def evaluate(score_path, result_file_path):
"""
Evaluate both douban and ubuntu dataset
"""
if args.ext_eval:
result = eva.evaluate_douban(score_path)
else:
result = eva.evaluate_ubuntu(score_path)
#write evaluation result
with open(result_file_path, 'w') as out_file:
for p_at in result:
out_file.write(p_at + '\t' + str(result[p_at]) + '\n')
print('finish evaluation')
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
def test(args):
"""
Test
"""
if not os.path.exists(args.save_path):
mkdir(args.save_path)
if not os.path.exists(args.model_path):
raise ValueError("Invalid model init path %s" % args.model_path)
# data data_config
data_conf = {
"batch_size": args.batch_size,
"max_turn_num": args.max_turn_num,
"max_turn_len": args.max_turn_len,
"_EOS_": args._EOS_,
}
dam = Net(args.max_turn_num, args.max_turn_len, args.vocab_size,
args.emb_size, args.stack_num, args.channel1_num,
args.channel2_num)
dam.create_data_layers()
loss, logits = dam.create_network()
loss.persistable = True
logits.persistable = True
# gradient clipping
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByValue(max=1.0, min=-1.0))
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.exponential_decay(
learning_rate=args.learning_rate,
decay_steps=400,
decay_rate=0.9,
staircase=True))
optimizer.minimize(loss)
if args.use_cuda:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
#dev_count = multiprocessing.cpu_count()
dev_count = 1
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fluid.io.load_persistables(exe, args.model_path)
test_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda,
main_program=test_program)
print("start loading data ...")
with open(args.data_path, 'rb') as f:
if six.PY2:
train_data, val_data, test_data = pickle.load(f)
else:
train_data, val_data, test_data = pickle.load(f, encoding="bytes")
print("finish loading data ...")
test_batches = reader.build_batches(test_data, data_conf)
test_batch_num = len(test_batches["response"])
print("test batch num: %d" % test_batch_num)
print("begin inference ...")
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
score_path = os.path.join(args.save_path, 'score.txt')
score_file = open(score_path, 'w')
for it in six.moves.xrange(test_batch_num // dev_count):
feed_list = []
for dev in six.moves.xrange(dev_count):
index = it * dev_count + dev
batch_data = reader.make_one_batch_input(test_batches, index)
feed_dict = dict(zip(dam.get_feed_names(), batch_data))
feed_list.append(feed_dict)
predicts = test_exe.run(feed=feed_list, fetch_list=[logits.name])
scores = np.array(predicts[0])
print("step = %d" % it)
for dev in six.moves.xrange(dev_count):
index = it * dev_count + dev
for i in six.moves.xrange(args.batch_size):
score_file.write(
str(scores[args.batch_size * dev + i][0]) + '\t' +
str(test_batches["label"][index][i]) + '\n')
score_file.close()
#write evaluation result
if args.ext_eval:
result = eva.evaluate_douban(score_path)
else:
result = eva.evaluate_ubuntu(score_path)
result_file_path = os.path.join(args.save_path, 'result.txt')
with open(result_file_path, 'w') as out_file:
for metric in result:
out_file.write(metric + '\t' + str(result[metric]) + '\n')
print('finish test')
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))