# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import time import unittest import sys from multiprocessing import Process import signal import numpy import paddle.fluid as fluid import paddle.fluid.layers as layers from paddle.fluid.layers.io import ListenAndServ from paddle.fluid.layers.io import Recv from paddle.fluid.layers.io import Send import paddle.fluid.layers.ops as ops class TestProgram2Code(unittest.TestCase): @unittest.skipIf(sys.platform == "win32", "Windows does not support distribution") def test_print(self): place = fluid.CPUPlace() self.init_serv(place) self.init_client(place, 9123) def init_serv(self, place): main = fluid.Program() with fluid.program_guard(main): serv = ListenAndServ("127.0.0.1:0", ["X"], optimizer_mode=False) with serv.do(): out_var = main.global_block().create_var( name="scale_0.tmp_0", psersistable=True, dtype="float32", shape=[32, 32]) x = layers.data( shape=[32, 32], dtype='float32', name="X", append_batch_size=False) fluid.initializer.Constant(value=1.0)(x, main.global_block()) ops._scale(x=x, scale=10.0, out=out_var) print(main) def init_client(self, place, port): main = fluid.Program() with fluid.program_guard(main): x = layers.data( shape=[32, 32], dtype='float32', name='X', append_batch_size=False) fluid.initializer.Constant(value=2.3)(x, main.global_block()) get_var = main.global_block().create_var( name="scale_0.tmp_0", # server side var dtype="float32", persistable=False, shape=[32, 32]) fluid.initializer.Constant(value=2.3)(get_var, main.global_block()) Send("127.0.0.1:%d" % port, [x]) o = Recv("127.0.0.1:%d" % port, [get_var]) print(main) class TestProgramToReadableCode(unittest.TestCase): def setUp(self): self.program = fluid.Program() self.block = self.program.current_block() self.var = self.block.create_var( name="X", shape=[-1, 23, 48], dtype='float32') self.param = self.block.create_parameter( name="W", shape=[23, 48], dtype='float32', trainable=True) self.op = self.block.append_op( type="abs", inputs={"X": [self.var]}, outputs={"Out": [self.var]}) # add control flow op and sub block self.append_cond_op(self.program) def append_cond_op(self, program): def true_func(): return layers.fill_constant(shape=[2, 3], dtype='int32', value=2) def false_func(): return layers.fill_constant(shape=[3, 2], dtype='int32', value=-1) with fluid.program_guard(program): x = layers.fill_constant(shape=[1], dtype='float32', value=0.1) y = layers.fill_constant(shape=[1], dtype='float32', value=0.23) pred = layers.less_than(y, x) out = layers.cond(pred, true_func, false_func) def test_program_code(self): self.var._to_readable_code() self.param._to_readable_code() self.op._to_readable_code() self.block._to_readable_code() self.program._to_readable_code() def test_program_print(self): print(self.var) print(self.param) print(self.op) print(self.block) print(self.program) if __name__ == "__main__": unittest.main()