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117 lines
4.3 KiB
117 lines
4.3 KiB
# Copyright (c) 2016 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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from py_paddle import swig_paddle
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import paddle.proto.ParameterConfig_pb2
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import util
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import unittest
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import numpy
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class TestGradientMachine(unittest.TestCase):
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def test_create_gradient_machine(self):
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conf_file_path = "./testTrainConfig.py"
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trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
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conf_file_path)
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self.assertIsNotNone(trainer_config)
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opt_config = trainer_config.getOptimizationConfig()
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model_config = trainer_config.getModelConfig()
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self.assertIsNotNone(model_config)
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machine = swig_paddle.GradientMachine.createByModelConfig(
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model_config, swig_paddle.CREATE_MODE_NORMAL,
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swig_paddle.ParameterOptimizer.create(opt_config).getParameterTypes(
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))
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self.assertIsNotNone(machine)
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ipt, _ = util.loadMNISTTrainData()
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output = swig_paddle.Arguments.createArguments(0)
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optimizers = {}
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# Initial Machine Parameter all to 0.1
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for param in machine.getParameters():
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assert isinstance(param, swig_paddle.Parameter)
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val = param.getBuf(swig_paddle.PARAMETER_VALUE)
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assert isinstance(val, swig_paddle.Vector)
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arr = numpy.full((len(val), ), 0.1, dtype="float32")
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val.copyFromNumpyArray(arr)
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self.assertTrue(param.save(param.getName()))
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param_config = param.getConfig().toProto()
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assert isinstance(param_config,
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paddle.proto.ParameterConfig_pb2.ParameterConfig)
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opt = swig_paddle.ParameterOptimizer.create(opt_config)
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optimizers[param.getID()] = opt
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num_rows = param_config.dims[1]
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opt.init(num_rows, param.getConfig())
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for k in optimizers:
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opt = optimizers[k]
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opt.startPass()
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batch_size = ipt.getSlotValue(0).getHeight()
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for k in optimizers:
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opt = optimizers[k]
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opt.startBatch(batch_size)
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machine.forward(ipt, output, swig_paddle.PASS_TRAIN)
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self.assertEqual(1, output.getSlotNum())
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self.isCalled = False
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def backward_callback(param_):
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self.isCalled = isinstance(param_, swig_paddle.Parameter)
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assert isinstance(param_, swig_paddle.Parameter)
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vec = param_.getBuf(swig_paddle.PARAMETER_VALUE)
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assert isinstance(vec, swig_paddle.Vector)
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vec = vec.copyToNumpyArray()
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for val_ in vec:
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self.assertTrue(
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util.doubleEqual(val_, 0.1)) # Assert All Value is 0.1
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vecs = list(param_.getBufs())
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opt_ = optimizers[param_.getID()]
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opt_.update(vecs, param_.getConfig())
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machine.backward(backward_callback)
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for k in optimizers:
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opt = optimizers[k]
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opt.finishBatch()
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for k in optimizers:
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opt = optimizers[k]
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opt.finishPass()
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self.assertTrue(self.isCalled)
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for param in machine.getParameters():
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self.assertTrue(param.load(param.getName()))
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def test_train_one_pass(self):
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conf_file_path = './testTrainConfig.py'
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trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
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conf_file_path)
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model_config = trainer_config.getModelConfig()
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machine = swig_paddle.GradientMachine.createByModelConfig(model_config)
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at_end = False
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output = swig_paddle.Arguments.createArguments(0)
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if not at_end:
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input_, at_end = util.loadMNISTTrainData(1000)
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machine.forwardBackward(input_, output, swig_paddle.PASS_TRAIN)
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if __name__ == '__main__':
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swig_paddle.initPaddle('--use_gpu=0')
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unittest.main()
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