wehub-resource-sync
135 lines
4.1 KiB
Python
135 lines
4.1 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 random
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import shutil
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import tempfile
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import time
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import unittest
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import numpy as np
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from paddle import Model
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from paddle.hapi.callbacks import config_callbacks
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from paddle.static import InputSpec
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from paddle.vision.datasets import MNIST
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from paddle.vision.models import LeNet
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class MnistDataset(MNIST):
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def __init__(self, mode, return_label=True, sample_num=None):
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super().__init__(mode=mode)
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self.return_label = return_label
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if sample_num:
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self.images = self.images[:sample_num]
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self.labels = self.labels[:sample_num]
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def __getitem__(self, idx):
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img, label = self.images[idx], self.labels[idx]
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img = np.reshape(img, [1, 28, 28])
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if self.return_label:
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return img, np.array(self.labels[idx]).astype('int64')
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return (img,)
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def __len__(self):
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return len(self.images)
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class TestCallbacks(unittest.TestCase):
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def setUp(self):
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self.save_dir = tempfile.mkdtemp()
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def tearDown(self):
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shutil.rmtree(self.save_dir)
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def run_callback(self):
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epochs = 2
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steps = 5
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freq = 2
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eval_steps = 2
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inputs = [InputSpec([None, 1, 28, 28], 'float32', 'image')]
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lenet = Model(LeNet(), inputs)
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lenet.prepare()
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cbks = config_callbacks(
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model=lenet,
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batch_size=128,
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epochs=epochs,
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steps=steps,
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log_freq=freq,
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verbose=self.verbose,
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metrics=['loss', 'acc'],
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save_dir=self.save_dir,
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)
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cbks.on_begin('train')
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logs = {'loss': 50.341673, 'acc': 0.00256}
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for epoch in range(epochs):
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cbks.on_epoch_begin(epoch)
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for step in range(steps):
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cbks.on_batch_begin('train', step, logs)
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logs['loss'] -= random.random() * 0.1
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logs['acc'] += random.random() * 0.1
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time.sleep(0.005)
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cbks.on_batch_end('train', step, logs)
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cbks.on_epoch_end(epoch, logs)
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eval_logs = {'eval_loss': 20.341673, 'eval_acc': 0.256}
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params = {
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'steps': eval_steps,
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'metrics': ['eval_loss', 'eval_acc'],
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}
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cbks.on_begin('eval', params)
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for step in range(eval_steps):
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cbks.on_batch_begin('eval', step, eval_logs)
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eval_logs['eval_loss'] -= random.random() * 0.1
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eval_logs['eval_acc'] += random.random() * 0.1
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eval_logs['batch_size'] = 2
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time.sleep(0.005)
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cbks.on_batch_end('eval', step, eval_logs)
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cbks.on_end('eval', eval_logs)
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test_logs = {}
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params = {'steps': eval_steps}
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cbks.on_begin('predict', params)
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for step in range(eval_steps):
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cbks.on_batch_begin('predict', step, test_logs)
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test_logs['batch_size'] = 2
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time.sleep(0.005)
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cbks.on_batch_end('predict', step, test_logs)
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cbks.on_end('predict', test_logs)
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cbks.on_end('train')
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def test_callback_verbose_0(self):
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self.verbose = 0
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self.run_callback()
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def test_callback_verbose_1(self):
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self.verbose = 1
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self.run_callback()
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def test_callback_verbose_2(self):
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self.verbose = 2
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self.run_callback()
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def test_callback_verbose_3(self):
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self.verbose = 3
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self.run_callback()
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if __name__ == '__main__':
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unittest.main()
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