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predict_classifier.py
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# Copyright (c) 2019 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.
"""Load classifier's checkpoint to do prediction or save inference model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import multiprocessing
# NOTE(paddle-dev): All of these flags should be
# set before `import paddle`. Otherwise, it would
# not take any effect.
os.environ['FLAGS_eager_delete_tensor_gb'] = '0' # enable gc
import paddle.fluid as fluid
from reader.task_reader import ClassifyReader
from model.ernie import ErnieConfig
from finetune.classifier import create_model
from utils.args import ArgumentGroup, print_arguments
from utils.init import init_pretraining_params
from finetune_args import parser
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "options to init, resume and save model.")
model_g.add_arg("ernie_config_path", str, None, "Path to the json file for bert model config.")
model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
model_g.add_arg("save_inference_model_path", str, "inference_model", "If set, save the inference model to this path.")
model_g.add_arg("use_fp16", bool, False, "Whether to resume parameters from fp16 checkpoint.")
model_g.add_arg("num_labels", int, 2, "num labels for classify")
model_g.add_arg("ernie_version", str, "1.0", "ernie_version")
data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options.")
data_g.add_arg("predict_set", str, None, "Predict set file")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("label_map_config", str, None, "Label_map_config json file.")
data_g.add_arg("max_seq_len", int, 128, "Number of words of the longest seqence.")
data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("do_lower_case", bool, True,
"Whether to lower case the input text. Should be True for uncased models and False for cased models.")
run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
run_type_g.add_arg("do_prediction", bool, True, "Whether to do prediction on test set.")
args = parser.parse_args()
# yapf: enable.
def main(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
reader = ClassifyReader(
vocab_path=args.vocab_path,
label_map_config=args.label_map_config,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=False,
is_inference=True)
predict_prog = fluid.Program()
predict_startup = fluid.Program()
with fluid.program_guard(predict_prog, predict_startup):
with fluid.unique_name.guard():
predict_pyreader, probs, feed_target_names = create_model(
args,
pyreader_name='predict_reader',
ernie_config=ernie_config,
is_classify=True,
is_prediction=True,
ernie_version=args.ernie_version)
predict_prog = predict_prog.clone(for_test=True)
if args.use_cuda:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(predict_startup)
if args.init_checkpoint:
init_pretraining_params(exe, args.init_checkpoint, predict_prog)
else:
raise ValueError("args 'init_checkpoint' should be set for prediction!")
assert args.save_inference_model_path, "args save_inference_model_path should be set for prediction"
_, ckpt_dir = os.path.split(args.init_checkpoint.rstrip('/'))
dir_name = ckpt_dir + '_inference_model'
model_path = os.path.join(args.save_inference_model_path, dir_name)
print("save inference model to %s" % model_path)
fluid.io.save_inference_model(
model_path,
feed_target_names, [probs],
exe,
main_program=predict_prog)
print("load inference model from %s" % model_path)
infer_program, feed_target_names, probs = fluid.io.load_inference_model(
model_path, exe)
src_ids = feed_target_names[0]
sent_ids = feed_target_names[1]
pos_ids = feed_target_names[2]
input_mask = feed_target_names[3]
if args.ernie_version == "2.0":
task_ids = feed_target_names[4]
predict_data_generator = reader.data_generator(
input_file=args.predict_set,
batch_size=args.batch_size,
epoch=1,
shuffle=False)
print("-------------- prediction results --------------")
np.set_printoptions(precision=4, suppress=True)
index = 0
for sample in predict_data_generator():
src_ids_data = sample[0]
sent_ids_data = sample[1]
pos_ids_data = sample[2]
task_ids_data = sample[3]
input_mask_data = sample[4]
if args.ernie_version == "1.0":
output = exe.run(
infer_program,
feed={src_ids: src_ids_data,
sent_ids: sent_ids_data,
pos_ids: pos_ids_data,
input_mask: input_mask_data},
fetch_list=probs)
elif args.ernie_version == "2.0":
output = exe.run(
infer_program,
feed={src_ids: src_ids_data,
sent_ids: sent_ids_data,
pos_ids: pos_ids_data,
task_ids: task_ids_data,
input_mask: input_mask_data},
fetch_list=probs)
else:
raise ValueError("ernie_version must be 1.0 or 2.0")
for single_result in output[0]:
print("example_index:{}\t{}".format(index, single_result))
index += 1
if __name__ == '__main__':
print_arguments(args)
main(args)