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133 lines
4.6 KiB
133 lines
4.6 KiB
# 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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from __future__ import division
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from __future__ import print_function
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import os
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.base import to_variable
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from paddle.incubate.hapi.metrics import *
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from paddle.incubate.hapi.utils import to_list
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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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correct = (pred == np.repeat(label, maxk, 1))
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batch_size = label.shape[0]
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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(correct_k / batch_size)
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return res
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def convert_to_one_hot(y, C):
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oh = np.random.random((y.shape[0], C)).astype('float32') * .5
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for i in range(y.shape[0]):
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oh[i, int(y[i])] = 1.
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return oh
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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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def random_pred_label(self):
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label = np.random.randint(0, self.class_num,
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(self.sample_num, 1)).astype('int64')
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pred = np.random.randint(0, self.class_num,
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(self.sample_num, 1)).astype('int32')
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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 fluid.dygraph.guard(fluid.CPUPlace()):
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acc = 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 = to_variable(label)
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pred_var = to_variable(pred)
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state = to_list(acc.add_metric_op(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(np.isclose(np.array(res_m, dtype='float64'), np.array(res_f, dtype='float64'), rtol=1e-3)), \
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"Accuracy precision error: {} != {}".format(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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class TestAccuracyStatic(TestAccuracyDynamic):
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def test_main(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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pred = fluid.data(
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name='pred', shape=[None, self.class_num], dtype='float32')
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label = fluid.data(name='label', shape=[None, 1], dtype='int64')
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acc = Accuracy(topk=self.topk, name=self.name)
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state = acc.add_metric_op(pred, label)
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exe = fluid.Executor(fluid.CPUPlace())
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compiled_main_prog = fluid.CompiledProgram(main_prog)
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for _ in range(10):
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label, pred = self.random_pred_label()
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state_ret = exe.run(compiled_main_prog,
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feed={'pred': pred,
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'label': label},
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fetch_list=[s.name for s in to_list(state)],
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return_numpy=True)
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acc.update(*state_ret)
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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(np.isclose(np.array(res_m, dtype='float64'), np.array(res_f, dtype='float64'), rtol=1e-3)), \
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"Accuracy precision error: {} != {}".format(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 TestAccuracyStaticMultiTopk(TestAccuracyStatic):
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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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if __name__ == '__main__':
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unittest.main()
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