test_metrics.py

# 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.fluid as fluid
from paddle.fluid.dygraph.base import to_variable

from paddle.incubate.hapi.metrics import *
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(correct_k / batch_size)
    return res


def convert_to_one_hot(y, C):
    oh = np.random.random((y.shape[0], C)).astype('float32') * .5
    for i in range(y.shape[0]):
        oh[i, int(y[i])] = 1.
    return oh


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 = Accuracy(topk=self.topk, name=self.name)
            for _ in range(10):
                label, pred = self.random_pred_label()
                label_var = to_variable(label)
                pred_var = to_variable(pred)
                state = to_list(acc.add_metric_op(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()
        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 = Accuracy(topk=self.topk, name=self.name)
            state = acc.add_metric_op(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, 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 TestAccuracyStaticMultiTopk(TestAccuracyStatic):
    def setUp(self):
        self.topk = (1, 5)
        self.class_num = 10
        self.sample_num = 1000
        self.name = "accuracy"


if __name__ == '__main__':
    unittest.main()