103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import cv2
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import utils
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import argparse
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
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import paddle
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from paddle.distributed import ParallelEnv
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from resnet import ResNet50
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from ppcls.utils.save_load import load_dygraph_pretrain
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def parse_args():
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--image_file", type=str)
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parser.add_argument("-c", "--channel_num", type=int)
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parser.add_argument("-p", "--pretrained_model", type=str)
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parser.add_argument("--show", type=str2bool, default=False)
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parser.add_argument("--interpolation", type=int, default=1)
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parser.add_argument("--save_path", type=str, default=None)
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument(
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"--load_static_weights",
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type=str2bool,
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default=False,
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help='Whether to load the pretrained weights saved in static mode')
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return parser.parse_args()
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def create_operators(interpolation=1):
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size = 224
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img_mean = [0.485, 0.456, 0.406]
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img_std = [0.229, 0.224, 0.225]
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img_scale = 1.0 / 255.0
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resize_op = utils.ResizeImage(
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resize_short=256, interpolation=interpolation)
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crop_op = utils.CropImage(size=(size, size))
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normalize_op = utils.NormalizeImage(
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scale=img_scale, mean=img_mean, std=img_std)
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totensor_op = utils.ToTensor()
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return [resize_op, crop_op, normalize_op, totensor_op]
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def preprocess(data, ops):
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for op in ops:
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data = op(data)
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return data
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def main():
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args = parse_args()
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operators = create_operators(args.interpolation)
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# assign the place
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place = 'gpu:{}'.format(ParallelEnv().dev_id) if args.use_gpu else 'cpu'
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place = paddle.set_device(place)
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net = ResNet50()
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load_dygraph_pretrain(net, args.pretrained_model, args.load_static_weights)
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img = cv2.imread(args.image_file, cv2.IMREAD_COLOR)
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data = preprocess(img, operators)
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data = np.expand_dims(data, axis=0)
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data = paddle.to_tensor(data)
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net.eval()
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_, fm = net(data)
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assert args.channel_num >= 0 and args.channel_num <= fm.shape[
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1], "the channel is out of the range, should be in {} but got {}".format(
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[0, fm.shape[1]], args.channel_num)
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fm = (np.squeeze(fm[0][args.channel_num].numpy()) * 255).astype(np.uint8)
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fm = cv2.resize(fm, (img.shape[1], img.shape[0]))
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if args.save_path is not None:
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print("the feature map is saved in path: {}".format(args.save_path))
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cv2.imwrite(args.save_path, fm)
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if __name__ == "__main__":
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main()
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