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193 lines
6.4 KiB
193 lines
6.4 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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from op_test import OpTest
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class TestClipOp(OpTest):
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def setUp(self):
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self.max_relative_error = 0.006
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self.inputs = {}
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self.initTestCase()
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self.op_type = "clip"
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self.attrs = {}
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self.attrs['min'] = self.min
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self.attrs['max'] = self.max
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if 'Min' in self.inputs:
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min_v = self.inputs['Min']
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else:
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min_v = self.attrs['min']
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if 'Max' in self.inputs:
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max_v = self.inputs['Max']
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else:
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max_v = self.attrs['max']
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input = np.random.random(self.shape).astype("float32")
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input[np.abs(input - min_v) < self.max_relative_error] = 0.5
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input[np.abs(input - max_v) < self.max_relative_error] = 0.5
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self.inputs['X'] = input
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self.outputs = {'Out': np.clip(self.inputs['X'], min_v, max_v)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out')
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def initTestCase(self):
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self.shape = (4, 10, 10)
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self.max = 0.8
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self.min = 0.3
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self.inputs['Max'] = np.array([0.8]).astype('float32')
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self.inputs['Min'] = np.array([0.1]).astype('float32')
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class TestCase1(TestClipOp):
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def initTestCase(self):
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self.shape = (8, 16, 8)
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self.max = 0.7
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self.min = 0.0
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class TestCase2(TestClipOp):
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def initTestCase(self):
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self.shape = (8, 16)
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self.max = 1.0
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self.min = 0.0
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class TestCase3(TestClipOp):
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def initTestCase(self):
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self.shape = (4, 8, 16)
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self.max = 0.7
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self.min = 0.2
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class TestCase4(TestClipOp):
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def initTestCase(self):
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self.shape = (4, 8, 8)
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self.max = 0.7
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self.min = 0.2
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self.inputs['Max'] = np.array([0.8]).astype('float32')
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self.inputs['Min'] = np.array([0.3]).astype('float32')
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class TestCase5(TestClipOp):
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def initTestCase(self):
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self.shape = (4, 8, 16)
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self.max = 0.5
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self.min = 0.5
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class TestClipOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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input_data = np.random.random((2, 4)).astype("float32")
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def test_Variable():
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fluid.layers.clip(x=input_data, min=-1.0, max=1.0)
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self.assertRaises(TypeError, test_Variable)
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def test_dtype():
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x2 = fluid.layers.data(name='x2', shape=[1], dtype='int32')
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fluid.layers.clip(x=x2, min=-1.0, max=1.0)
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self.assertRaises(TypeError, test_dtype)
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class TestClipAPI(unittest.TestCase):
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def test_clip(self):
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paddle.enable_static()
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data_shape = [1, 9, 9, 4]
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data = np.random.random(data_shape).astype('float32')
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images = fluid.data(name='image', shape=data_shape, dtype='float32')
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min = fluid.data(name='min', shape=[1], dtype='float32')
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max = fluid.data(name='max', shape=[1], dtype='float32')
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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out_1 = paddle.clip(images, min=min, max=max)
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out_2 = paddle.clip(images, min=0.2, max=0.9)
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out_3 = paddle.clip(images, min=0.3)
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out_4 = paddle.clip(images, max=0.7)
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out_5 = paddle.clip(images, min=min)
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out_6 = paddle.clip(images, max=max)
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out_7 = paddle.clip(images, max=-1.)
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out_8 = paddle.clip(images)
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out_9 = paddle.clip(paddle.cast(images, 'float64'), min=0.2, max=0.9)
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res1, res2, res3, res4, res5, res6, res7, res8, res9 = exe.run(
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fluid.default_main_program(),
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feed={
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"image": data,
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"min": np.array([0.2]).astype('float32'),
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"max": np.array([0.8]).astype('float32')
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},
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fetch_list=[
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out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8, out_9
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])
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self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
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self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
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self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
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self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
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self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
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self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
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self.assertTrue(np.allclose(res7, data.clip(max=-1)))
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self.assertTrue(np.allclose(res8, data))
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self.assertTrue(
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np.allclose(res9, data.astype(np.float64).clip(0.2, 0.9)))
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def test_clip_dygraph(self):
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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paddle.disable_static(place)
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data_shape = [1, 9, 9, 4]
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data = np.random.random(data_shape).astype('float32')
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images = paddle.to_tensor(data, dtype='float32')
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v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
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v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))
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out_1 = paddle.clip(images, min=0.2, max=0.8)
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out_2 = paddle.clip(images, min=0.2, max=0.9)
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out_3 = paddle.clip(images, min=v_min, max=v_max)
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self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
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self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
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self.assertTrue(np.allclose(out_3.numpy(), data.clip(0.2, 0.8)))
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def test_errors(self):
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paddle.enable_static()
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x1 = fluid.data(name='x1', shape=[1], dtype="int16")
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x2 = fluid.data(name='x2', shape=[1], dtype="int8")
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self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
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self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
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if __name__ == '__main__':
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unittest.main()
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