wehub-resource-sync
302 lines
8.7 KiB
Python
302 lines
8.7 KiB
Python
# 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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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.hapi.model import to_list
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def one_hot(x, n_class):
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res = np.eye(n_class)[np.array(x).reshape(-1)]
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res = res.reshape([*x.shape, n_class])
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return res
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def accuracy(pred, label, topk=(1,)):
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maxk = max(topk)
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pred = np.argsort(pred)[..., ::-1][..., :maxk]
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if len(label.shape) == 1:
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label = label.reshape(-1, 1)
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elif label.shape[-1] != 1:
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label = np.argmax(label, axis=-1)
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label = label[..., np.newaxis]
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correct = pred == np.repeat(label, maxk, -1)
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total = np.prod(np.array(label.shape[:-1]))
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res = []
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for k in topk:
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correct_k = correct[..., :k].sum()
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res.append(float(correct_k) / total)
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return res
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def convert_to_one_hot(y, C):
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oh = np.random.choice(np.arange(C), C, replace=False).astype('float32') / C
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oh = np.tile(oh[np.newaxis, :], (y.shape[0], 1))
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labels = np.asarray(y).reshape(-1)
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for i in range(y.shape[0]):
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oh[i, int(labels[i])] = 1.0
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return oh
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class TestAccuracy(unittest.TestCase):
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def test_acc(self, squeeze_y=False):
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x = paddle.to_tensor(
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np.array(
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[
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[0.1, 0.2, 0.3, 0.4],
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[0.1, 0.4, 0.3, 0.2],
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[0.1, 0.2, 0.4, 0.3],
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[0.1, 0.2, 0.3, 0.4],
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]
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)
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)
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y = np.array([[0], [1], [2], [3]])
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if squeeze_y:
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y = y.squeeze()
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y = paddle.to_tensor(y)
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m = paddle.metric.Accuracy(name='my_acc')
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# check name
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self.assertEqual(m.name(), ['my_acc'])
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correct = m.compute(x, y)
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# check shape and results
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self.assertEqual(correct.shape, [4, 1])
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self.assertEqual(m.update(correct), 0.75)
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self.assertEqual(m.accumulate(), 0.75)
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x = paddle.to_tensor(
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np.array(
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[
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[0.1, 0.2, 0.3, 0.4],
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[0.1, 0.3, 0.4, 0.2],
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[0.1, 0.2, 0.4, 0.3],
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[0.1, 0.2, 0.3, 0.4],
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]
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)
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)
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y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
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correct = m.compute(x, y)
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# check results
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self.assertEqual(m.update(correct), 0.5)
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self.assertEqual(m.accumulate(), 0.625)
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# check reset
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m.reset()
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self.assertEqual(m.total[0], 0.0)
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self.assertEqual(m.count[0], 0.0)
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def test_1d_label(self):
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self.test_acc(True)
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def compare(self, x_np, y_np, k=(1,)):
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x = paddle.to_tensor(x_np)
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y = paddle.to_tensor(y_np)
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m = paddle.metric.Accuracy(name='my_acc', topk=k)
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correct = m.compute(x, y)
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acc_np = accuracy(x_np, y_np, k)
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acc_np = acc_np[0] if len(acc_np) == 1 else acc_np
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# check shape and results
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self.assertEqual(correct.shape, [*list(x_np.shape)[:-1], max(k)])
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self.assertEqual(m.update(correct), acc_np)
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self.assertEqual(m.accumulate(), acc_np)
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def test_3d(self):
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x_np = np.random.rand(2, 3, 4)
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y_np = np.random.randint(4, size=(2, 3, 1))
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self.compare(x_np, y_np)
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def test_one_hot(self):
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x_np = np.random.rand(2, 3, 4)
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y_np = np.random.randint(4, size=(2, 3))
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y_one_hot_np = one_hot(y_np, 4)
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self.compare(x_np, y_one_hot_np, (1, 2))
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class TestAccuracyDynamic(unittest.TestCase):
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def setUp(self):
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self.topk = (1,)
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self.class_num = 5
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self.sample_num = 1000
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self.name = None
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self.squeeze_label = False
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def random_pred_label(self):
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label = np.random.randint(
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0, self.class_num, (self.sample_num, 1)
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).astype('int64')
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pred = np.random.randint(
