# Copyright (c) 2020 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 paddle import paddle.fluid as fluid import numpy as np import unittest def smooth_l1_loss_forward(val, delta): abs_val = abs(val) if abs_val <= delta: return 0.5 * val * val else: return delta * (abs_val - 0.5 * delta) def smooth_l1_loss_np(input, label, reduction='mean', delta=1.0): diff = input - label out = np.vectorize(smooth_l1_loss_forward)(diff, delta) if reduction == 'sum': return np.sum(out) elif reduction == 'mean': return np.mean(out) elif reduction == 'none': return out class SmoothL1Loss(unittest.TestCase): def setUp(self): np.random.seed(123) def test_smooth_l1_loss_mean(self): input_np = np.random.random([100, 200]).astype(np.float32) label_np = np.random.random([100, 200]).astype(np.float32) prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data(name='input', shape=[100, 200], dtype='float32') label = fluid.data(name='label', shape=[100, 200], dtype='float32') smooth_l1_loss = paddle.nn.loss.SmoothL1Loss() ret = smooth_l1_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): smooth_l1_loss = paddle.nn.loss.SmoothL1Loss() dy_ret = smooth_l1_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = smooth_l1_loss_np(input_np, label_np, reduction='mean') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_smooth_l1_loss_sum(self): input_np = np.random.random([100, 200]).astype(np.float32) label_np = np.random.random([100, 200]).astype(np.float32) prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data(name='input', shape=[100, 200], dtype='float32') label = fluid.data(name='label', shape=[100, 200], dtype='float32') smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(reduction='sum') ret = smooth_l1_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(reduction='sum') dy_ret = smooth_l1_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = smooth_l1_loss_np(input_np, label_np, reduction='sum') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_smooth_l1_loss_none(self): input_np = np.random.random([100, 200]).astype(np.float32) label_np = np.random.random([100, 200]).astype(np.float32) prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data(name='input', shape=[100, 200], dtype='float32') label = fluid.data(name='label', shape=[100, 200], dtype='float32') smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(reduction='none') ret = smooth_l1_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(reduction='none') dy_ret = smooth_l1_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = smooth_l1_loss_np(input_np, label_np, reduction='none') self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) def test_smooth_l1_loss_delta(self): input_np = np.random.random([100, 200]).astype(np.float32) label_np = np.random.random([100, 200]).astype(np.float32) delta = np.random.rand() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data(name='input', shape=[100, 200], dtype='float32') label = fluid.data(name='label', shape=[100, 200], dtype='float32') smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(delta=delta) ret = smooth_l1_loss(input, label) exe = fluid.Executor(place) static_ret = exe.run(prog, feed={ 'input': input_np, 'label': label_np, }, fetch_list=[ret]) self.assertIsNotNone(static_ret) with fluid.dygraph.guard(): smooth_l1_loss = paddle.nn.loss.SmoothL1Loss(delta=delta) dy_ret = smooth_l1_loss( fluid.dygraph.to_variable(input_np), fluid.dygraph.to_variable(label_np)) dy_ret_value = dy_ret.numpy() self.assertIsNotNone(dy_ret_value) expected = smooth_l1_loss_np(input_np, label_np, delta=delta) self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, expected)) self.assertTrue(np.allclose(dy_ret_value, expected)) if __name__ == "__main__": unittest.main()