90 lines
3.3 KiB
Python
90 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 os
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import sys
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import paddle
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import paddle.nn.functional as F
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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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from ppcls.utils.save_load import load_dygraph_pretrain
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from ppcls.utils import logger
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from ppcls.modeling import architectures
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from utils import parse_args, get_image_list, preprocess, postprocess, save_prelabel_results
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def main():
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args = parse_args()
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# assign the place
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place = paddle.set_device('gpu' if args.use_gpu else 'cpu')
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multilabel = True if args.multilabel else False
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net = architectures.__dict__[args.model](class_dim=args.class_num)
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load_dygraph_pretrain(net, args.pretrained_model, args.load_static_weights)
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image_list = get_image_list(args.image_file)
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batch_input_list = []
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img_path_list = []
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cnt = 0
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for idx, img_path in enumerate(image_list):
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img = cv2.imread(img_path)
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if img is None:
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logger.warning(
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"Image file failed to read and has been skipped. The path: {}".
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format(img_path))
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continue
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else:
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img = img[:, :, ::-1]
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data = preprocess(img, args)
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batch_input_list.append(data)
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img_path_list.append(img_path)
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cnt += 1
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if cnt % args.batch_size == 0 or (idx + 1) == len(image_list):
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batch_tensor = paddle.to_tensor(batch_input_list)
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net.eval()
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batch_outputs = net(batch_tensor)
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if args.model == "GoogLeNet":
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batch_outputs = batch_outputs[0]
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if multilabel:
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batch_outputs = F.sigmoid(batch_outputs)
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else:
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batch_outputs = F.softmax(batch_outputs)
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batch_outputs = batch_outputs.numpy()
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batch_result_list = postprocess(batch_outputs, args.top_k, multilabel=multilabel)
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for number, result_dict in enumerate(batch_result_list):
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filename = img_path_list[number].split("/")[-1]
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clas_ids = result_dict["clas_ids"]
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scores_str = "[{}]".format(", ".join("{:.2f}".format(
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r) for r in result_dict["scores"]))
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print("File:{}, Top-{} result: class id(s): {}, score(s): {}".
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format(filename, args.top_k, clas_ids, scores_str))
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if args.pre_label_image:
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save_prelabel_results(clas_ids[0], img_path_list[number],
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args.pre_label_out_idr)
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batch_input_list = []
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img_path_list = []
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if __name__ == "__main__":
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main()
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