PaddleClas/tools/eval.py

113 lines
3.5 KiB
Python

# 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 paddle
import paddle.nn.functional as F
import argparse
import os
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 ppcls.utils import logger
from ppcls.utils.save_load import init_model
from ppcls.utils.config import get_config
from ppcls.utils import multi_hot_encode
from ppcls.utils import accuracy_score
from ppcls.utils import mean_average_precision
from ppcls.utils import precision_recall_fscore
from ppcls.data import Reader
import program
import numpy as np
def parse_args():
parser = argparse.ArgumentParser("PaddleClas eval script")
parser.add_argument(
'-c',
'--config',
type=str,
default='./configs/eval.yaml',
help='config file path')
parser.add_argument(
'-o',
'--override',
action='append',
default=[],
help='config options to be overridden')
args = parser.parse_args()
return args
def main(args, return_dict={}):
config = get_config(args.config, overrides=args.override, show=True)
config.mode = "valid"
# assign place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
multilabel = config.get("multilabel", False)
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
init_model(config, net, optimizer=None)
valid_dataloader = Reader(config, 'valid', places=place)()
net.eval()
with paddle.no_grad():
if not multilabel:
top1_acc = program.run(valid_dataloader, config, net, None, None,
0, 'valid')
return_dict["top1_acc"] = top1_acc
return top1_acc
else:
all_outs = []
targets = []
for _, batch in enumerate(valid_dataloader()):
feeds = program.create_feeds(batch, False, config.classes_num,
multilabel)
out = net(feeds["image"])
out = F.sigmoid(out)
use_distillation = config.get("use_distillation", False)
if use_distillation:
out = out[1]
all_outs.extend(list(out.numpy()))
targets.extend(list(feeds["label"].numpy()))
all_outs = np.array(all_outs)
targets = np.array(targets)
mAP = mean_average_precision(all_outs, targets)
return_dict["mean average precision"] = mAP
return mAP
if __name__ == '__main__':
args = parse_args()
return_dict = {}
main(args, return_dict)
print(return_dict)