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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.
- import numpy as np
- import cv2
- import utils
- 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__, '../../..')))
- import paddle
- from paddle.distributed import ParallelEnv
- from resnet import ResNet50
- from paddlex.ppcls.utils.save_load import load_dygraph_pretrain
- def parse_args():
- def str2bool(v):
- return v.lower() in ("true", "t", "1")
- parser = argparse.ArgumentParser()
- parser.add_argument("-i", "--image_file", required=True, type=str)
- parser.add_argument("-c", "--channel_num", type=int)
- parser.add_argument("-p", "--pretrained_model", type=str)
- parser.add_argument("--show", type=str2bool, default=False)
- parser.add_argument("--interpolation", type=int, default=1)
- parser.add_argument("--save_path", type=str, default=None)
- parser.add_argument("--use_gpu", type=str2bool, default=True)
- return parser.parse_args()
- def create_operators(interpolation=1):
- size = 224
- img_mean = [0.485, 0.456, 0.406]
- img_std = [0.229, 0.224, 0.225]
- img_scale = 1.0 / 255.0
- resize_op = utils.ResizeImage(
- resize_short=256, interpolation=interpolation)
- crop_op = utils.CropImage(size=(size, size))
- normalize_op = utils.NormalizeImage(
- scale=img_scale, mean=img_mean, std=img_std)
- totensor_op = utils.ToTensor()
- return [resize_op, crop_op, normalize_op, totensor_op]
- def preprocess(data, ops):
- for op in ops:
- data = op(data)
- return data
- def main():
- args = parse_args()
- operators = create_operators(args.interpolation)
- # assign the place
- place = 'gpu:{}'.format(ParallelEnv().dev_id) if args.use_gpu else 'cpu'
- place = paddle.set_device(place)
- net = ResNet50()
- load_dygraph_pretrain(net, args.pretrained_model)
- img = cv2.imread(args.image_file, cv2.IMREAD_COLOR)
- data = preprocess(img, operators)
- data = np.expand_dims(data, axis=0)
- data = paddle.to_tensor(data)
- net.eval()
- _, fm = net(data)
- assert args.channel_num >= 0 and args.channel_num <= fm.shape[
- 1], "the channel is out of the range, should be in {} but got {}".format(
- [0, fm.shape[1]], args.channel_num)
- fm = (np.squeeze(fm[0][args.channel_num].numpy()) * 255).astype(np.uint8)
- fm = cv2.resize(fm, (img.shape[1], img.shape[0]))
- if args.save_path is not None:
- print("the feature map is saved in path: {}".format(args.save_path))
- cv2.imwrite(args.save_path, fm)
- if __name__ == "__main__":
- main()
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