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static_infer.py
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# Copyright (c) 2020 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.
from __future__ import print_function
import os
import warnings
import logging
import paddle
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
#sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from utils.utils_single import load_yaml, load_static_model_class, get_abs_model, create_data_loader, reset_auc
from utils.save_load import save_static_model, load_static_model, save_data
import time
import argparse
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser("PaddleRec train static script")
parser.add_argument("-m", "--config_yaml", type=str)
parser.add_argument("-o", "--opt", nargs='*', type=str)
args = parser.parse_args()
args.abs_dir = os.path.dirname(os.path.abspath(args.config_yaml))
args.config_yaml = get_abs_model(args.config_yaml)
return args
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# modify config from command
if args.opt:
for parameter in args.opt:
parameter = parameter.strip()
key, value = parameter.split("=")
if type(config.get(key)) is int:
value = int(value)
if type(config.get(key)) is float:
value = float(value)
if type(config.get(key)) is bool:
value = (True if value.lower() == "true" else False)
config[key] = value
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_xpu = config.get("runner.use_xpu", False)
use_auc = config.get("runner.use_auc", False)
use_visual = config.get("runner.use_visual", False)
auc_num = config.get("runner.auc_num", 1)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
use_save_data = config.get("runner.use_save_data", False)
reader_type = config.get("runner.reader_type", "DataLoader")
use_fleet = config.get("runner.use_fleet", False)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, use_xpu: {}, use_visual: {}, infer_batch_size: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}".
format(use_gpu, use_xpu, use_visual, batch_size, test_data_dir,
start_epoch, end_epoch, print_interval, model_load_path))
logger.info("**************common.configs**********")
if use_xpu:
xpu_device = 'xpu:{0}'.format(os.getenv('FLAGS_selected_xpus', 0))
place = paddle.set_device(xpu_device)
else:
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
if reader_type == 'DataLoader':
test_dataloader = create_data_loader(
config=config, place=place, mode="test")
elif reader_type == "CustomizeDataLoader":
test_dataloader = static_model_class.create_data_loader()
# Create a log_visual object and store the data in the path
if use_visual:
from visualdl import LogWriter
log_visual = LogWriter(args.abs_dir + "/visualDL_log/infer")
step_num = 0
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(
paddle.static.default_main_program(),
model_path,
prefix='rec_static')
epoch_begin = time.time()
interval_begin = time.time()
infer_reader_cost = 0.0
infer_run_cost = 0.0
reader_start = time.time()
if use_auc:
reset_auc(use_fleet, auc_num)
#we will drop the last incomplete batch when dataset size is not divisible by the batch size
assert any(test_dataloader(
)), "test_dataloader's size is null, please ensure batch size < dataset size!"
for batch_id, batch_data in enumerate(test_dataloader()):
infer_reader_cost += time.time() - reader_start
infer_start = time.time()
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data)),
fetch_list=[var for _, var in fetch_vars.items()])
infer_run_cost += time.time() - infer_start
if batch_id % print_interval == 0:
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx])
if use_visual:
log_visual.add_scalar(
tag="infer/" + var_name,
step=step_num,
value=fetch_batch_var[var_idx])
logger.info(
"epoch: {}, batch_id: {}, ".format(epoch_id,
batch_id) + metric_str +
"avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.2f} ins/s".
format(infer_reader_cost / print_interval, (
infer_reader_cost + infer_run_cost) / print_interval,
batch_size, print_interval * batch_size / (
time.time() + 0.0001 - interval_begin)))
interval_begin = time.time()
infer_reader_cost = 0.0
infer_run_cost = 0.0
reader_start = time.time()
step_num = step_num + 1
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name, fetch_batch_var[var_idx])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
if use_save_data:
save_data(fetch_batch_var, model_load_path)
if __name__ == "__main__":
paddle.enable_static()
args = parse_args()
main(args)