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- # 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 import base
- from paddle.base import Program, core, program_guard
-
-
- class XPUTestClipOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'clip'
- self.use_dynamic_create_class = False
-
- class TestClipOp(XPUOpTest):
- def setUp(self):
- self.init_dtype()
- self.set_xpu()
- self.op_type = "clip"
- self.place = paddle.XPUPlace(0)
- self.inputs = {}
- self.init_data()
- self.set_attrs()
- self.set_inputs()
- self.outputs = {
- 'Out': np.clip(self.inputs['X'], self.min_v, self.max_v)
- }
-
- def set_xpu(self):
- self.__class__.use_xpu = True
- self.__class__.no_need_check_grad = False
- self.__class__.op_type = self.dtype
-
- def init_data(self):
- self.shape = (4, 10, 10)
- self.max = 0.8
- self.min = 0.3
-
- def set_inputs(self):
- if 'Min' in self.inputs:
- min_v = self.inputs['Min']
- else:
- min_v = self.attrs['min']
-
- if 'Max' in self.inputs:
- max_v = self.inputs['Max']
- else:
- max_v = self.attrs['max']
-
- self.min_v = min_v
- self.max_v = max_v
- self.max_relative_error = 0.006
- input = np.random.random(self.shape).astype("float32")
- input[np.abs(input - min_v) < self.max_relative_error] = 0.5
- input[np.abs(input - max_v) < self.max_relative_error] = 0.5
- self.inputs['X'] = input
-
- def set_attrs(self):
- self.attrs = {}
- self.attrs['min'] = self.min
- self.attrs['max'] = self.max
-
- def init_dtype(self):
- self.dtype = self.in_type
-
- def test_check_output(self):
- paddle.enable_static()
- self.check_output_with_place(self.place)
- paddle.disable_static()
-
- def test_check_grad(self):
- if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
- return
- if core.is_compiled_with_xpu():
- paddle.enable_static()
- self.check_grad_with_place(
- self.place, ['X'], 'Out', check_dygraph=True
- )
- paddle.disable_static()
-
- class TestClipOp1(TestClipOp):
- def init_data(self):
- self.shape = (8, 16, 8)
- self.max = 0.7
- self.min = 0.0
-
- class TestClipOp2(TestClipOp):
- def init_data(self):
- self.shape = (8, 16)
- self.max = 1.0
- self.min = 0.0
-
- class TestClipOp3(TestClipOp):
- def init_data(self):
- self.shape = (4, 8, 16)
- self.max = 0.7
- self.min = 0.2
-
- class TestClipOp4(TestClipOp):
- def init_data(self):
- self.shape = (4, 8, 8)
- self.max = 0.7
- self.min = 0.2
- self.inputs['Max'] = np.array([0.8]).astype('float32')
- self.inputs['Min'] = np.array([0.3]).astype('float32')
-
- class TestClipOp5(TestClipOp):
- def init_data(self):
- self.shape = (4, 8, 16)
- self.max = 0.5
- self.min = 0.5
-
-
- class TestClipOpError(unittest.TestCase):
- def test_errors(self):
- paddle.enable_static()
- with program_guard(Program(), Program()):
- input_data = np.random.random((2, 4)).astype("float32")
-
- def test_Variable():
- paddle.clip(x=input_data, min=-1.0, max=1.0)
-
- self.assertRaises(TypeError, test_Variable)
- paddle.disable_static()
-
-
- class TestClipAPI(unittest.TestCase):
- def _executed_api(self, x, min=None, max=None):
- return paddle.clip(x, min, max)
-
- def test_clip(self):
- paddle.enable_static()
- data_shape = [1, 9, 9, 4]
- data = np.random.random(data_shape).astype('float32')
- images = paddle.static.data(
- name='image', shape=data_shape, dtype='float32'
- )
- min = paddle.static.data(name='min', shape=[1], dtype='float32')
- max = paddle.static.data(name='max', shape=[1], dtype='float32')
-
- place = (
- base.XPUPlace(0)
- if base.core.is_compiled_with_xpu()
- else base.CPUPlace()
- )
- exe = base.Executor(place)
-
- out_1 = self._executed_api(images, min=min, max=max)
- out_2 = self._executed_api(images, min=0.2, max=0.9)
- out_3 = self._executed_api(images, min=0.3)
- out_4 = self._executed_api(images, max=0.7)
- out_5 = self._executed_api(images, min=min)
- out_6 = self._executed_api(images, max=max)
- out_7 = self._executed_api(images, max=-1.0)
- out_8 = self._executed_api(images)
-
- res1, res2, res3, res4, res5, res6, res7, res8 = exe.run(
- base.default_main_program(),
- feed={
- "image": data,
- "min": np.array([0.2]).astype('float32'),
- "max": np.array([0.8]).astype('float32'),
- },
- fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8],
- )
-
- np.testing.assert_allclose(res1, data.clip(0.2, 0.8))
- np.testing.assert_allclose(res2, data.clip(0.2, 0.9))
- np.testing.assert_allclose(res3, data.clip(min=0.3))
- np.testing.assert_allclose(res4, data.clip(max=0.7))
- np.testing.assert_allclose(res5, data.clip(min=0.2))
- np.testing.assert_allclose(res6, data.clip(max=0.8))
- np.testing.assert_allclose(res7, data.clip(max=-1))
- np.testing.assert_allclose(res8, data)
- paddle.disable_static()
-
- def test_clip_dygraph(self):
- paddle.disable_static()
- place = (
- base.XPUPlace(0)
- if base.core.is_compiled_with_xpu()
- else base.CPUPlace()
- )
- paddle.disable_static(place)
- data_shape = [1, 9, 9, 4]
- data = np.random.random(data_shape).astype('float32')
- images = paddle.to_tensor(data, dtype='float32')
- v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
- v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))
-
- out_1 = self._executed_api(images, min=0.2, max=0.8)
- images = paddle.to_tensor(data, dtype='float32')
- out_2 = self._executed_api(images, min=0.2, max=0.9)
- images = paddle.to_tensor(data, dtype='float32')
- out_3 = self._executed_api(images, min=v_min, max=v_max)
-
- np.testing.assert_allclose(out_1.numpy(), data.clip(0.2, 0.8))
- np.testing.assert_allclose(out_2.numpy(), data.clip(0.2, 0.9))
- np.testing.assert_allclose(out_3.numpy(), data.clip(0.2, 0.8))
-
- def test_errors(self):
- paddle.enable_static()
- x1 = paddle.static.data(name='x1', shape=[1], dtype="int16")
- x2 = paddle.static.data(name='x2', shape=[1], dtype="int8")
- self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
- self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
- paddle.disable_static()
-
-
- class TestInplaceClipAPI(TestClipAPI):
- def _executed_api(self, x, min=None, max=None):
- return x.clip_(min, max)
-
-
- support_types = get_xpu_op_support_types('clip')
- for stype in support_types:
- create_test_class(globals(), XPUTestClipOp, stype)
-
- if __name__ == '__main__':
- unittest.main()
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