File size: 2,157 Bytes
7b7527a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | # Copyright (c) 2021 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 absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
from ppdet.core.workspace import load_config
from ppdet.engine import Trainer
class TestMultiScaleInference(unittest.TestCase):
def setUp(self):
self.set_config()
def set_config(self):
self.mstest_cfg_file = 'configs/faster_rcnn/faster_rcnn_r34_fpn_multiscaletest_1x_coco.yml'
# test evaluation with multi scale test
def test_eval_mstest(self):
cfg = load_config(self.mstest_cfg_file)
trainer = Trainer(cfg, mode='eval')
cfg.weights = 'https://paddledet.bj.bcebos.com/models/faster_rcnn_r34_fpn_1x_coco.pdparams'
trainer.load_weights(cfg.weights)
trainer.evaluate()
# test inference with multi scale test
def test_infer_mstest(self):
cfg = load_config(self.mstest_cfg_file)
trainer = Trainer(cfg, mode='test')
cfg.weights = 'https://paddledet.bj.bcebos.com/models/faster_rcnn_r34_fpn_1x_coco.pdparams'
trainer.load_weights(cfg.weights)
tests_img_root = os.path.join(os.path.dirname(__file__), 'imgs')
# input images to predict
imgs = [
'coco2017_val2017_000000000139.jpg',
'coco2017_val2017_000000000724.jpg'
]
imgs = [os.path.join(tests_img_root, img) for img in imgs]
trainer.predict(
imgs, draw_threshold=0.5, output_dir='output', save_results=False)
if __name__ == '__main__':
unittest.main()
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