# 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.
from __future__ import print_function
import paddle
import paddle.fluid.layers as layers
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.executor import Executor
from paddle.fluid.optimizer import MomentumOptimizer
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.layers.control_flow import split_lod_tensor
from paddle.fluid.layers.control_flow import merge_lod_tensor
from paddle.fluid.layers.control_flow import ConditionalBlock
import unittest
import numpy as np
class TestMNISTIfElseOp(unittest.TestCase):
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def not_test_raw_api(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = split_lod_tensor(input=image, mask=cond)
true_out = layers.create_tensor(dtype='float32')
true_cond = ConditionalBlock([cond])
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=true_out)
false_out = layers.create_tensor(dtype='float32')
false_cond = ConditionalBlock([cond])
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
prob = merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=10)
place = core.CPUPlace()
exe = Executor(place)
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print(outs[0])
if outs[0] < 1.0:
return
self.assertFalse(True)
# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
def not_test_ifelse(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
ie = layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
loss = layers.cross_entropy(input=prob[0], label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=200)
place = core.CPUPlace()
exe = Executor(place)
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = y_data.reshape((y_data.shape[0], 1))
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print(outs[0])
if outs[0] < 1.0:
return
self.assertFalse(True)
class TestIfElse(unittest.TestCase):
def set_test_case(self):
# condiction is: self.data < self.cond_value
self.cond_value = 0.5
self.data = np.random.rand(25, 1).astype(np.float32)
def numpy_cal(self):
s1 = self.data[np.where(self.data < self.cond_value)]
res = np.sum(np.exp(s1))
s2 = self.data[np.where(self.data >= self.cond_value)]
res += np.sum(np.tanh(s2))
return res
def compare_ifelse_op_and_numpy(self, place):
self.set_test_case()
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
src = layers.data(name='data', shape=[1], dtype='float32')
cond = layers.fill_constant(
[1], dtype='float32', value=self.cond_value)
ifcond = layers.less_than(x=src, y=cond)
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
ie.output(true_target)
with ie.false_block():
false_target = ie.input(src)
false_target = fluid.layers.tanh(false_target)
ie.output(false_target)
if_out = ie()
out = layers.reduce_sum(if_out)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fetch_list = [out]
o1, = exe.run(fluid.default_main_program(),
feed={'data': self.data},
fetch_list=[out])
o2 = self.numpy_cal()
self.assertTrue(
np.allclose(
o1, o2, atol=1e-8),
"IfElse result : " + str(o1) + "\n Numpy result :" + str(o2))
def test_cpu(self):
self.compare_ifelse_op_and_numpy(fluid.CPUPlace())
def test_cuda(self):
if not core.is_compiled_with_cuda():
return
self.compare_ifelse_op_and_numpy(fluid.CUDAPlace(0))
class TestIfElseTrueBranch(TestIfElse):
def set_test_case(self):
# condiction is: self.data < self.cond_value
self.cond_value = 10.
self.data = np.random.rand(25, 1).astype(np.float32)
class TestIfElseFalseBranch(TestIfElse):
def set_test_case(self):
# condiction is: self.data < self.cond_value
self.cond_value = -10.
self.data = np.random.rand(25, 1).astype(np.float32)
if __name__ == '__main__':
unittest.main()