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186 lines
7.2 KiB
186 lines
7.2 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 TestFunctionalL1Loss(unittest.TestCase):
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def setUp(self):
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self.input_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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self.label_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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def run_imperative(self):
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input = paddle.to_tensor(self.input_np)
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label = paddle.to_tensor(self.label_np)
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dy_result = paddle.nn.functional.l1_loss(input, label)
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expected = np.mean(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [1])
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dy_result = paddle.nn.functional.l1_loss(input, label, reduction='sum')
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expected = np.sum(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [1])
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dy_result = paddle.nn.functional.l1_loss(input, label, reduction='none')
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expected = np.abs(self.input_np - self.label_np)
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [10, 10, 5])
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def run_static(self, use_gpu=False):
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input = paddle.data(name='input', shape=[10, 10, 5], dtype='float32')
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label = paddle.data(name='label', shape=[10, 10, 5], dtype='float32')
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result0 = paddle.nn.functional.l1_loss(input, label)
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result1 = paddle.nn.functional.l1_loss(input, label, reduction='sum')
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result2 = paddle.nn.functional.l1_loss(input, label, reduction='none')
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y = paddle.nn.functional.l1_loss(input, label, name='aaa')
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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static_result = exe.run(
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feed={"input": self.input_np,
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"label": self.label_np},
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fetch_list=[result0, result1, result2])
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expected = np.mean(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(static_result[0], expected))
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expected = np.sum(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(static_result[1], expected))
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expected = np.abs(self.input_np - self.label_np)
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self.assertTrue(np.allclose(static_result[2], expected))
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self.assertTrue('aaa' in y.name)
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def test_cpu(self):
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paddle.disable_static(place=paddle.fluid.CPUPlace())
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static()
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def test_gpu(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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paddle.disable_static(place=paddle.fluid.CUDAPlace(0))
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static(use_gpu=True)
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# test case the raise message
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def test_errors(self):
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def test_value_error():
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input = paddle.data(
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name='input', shape=[10, 10, 5], dtype='float32')
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label = paddle.data(
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name='label', shape=[10, 10, 5], dtype='float32')
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loss = paddle.nn.functional.l1_loss(
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input, label, reduction='reduce_mean')
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self.assertRaises(ValueError, test_value_error)
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class TestClassL1Loss(unittest.TestCase):
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def setUp(self):
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self.input_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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self.label_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
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def run_imperative(self):
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input = paddle.to_tensor(self.input_np)
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label = paddle.to_tensor(self.label_np)
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l1_loss = paddle.nn.loss.L1Loss()
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dy_result = l1_loss(input, label)
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expected = np.mean(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [1])
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l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
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dy_result = l1_loss(input, label)
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expected = np.sum(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [1])
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l1_loss = paddle.nn.loss.L1Loss(reduction='none')
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dy_result = l1_loss(input, label)
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expected = np.abs(self.input_np - self.label_np)
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self.assertTrue(np.allclose(dy_result.numpy(), expected))
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self.assertTrue(dy_result.shape, [10, 10, 5])
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def run_static(self, use_gpu=False):
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input = paddle.data(name='input', shape=[10, 10, 5], dtype='float32')
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label = paddle.data(name='label', shape=[10, 10, 5], dtype='float32')
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l1_loss = paddle.nn.loss.L1Loss()
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result0 = l1_loss(input, label)
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l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
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result1 = l1_loss(input, label)
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l1_loss = paddle.nn.loss.L1Loss(reduction='none')
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result2 = l1_loss(input, label)
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l1_loss = paddle.nn.loss.L1Loss(name='aaa')
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result3 = l1_loss(input, label)
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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static_result = exe.run(
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feed={"input": self.input_np,
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"label": self.label_np},
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fetch_list=[result0, result1, result2])
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expected = np.mean(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(static_result[0], expected))
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expected = np.sum(np.abs(self.input_np - self.label_np))
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self.assertTrue(np.allclose(static_result[1], expected))
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expected = np.abs(self.input_np - self.label_np)
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self.assertTrue(np.allclose(static_result[2], expected))
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self.assertTrue('aaa' in result3.name)
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def test_cpu(self):
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paddle.disable_static(place=paddle.fluid.CPUPlace())
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static()
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def test_gpu(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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paddle.disable_static(place=paddle.fluid.CUDAPlace(0))
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self.run_imperative()
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paddle.enable_static()
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with fluid.program_guard(fluid.Program()):
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self.run_static(use_gpu=True)
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# test case the raise message
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def test_errors(self):
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def test_value_error():
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loss = paddle.nn.loss.L1Loss(reduction="reduce_mean")
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self.assertRaises(ValueError, test_value_error)
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if __name__ == "__main__":
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unittest.main()
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