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Paddle/python/paddle/fluid/tests/unittests/test_print_op.py

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# 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 unittest
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
import paddle.fluid.layers as layers
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import switch_main_program
from paddle.fluid.framework import Program
import numpy as np
class TestPrintOpCPU(unittest.TestCase):
def setUp(self):
self.place = core.CPUPlace()
self.x_tensor = core.LoDTensor()
tensor_np = np.random.random(size=(2, 3)).astype('float32')
self.x_tensor.set(tensor_np, self.place)
self.x_tensor.set_lod([[0, 1, 1]])
def build_network(self, only_forward, **kargs):
x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
x.stop_gradient = False
printed = layers.Print(input=x, **kargs)
if only_forward: return printed
loss = layers.mean(printed)
append_backward(loss=loss)
return loss
def test_forward(self):
switch_main_program(Program())
printed = self.build_network(True, print_phase='forward')
exe = Executor(self.place)
outs = exe.run(feed={'x': self.x_tensor},
fetch_list=[printed],
return_numpy=False)
def test_backward(self):
switch_main_program(Program())
loss = self.build_network(False, print_phase='backward')
exe = Executor(self.place)
outs = exe.run(feed={'x': self.x_tensor},
fetch_list=[loss],
return_numpy=False)
class TestPrintOpGPU(TestPrintOpCPU):
def setUp(self):
self.place = core.CUDAPlace(0)
self.x_tensor = core.LoDTensor()
tensor_np = np.random.random(size=(2, 3)).astype('float32')
self.x_tensor.set(tensor_np, self.place)
self.x_tensor.set_lod([[0, 1, 1]])
if __name__ == '__main__':
unittest.main()