique rather than base.layers.unique
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}
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self.outputs = {
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'Out': np.array([1, 2, 3, 5], dtype=self.dtype),
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'Indices': np.array([3, 0, 1, 4], dtype='int32'),
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'Index': np.array([1, 2, 2, 0, 3, 2]),
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}
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def test_check_output(self):
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self.check_output_with_place(paddle.XPUPlace(0))
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class TestOne(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': np.array([2], dtype=self.dtype),
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}
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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'return_index': True,
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'return_inverse': True,
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'is_sorted': True,
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}
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self.outputs = {
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'Out': np.array([2], dtype=self.dtype),
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'Indices': np.array([0], dtype='int32'),
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'Index': np.array([0], dtype='int32'),
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}
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class TestRandom(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': (np.random.random([150]) * 100.0).astype(self.dtype)
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}
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT64),
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'return_index': True,
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'return_inverse': True,
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'return_counts': True,
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'is_sorted': True,
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}
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np_unique, np_index, reverse_index, np_counts = np.unique(
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self.inputs['X'],
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True,
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True,
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True,
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)
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self.outputs = {
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'Out': np_unique,
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'Indices': np_index,
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'Index': reverse_index,
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'Counts': np_counts,
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}
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class TestRandom2(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': (np.random.random([4, 7, 10]) * 100.0).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=None,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT64),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": None,
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestEmpty(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.ones([0, 4], dtype=self.dtype)}
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT64),
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'return_index': True,
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'return_inverse': True,
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'return_counts': True,
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'is_sorted': True,
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}
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self.outputs = {
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'Out': np.ones([0], dtype=self.dtype),
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'Indices': np.ones([0], dtype=self.dtype),
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'Index': np.ones([0], dtype=self.dtype),
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'Counts': np.ones([0], dtype=self.dtype),
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}
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class TestUniqueOpAxis1(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': (np.random.random([3, 8, 8]) * 100.0).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=1,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": [1],
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestUniqueOpAxis2(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': (np.random.random([1, 10]) * 100.0).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=0,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": [0],
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestUniqueOpAxisNeg(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': (np.random.random([6, 1, 8]) * 100.0).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=-1,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": [-1],
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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support_types = get_xpu_op_support_types("unique")
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for stype in support_types:
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create_test_class(globals(), XPUTestUniqueOp, stype)
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if __name__ == "__main__":
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unittest.main()
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