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107 lines
3.9 KiB
107 lines
3.9 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 numpy as np
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import paddle
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import paddle.vision.transforms as T
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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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from paddle.vision.datasets import MNIST
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from paddle.metric import Accuracy
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from paddle.nn.layer.loss import CrossEntropyLoss
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# Accelerate unittest
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class CustomMnist(MNIST):
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def __len__(self):
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return 8
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class TestReduceLROnPlateau(unittest.TestCase):
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def test_reduce_lr_on_plateau(self):
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transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
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train_dataset = CustomMnist(mode='train', transform=transform)
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val_dataset = CustomMnist(mode='test', transform=transform)
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net = LeNet()
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optim = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=net.parameters())
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inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
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labels = [InputSpec([None, 1], 'int64', 'label')]
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model = Model(net, inputs=inputs, labels=labels)
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model.prepare(optim, loss=CrossEntropyLoss(), metrics=[Accuracy()])
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callbacks = paddle.callbacks.ReduceLROnPlateau(
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patience=1, verbose=1, cooldown=1)
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model.fit(train_dataset,
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val_dataset,
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batch_size=8,
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log_freq=1,
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save_freq=10,
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epochs=10,
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callbacks=[callbacks])
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def test_warn_or_error(self):
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with self.assertRaises(ValueError):
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paddle.callbacks.ReduceLROnPlateau(factor=2.0)
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# warning
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paddle.callbacks.ReduceLROnPlateau(mode='1', patience=3, verbose=1)
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transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
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train_dataset = CustomMnist(mode='train', transform=transform)
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val_dataset = CustomMnist(mode='test', transform=transform)
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net = LeNet()
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optim = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=net.parameters())
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inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
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labels = [InputSpec([None, 1], 'int64', 'label')]
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model = Model(net, inputs=inputs, labels=labels)
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model.prepare(optim, loss=CrossEntropyLoss(), metrics=[Accuracy()])
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callbacks = paddle.callbacks.ReduceLROnPlateau(
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monitor='miou', patience=3, verbose=1)
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model.fit(train_dataset,
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val_dataset,
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batch_size=8,
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log_freq=1,
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save_freq=10,
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epochs=1,
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callbacks=[callbacks])
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optim = paddle.optimizer.Adam(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay([0.001, 0.0001],
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[5, 10]),
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parameters=net.parameters())
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model.prepare(optim, loss=CrossEntropyLoss(), metrics=[Accuracy()])
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callbacks = paddle.callbacks.ReduceLROnPlateau(
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monitor='acc', mode='max', patience=3, verbose=1, cooldown=1)
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model.fit(train_dataset,
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val_dataset,
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batch_size=8,
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log_freq=1,
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save_freq=10,
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epochs=3,
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callbacks=[callbacks])
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if __name__ == '__main__':
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unittest.main()
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