wehub-resource-sync
138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
# Copyright (c) 2022 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.framework import convert_nptype_to_datatype_or_vartype
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paddle.enable_static()
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class XPUTestEmptyOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'empty'
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self.use_dynamic_create_class = False
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# Situation 1: Attr(shape) is a list(without tensor)
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class TestEmptyOp(XPUOpTest):
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def setUp(self):
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self.op_type = "empty"
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self.init_dtype()
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self.set_xpu()
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self.place = paddle.XPUPlace(0)
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self.set_shape()
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self.set_inputs()
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self.init_config()
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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def verify_output(self, outs):
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data_type = outs[0].dtype
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if data_type in [
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'float32',
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'float64',
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'int32',
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'int64',
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'int8',
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'uint8',
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'float16',
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'int16',
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'uint16',
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]:
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max_value = np.nanmax(outs[0])
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min_value = np.nanmin(outs[0])
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always_full_zero = max_value == 0.0 and min_value == 0.0
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always_non_full_zero = max_value >= min_value
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self.assertTrue(
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always_full_zero or always_non_full_zero,
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'always_full_zero or always_non_full_zero.',
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)
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elif data_type in ['bool']:
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total_num = outs[0].size
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true_num = np.sum(outs[0])
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false_num = np.sum(~outs[0])
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self.assertTrue(
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total_num == true_num + false_num,
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'The value should always be True or False.',
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)
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else:
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# pass
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self.assertTrue(False, 'invalid data type')
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def set_shape(self):
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self.shape = [500, 3]
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def set_inputs(self):
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self.inputs = {}
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def init_config(self):
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dtype_inner = convert_nptype_to_datatype_or_vartype(self.dtype)
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self.attrs = {'shape': self.shape, 'dtype': dtype_inner}
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self.outputs = {'Out': np.zeros(self.shape).astype(self.dtype)}
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def init_dtype(self):
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self.dtype = self.in_type
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def set_xpu(self):
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self.__class__.use_xpu = True
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self.__class__.no_need_check_grad = True
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self.__class__.op_type = self.op_type
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class TestEmptyOpCase1(TestEmptyOp):
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def set_shape(self):
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self.shape = [50]
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class TestEmptyOpCase2(TestEmptyOp):
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def set_shape(self):
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self.shape = [1, 50, 3, 4]
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class TestEmptyOpCase3(TestEmptyOp):
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def set_shape(self):
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self.shape = [5, 5, 5]
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# Situation 2: shape is a tensor
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class TestEmptyOp_ShapeTensor(TestEmptyOp):
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def set_inputs(self):
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self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
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# Situation 3: Attr(shape) is a list(with tensor)
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class TestEmptyOp_ShapeTensorList(TestEmptyOp):
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def set_inputs(self):
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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support_types = get_xpu_op_support_types('empty')
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for stype in support_types:
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create_test_class(globals(), XPUTestEmptyOp, stype)
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if __name__ == '__main__':
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unittest.main()
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