def setUp(self):
self.conf_path = "./regression_tests/conf/nmt.conf"
self.log_dir = "./regression_tests/logs/nmt/"
self.runner = PaddleRunner()
self.runner.FLAGS_config(self.conf_path)
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
def setUp(self):
self.conf_path = "./regression_tests/conf/nmt_gen.conf"
self.log_dir = "./regression_tests/logs/nmt_gen/"
self.out_path = "./regression_tests/data/nmt_gen/"
self.config_args = "generating=1,gen_trans_file=" + self.log_dir
self.runner = PaddleRunner()
self.runner.FLAGS_config(self.conf_path)
self.runner.FLAGS_job("test")
self.runner.FLAGS_save_dir("./regression_tests/data/nmt_gen/model")
self.runner.FLAGS_trainer_count(1)
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
self.runner.FLAGS_test_pass(12)
self.runner.FLAGS_num_passes(13)
class test_NMT_gen(unittest.TestCase):
"""
Run regression test of neural machine translation (NMT) from French to English.
Check the final generating English sequences are within reasonable range.
"""
@classmethod
def setUpClass(cls):
"""
Down nmt data
"""
out_path = "./regression_tests/data/nmt_gen/"
file_list = ["gen.lst", "gen_data", "fr.dict", "en.dict"] + \
["model.tar.gz", "cpu_result", "gpu_result"]
baseurl = "http://m1-idl-gpu2-bak31.m1.baidu.com:8088/" \
"paddle_regression_test/nmt_gen/"
download_file_list(baseurl, file_list, out_path)
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
self.conf_path = "./regression_tests/conf/nmt_gen.conf"
self.log_dir = "./regression_tests/logs/nmt_gen/"
self.out_path = "./regression_tests/data/nmt_gen/"
self.config_args = "generating=1,gen_trans_file=" + self.log_dir
self.runner = PaddleRunner()
self.runner.FLAGS_config(self.conf_path)
self.runner.FLAGS_job("test")
self.runner.FLAGS_save_dir("./regression_tests/data/nmt_gen/model")
self.runner.FLAGS_trainer_count(1)
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
self.runner.FLAGS_test_pass(12)
self.runner.FLAGS_num_passes(13)
def tearDown(self):
pass
def compare_beam_search_result(self, result_path1, result_path2):
"""
Compare the beam search results. The benchmark of the same results are:
1. generating sequence must be the same.
2. error of score should within 0.001.
"""
score1, seqHash1 = PaddleBeamSearchParser(result_path1).find_info()
score2, seqHash2 = PaddleBeamSearchParser(result_path2).find_info()
self.assertEquals(seqHash1, seqHash2)
self.assertEquals(len(score1), len(score2))
for i in range(1, len(score1)):
self.assertLess(abs(score1[i] - score2[i]), 0.001)
@with_gpu()
@with_pydataprovider()
def test_gpu(self):
self.runner.FLAGS_use_gpu(True)
self.runner.FLAGS_config_args(self.config_args + "gpu/gen.txt")
self.runner.ENV_logdir(self.log_dir + "gpu/")
self.log_path = self.log_dir + "gpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
time.sleep(10)
self.compare_beam_search_result(self.log_dir + "gpu/gen.txt",
self.out_path + "gpu_result")
@with_pydataprovider()
def test_cpu(self):
self.runner.FLAGS_use_gpu(False)
self.runner.FLAGS_config_args(self.config_args + "cpu/gen.txt")
self.runner.ENV_logdir(self.log_dir + "cpu/")
self.log_path = self.log_dir + "cpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
self.compare_beam_search_result(self.log_dir + "cpu/gen.txt",
self.out_path + "cpu_result")
class test_NMT(unittest.TestCase):
"""
Run regression test of training a sequence generation model that generates
one target sequence from multiple source sequences.
Check the final training and test error are within reasonable range.
"""
@classmethod
def setUpClass(cls):
"""
Down nmt data
"""
out_path = "./regression_tests/data/nmt/"
file_list = ["train.lst", "test.lst", "src.dict"] + \
["train_data", "test_data", "trg.dict"]
baseurl = "http://m1-idl-gpu2-bak31.m1.baidu.com:8088/" \
"paddle_regression_test/nmt/"
download_file_list(baseurl, file_list, out_path)
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
self.conf_path = "./regression_tests/conf/nmt.conf"
self.log_dir = "./regression_tests/logs/nmt/"
self.runner = PaddleRunner()
self.runner.FLAGS_config(self.conf_path)
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
def tearDown(self):
pass
@with_gpu()
@with_pydataprovider()
def test_gpu(self):
self.runner.FLAGS_use_gpu(True)
self.runner.FLAGS_trainer_count(2)
self.runner.FLAGS_log_period(2)
self.runner.ENV_logdir(self.log_dir + "gpu/")
self.log_path = self.log_dir + "gpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
time.sleep(10)
parser = PaddleLogParser(self.log_path)
self.assertLess(parser.last_train_error(), 0.45)
self.assertLess(parser.last_test_error(), 0.55)
@with_pydataprovider()
def test_cpu(self):
self.runner.FLAGS_use_gpu(False)
self.runner.FLAGS_trainer_count(6)
self.runner.FLAGS_log_period(2)
self.runner.ENV_logdir(self.log_dir + "cpu/")
self.log_path = self.log_dir + "cpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
parser = PaddleLogParser(self.log_path)
self.assertLess(parser.last_train_error(), 0.45)
self.assertLess(parser.last_test_error(), 0.55)
class test_MNIST(unittest.TestCase):
"""
Run regression test of MNIST image classification.
