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Paddle/python/paddle/tests/test_callbacks.py

108 lines
3.3 KiB

# 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.
import unittest
import time
import random
import tempfile
import shutil
from paddle import Model
from paddle.static import InputSpec
from paddle.vision.models import LeNet
from paddle.hapi.callbacks import config_callbacks
class TestCallbacks(unittest.TestCase):
def setUp(self):
self.save_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.save_dir)
def run_callback(self):
epochs = 2
steps = 50
freq = 2
eval_steps = 20
inputs = [InputSpec([None, 1, 28, 28], 'float32', 'image')]
lenet = Model(LeNet(), inputs)
lenet.prepare()
cbks = config_callbacks(
model=lenet,
batch_size=128,
epochs=epochs,
steps=steps,
log_freq=freq,
verbose=self.verbose,
metrics=['loss', 'acc'],
save_dir=self.save_dir)
cbks.on_begin('train')
logs = {'loss': 50.341673, 'acc': 0.00256}
for epoch in range(epochs):
cbks.on_epoch_begin(epoch)
for step in range(steps):
cbks.on_batch_begin('train', step, logs)
logs['loss'] -= random.random() * 0.1
logs['acc'] += random.random() * 0.1
time.sleep(0.005)
cbks.on_batch_end('train', step, logs)
cbks.on_epoch_end(epoch, logs)
eval_logs = {'eval_loss': 20.341673, 'eval_acc': 0.256}
params = {
'steps': eval_steps,
'metrics': ['eval_loss', 'eval_acc'],
}
cbks.on_begin('eval', params)
for step in range(eval_steps):
cbks.on_batch_begin('eval', step, eval_logs)
eval_logs['eval_loss'] -= random.random() * 0.1
eval_logs['eval_acc'] += random.random() * 0.1
eval_logs['batch_size'] = 2
time.sleep(0.005)
cbks.on_batch_end('eval', step, eval_logs)
cbks.on_end('eval', eval_logs)
test_logs = {}
params = {'steps': eval_steps}
cbks.on_begin('test', params)
for step in range(eval_steps):
cbks.on_batch_begin('test', step, test_logs)
test_logs['batch_size'] = 2
time.sleep(0.005)
cbks.on_batch_end('test', step, test_logs)
cbks.on_end('test', test_logs)
cbks.on_end('train')
def test_callback_verbose_0(self):
self.verbose = 0
self.run_callback()
def test_callback_verbose_1(self):
self.verbose = 1
self.run_callback()
def test_callback_verbose_2(self):
self.verbose = 2
self.run_callback()
if __name__ == '__main__':
unittest.main()