paddlepaddle--paddle
wehub-resource-sync
378 行
15 KiB
Python
378 行
15 KiB
Python
# Copyright (c) 2019 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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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from utils import dygraph_guard
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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from paddle.base.executor import Executor
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class TestMseLoss(unittest.TestCase):
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def test_mse_loss(self):
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input_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
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label_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
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sub = input_val - label_val
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np_result = np.mean(sub * sub)
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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input_var = paddle.static.data(
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name="input", shape=[-1, 3], dtype="float32"
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)
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label_var = paddle.static.data(
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name="label", shape=[-1, 3], dtype="float32"
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)
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output = paddle.nn.functional.mse_loss(
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input=input_var, label=label_var
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)
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = Executor(place)
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(result,) = exe.run(
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main,
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feed={"input": input_val, "label": label_val},
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fetch_list=[output],
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)
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np.testing.assert_allclose(np_result, result, rtol=1e-05)
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class TestMseInvalidInput(unittest.TestCase):
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def test_error(self):
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def test_invalid_input():
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input = [256, 3]
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label = paddle.static.data(
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name='label1', shape=[None, 3], dtype='float32'
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)
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loss = paddle.nn.functional.mse_loss(input, label)
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self.assertRaises(TypeError, test_invalid_input)
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def test_invalid_label():
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input = paddle.static.data(
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name='input1', shape=[None, 3], dtype='float32'
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)
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label = [256, 3]
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loss = paddle.nn.functional.mse_loss(input, label)
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self.assertRaises(TypeError, test_invalid_label)
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def test_invalid_tuple_input():
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with dygraph_guard():
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input = paddle.randn(shape=[256, 3], dtype='float32')
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label = [256, 3]
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loss = paddle.nn.functional.mse_loss((input,), label)
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self.assertRaises(ValueError, test_invalid_tuple_input)
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class TestNNMseLoss(unittest.TestCase):
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def test_NNMseLoss_mean(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = base.Program()
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startup_prog = base.Program()
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place = get_device_place()
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with base.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.loss.MSELoss()
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ret = mse_loss(input, label)
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exe = base.Executor(place)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "label": label_np},
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fetch_list=[ret],
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)
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with base.dygraph.guard():
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mse_loss = paddle.nn.loss.MSELoss()
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dy_ret = mse_loss(
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paddle.to_tensor(input_np),
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paddle.to_tensor(label_np),
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)
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dy_result = dy_ret.numpy()
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sub = input_np - label_np
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expected = np.mean(sub * sub)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, ())
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def test_NNMseLoss_sum(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = base.Program()
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startup_prog = base.Program()
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place = get_device_place()
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with base.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
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ret = mse_loss(input, label)
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exe = base.Executor(place)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "label": label_np},
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fetch_list=[ret],
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)
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with base.dygraph.guard():
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mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
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dy_ret = mse_loss(
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paddle.to_tensor(input_np),
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paddle.to_tensor(label_np),
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)
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dy_result = dy_ret.numpy()
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sub = input_np - label_np
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expected = np.sum(sub * sub)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, ())
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def test_NNMseLoss_none(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = base.Program()
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startup_prog = base.Program()
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place = get_device_place()
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with base.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.loss.MSELoss(reduction='none')
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ret = mse_loss(input, label)
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exe = base.Executor(place)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "label": label_np},
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fetch_list=[ret],
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)
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with base.dygraph.guard():
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mse_loss = paddle.nn.loss.MSELoss(reduction='none')
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dy_ret = mse_loss(
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paddle.to_tensor(input_np),
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paddle.to_tensor(label_np),
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)
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dy_result = dy_ret.numpy()
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sub = input_np - label_np
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expected = sub * sub
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, tuple(dim))
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class TestNNFunctionalMseLoss(unittest.TestCase):
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def test_NNFunctionalMseLoss_mean(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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place = get_device_place()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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target = paddle.static.data(
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name='target', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.functional.mse_loss(input, target, 'mean')
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "target": target_np},
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fetch_list=[mse_loss],
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)
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paddle.disable_static()
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dy_ret = paddle.nn.functional.mse_loss(
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paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'mean'
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)
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dy_result = dy_ret.numpy()
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sub = input_np - target_np
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expected = np.mean(sub * sub)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, ())
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def test_NNFunctionalMseLoss_sum(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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place = get_device_place()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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target = paddle.static.data(
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name='target', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.functional.mse_loss(input, target, 'sum')
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "target": target_np},
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fetch_list=[mse_loss],
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)
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paddle.disable_static()
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dy_ret = paddle.nn.functional.mse_loss(
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paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'sum'
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)
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dy_result = dy_ret.numpy()
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sub = input_np - target_np
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expected = np.sum(sub * sub)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, ())
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def test_NNFunctionalMseLoss_none(self):
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for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.enable_static()
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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place = get_device_place()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=dim, dtype='float32'
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)
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target = paddle.static.data(
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name='target', shape=dim, dtype='float32'
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)
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mse_loss = paddle.nn.functional.mse_loss(input, target, 'none')
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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(static_result,) = exe.run(
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prog,
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feed={"input": input_np, "target": target_np},
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fetch_list=[mse_loss],
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)
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paddle.disable_static()
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dy_ret = paddle.nn.functional.mse_loss(
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paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'none'
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)
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dy_result = dy_ret.numpy()
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sub = input_np - target_np
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expected = sub * sub
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, tuple(dim))
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class TestNNFunctionalMseLoss_ZeroSize(unittest.TestCase):
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def test_dygraph_and_grad(self):
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for dim in [[0, 0], [2, 0, 10]]:
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input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
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paddle.disable_static()
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x = paddle.to_tensor(input_np)
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x.stop_gradient = False
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dy_ret = paddle.nn.functional.mse_loss(
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x, paddle.to_tensor(target_np), 'mean'
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)
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dy_result = dy_ret.numpy()
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sub = input_np - target_np
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expected = np.mean(sub * sub)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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self.assertEqual(dy_result.shape, ())
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loss = paddle.sum(dy_ret)
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loss.backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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class TestNNFunctionalMseLossAlias(unittest.TestCase):
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def test_target_alias_dygraph(self):
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with base.dygraph.guard():
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x = paddle.randn([4, 5], dtype="float32")
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y = paddle.randn([4, 5], dtype="float32")
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out1 = F.mse_loss(input=x, label=y, reduction="none")
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out2 = F.mse_loss(input=x, target=y, reduction="none")
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np.testing.assert_allclose(
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out1.numpy(), out2.numpy(), rtol=1e-6, atol=0.0
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)
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out3 = F.mse_loss(input=x, label=y, reduction="mean")
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out4 = F.mse_loss(input=x, target=y, reduction="mean")
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np.testing.assert_allclose(
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out3.numpy(), out4.numpy(), rtol=1e-6, atol=0.0
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)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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