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.
276 lines
8.8 KiB
276 lines
8.8 KiB
# 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 division
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
import unittest
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
|
|
from paddle.incubate.hapi.utils import to_list
|
|
|
|
|
|
def accuracy(pred, label, topk=(1, )):
|
|
maxk = max(topk)
|
|
pred = np.argsort(pred)[:, ::-1][:, :maxk]
|
|
correct = (pred == np.repeat(label, maxk, 1))
|
|
|
|
batch_size = label.shape[0]
|
|
res = []
|
|
for k in topk:
|
|
correct_k = correct[:, :k].sum()
|
|
res.append(float(correct_k) / batch_size)
|
|
return res
|
|
|
|
|
|
def convert_to_one_hot(y, C):
|
|
oh = np.random.choice(np.arange(C), C, replace=False).astype('float32') / C
|
|
oh = np.tile(oh[np.newaxis, :], (y.shape[0], 1))
|
|
for i in range(y.shape[0]):
|
|
oh[i, int(y[i])] = 1.
|
|
return oh
|
|
|
|
|
|
class TestAccuracy(unittest.TestCase):
|
|
def test_acc(self):
|
|
paddle.disable_static()
|
|
|
|
x = paddle.to_tensor(
|
|
np.array([[0.1, 0.2, 0.3, 0.4], [0.1, 0.4, 0.3, 0.2],
|
|
[0.1, 0.2, 0.4, 0.3], [0.1, 0.2, 0.3, 0.4]]))
|
|
y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
|
|
|
|
m = paddle.metric.Accuracy(name='my_acc')
|
|
|
|
# check name
|
|
self.assertEqual(m.name(), ['my_acc'])
|
|
|
|
correct = m.compute(x, y)
|
|
# check results
|
|
self.assertEqual(m.update(correct), 0.75)
|
|
self.assertEqual(m.accumulate(), 0.75)
|
|
|
|
x = paddle.to_tensor(
|
|
np.array([[0.1, 0.2, 0.3, 0.4], [0.1, 0.3, 0.4, 0.2],
|
|
[0.1, 0.2, 0.4, 0.3], [0.1, 0.2, 0.3, 0.4]]))
|
|
y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
|
|
correct = m.compute(x, y)
|
|
# check results
|
|
self.assertEqual(m.update(correct), 0.5)
|
|
self.assertEqual(m.accumulate(), 0.625)
|
|
|
|
# check reset
|
|
m.reset()
|
|
self.assertEqual(m.total[0], 0.0)
|
|
self.assertEqual(m.count[0], 0.0)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestAccuracyDynamic(unittest.TestCase):
|
|
def setUp(self):
|
|
self.topk = (1, )
|
|
self.class_num = 5
|
|
self.sample_num = 1000
|
|
self.name = None
|
|
|
|
def random_pred_label(self):
|
|
label = np.random.randint(0, self.class_num,
|
|
(self.sample_num, 1)).astype('int64')
|
|
pred = np.random.randint(0, self.class_num,
|
|
(self.sample_num, 1)).astype('int32')
|
|
pred_one_hot = convert_to_one_hot(pred, self.class_num)
|
|
pred_one_hot = pred_one_hot.astype('float32')
|
|
|
|
return label, pred_one_hot
|
|
|
|
def test_main(self):
|
|
with fluid.dygraph.guard(fluid.CPUPlace()):
|
|
acc = paddle.metric.Accuracy(topk=self.topk, name=self.name)
|
|
for _ in range(10):
|
|
label, pred = self.random_pred_label()
|
|
label_var = paddle.to_tensor(label)
|
|
pred_var = paddle.to_tensor(pred)
|
|
state = to_list(acc.compute(pred_var, label_var))
|
|
acc.update(* [s.numpy() for s in state])
|
|
res_m = acc.accumulate()
|
|
res_f = accuracy(pred, label, self.topk)
|
|
assert np.all(np.isclose(np.array(res_m, dtype='float64'),
|
|
np.array(res_f, dtype='float64'), rtol=1e-3)), \
|
|
"Accuracy precision error: {} != {}".format(res_m, res_f)
|
|
acc.reset()
|
|
assert np.sum(acc.total) == 0
|
|
assert np.sum(acc.count) == 0
|
|
|
|
|
|
class TestAccuracyDynamicMultiTopk(TestAccuracyDynamic):
|
|
def setUp(self):
|
|
self.topk = (1, 5)
|
|
self.class_num = 10
|
|
self.sample_num = 1000
|
|
self.name = "accuracy"
|
|
|
|
|
|
class TestAccuracyStatic(TestAccuracyDynamic):
|
|
def test_main(self):
|
|
main_prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
main_prog.random_seed = 1024
|
|
startup_prog.random_seed = 1024
|
|
with fluid.program_guard(main_prog, startup_prog):
|
|
pred = fluid.data(
|
|
