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132 lines
5.1 KiB
132 lines
5.1 KiB
# Copyright (c) 2020 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 paddle
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import paddle.fluid as fluid
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import numpy as np
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import unittest
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class TestL1Loss(unittest.TestCase):
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def test_L1Loss_mean(self):
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input_np = np.random.random(size=(10, 1)).astype(np.float32)
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label_np = np.random.random(size=(10, 1)).astype(np.float32)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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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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with fluid.program_guard(prog, startup_prog):
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input = fluid.layers.data(
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name='input', shape=[10, 1], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[10, 1], dtype='float32')
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l1_loss = paddle.nn.loss.L1Loss()
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ret = l1_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[ret])
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with fluid.dygraph.guard():
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l1_loss = paddle.nn.loss.L1Loss()
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dy_ret = l1_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_ret.numpy()
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expected = np.mean(np.abs(input_np - label_np))
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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self.assertTrue(dy_result.shape, [1])
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def test_L1Loss_sum(self):
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input_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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label_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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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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with fluid.program_guard(prog, startup_prog):
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input = fluid.layers.data(
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name='input', shape=[10, 10, 5], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[10, 10, 5], dtype='float32')
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l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
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ret = l1_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[ret])
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with fluid.dygraph.guard():
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l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
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dy_ret = l1_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_ret.numpy()
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expected = np.sum(np.abs(input_np - label_np))
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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self.assertTrue(dy_result.shape, [1])
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def test_L1Loss_none(self):
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input_np = np.random.random(size=(10, 5)).astype(np.float32)
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label_np = np.random.random(size=(10, 5)).astype(np.float32)
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prog = fluid.Program()
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startup_prog = fluid.Program()
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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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with fluid.program_guard(prog, startup_prog):
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input = fluid.layers.data(
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name='input', shape=[10, 5], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[10, 5], dtype='float32')
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l1_loss = paddle.nn.loss.L1Loss(reduction='none')
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ret = l1_loss(input, label)
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exe = fluid.Executor(place)
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static_result = exe.run(
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prog,
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feed={"input": input_np,
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"label": label_np},
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fetch_list=[ret])
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with fluid.dygraph.guard():
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l1_loss = paddle.nn.loss.L1Loss(reduction='none')
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dy_ret = l1_loss(
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fluid.dygraph.to_variable(input_np),
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fluid.dygraph.to_variable(label_np))
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dy_result = dy_ret.numpy()
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expected = np.abs(input_np - label_np)
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self.assertTrue(np.allclose(static_result, expected))
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self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
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self.assertTrue(dy_result.shape, input.shape)
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if __name__ == "__main__":
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unittest.main()
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