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97 lines
3.1 KiB
97 lines
3.1 KiB
# 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", required=True, 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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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) |
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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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