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105 lines
3.6 KiB
105 lines
3.6 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.fluid as fluid
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import paddle.fluid.layers as layers
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import numpy as np
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import os
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import paddle.fluid.core as core
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import unittest
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from paddle.fluid.layers.nn import _pull_box_sparse
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class TestBoxPSPreload(unittest.TestCase):
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""" TestCases for BoxPS Preload """
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def test_boxps_cpu(self):
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self.run_boxps_preload(True)
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def test_boxps_gpu(self):
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self.run_boxps_preload(False)
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def run_boxps_preload(self, is_cpu=True):
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x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
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y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
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emb_x, emb_y = _pull_box_sparse([x, y], size=2)
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emb_xp = _pull_box_sparse(x, size=2)
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layers.Print(emb_xp)
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concat = layers.concat([emb_x, emb_y], axis=1)
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fc = layers.fc(input=concat,
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name="fc",
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size=1,
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num_flatten_dims=1,
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bias_attr=False)
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loss = layers.reduce_mean(fc)
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layers.Print(loss)
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place = fluid.CPUPlace() if is_cpu or not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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optimizer = fluid.optimizer.SGD(learning_rate=0.5)
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batch_size = 2
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def binary_print(slot, fout):
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fout.write(str(len(slot)) + " ")
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for e in slot:
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fout.write(str(e) + " ")
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batch1 = np.ones(
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(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
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filelist = []
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place_str = "cpu" if is_cpu else "gpu"
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for i in range(2):
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filelist.append("test_hdfs_" + place_str + "_" + str(i))
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for f in filelist:
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with open(f, "w") as fout:
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for ins in batch1:
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for slot in ins:
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binary_print(slot, fout)
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fout.write("\n")
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def create_dataset():
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dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
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dataset.set_use_var([x, y])
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dataset.set_batch_size(2)
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dataset.set_thread(1)
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dataset.set_filelist(filelist)
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return dataset
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datasets = []
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datasets.append(create_dataset())
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datasets.append(create_dataset())
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optimizer.minimize(loss)
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exe.run(fluid.default_startup_program())
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datasets[0].load_into_memory()
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datasets[0].begin_pass()
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datasets[1].preload_into_memory()
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exe.train_from_dataset(
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program=fluid.default_main_program(),
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dataset=datasets[0],
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print_period=1)
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datasets[0].end_pass()
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datasets[1].wait_preload_done()
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datasets[1].begin_pass()
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exe.train_from_dataset(
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program=fluid.default_main_program(),
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dataset=datasets[1],
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print_period=1)
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datasets[1].end_pass()
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for f in filelist:
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os.remove(f)
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if __name__ == '__main__':
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unittest.main()
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