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eval.py
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eval.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#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.
import os
import sys
import time
import logging
import argparse
import ast
import paddle.fluid as fluid
from utils.config_utils import *
import models
from reader import get_reader
from metrics import get_metrics
from utils.utility import check_cuda
from utils.utility import check_version
logging.root.handlers = []
FORMAT = '[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT, stream=sys.stdout)
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name',
type=str,
default='AttentionCluster',
help='name of model to train.')
parser.add_argument('--config',
type=str,
default='configs/attention_cluster.txt',
help='path to config file of model')
parser.add_argument(
'--batch_size',
type=int,
default=None,
help='test batch size. None to use config file setting.')
parser.add_argument('--use_gpu',
type=ast.literal_eval,
default=True,
help='default use gpu.')
parser.add_argument(
'--weights',
type=str,
default='./data/checkpoints/AttentionLSTM_epoch9.pdparams',
help='weight path.')
parser.add_argument(
'--save_dir',
type=str,
default=os.path.join('data', 'evaluate_results'),
help='output dir path, default to use ./data/evaluate_results')
parser.add_argument('--log_interval',
type=int,
default=1,
help='mini-batch interval to log.')
args = parser.parse_args()
return args
def test(args):
# parse config
config = parse_config(args.config)
test_config = merge_configs(config, 'test', vars(args))
print_configs(test_config, "Test")
use_dali = test_config['TEST'].get('use_dali', False)
# build model
test_model = models.get_model(args.model_name, test_config, mode='test')
test_model.build_input(use_dataloader=False)
test_model.build_model()
test_feeds = test_model.feeds()
test_fetch_list = test_model.fetches()
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if args.weights:
assert os.path.exists(
args.weights), "Given weight dir {} not exist.".format(args.weights)
weights = args.weights or test_model.get_weights()
logger.info('load test weights from {}'.format(weights))
test_model.load_test_weights(exe, weights, fluid.default_main_program())
# get reader and metrics
test_reader = get_reader(args.model_name.upper(), 'test', test_config)
test_metrics = get_metrics(args.model_name.upper(), 'test', test_config)
test_feeder = fluid.DataFeeder(place=place, feed_list=test_feeds)
epoch_period = []
for test_iter, data in enumerate(test_reader()):
cur_time = time.time()
test_outs = exe.run(fetch_list=test_fetch_list,
feed=test_feeder.feed(data))
period = time.time() - cur_time
epoch_period.append(period)
test_metrics.accumulate(test_outs)
# metric here
if args.log_interval > 0 and test_iter % args.log_interval == 0:
info_str = '[EVAL] Batch {}'.format(test_iter)
test_metrics.calculate_and_log_out(test_outs, info_str)
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
test_metrics.finalize_and_log_out("[EVAL] eval finished. ", args.save_dir)
if __name__ == "__main__":
args = parse_args()
# check whether the installed paddle is compiled with GPU
check_cuda(args.use_gpu)
check_version()
logger.info(args)
test(args)