提交 b8d1f503 编写于 作者: Z Zhen Wang

Add the executor test for the graph clone API. test=develop

上级 ac6ef06f
......@@ -13,59 +13,92 @@
# limitations under the license.
from __future__ import print_function
import os
import six
import unittest
import paddle
import paddle.fluid as fluid
import six
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["CPU_NUM"] = "1"
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
def conv_block():
img = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return [img, label], avg_loss
class TestGraph(unittest.TestCase):
def test_graph_functions(self, for_ci=True):
def graph_apis(self, use_cuda=False, for_ci=True):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
feeds, loss = conv_block()
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
backup_graph = graph.clone()
self.assertEqual(len(graph.all_nodes()), len(backup_graph.all_nodes()))
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
origin_binary = fluid.CompiledProgram(graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
backup_binary = fluid.CompiledProgram(
backup_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
iters = 5
batch_size = 8
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
def train(binary):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss.name])
print('{}: {}'.format('loss', loss_v))
train(origin_binary)
train(backup_binary)
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
if not for_ci:
graph.draw('.', 'residual', marked_nodes)
backup_marked_nodes = set()
for op in backup_graph.all_op_nodes():
if op.name().find('conv2d') > -1:
backup_marked_nodes.add(op)
backup_graph.draw('.', 'backup', backup_marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
......@@ -75,14 +108,13 @@ class TestGraph(unittest.TestCase):
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
backup_graph = graph.clone()
self.assertEqual(len(graph.all_nodes()), len(backup_graph.all_nodes()))
if not for_ci:
backup_marked_nodes = set()
for op in backup_graph.all_op_nodes():
if op.name().find('conv2d') > -1:
backup_marked_nodes.add(op)
backup_graph.draw('.', 'backup', backup_marked_nodes)
def test_graph_apis_cpu(self):
self.graph_apis(use_cuda=False, for_ci=True)
def test_graph_apis_cuda(self):
if fluid.core.is_compiled_with_cuda():
self.graph_apis(use_cuda=True, for_ci=True)
if __name__ == '__main__':
......
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