Check the final training and testing error are within reasonable range.
"""
@classmethod
def setUpClass(cls):
"""
Down mnist data
"""
out_path = "./regression_tests/data/mnist/"
file_list = ["train.lst", "test.lst", "mnist.meta"] + \
["train_batch_%03d" % i for i in range(12)] + \
["test_batch_%03d" % i for i in range(2)]
baseurl = "http://m1-idl-gpu2-bak31.m1.baidu.com:8088/" \
"paddle_regression_test/mnist/"
download_file_list(baseurl, file_list, out_path)
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
self.conf_path = "./regression_tests/conf/mnist.conf"
self.log_dir = "./regression_tests/logs/mnist/"
self.runner = PaddleRunner()
self.runner.FLAGS_config(self.conf_path)
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
def tearDown(self):
pass
@with_gpu()
@with_pydataprovider()
def test_gpu(self):
self.runner.FLAGS_use_gpu(True)
self.runner.FLAGS_trainer_count(2)
self.runner.ENV_logdir(self.log_dir + "gpu/")
self.log_path = self.log_dir + "gpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
time.sleep(10)
parser = PaddleLogParser(self.log_path)
self.assertLess(parser.last_train_error(), 0.02)
self.assertLess(parser.last_test_error(), 0.03)
@with_pydataprovider()
def test_cpu(self):
self.runner.FLAGS_use_gpu(False)
self.runner.FLAGS_trainer_count(2)
self.runner.ENV_logdir(self.log_dir + "cpu/")
self.log_path = self.log_dir + "cpu/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
parser = PaddleLogParser(self.log_path)
self.assertLess(parser.last_train_error(), 0.02)
self.assertLess(parser.last_test_error(), 0.03)
def setUp(self):
self.log_dir = "./regression_tests/logs/chunking_rnn/"
self.runner = PaddleRunner()
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
class test_rnn_chunking(unittest.TestCase):
"""
Run regression test of rnn chunking.
Check the final training and testing error are within reasonable range.
"""
@classmethod
def setUpClass(cls):
"""
Down chunking data
"""
out_path = "./regression_tests/data/chunking/"
file_list = ["train_files.txt", "test_files.txt"] + \
["train_proto.bin", "test_proto.bin"]
baseurl = "http://m1-idl-gpu2-bak31.m1.baidu.com:8088/" \
"paddle_regression_test/chunking/"
download_file_list(baseurl, file_list, out_path)
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
self.log_dir = "./regression_tests/logs/chunking_rnn/"
self.runner = PaddleRunner()
with open("./regression_tests/default.json", 'r') as f:
parse_json(self.runner, json.load(f))
def tearDown(self):
pass
@with_pydataprovider()
def test_cpu_dense(self):
"""
test chunking with cpu and use dense updater
"""
self.runner.FLAGS_config("./regression_tests/conf/chunking_rnn.conf")
self.runner.FLAGS_use_gpu(False)
self.runner.FLAGS_trainer_count(12)
self.runner.ENV_logdir(self.log_dir + "cpu/dense/")
self.log_path = self.log_dir + "cpu/dense/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
parser = PaddleLogParser(self.log_path)
print 'last train error: ' + str(parser.last_train_error())
print 'last test error: ' + str(parser.last_test_error())
self.assertLess(parser.last_train_error(), 0.02)
self.assertLess(parser.last_test_error(), 0.05)
@with_pydataprovider()
def test_cpu_sparse(self):
"""
test chunking with cup and use sparse updater
"""
self.runner.FLAGS_config(
"./regression_tests/conf/chunking_rnn.sparse.conf")
self.runner.FLAGS_use_gpu(False)
self.runner.FLAGS_trainer_count(12)
self.runner.ENV_logdir(self.log_dir + "cpu/sparse/")
self.log_path = self.log_dir + "cpu/sparse/paddle_trainer.INFO"
print self.runner
p = self.runner.exec_()
p.wait()
parser = PaddleLogParser(self.log_path)
print 'last train error: ' + str(parser.last_train_error())
print 'last test error: ' + str(parser.last_test_error())
self.assertLess(parser.last_train_error(), 0.02)
self.assertLess(parser.last_test_error(), 0.05)