From b8d1f5038e268b574379114378eae4e0c5ed4537 Mon Sep 17 00:00:00 2001 From: Zhen Wang Date: Mon, 11 Mar 2019 23:03:45 +0800 Subject: [PATCH] Add the executor test for the graph clone API. test=develop --- .../fluid/contrib/slim/tests/test_graph.py | 108 ++++++++++++------ 1 file changed, 70 insertions(+), 38 deletions(-) diff --git a/python/paddle/fluid/contrib/slim/tests/test_graph.py b/python/paddle/fluid/contrib/slim/tests/test_graph.py index 3245dfc06b..3629fed160 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_graph.py +++ b/python/paddle/fluid/contrib/slim/tests/test_graph.py @@ -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__': -- GitLab