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140 lines
4.4 KiB
140 lines
4.4 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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import sys
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import unittest
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import time
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import random
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import tempfile
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import shutil
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import paddle
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from paddle import Model
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from paddle.static import InputSpec
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from paddle.vision.models import LeNet
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from paddle.hapi.callbacks import config_callbacks
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import paddle.vision.transforms as T
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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 = 50
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freq = 2
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eval_steps = 20
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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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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('test', params)
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for step in range(eval_steps):
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cbks.on_batch_begin('test', 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('test', step, test_logs)
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cbks.on_end('test', 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_visualdl_callback(self):
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# visualdl not support python2
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if sys.version_info < (3, ):
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return
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inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
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labels = [InputSpec([None, 1], 'int64', 'label')]
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transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
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train_dataset = paddle.vision.datasets.MNIST(
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mode='train', transform=transform)
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eval_dataset = paddle.vision.datasets.MNIST(
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mode='test', transform=transform)
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net = paddle.vision.LeNet()
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model = paddle.Model(net, inputs, labels)
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optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
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model.prepare(
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optimizer=optim,
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loss=paddle.nn.CrossEntropyLoss(),
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metrics=paddle.metric.Accuracy())
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callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
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model.fit(train_dataset,
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eval_dataset,
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batch_size=64,
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callbacks=callback)
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if __name__ == '__main__':
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unittest.main()
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