mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
66 lines
2.0 KiB
Python
66 lines
2.0 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 argparse
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from ppcls.modeling import architectures
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from ppcls.utils.save_load import load_dygraph_pretrain
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import paddle
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import paddle.nn.functional as F
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from paddle.jit import to_static
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", type=str)
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parser.add_argument("-p", "--pretrained_model", type=str)
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parser.add_argument("-o", "--output_path", type=str)
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parser.add_argument("--class_dim", type=int, default=1000)
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# parser.add_argument("--img_size", type=int, default=224)
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return parser.parse_args()
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class Net(paddle.nn.Layer):
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def __init__(self, net, to_static, class_dim):
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super(Net, self).__init__()
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self.pre_net = net(class_dim=class_dim)
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self.to_static = to_static
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# 请根据实际需求修改shape
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@to_static(input_spec=[
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paddle.static.InputSpec(
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shape=[None, 3, 224, 224], dtype='float32')
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])
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def forward(self, inputs):
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x = self.pre_net(inputs)
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x = F.softmax(x)
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return x
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def main():
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args = parse_args()
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paddle.disable_static()
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net = architectures.__dict__[args.model]
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model = Net(net, to_static, args.class_dim)
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para_state_dict = paddle.io.load_program_state(args.pretrained_model)
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load_dygraph_pretrain(model, args.pretrained_model, True)
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paddle.jit.save(model, args.output_path)
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if __name__ == "__main__":
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main()
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