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83 lines
3.0 KiB
83 lines
3.0 KiB
# copyright (c) 2019 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 paddle
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import unittest
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import paddle.fluid as fluid
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from mobilenet import MobileNet
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from paddle.fluid.contrib.slim.core import Compressor
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from paddle.fluid.contrib.slim.graph import GraphWrapper
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class TestQuantizationStrategy(unittest.TestCase):
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"""
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Test API of quantization strategy.
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"""
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def test_compression(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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class_dim = 10
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image_shape = [1, 28, 28]
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image = fluid.layers.data(
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name='image', shape=image_shape, dtype='float32')
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image.stop_gradient = False
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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out = MobileNet(name='quan').net(input=image, class_dim=class_dim)
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acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
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acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
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val_program = fluid.default_main_program().clone(for_test=False)
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cost = fluid.layers.cross_entropy(input=out, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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optimizer = fluid.optimizer.Momentum(
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momentum=0.9,
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learning_rate=0.01,
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regularization=fluid.regularizer.L2Decay(4e-5))
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place = fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
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val_feed_list = [('img', image.name), ('label', label.name)]
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val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
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acc_top5.name)]
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128)
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train_feed_list = [('img', image.name), ('label', label.name)]
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train_fetch_list = [('loss', avg_cost.name)]
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com_pass = Compressor(
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place,
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fluid.global_scope(),
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fluid.default_main_program(),
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train_reader=train_reader,
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train_feed_list=train_feed_list,
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train_fetch_list=train_fetch_list,
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eval_program=val_program,
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eval_reader=val_reader,
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eval_feed_list=val_feed_list,
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eval_fetch_list=val_fetch_list,
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train_optimizer=optimizer)
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com_pass.config('./quantization/compress.yaml')
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eval_graph = com_pass.run()
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if __name__ == '__main__':
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unittest.main()
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