You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
289 lines
12 KiB
289 lines
12 KiB
# Copyright (c) 2019 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.core as core
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.layers as layers
|
|
from paddle.fluid.executor import Executor
|
|
|
|
|
|
class TestMseLoss(unittest.TestCase):
|
|
def test_mse_loss(self):
|
|
input_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
|
|
label_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
|
|
|
|
sub = input_val - label_val
|
|
np_result = np.mean(sub * sub)
|
|
|
|
input_var = layers.create_tensor(dtype="float32", name="input")
|
|
label_var = layers.create_tensor(dtype="float32", name="label")
|
|
|
|
output = layers.mse_loss(input=input_var, label=label_var)
|
|
for use_cuda in ([False, True]
|
|
if core.is_compiled_with_cuda() else [False]):
|
|
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
|
|
exe = Executor(place)
|
|
result = exe.run(fluid.default_main_program(),
|
|
feed={"input": input_val,
|
|
"label": label_val},
|
|
fetch_list=[output])
|
|
|
|
self.assertTrue(np.isclose(np_result, result).all())
|
|
|
|
|
|
class TestMseInvalidInput(unittest.TestCase):
|
|
def test_error(self):
|
|
def test_invalid_input():
|
|
input = [256, 3]
|
|
label = fluid.data(name='label', shape=[None, 3], dtype='float32')
|
|
loss = fluid.layers.mse_loss(input, label)
|
|
|
|
self.assertRaises(TypeError, test_invalid_input)
|
|
|
|
def test_invalid_label():
|
|
input = fluid.data(name='input1', shape=[None, 3], dtype='float32')
|
|
label = [256, 3]
|
|
loss = fluid.layers.mse_loss(input, label)
|
|
|
|
self.assertRaises(TypeError, test_invalid_label)
|
|
|
|
|
|
class TestNNMseLoss(unittest.TestCase):
|
|
def test_NNMseLoss_mean(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
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.layers.data(
|
|
name='input', shape=dim, dtype='float32')
|
|
label = fluid.layers.data(
|
|
name='label', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.loss.MSELoss()
|
|
ret = mse_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"label": label_np},
|
|
fetch_list=[ret])
|
|
|
|
with fluid.dygraph.guard():
|
|
mse_loss = paddle.nn.loss.MSELoss()
|
|
dy_ret = mse_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - label_np
|
|
expected = np.mean(sub * sub)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
def test_NNMseLoss_sum(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
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.layers.data(
|
|
name='input', shape=dim, dtype='float32')
|
|
label = fluid.layers.data(
|
|
name='label', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
|
|
ret = mse_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"label": label_np},
|
|
fetch_list=[ret])
|
|
|
|
with fluid.dygraph.guard():
|
|
mse_loss = paddle.nn.loss.MSELoss(reduction='sum')
|
|
dy_ret = mse_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - label_np
|
|
expected = np.sum(sub * sub)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
def test_NNMseLoss_none(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
label_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
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.layers.data(
|
|
name='input', shape=dim, dtype='float32')
|
|
label = fluid.layers.data(
|
|
name='label', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.loss.MSELoss(reduction='none')
|
|
ret = mse_loss(input, label)
|
|
|
|
exe = fluid.Executor(place)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"label": label_np},
|
|
fetch_list=[ret])
|
|
|
|
with fluid.dygraph.guard():
|
|
mse_loss = paddle.nn.loss.MSELoss(reduction='none')
|
|
dy_ret = mse_loss(
|
|
fluid.dygraph.to_variable(input_np),
|
|
fluid.dygraph.to_variable(label_np))
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - label_np
|
|
expected = (sub * sub)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
|
|
class TestNNFunctionalMseLoss(unittest.TestCase):
|
|
def test_NNFunctionalMseLoss_mean(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
|
|
) else paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
input = paddle.fluid.data(name='input', shape=dim, dtype='float32')
|
|
target = paddle.fluid.data(name='target', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.functional.mse_loss(input, target, 'mean')
|
|
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_prog)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"target": target_np},
|
|
fetch_list=[mse_loss])
|
|
|
|
paddle.disable_static()
|
|
dy_ret = paddle.nn.functional.mse_loss(
|
|
paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'mean')
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - target_np
|
|
expected = np.mean(sub * sub)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
def test_NNFunctionalMseLoss_sum(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
|
|
) else paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
input = paddle.fluid.data(name='input', shape=dim, dtype='float32')
|
|
target = paddle.fluid.data(name='target', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.functional.mse_loss(input, target, 'sum')
|
|
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_prog)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"target": target_np},
|
|
fetch_list=[mse_loss])
|
|
|
|
paddle.disable_static()
|
|
dy_ret = paddle.nn.functional.mse_loss(
|
|
paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'sum')
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - target_np
|
|
expected = np.sum(sub * sub)
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
def test_NNFunctionalMseLoss_none(self):
|
|
for dim in [[10, 10], [2, 10, 10], [3, 3, 10, 10]]:
|
|
input_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
target_np = np.random.uniform(0.1, 0.5, dim).astype("float32")
|
|
paddle.enable_static()
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
|
|
) else paddle.CPUPlace()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
input = paddle.fluid.data(name='input', shape=dim, dtype='float32')
|
|
target = paddle.fluid.data(name='target', shape=dim, dtype='float32')
|
|
mse_loss = paddle.nn.functional.mse_loss(input, target, 'none')
|
|
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_prog)
|
|
static_result = exe.run(
|
|
prog,
|
|
feed={"input": input_np,
|
|
"target": target_np},
|
|
fetch_list=[mse_loss])
|
|
|
|
paddle.disable_static()
|
|
dy_ret = paddle.nn.functional.mse_loss(
|
|
paddle.to_tensor(input_np), paddle.to_tensor(target_np), 'none')
|
|
dy_result = dy_ret.numpy()
|
|
|
|
sub = input_np - target_np
|
|
expected = sub * sub
|
|
self.assertTrue(np.allclose(static_result, expected))
|
|
self.assertTrue(np.allclose(static_result, dy_result))
|
|
self.assertTrue(np.allclose(dy_result, expected))
|
|
self.assertTrue(dy_result.shape, [1])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|