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125 lines
4.2 KiB
125 lines
4.2 KiB
# Copyright (c) 2018 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 os
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import time
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import unittest
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import sys
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from multiprocessing import Process
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import signal
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import numpy
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.layers.io import ListenAndServ
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from paddle.fluid.layers.io import Recv
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from paddle.fluid.layers.io import Send
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import paddle.fluid.layers.ops as ops
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class TestProgram2Code(unittest.TestCase):
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@unittest.skipIf(sys.platform == "win32",
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"Windows does not support distribution")
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def test_print(self):
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place = fluid.CPUPlace()
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self.init_serv(place)
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self.init_client(place, 9123)
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def init_serv(self, place):
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main = fluid.Program()
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with fluid.program_guard(main):
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serv = ListenAndServ("127.0.0.1:0", ["X"], optimizer_mode=False)
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with serv.do():
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out_var = main.global_block().create_var(
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name="scale_0.tmp_0",
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psersistable=True,
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dtype="float32",
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shape=[32, 32])
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x = layers.data(
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shape=[32, 32],
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dtype='float32',
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name="X",
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append_batch_size=False)
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fluid.initializer.Constant(value=1.0)(x, main.global_block())
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ops._scale(x=x, scale=10.0, out=out_var)
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print(main)
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def init_client(self, place, port):
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main = fluid.Program()
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with fluid.program_guard(main):
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x = layers.data(
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shape=[32, 32],
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dtype='float32',
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name='X',
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append_batch_size=False)
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fluid.initializer.Constant(value=2.3)(x, main.global_block())
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get_var = main.global_block().create_var(
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name="scale_0.tmp_0", # server side var
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dtype="float32",
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persistable=False,
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shape=[32, 32])
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fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
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Send("127.0.0.1:%d" % port, [x])
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o = Recv("127.0.0.1:%d" % port, [get_var])
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print(main)
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class TestProgramToReadableCode(unittest.TestCase):
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def setUp(self):
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self.program = fluid.Program()
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self.block = self.program.current_block()
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self.var = self.block.create_var(
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name="X", shape=[-1, 23, 48], dtype='float32')
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self.param = self.block.create_parameter(
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name="W", shape=[23, 48], dtype='float32', trainable=True)
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self.op = self.block.append_op(
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type="abs", inputs={"X": [self.var]}, outputs={"Out": [self.var]})
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# add control flow op and sub block
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self.append_cond_op(self.program)
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def append_cond_op(self, program):
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def true_func():
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return layers.fill_constant(shape=[2, 3], dtype='int32', value=2)
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def false_func():
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return layers.fill_constant(shape=[3, 2], dtype='int32', value=-1)
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with fluid.program_guard(program):
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x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
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y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
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pred = layers.less_than(y, x)
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out = layers.cond(pred, true_func, false_func)
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def test_program_code(self):
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self.var._to_readable_code()
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self.param._to_readable_code()
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self.op._to_readable_code()
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self.block._to_readable_code()
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self.program._to_readable_code()
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def test_program_print(self):
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print(self.var)
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print(self.param)
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print(self.op)
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print(self.block)
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print(self.program)
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if __name__ == "__main__":
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unittest.main()
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