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0, self.class_num, (self.sample_num, 1)
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).astype('int32')
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if self.squeeze_label:
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label = label.squeeze()
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pred_one_hot = convert_to_one_hot(pred, self.class_num)
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pred_one_hot = pred_one_hot.astype('float32')
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return label, pred_one_hot
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def test_main(self):
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with base.dygraph.guard(base.CPUPlace()):
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acc = paddle.metric.Accuracy(topk=self.topk, name=self.name)
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for _ in range(10):
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label, pred = self.random_pred_label()
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label_var = paddle.to_tensor(label)
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pred_var = paddle.to_tensor(pred)
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state = to_list(acc.compute(pred_var, label_var))
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acc.update(*[s.numpy() for s in state])
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res_m = acc.accumulate()
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res_f = accuracy(pred, label, self.topk)
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assert np.all(
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np.isclose(
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np.array(res_m, dtype='float64'),
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np.array(res_f, dtype='float64'),
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rtol=1e-3,
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)
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), f"Accuracy precision error: {res_m} != {res_f}"
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acc.reset()
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assert np.sum(acc.total) == 0
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assert np.sum(acc.count) == 0
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class TestAccuracyDynamicMultiTopk(TestAccuracyDynamic):
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def setUp(self):
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self.topk = (1, 5)
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self.class_num = 10
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self.sample_num = 1000
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self.name = "accuracy"
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self.squeeze_label = True
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class TestPrecision(unittest.TestCase):
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def test_1d(self):
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x = np.array([0.1, 0.5, 0.6, 0.7])
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y = np.array([1, 0, 1, 1])
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m = paddle.metric.Precision()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2.0 / 3.0)
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x = np.array([0.1, 0.5, 0.6, 0.7, 0.2])
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y = np.array([1, 0, 1, 1, 1])
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 4.0 / 6.0)
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def test_2d(self):
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x = np.array([0.1, 0.5, 0.6, 0.7]).reshape(-1, 1)
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y = np.array([1, 0, 1, 1]).reshape(-1, 1)
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m = paddle.metric.Precision()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2.0 / 3.0)
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x = np.array([0.1, 0.5, 0.6, 0.7, 0.2]).reshape(-1, 1)
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y = np.array([1, 0, 1, 1, 1]).reshape(-1, 1)
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 4.0 / 6.0)
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# check reset
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m.reset()
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self.assertEqual(m.tp, 0.0)
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self.assertEqual(m.fp, 0.0)
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self.assertEqual(m.accumulate(), 0.0)
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class TestRecall(unittest.TestCase):
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def test_1d(self):
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x = np.array([0.1, 0.5, 0.6, 0.7])
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y = np.array([1, 0, 1, 1])
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m = paddle.metric.Recall()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2.0 / 3.0)
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x = np.array([0.1, 0.5, 0.6, 0.7])
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y = np.array([1, 0, 0, 1])
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 3.0 / 5.0)
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# check reset
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m.reset()
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self.assertEqual(m.tp, 0.0)
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self.assertEqual(m.fn, 0.0)
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self.assertEqual(m.accumulate(), 0.0)
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class TestAuc(unittest.TestCase):
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def test_auc_numpy(self):
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x = np.array(
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[
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[0.78, 0.22],
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[0.62, 0.38],
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[0.55, 0.45],
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[0.30, 0.70],
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[0.14, 0.86],
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[0.59, 0.41],
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[0.91, 0.08],
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[0.16, 0.84],
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]
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)
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y = np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
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m = paddle.metric.Auc()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 0.8125)
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m.reset()
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self.assertEqual(m.accumulate(), 0.0)
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def test_auc_tensor(self):
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x = np.array(
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[
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[0.78, 0.22],
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[0.62, 0.38],
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[0.55, 0.45],
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[0.30, 0.70],
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[0.14, 0.86],
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[0.59, 0.41],
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[0.91, 0.08],
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[0.16, 0.84],
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]
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)
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y = np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
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m = paddle.metric.Auc()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 0.8125)
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m.reset()
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self.assertEqual(m.accumulate(), 0.0)
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if __name__ == '__main__':
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unittest.main()
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