name='pred', shape=[None, self.class_num], dtype='float32')
|
|
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
|
|
acc = paddle.metric.Accuracy(topk=self.topk, name=self.name)
|
|
state = acc.compute(pred, label)
|
|
|
|
exe = fluid.Executor(fluid.CPUPlace())
|
|
compiled_main_prog = fluid.CompiledProgram(main_prog)
|
|
|
|
for _ in range(10):
|
|
label, pred = self.random_pred_label()
|
|
state_ret = exe.run(compiled_main_prog,
|
|
feed={'pred': pred,
|
|
'label': label},
|
|
fetch_list=[s.name for s in to_list(state)],
|
|
return_numpy=True)
|
|
acc.update(*state_ret)
|
|
res_m = acc.accumulate()
|
|
res_f = accuracy(pred, label, self.topk)
|
|
assert np.all(np.isclose(np.array(res_m), np.array(res_f), rtol=1e-3)), \
|
|
"Accuracy precision error: {} != {}".format(res_m, res_f)
|
|
acc.reset()
|
|
assert np.sum(acc.total) == 0
|
|
assert np.sum(acc.count) == 0
|
|
|
|
|
|
class TestAccuracyStaticMultiTopk(TestAccuracyStatic):
|
|
def setUp(self):
|
|
self.topk = (1, 5)
|
|
self.class_num = 10
|
|
self.sample_num = 100
|
|
self.name = "accuracy"
|
|
|
|
|
|
class TestPrecision(unittest.TestCase):
|
|
def test_1d(self):
|
|
paddle.disable_static()
|
|
|
|
x = np.array([0.1, 0.5, 0.6, 0.7])
|
|
y = np.array([1, 0, 1, 1])
|
|
|
|
m = paddle.metric.Precision()
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 2. / 3.)
|
|
|
|
x = paddle.to_tensor(np.array([0.1, 0.5, 0.6, 0.7, 0.2]))
|
|
y = paddle.to_tensor(np.array([1, 0, 1, 1, 1]))
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 4. / 6.)
|
|
|
|
paddle.enable_static()
|
|
|
|
def test_2d(self):
|
|
paddle.disable_static()
|
|
|
|
x = np.array([0.1, 0.5, 0.6, 0.7]).reshape(-1, 1)
|
|
y = np.array([1, 0, 1, 1]).reshape(-1, 1)
|
|
|
|
m = paddle.metric.Precision()
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 2. / 3.)
|
|
|
|
x = np.array([0.1, 0.5, 0.6, 0.7, 0.2]).reshape(-1, 1)
|
|
y = np.array([1, 0, 1, 1, 1]).reshape(-1, 1)
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 4. / 6.)
|
|
|
|
# check reset
|
|
m.reset()
|
|
self.assertEqual(m.tp, 0.0)
|
|
self.assertEqual(m.fp, 0.0)
|
|
self.assertEqual(m.accumulate(), 0.0)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestRecall(unittest.TestCase):
|
|
def test_1d(self):
|
|
paddle.disable_static()
|
|
|
|
x = np.array([0.1, 0.5, 0.6, 0.7])
|
|
y = np.array([1, 0, 1, 1])
|
|
|
|
m = paddle.metric.Recall()
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 2. / 3.)
|
|
|
|
x = paddle.to_tensor(np.array([0.1, 0.5, 0.6, 0.7]))
|
|
y = paddle.to_tensor(np.array([1, 0, 0, 1]))
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 3. / 5.)
|
|
|
|
# check reset
|
|
m.reset()
|
|
self.assertEqual(m.tp, 0.0)
|
|
self.assertEqual(m.fn, 0.0)
|
|
self.assertEqual(m.accumulate(), 0.0)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestAuc(unittest.TestCase):
|
|
def test_auc_numpy(self):
|
|
paddle.disable_static()
|
|
x = np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
|
|
[0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]])
|
|
y = np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
|
|
m = paddle.metric.Auc()
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 0.8125)
|
|
|
|
m.reset()
|
|
self.assertEqual(m.accumulate(), 0.0)
|
|
|
|
paddle.enable_static()
|
|
|
|
def test_auc_tensor(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
|
|
[0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]]))
|
|
y = paddle.to_tensor(np.array([[0], [1], [1], [0], [1], [0], [0], [1]]))
|
|
m = paddle.metric.Auc()
|
|
m.update(x, y)
|
|
r = m.accumulate()
|
|
self.assertAlmostEqual(r, 0.8125)
|
|
|
|
m.reset()
|
|
self.assertEqual(m.accumulate(), 0.0)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|