# Copyright (c) 2018 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. from __future__ import print_function import unittest import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid import Program, program_guard from op_test import OpTest class TestClipOp(OpTest): def setUp(self): self.max_relative_error = 0.006 self.inputs = {} self.initTestCase() self.op_type = "clip" self.attrs = {} self.attrs['min'] = self.min self.attrs['max'] = self.max 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'] 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 self.outputs = {'Out': np.clip(self.inputs['X'], min_v, max_v)} def test_check_output(self): paddle.enable_static() self.check_output() paddle.disable_static() def test_check_grad_normal(self): paddle.enable_static() self.check_grad(['X'], 'Out') paddle.disable_static() def initTestCase(self): self.shape = (4, 10, 10) self.max = 0.8 self.min = 0.3 self.inputs['Max'] = np.array([0.8]).astype('float32') self.inputs['Min'] = np.array([0.1]).astype('float32') class TestCase1(TestClipOp): def initTestCase(self): self.shape = (8, 16, 8) self.max = 0.7 self.min = 0.0 class TestCase2(TestClipOp): def initTestCase(self): self.shape = (8, 16) self.max = 1.0 self.min = 0.0 class TestCase3(TestClipOp): def initTestCase(self): self.shape = (4, 8, 16) self.max = 0.7 self.min = 0.2 class TestCase4(TestClipOp): def initTestCase(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 TestCase5(TestClipOp): def initTestCase(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(): fluid.layers.clip(x=input_data, min=-1.0, max=1.0) self.assertRaises(TypeError, test_Variable) def test_dtype(): x2 = fluid.layers.data(name='x2', shape=[1], dtype='int32') fluid.layers.clip(x=x2, min=-1.0, max=1.0) self.assertRaises(TypeError, test_dtype) paddle.disable_static() class TestClipAPI(unittest.TestCase): def test_clip(self): paddle.enable_static() data_shape = [1, 9, 9, 4] data = np.random.random(data_shape).astype('float32') images = fluid.data(name='image', shape=data_shape, dtype='float32') min = fluid.data(name='min', shape=[1], dtype='float32') max = fluid.data(name='max', shape=[1], dtype='float32') place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() exe = fluid.Executor(place) out_1 = paddle.clip(images, min=min, max=max) out_2 = paddle.clip(images, min=0.2, max=0.9) out_3 = paddle.clip(images, min=0.3) out_4 = paddle.clip(images, max=0.7) out_5 = paddle.clip(images, min=min) out_6 = paddle.clip(images, max=max) out_7 = paddle.clip(images, max=-1.) out_8 = paddle.clip(images) out_9 = paddle.clip(paddle.cast(images, 'float64'), min=0.2, max=0.9) out_10 = paddle.clip(paddle.cast(images * 10, 'int32'), min=2, max=8) out_11 = paddle.clip(paddle.cast(images * 10, 'int64'), min=2, max=8) res1, res2, res3, res4, res5, res6, res7, res8, res9, res10, res11 = exe.run( fluid.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, out_9, out_10, out_11 ]) self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8))) self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9))) self.assertTrue(np.allclose(res3, data.clip(min=0.3))) self.assertTrue(np.allclose(res4, data.clip(max=0.7))) self.assertTrue(np.allclose(res5, data.clip(min=0.2))) self.assertTrue(np.allclose(res6, data.clip(max=0.8))) self.assertTrue(np.allclose(res7, data.clip(max=-1))) self.assertTrue(np.allclose(res8, data)) self.assertTrue( np.allclose(res9, data.astype(np.float64).clip(0.2, 0.9))) self.assertTrue( np.allclose(res10, (data * 10).astype(np.int32).clip(2, 8))) self.assertTrue( np.allclose(res11, (data * 10).astype(np.int64).clip(2, 8))) paddle.disable_static() def test_clip_dygraph(self): paddle.disable_static() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.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 = paddle.clip(images, min=0.2, max=0.8) out_2 = paddle.clip(images, min=0.2, max=0.9) out_3 = paddle.clip(images, min=v_min, max=v_max) out_4 = paddle.clip(paddle.cast(images * 10, 'int32'), min=2, max=8) out_5 = paddle.clip(paddle.cast(images * 10, 'int64'), min=2, max=8) self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8))) self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9))) self.assertTrue(np.allclose(out_3.numpy(), data.clip(0.2, 0.8))) self.assertTrue( np.allclose(out_4.numpy(), (data * 10).astype(np.int32).clip(2, 8))) self.assertTrue( np.allclose(out_5.numpy(), (data * 10).astype(np.int64).clip(2, 8))) def test_errors(self): paddle.enable_static() x1 = fluid.data(name='x1', shape=[1], dtype="int16") x2 = fluid.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() if __name__ == '__main__': unittest.main()