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inference_model_ernie.py
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# -*- coding: utf_8 -*-
import os
import sys
sys.path.append("../shared_modules/")
sys.path.append("../shared_modules/models/classification")
import paddle
import paddle.fluid as fluid
import numpy as np
from models.model_check import check_cuda
from config import PDConfig
from run_ernie_classifier import create_model
import utils
import reader
from run_ernie_classifier import ernie_pyreader
from models.representation.ernie import ErnieConfig
from models.representation.ernie import ernie_encoder, ernie_encoder_with_paddle_hub
from preprocess.ernie import task_reader
def do_save_inference_model(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
if args.use_cuda:
dev_count = fluid.core.get_cuda_device_count()
place = fluid.CUDAPlace(0)
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
exe = fluid.Executor(place)
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, ernie_inputs, labels = ernie_pyreader(
args, pyreader_name="infer_reader")
if args.use_paddle_hub:
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs,
args.max_seq_len)
else:
embeddings = ernie_encoder(
ernie_inputs, ernie_config=ernie_config)
probs = create_model(
args, embeddings, labels=labels, is_prediction=True)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
assert (args.init_checkpoint)
if args.init_checkpoint:
utils.init_checkpoint(exe, args.init_checkpoint, test_prog)
fluid.io.save_inference_model(
args.inference_model_dir,
feeded_var_names=[
ernie_inputs["src_ids"].name, ernie_inputs["sent_ids"].name,
ernie_inputs["pos_ids"].name, ernie_inputs["input_mask"].name,
ernie_inputs["seq_lens"].name
],
target_vars=[probs],
executor=exe,
main_program=test_prog,
model_filename="model.pdmodel",
params_filename="params.pdparams")
print("save inference model at %s" % (args.inference_model_dir))
def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase):
"""
Inference Function
"""
print("=================")
test_pyreader.start()
while True:
try:
np_props = exe.run(program=test_program,
fetch_list=fetch_list,
return_numpy=True)
for probs in np_props[0]:
print("%d\t%f\t%f" % (np.argmax(probs), probs[0], probs[1]))
except fluid.core.EOFException:
test_pyreader.reset()
break
def test_inference_model(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
if args.use_cuda:
dev_count = fluid.core.get_cuda_device_count()
place = fluid.CUDAPlace(0)
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
exe = fluid.Executor(place)
reader = task_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,
random_seed=args.random_seed)
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, ernie_inputs, labels = ernie_pyreader(
args, pyreader_name="infer_pyreader")
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
probs = create_model(
args, embeddings, labels=labels, is_prediction=True)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
assert (args.inference_model_dir)
infer_data_generator = reader.data_generator(
input_file=args.test_set,
batch_size=args.batch_size / dev_count,
phase="infer",
epoch=1,
shuffle=False)
infer_program, feed_names, fetch_targets = fluid.io.load_inference_model(
dirname=args.inference_model_dir,
executor=exe,
model_filename="model.pdmodel",
params_filename="params.pdparams")
infer_pyreader.set_batch_generator(infer_data_generator)
inference(exe, test_prog, infer_pyreader, [probs.name], "infer")
if __name__ == "__main__":
args = PDConfig()
args.build()
args.print_arguments()
check_cuda(args.use_cuda)
if args.do_save_inference_model:
do_save_inference_model(args)
else:
test_inference_model(args)